Part2-3_Purchase_Analytics_Modelling_Brand_Choice_by_Segment.ipynb

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    "# Customer Analytics in FMGC Industry (Part 2)\n",
    "#### by Sooyeon Won\n",
    "\n",
    "### Keywords \n",
    "\n",
    "- Marketing Mix \n",
    "- STP framework\n",
    "- Purchase Analytics \n",
    "- Predictive Analysis\n",
    "- Price Elasticity\n",
    "- Modeling Brand Choice\n",
    "\n",
    "\n",
    "### Contents \n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#Introduction\">1. Introduction</a></li> \n",
    "<li><a href=\"#Preparation\">2. Data Preparation</a></li>\n",
    "<li><a href=\"#Exploration\">3. Data Exploration</a></li>\n",
    "<li><a href=\"#Analysis\">4. Data Analysis</a></li>\n",
    "&emsp;4.1. Customer Analytics<br>\n",
    "&emsp;4.2. Purchase Analytics <br>\n",
    "&emsp;&emsp;&emsp;i. Descriptive Analyses by Segment <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-1. The Proportion of each Segment <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-2. Purchase Occasions and Purchase Incidences <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-3. Brand Chocie <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-4. Revenue Comparison between segments <br>\n",
    "&emsp;&emsp;&emsp;ii. Predictive Analyses <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-1. Modeling Purchase Incidence<br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-2. Modeling Brand Choice <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-3. Modeling Purchase Quantity <br>\n",
    "<li><a href=\"#Conclusion\">5. Conclusion</a></li>\n",
    "</ul>\n"
   ]
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    "## 4. Data Analysis\n",
    "### 4.2. Purchase Analytics\n",
    "- Data Preparation from Part 1\n",
    "- ii. Predictive Analyses <br>\n",
    "&emsp;&emsp;&emsp; ii-1. Modeling Purchase Incidence<br>\n",
    "&emsp;&emsp;&emsp; **ii-2. Modeling Brand Choice** <br>\n",
    "&emsp;&emsp;&emsp; ii-3. Modeling Purchase Quantity <br>\n",
    "\n",
    "### Data Preparation from Part 1"
   ]
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    "# Import the relevant libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.axes as axs\n",
    "%matplotlib inline\n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.cluster import KMeans \n",
    "\n",
    "# Import pickle in order to be able to load our pickled objects.\n",
    "import pickle\n",
    "\n",
    "# Import the Logistic Regression module from sk learn for the purchase probability model.\n",
    "from sklearn.linear_model import LogisticRegression "
   ]
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    "# Load data\n",
    "purchase_df = pd.read_csv('purchase data.csv', index_col = 0)\n",
    "price_elasticities = pd.read_csv('price_elasticities.csv', index_col = 0)\n",
    "\n",
    "# Import Scaler, PCA, K-Means\n",
    "scaler = pickle.load(open('scaler.pickle', 'rb'))\n",
    "pca = pickle.load(open('pca.pickle', 'rb'))\n",
    "kmeans_pca = pickle.load(open('kmeans_pca.pickle', 'rb'))\n",
    "\n",
    "# Standardisation\n",
    "features = purchase_df[['Sex', 'Marital status', 'Age', 'Education', 'Income', 'Occupation', 'Settlement size']]\n",
    "purchase_segm_std = scaler.transform(features)\n",
    "# Apply PCA\n",
    "purchase_segm_pca = pca.transform(purchase_segm_std)\n",
    "# Segment data\n",
    "purchase_segm_kmeans_pca = kmeans_pca.predict(purchase_segm_pca)\n",
    "\n",
    "# Create a copy of the data frame\n",
    "purchase_predictors = purchase_df.copy()\n",
    "\n",
    "# Add segment labels\n",
    "purchase_predictors['Segment'] = purchase_segm_kmeans_pca\n",
    "segment_dummies = pd.get_dummies(purchase_segm_kmeans_pca, prefix = 'Segment', prefix_sep = '_')\n",
    "purchase_predictors = purchase_predictors.reset_index()\n",
    "purchase_predictors = purchase_predictors.merge(segment_dummies, how='inner',\n",
    "                                                left_on = purchase_predictors.index,\n",
    "                                                right_on = segment_dummies.index)"
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       "           Day  Incidence  Brand  Quantity  Last_Inc_Brand  Last_Inc_Quantity  \\\n",
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    "df_pa = purchase_predictors.set_index('ID').iloc[:,1:].copy()\n",
    "df_pa.head() # Data is ready to conduct predictive analysis "
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    "### ii. Predictive Analysis \n",
    "In this section, I mainly conduct predictive analytics. Using machine learning models I estimated price elasticities. The situation, in this part of analysis, is that customers are in the store and have decided to purchase a product we are interested in: which brand they'll choose. Furthermore, I analysed how the choice of customer would be changed as competitors’ price is changing.\n",
    "\n",
    "### ii-2. Modeling Brand Choice <br>\n",
    "<li><a href=\"#BrandChoice\">ii-2-1. Brand Choice</a></li>\n",
    "<li><a href=\"#OPEB5\">ii-2-2. Own Price Elasticity Brand 5</a></li>\n",
    "<li><a href=\"#CPEB54\">ii-2-3. Cross Price Elasticity Brand 5, Cross Brand 4</a></li> \n",
    "<li><a href=\"#OaCPEbySeg\">ii-2-4. Own and Cross-Price Elasticity by Segment</a></li> \n",
    "\n",
    "> The chocolate brand has values from 0 to 5 indicating which of the 5 brands are chosen. The entry is indicated as 0, if no purchase was made during the particular shopping trip. To investigate the brand choice, I focused on these entries where brand is from 1 to 5. By doing s, we can be sure that a purchase was made and a certain brand was preferred. <br><br>\n",
    "The final goal of the brand choice model is to determine what the probability to choose a certain brand is. This would help marketers analyse their customers‘ behaviour. Based on the analysis, the marketers can increase sales and certainly the goal would help to increase customer satisfaction. I take advantage of a \"Logistic Regression Algorithm\". This time I apply it to a multi class scenario, because now there are 5 classes (brands). This approach is known as a **\"Multi-Nominal-Logistic-Regression / Classifier\"**.\n"
   ]
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   "source": [
    "<a id='BrandChoice'></a>\n",
    "### ii-2-1. Brand Choice"
   ]
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.89</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Day  Incidence  Brand  Quantity  Last_Inc_Brand  Last_Inc_Quantity  \\\n",
       "ID                                                                              \n",
       "200000001   28          1      2         2               0                  0   \n",
       "200000001   58          1      5         1               0                  0   \n",
       "200000001  110          1      1         2               0                  0   \n",
       "200000001  142          1      4         1               0                  0   \n",
       "200000001  183          1      2         3               0                  0   \n",
       "\n",
       "           Price_1  Price_2  Price_3  Price_4  ...  Age  Education  Income  \\\n",
       "ID                                             ...                           \n",
       "200000001     1.50     1.90     1.99     2.09  ...   47          1  110866   \n",
       "200000001     1.39     1.90     1.91     2.12  ...   47          1  110866   \n",
       "200000001     1.47     1.90     1.99     1.97  ...   47          1  110866   \n",
       "200000001     1.21     1.35     1.99     2.16  ...   47          1  110866   \n",
       "200000001     1.46     1.88     1.97     1.89  ...   47          1  110866   \n",
       "\n",
       "           Occupation  Settlement size  Segment  Segment_0  Segment_1  \\\n",
       "ID                                                                      \n",
       "200000001           1                0        0          1          0   \n",
       "200000001           1                0        0          1          0   \n",
       "200000001           1                0        0          1          0   \n",
       "200000001           1                0        0          1          0   \n",
       "200000001           1                0        0          1          0   \n",
       "\n",
       "           Segment_2  Segment_3  \n",
       "ID                               \n",
       "200000001          0          0  \n",
       "200000001          0          0  \n",
       "200000001          0          0  \n",
       "200000001          0          0  \n",
       "200000001          0          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_choice = df_pa.query('Incidence ==1')\n",
    "brand_choice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(multi_class='multinomial', solver='sag')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The target variable of the model is \"brand\".\n",
    "output = brand_choice['Brand']\n",
    "# Predict based on the prices for the five brands.\n",
    "features = ['Price_1', 'Price_2', 'Price_3', 'Price_4', 'Price_5']\n",
    "inputs = brand_choice[features]\n",
    "# Brand Choice Model fit.\n",
    "model_brand_choice = LogisticRegression(solver = 'sag', multi_class = 'multinomial')\n",
    "model_brand_choice.fit(inputs, output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Like the previous analysis, I predict the brand  based on price, but now we're interested in the prices of each individual\n",
    "brand and the interactions between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef_Brand_1</th>\n",
       "      <th>Coef_Brand_2</th>\n",
       "      <th>Coef_Brand_3</th>\n",
       "      <th>Coef_Brand_4</th>\n",
       "      <th>Coef_Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_1</th>\n",
       "      <td>-3.92</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_2</th>\n",
       "      <td>0.66</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_3</th>\n",
       "      <td>2.42</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_4</th>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_5</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef_Brand_1  Coef_Brand_2  Coef_Brand_3  Coef_Brand_4  Coef_Brand_5\n",
       "Price_1         -3.92          1.27          1.62          0.57          0.44\n",
       "Price_2          0.66         -1.88          0.56          0.40          0.26\n",
       "Price_3          2.42         -0.21          0.50         -1.40         -1.31\n",
       "Price_4          0.70         -0.21          1.04         -1.25         -0.29\n",
       "Price_5         -0.20          0.59          0.45          0.25         -1.09"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn off scientific notation in pandas \n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "# Make some transformations on the coefficients data frame to increase readability.\n",
    "# Tanspose the data frame, to keep with the conventional representation of results.\n",
    "# Add labels for the columns and the index, which represent the coefficients of the brands and prices, respectively. \n",
    "coefficients = ['Coef_Brand_1', 'Coef_Brand_2', 'Coef_Brand_3', 'Coef_Brand_4', 'Coef_Brand_5']\n",
    "prices = ['Price_1', 'Price_2', 'Price_3', 'Price_4', 'Price_5']\n",
    "bc_coef = pd.DataFrame(np.transpose(model_brand_choice.coef_), columns = coefficients, index = prices)\n",
    "\n",
    "bc_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The Dataframe \"bc_coef\" presents the beta values. \n",
    ">- \"Coef_Brand_1\": The Brand 1 the coefficient for the own brand with respect to price is negative (-3,92), while it's positive for all other prices except Brand 5. We know that the higher the price of the own product, the lower the probability for it to be purchased.\n",
    "So it makes sense for the own brand price coefficient to be negative.\n",
    ">- On the other hand, the more the price of a competitor increases, the higher the probability of customers switching to our own brand would be. Hence there is a positive relationship between our own brands (Brand 5) purchase probability and a competitive brand increasing their price. \n",
    ">- It can be easily realized that the choice probability for own brand and the choice probabilities for all the other brands are interrelated and a marketing mix tool of own brand reflects not only the choice probability for that brand but the choice probabilities for all other brands as well. These effects are known as own **\"Brand Effects\"** and **\"Cross Brand Effects\"**. Throughout this analysis, I examined both in more detail.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='OPEB5'></a>\n",
    "### ii-2-2. Own Price Elasticity Brand 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section I chose the Brand 5 and regarded as the own product which should be analysed. Then I expand the findings into developing a strategy to target customers. \n",
    "\n",
    "Firstly, I examine the effects of price changes to own brand price. Then I explore the effects of competitors' product price changing on the brand 5 product. This information contains in the Brand 5 own price elasticity and the cross price elasticities respectively. \n",
    "\n",
    "For this analysis, I chose Brand 5 chocolate product, the most expensive brand. Like before, in order to predict the brand elasticities, I generated a dataframe which contains the same features to fit the brand choice model. Basically I predict the purchase probability of brand 5 for different price points to calculate the predicted probabilities, Then apply the predict proper method on the brand choice model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_range = np.arange(0.5, 3.5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price_1</th>\n",
       "      <th>Price_2</th>\n",
       "      <th>Price_3</th>\n",
       "      <th>Price_4</th>\n",
       "      <th>Price_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price_1  Price_2  Price_3  Price_4  Price_5\n",
       "0     1.38     1.76     2.01     2.16     0.50\n",
       "1     1.38     1.76     2.01     2.16     0.51\n",
       "2     1.38     1.76     2.01     2.16     0.52\n",
       "3     1.38     1.76     2.01     2.16     0.53\n",
       "4     1.38     1.76     2.01     2.16     0.54"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate price elasticity of brand choice.\n",
    "# Create a data frame with price columns, which the model will use to predict the brand choice probabilities.\n",
    "own_brand_5 = pd.DataFrame( {'Price_1':  brand_choice['Price_1'].mean(),\n",
    "                             'Price_2':  brand_choice['Price_2'].mean(), \n",
    "                             'Price_3':  brand_choice['Price_3'].mean(),\n",
    "                             'Price_4':  brand_choice['Price_4'].mean(),\n",
    "                             'Price_5':  price_range} , index = np.arange(price_range.size))\n",
    "own_brand_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0879"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Brand Choice Model prediction.\n",
    "pred_brand_5 = model_brand_choice.predict_proba(own_brand_5)\n",
    "\n",
    "# The model returns the probabilities of choosing each of the 5 brands. \n",
    "# Since, we are interested in the probability for the fifth brand we need to obtain the last column located on position 4,\n",
    "# as we're starting to count from 0.\n",
    "pred_own_brand_5 = pred_brand_5[: ][:, 4]\n",
    "\n",
    "# We're interested in choosing brand 5. \n",
    "# Therefore, the beta coefficient we require is that of the brand 5 coefficient and price 5.\n",
    "beta5 = bc_coef.iloc[4, 4]\n",
    "np.round(beta5, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_Point</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>...</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_PE</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.89</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>-7.96</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>-8.01</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-8.06</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>-8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_0_FO</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>-12.27</td>\n",
       "      <td>-12.31</td>\n",
       "      <td>-12.34</td>\n",
       "      <td>-12.38</td>\n",
       "      <td>-12.42</td>\n",
       "      <td>-12.45</td>\n",
       "      <td>-12.49</td>\n",
       "      <td>-12.53</td>\n",
       "      <td>-12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_1_Cf</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.62</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-5.76</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_2_Strd</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>-5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_3_WO</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_1</th>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.77</td>\n",
       "      <td>-4.79</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_0</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_Avg</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0     1     2     3     4     5     6     7     8  \\\n",
       "Price_Point             0.50  0.51  0.52  0.53  0.54  0.55  0.56  0.57  0.58   \n",
       "Mean_PE                -0.10 -0.10 -0.10 -0.11 -0.11 -0.12 -0.12 -0.13 -0.13   \n",
       "PE_Segment_0_FO        -0.03 -0.03 -0.04 -0.04 -0.04 -0.04 -0.04 -0.05 -0.05   \n",
       "PE_Segment_1_Cf        -0.12 -0.12 -0.13 -0.13 -0.14 -0.14 -0.15 -0.15 -0.15   \n",
       "PE_Segment_2_Strd      -0.21 -0.22 -0.22 -0.23 -0.24 -0.25 -0.25 -0.26 -0.27   \n",
       "PE_Segment_3_WO        -0.17 -0.17 -0.18 -0.18 -0.19 -0.19 -0.20 -0.20 -0.21   \n",
       "Elasticity_Promotion_1 -0.13 -0.13 -0.13 -0.14 -0.14 -0.15 -0.15 -0.16 -0.16   \n",
       "Elasticity_Promotion_0 -0.20 -0.20 -0.21 -0.21 -0.22 -0.23 -0.23 -0.24 -0.25   \n",
       "Brand5_own_Avg         -0.05 -0.05 -0.05 -0.05 -0.06 -0.06 -0.06 -0.06 -0.06   \n",
       "\n",
       "                           9  ...    290    291    292    293    294    295  \\\n",
       "Price_Point             0.59  ...   3.40   3.41   3.42   3.43   3.44   3.45   \n",
       "Mean_PE                -0.14  ...  -7.89  -7.91  -7.94  -7.96  -7.99  -8.01   \n",
       "PE_Segment_0_FO        -0.05  ... -12.23 -12.27 -12.31 -12.34 -12.38 -12.42   \n",
       "PE_Segment_1_Cf        -0.16  ...  -5.62  -5.64  -5.66  -5.68  -5.70  -5.72   \n",
       "PE_Segment_2_Strd      -0.27  ...  -4.97  -4.98  -5.00  -5.02  -5.03  -5.05   \n",
       "PE_Segment_3_WO        -0.21  ...  -4.08  -4.10  -4.12  -4.13  -4.15  -4.16   \n",
       "Elasticity_Promotion_1 -0.17  ...  -4.77  -4.79  -4.81  -4.83  -4.84  -4.86   \n",
       "Elasticity_Promotion_0 -0.25  ...  -4.90  -4.92  -4.93  -4.95  -4.97  -4.98   \n",
       "Brand5_own_Avg         -0.06  ...  -3.14  -3.16  -3.18  -3.19  -3.21  -3.23   \n",
       "\n",
       "                          296    297    298    299  \n",
       "Price_Point              3.46   3.47   3.48   3.49  \n",
       "Mean_PE                 -8.04  -8.06  -8.09  -8.11  \n",
       "PE_Segment_0_FO        -12.45 -12.49 -12.53 -12.56  \n",
       "PE_Segment_1_Cf         -5.74  -5.76  -5.78  -5.80  \n",
       "PE_Segment_2_Strd       -5.07  -5.09  -5.10  -5.12  \n",
       "PE_Segment_3_WO         -4.18  -4.19  -4.21  -4.22  \n",
       "Elasticity_Promotion_1  -4.88  -4.90  -4.92  -4.93  \n",
       "Elasticity_Promotion_0  -5.00  -5.02  -5.03  -5.05  \n",
       "Brand5_own_Avg          -3.24  -3.26  -3.27  -3.29  \n",
       "\n",
       "[9 rows x 300 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "own_price_elasticity_brand_5 = beta5 * price_range * (1 - pred_own_brand_5)\n",
    "price_elasticities.loc['Brand5_own_Avg'] = own_price_elasticity_brand_5\n",
    "price_elasticities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Own Price Elasticity of Purchase Probability for Brand 5')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot elasticities of purchase probability for brand 5.\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(price_range, own_price_elasticity_brand_5, color = 'grey')\n",
    "plt.xlabel('Price 5')\n",
    "plt.ylabel('Elasticity')\n",
    "plt.title('Own Price Elasticity of Purchase Probability for Brand 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='CPEB54'></a>\n",
    "### ii-2-3. Cross Price Elasticity Brand 5, Cross Brand 4\n",
    "\n",
    "Now, what would happen to the purchase probability of brand 5, if a competitor changed their pricing? Among other brands, I compared the Brand 5 with Brand 4. The reason is the most expensive chocolate: Brand 5 is highly likely one of the highest qualities (certainly not always, though). Based on this assumption, it seems that the brand 4 seems the closest to the brand 5. Therefore, it would make the most sense to compare these two. I determine the cross price elasticities of \"Brand 5\" with respect to \"Brand 4\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B5CB4_Price_1</th>\n",
       "      <th>B5CB4_Price_2</th>\n",
       "      <th>B5CB4_Price_3</th>\n",
       "      <th>B5CB4_Price_4</th>\n",
       "      <th>B5CB4_Price_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B5CB4_Price_1  B5CB4_Price_2  B5CB4_Price_3  B5CB4_Price_4  B5CB4_Price_5\n",
       "0           1.38           1.76           2.01           0.50           2.65\n",
       "1           1.38           1.76           2.01           0.51           2.65\n",
       "2           1.38           1.76           2.01           0.52           2.65\n",
       "3           1.38           1.76           2.01           0.53           2.65\n",
       "4           1.38           1.76           2.01           0.54           2.65"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand5_cross_brand4 = pd.DataFrame({'B5CB4_Price_1':  brand_choice['Price_1'].mean(),\n",
    "                                     'B5CB4_Price_2':  brand_choice['Price_2'].mean(), \n",
    "                                     'B5CB4_Price_3':  brand_choice['Price_3'].mean(),\n",
    "                                     'B5CB4_Price_4':  price_range,\n",
    "                                     'B5CB4_Price_5':  brand_choice['Price_5'].mean()}, index = np.arange(price_range.size))\n",
    "\n",
    "brand5_cross_brand4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the probabilities by using the previous model brand choice predict probabilities\n",
    "pred_brand5_cross_brand4 = model_brand_choice.predict_proba(brand5_cross_brand4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The probability of choosing the competitor brand \n",
    "# Select the purchase probability for brand 4, contained in the 4th column with index 3. \n",
    "pred_brand_4 = pred_brand5_cross_brand4[:][:, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0879"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We're interested in choosing brand 5. \n",
    "# Therefore, the beta coefficient we require is that of the brand 5 coefficient and price 5.\n",
    "np.round(beta5, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Price Elasticity - Reference](https://365datascience.com/price-elasticity/)\n",
    "$$ E = -\\beta(Own Product Price)*price(Cross Brand)*Pr(Cross Brand) $$ \n",
    "\n",
    "Price Elasticity of Probability for Brand Choice is equal to negative the price coefficient of the own brand multiplied by the price of the cross brand,further multiplied by the probability for choosing the cross brand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand5_cross_brand4_price_elasticity = -beta5 * price_range * pred_brand_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
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       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
       "      <th>152</th>\n",
       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "      <th>216</th>\n",
       "      <th>217</th>\n",
       "      <th>218</th>\n",
       "      <th>219</th>\n",
       "      <th>220</th>\n",
       "      <th>221</th>\n",
       "      <th>222</th>\n",
       "      <th>223</th>\n",
       "      <th>224</th>\n",
       "      <th>225</th>\n",
       "      <th>226</th>\n",
       "      <th>227</th>\n",
       "      <th>228</th>\n",
       "      <th>229</th>\n",
       "      <th>230</th>\n",
       "      <th>231</th>\n",
       "      <th>232</th>\n",
       "      <th>233</th>\n",
       "      <th>234</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "      <th>245</th>\n",
       "      <th>246</th>\n",
       "      <th>247</th>\n",
       "      <th>248</th>\n",
       "      <th>249</th>\n",
       "      <th>250</th>\n",
       "      <th>251</th>\n",
       "      <th>252</th>\n",
       "      <th>253</th>\n",
       "      <th>254</th>\n",
       "      <th>255</th>\n",
       "      <th>256</th>\n",
       "      <th>257</th>\n",
       "      <th>258</th>\n",
       "      <th>259</th>\n",
       "      <th>260</th>\n",
       "      <th>261</th>\n",
       "      <th>262</th>\n",
       "      <th>263</th>\n",
       "      <th>264</th>\n",
       "      <th>265</th>\n",
       "      <th>266</th>\n",
       "      <th>267</th>\n",
       "      <th>268</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "      <th>279</th>\n",
       "      <th>280</th>\n",
       "      <th>281</th>\n",
       "      <th>282</th>\n",
       "      <th>283</th>\n",
       "      <th>284</th>\n",
       "      <th>285</th>\n",
       "      <th>286</th>\n",
       "      <th>287</th>\n",
       "      <th>288</th>\n",
       "      <th>289</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_Point</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.87</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.34</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.73</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.09</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.17</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_PE</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.35</td>\n",
       "      <td>-4.39</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.46</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.82</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.89</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.96</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>-5.13</td>\n",
       "      <td>-5.16</td>\n",
       "      <td>-5.19</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>-5.26</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.42</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.49</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-5.62</td>\n",
       "      <td>-5.65</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>-5.84</td>\n",
       "      <td>-5.87</td>\n",
       "      <td>-5.90</td>\n",
       "      <td>-5.93</td>\n",
       "      <td>-5.96</td>\n",
       "      <td>-5.99</td>\n",
       "      <td>-6.02</td>\n",
       "      <td>-6.05</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>-6.11</td>\n",
       "      <td>-6.14</td>\n",
       "      <td>-6.17</td>\n",
       "      <td>-6.20</td>\n",
       "      <td>-6.23</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>-6.29</td>\n",
       "      <td>-6.32</td>\n",
       "      <td>-6.35</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>-6.40</td>\n",
       "      <td>-6.43</td>\n",
       "      <td>-6.46</td>\n",
       "      <td>-6.49</td>\n",
       "      <td>-6.52</td>\n",
       "      <td>-6.55</td>\n",
       "      <td>-6.58</td>\n",
       "      <td>-6.60</td>\n",
       "      <td>-6.63</td>\n",
       "      <td>-6.66</td>\n",
       "      <td>-6.69</td>\n",
       "      <td>-6.72</td>\n",
       "      <td>-6.75</td>\n",
       "      <td>-6.77</td>\n",
       "      <td>-6.80</td>\n",
       "      <td>-6.83</td>\n",
       "      <td>-6.86</td>\n",
       "      <td>-6.88</td>\n",
       "      <td>-6.91</td>\n",
       "      <td>-6.94</td>\n",
       "      <td>-6.97</td>\n",
       "      <td>-6.99</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>-7.05</td>\n",
       "      <td>-7.07</td>\n",
       "      <td>-7.10</td>\n",
       "      <td>-7.13</td>\n",
       "      <td>-7.16</td>\n",
       "      <td>-7.18</td>\n",
       "      <td>-7.21</td>\n",
       "      <td>-7.24</td>\n",
       "      <td>-7.26</td>\n",
       "      <td>-7.29</td>\n",
       "      <td>-7.32</td>\n",
       "      <td>-7.34</td>\n",
       "      <td>-7.37</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>-7.42</td>\n",
       "      <td>-7.45</td>\n",
       "      <td>-7.47</td>\n",
       "      <td>-7.50</td>\n",
       "      <td>-7.53</td>\n",
       "      <td>-7.55</td>\n",
       "      <td>-7.58</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>-7.63</td>\n",
       "      <td>-7.66</td>\n",
       "      <td>-7.68</td>\n",
       "      <td>-7.71</td>\n",
       "      <td>-7.73</td>\n",
       "      <td>-7.76</td>\n",
       "      <td>-7.78</td>\n",
       "      <td>-7.81</td>\n",
       "      <td>-7.83</td>\n",
       "      <td>-7.86</td>\n",
       "      <td>-7.89</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>-7.96</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>-8.01</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-8.06</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>-8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_0_FO</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.23</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.46</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-4.91</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-5.13</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-5.28</td>\n",
       "      <td>-5.35</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>-5.57</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-5.85</td>\n",
       "      <td>-5.92</td>\n",
       "      <td>-5.99</td>\n",
       "      <td>-6.06</td>\n",
       "      <td>-6.13</td>\n",
       "      <td>-6.19</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>-6.33</td>\n",
       "      <td>-6.39</td>\n",
       "      <td>-6.46</td>\n",
       "      <td>-6.52</td>\n",
       "      <td>-6.59</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>-6.71</td>\n",
       "      <td>-6.78</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>-6.96</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>-7.08</td>\n",
       "      <td>-7.14</td>\n",
       "      <td>-7.20</td>\n",
       "      <td>-7.26</td>\n",
       "      <td>-7.31</td>\n",
       "      <td>-7.37</td>\n",
       "      <td>-7.43</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>-7.54</td>\n",
       "      <td>-7.59</td>\n",
       "      <td>-7.65</td>\n",
       "      <td>-7.70</td>\n",
       "      <td>-7.76</td>\n",
       "      <td>-7.81</td>\n",
       "      <td>-7.86</td>\n",
       "      <td>-7.92</td>\n",
       "      <td>-7.97</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>-8.07</td>\n",
       "      <td>-8.12</td>\n",
       "      <td>-8.17</td>\n",
       "      <td>-8.22</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>-8.32</td>\n",
       "      <td>-8.37</td>\n",
       "      <td>-8.42</td>\n",
       "      <td>-8.47</td>\n",
       "      <td>-8.52</td>\n",
       "      <td>-8.56</td>\n",
       "      <td>-8.61</td>\n",
       "      <td>-8.66</td>\n",
       "      <td>-8.70</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>-8.80</td>\n",
       "      <td>-8.84</td>\n",
       "      <td>-8.89</td>\n",
       "      <td>-8.93</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-9.02</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>-9.11</td>\n",
       "      <td>-9.16</td>\n",
       "      <td>-9.20</td>\n",
       "      <td>-9.24</td>\n",
       "      <td>-9.29</td>\n",
       "      <td>-9.33</td>\n",
       "      <td>-9.37</td>\n",
       "      <td>-9.41</td>\n",
       "      <td>-9.46</td>\n",
       "      <td>-9.50</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>-9.58</td>\n",
       "      <td>-9.62</td>\n",
       "      <td>-9.67</td>\n",
       "      <td>-9.71</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>-9.79</td>\n",
       "      <td>-9.83</td>\n",
       "      <td>-9.87</td>\n",
       "      <td>-9.91</td>\n",
       "      <td>-9.95</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-10.03</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>-10.11</td>\n",
       "      <td>-10.15</td>\n",
       "      <td>-10.19</td>\n",
       "      <td>-10.23</td>\n",
       "      <td>-10.27</td>\n",
       "      <td>-10.31</td>\n",
       "      <td>-10.35</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>-10.43</td>\n",
       "      <td>-10.47</td>\n",
       "      <td>-10.51</td>\n",
       "      <td>-10.55</td>\n",
       "      <td>-10.58</td>\n",
       "      <td>-10.62</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-10.70</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>-10.78</td>\n",
       "      <td>-10.82</td>\n",
       "      <td>-10.85</td>\n",
       "      <td>-10.89</td>\n",
       "      <td>-10.93</td>\n",
       "      <td>-10.97</td>\n",
       "      <td>-11.01</td>\n",
       "      <td>-11.04</td>\n",
       "      <td>-11.08</td>\n",
       "      <td>-11.12</td>\n",
       "      <td>-11.16</td>\n",
       "      <td>-11.19</td>\n",
       "      <td>-11.23</td>\n",
       "      <td>-11.27</td>\n",
       "      <td>-11.31</td>\n",
       "      <td>-11.34</td>\n",
       "      <td>-11.38</td>\n",
       "      <td>-11.42</td>\n",
       "      <td>-11.46</td>\n",
       "      <td>-11.49</td>\n",
       "      <td>-11.53</td>\n",
       "      <td>-11.57</td>\n",
       "      <td>-11.61</td>\n",
       "      <td>-11.64</td>\n",
       "      <td>-11.68</td>\n",
       "      <td>-11.72</td>\n",
       "      <td>-11.75</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>-11.83</td>\n",
       "      <td>-11.86</td>\n",
       "      <td>-11.90</td>\n",
       "      <td>-11.94</td>\n",
       "      <td>-11.98</td>\n",
       "      <td>-12.01</td>\n",
       "      <td>-12.05</td>\n",
       "      <td>-12.09</td>\n",
       "      <td>-12.12</td>\n",
       "      <td>-12.16</td>\n",
       "      <td>-12.20</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>-12.27</td>\n",
       "      <td>-12.31</td>\n",
       "      <td>-12.34</td>\n",
       "      <td>-12.38</td>\n",
       "      <td>-12.42</td>\n",
       "      <td>-12.45</td>\n",
       "      <td>-12.49</td>\n",
       "      <td>-12.53</td>\n",
       "      <td>-12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_1_Cf</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.86</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.63</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.70</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.77</td>\n",
       "      <td>-4.79</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.94</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.01</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>-5.12</td>\n",
       "      <td>-5.14</td>\n",
       "      <td>-5.16</td>\n",
       "      <td>-5.18</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>-5.22</td>\n",
       "      <td>-5.25</td>\n",
       "      <td>-5.27</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-5.31</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-5.35</td>\n",
       "      <td>-5.37</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.41</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.48</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-5.54</td>\n",
       "      <td>-5.56</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>-5.60</td>\n",
       "      <td>-5.62</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-5.76</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_2_Strd</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.73</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.25</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.48</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>-5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_3_WO</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.94</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_1</th>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.73</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.26</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.42</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.49</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.60</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.70</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.77</td>\n",
       "      <td>-4.79</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_0</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.25</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.48</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_Avg</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_Avg</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0     1     2     3     4     5     6     7  \\\n",
       "Price_Point               0.50  0.51  0.52  0.53  0.54  0.55  0.56  0.57   \n",
       "Mean_PE                  -0.10 -0.10 -0.10 -0.11 -0.11 -0.12 -0.12 -0.13   \n",
       "PE_Segment_0_FO          -0.03 -0.03 -0.04 -0.04 -0.04 -0.04 -0.04 -0.05   \n",
       "PE_Segment_1_Cf          -0.12 -0.12 -0.13 -0.13 -0.14 -0.14 -0.15 -0.15   \n",
       "PE_Segment_2_Strd        -0.21 -0.22 -0.22 -0.23 -0.24 -0.25 -0.25 -0.26   \n",
       "PE_Segment_3_WO          -0.17 -0.17 -0.18 -0.18 -0.19 -0.19 -0.20 -0.20   \n",
       "Elasticity_Promotion_1   -0.13 -0.13 -0.13 -0.14 -0.14 -0.15 -0.15 -0.16   \n",
       "Elasticity_Promotion_0   -0.20 -0.20 -0.21 -0.21 -0.22 -0.23 -0.23 -0.24   \n",
       "Brand5_own_Avg           -0.05 -0.05 -0.05 -0.05 -0.06 -0.06 -0.06 -0.06   \n",
       "Brand5_Cross_Brand_4_Avg  0.33  0.34  0.34  0.35  0.35  0.36  0.36  0.37   \n",
       "\n",
       "                             8     9    10    11    12    13    14    15  \\\n",
       "Price_Point               0.58  0.59  0.60  0.61  0.62  0.63  0.64  0.65   \n",
       "Mean_PE                  -0.13 -0.14 -0.14 -0.15 -0.15 -0.16 -0.17 -0.17   \n",
       "PE_Segment_0_FO          -0.05 -0.05 -0.06 -0.06 -0.06 -0.06 -0.07 -0.07   \n",
       "PE_Segment_1_Cf          -0.15 -0.16 -0.16 -0.17 -0.18 -0.18 -0.19 -0.19   \n",
       "PE_Segment_2_Strd        -0.27 -0.27 -0.28 -0.29 -0.30 -0.31 -0.31 -0.32   \n",
       "PE_Segment_3_WO          -0.21 -0.21 -0.22 -0.22 -0.23 -0.24 -0.24 -0.25   \n",
       "Elasticity_Promotion_1   -0.16 -0.17 -0.17 -0.18 -0.18 -0.19 -0.19 -0.20   \n",
       "Elasticity_Promotion_0   -0.25 -0.25 -0.26 -0.27 -0.28 -0.28 -0.29 -0.30   \n",
       "Brand5_own_Avg           -0.06 -0.06 -0.07 -0.07 -0.07 -0.07 -0.07 -0.08   \n",
       "Brand5_Cross_Brand_4_Avg  0.37  0.38  0.38  0.39  0.39  0.40  0.40  0.40   \n",
       "\n",
       "                            16    17    18    19    20    21    22    23  \\\n",
       "Price_Point               0.66  0.67  0.68  0.69  0.70  0.71  0.72  0.73   \n",
       "Mean_PE                  -0.18 -0.19 -0.19 -0.20 -0.21 -0.21 -0.22 -0.23   \n",
       "PE_Segment_0_FO          -0.07 -0.08 -0.08 -0.09 -0.09 -0.10 -0.10 -0.11   \n",
       "PE_Segment_1_Cf          -0.20 -0.20 -0.21 -0.22 -0.22 -0.23 -0.23 -0.24   \n",
       "PE_Segment_2_Strd        -0.33 -0.34 -0.35 -0.36 -0.36 -0.37 -0.38 -0.39   \n",
       "PE_Segment_3_WO          -0.25 -0.26 -0.27 -0.27 -0.28 -0.28 -0.29 -0.30   \n",
       "Elasticity_Promotion_1   -0.20 -0.21 -0.21 -0.22 -0.22 -0.23 -0.24 -0.24   \n",
       "Elasticity_Promotion_0   -0.31 -0.31 -0.32 -0.33 -0.34 -0.35 -0.36 -0.36   \n",
       "Brand5_own_Avg           -0.08 -0.08 -0.08 -0.08 -0.09 -0.09 -0.09 -0.09   \n",
       "Brand5_Cross_Brand_4_Avg  0.41  0.41  0.42  0.42  0.43  0.43  0.43  0.44   \n",
       "\n",
       "                            24    25    26    27    28    29    30    31  \\\n",
       "Price_Point               0.74  0.75  0.76  0.77  0.78  0.79  0.80  0.81   \n",
       "Mean_PE                  -0.24 -0.24 -0.25 -0.26 -0.27 -0.28 -0.29 -0.30   \n",
       "PE_Segment_0_FO          -0.11 -0.12 -0.12 -0.13 -0.13 -0.14 -0.15 -0.15   \n",
       "PE_Segment_1_Cf          -0.25 -0.25 -0.26 -0.27 -0.28 -0.28 -0.29 -0.30   \n",
       "PE_Segment_2_Strd        -0.40 -0.41 -0.42 -0.43 -0.44 -0.45 -0.46 -0.47   \n",
       "PE_Segment_3_WO          -0.30 -0.31 -0.32 -0.33 -0.33 -0.34 -0.35 -0.35   \n",
       "Elasticity_Promotion_1   -0.25 -0.25 -0.26 -0.27 -0.27 -0.28 -0.29 -0.29   \n",
       "Elasticity_Promotion_0   -0.37 -0.38 -0.39 -0.40 -0.41 -0.42 -0.43 -0.44   \n",
       "Brand5_own_Avg           -0.10 -0.10 -0.10 -0.10 -0.11 -0.11 -0.11 -0.11   \n",
       "Brand5_Cross_Brand_4_Avg  0.44  0.45  0.45  0.45  0.46  0.46  0.46  0.47   \n",
       "\n",
       "                            32    33    34    35    36    37    38    39  \\\n",
       "Price_Point               0.82  0.83  0.84  0.85  0.86  0.87  0.88  0.89   \n",
       "Mean_PE                  -0.31 -0.32 -0.33 -0.34 -0.35 -0.36 -0.37 -0.38   \n",
       "PE_Segment_0_FO          -0.16 -0.17 -0.18 -0.19 -0.19 -0.20 -0.21 -0.22   \n",
       "PE_Segment_1_Cf          -0.31 -0.31 -0.32 -0.33 -0.34 -0.35 -0.36 -0.37   \n",
       "PE_Segment_2_Strd        -0.48 -0.49 -0.50 -0.51 -0.52 -0.53 -0.54 -0.55   \n",
       "PE_Segment_3_WO          -0.36 -0.37 -0.38 -0.38 -0.39 -0.40 -0.41 -0.42   \n",
       "Elasticity_Promotion_1   -0.30 -0.31 -0.32 -0.32 -0.33 -0.34 -0.35 -0.35   \n",
       "Elasticity_Promotion_0   -0.45 -0.46 -0.47 -0.48 -0.49 -0.50 -0.51 -0.52   \n",
       "Brand5_own_Avg           -0.12 -0.12 -0.12 -0.12 -0.13 -0.13 -0.13 -0.14   \n",
       "Brand5_Cross_Brand_4_Avg  0.47  0.47  0.48  0.48  0.48  0.49  0.49  0.49   \n",
       "\n",
       "                            40    41    42    43    44    45    46    47  \\\n",
       "Price_Point               0.90  0.91  0.92  0.93  0.94  0.95  0.96  0.97   \n",
       "Mean_PE                  -0.39 -0.41 -0.42 -0.43 -0.44 -0.46 -0.47 -0.48   \n",
       "PE_Segment_0_FO          -0.23 -0.24 -0.25 -0.27 -0.28 -0.29 -0.30 -0.32   \n",
       "PE_Segment_1_Cf          -0.37 -0.38 -0.39 -0.40 -0.41 -0.42 -0.43 -0.44   \n",
       "PE_Segment_2_Strd        -0.56 -0.58 -0.59 -0.60 -0.61 -0.62 -0.63 -0.65   \n",
       "PE_Segment_3_WO          -0.42 -0.43 -0.44 -0.45 -0.46 -0.47 -0.47 -0.48   \n",
       "Elasticity_Promotion_1   -0.36 -0.37 -0.38 -0.39 -0.39 -0.40 -0.41 -0.42   \n",
       "Elasticity_Promotion_0   -0.53 -0.54 -0.55 -0.56 -0.57 -0.58 -0.59 -0.60   \n",
       "Brand5_own_Avg           -0.14 -0.14 -0.15 -0.15 -0.15 -0.16 -0.16 -0.16   \n",
       "Brand5_Cross_Brand_4_Avg  0.50  0.50  0.50  0.51  0.51  0.51  0.51  0.52   \n",
       "\n",
       "                            48    49    50    51    52    53    54    55  \\\n",
       "Price_Point               0.98  0.99  1.00  1.01  1.02  1.03  1.04  1.05   \n",
       "Mean_PE                  -0.50 -0.51 -0.53 -0.54 -0.56 -0.57 -0.59 -0.61   \n",
       "PE_Segment_0_FO          -0.33 -0.34 -0.36 -0.37 -0.39 -0.41 -0.42 -0.44   \n",
       "PE_Segment_1_Cf          -0.45 -0.46 -0.47 -0.48 -0.49 -0.51 -0.52 -0.53   \n",
       "PE_Segment_2_Strd        -0.66 -0.67 -0.68 -0.70 -0.71 -0.72 -0.73 -0.75   \n",
       "PE_Segment_3_WO          -0.49 -0.50 -0.51 -0.52 -0.53 -0.54 -0.55 -0.56   \n",
       "Elasticity_Promotion_1   -0.43 -0.44 -0.45 -0.46 -0.47 -0.48 -0.48 -0.49   \n",
       "Elasticity_Promotion_0   -0.62 -0.63 -0.64 -0.65 -0.66 -0.68 -0.69 -0.70   \n",
       "Brand5_own_Avg           -0.17 -0.17 -0.17 -0.18 -0.18 -0.18 -0.19 -0.19   \n",
       "Brand5_Cross_Brand_4_Avg  0.52  0.52  0.52  0.53  0.53  0.53  0.53  0.53   \n",
       "\n",
       "                            56    57    58    59    60    61    62    63  \\\n",
       "Price_Point               1.06  1.07  1.08  1.09  1.10  1.11  1.12  1.13   \n",
       "Mean_PE                  -0.62 -0.64 -0.66 -0.67 -0.69 -0.71 -0.73 -0.75   \n",
       "PE_Segment_0_FO          -0.46 -0.48 -0.50 -0.52 -0.54 -0.56 -0.59 -0.61   \n",
       "PE_Segment_1_Cf          -0.54 -0.55 -0.56 -0.58 -0.59 -0.60 -0.61 -0.63   \n",
       "PE_Segment_2_Strd        -0.76 -0.77 -0.79 -0.80 -0.81 -0.83 -0.84 -0.85   \n",
       "PE_Segment_3_WO          -0.57 -0.58 -0.59 -0.60 -0.60 -0.61 -0.63 -0.64   \n",
       "Elasticity_Promotion_1   -0.50 -0.51 -0.52 -0.53 -0.54 -0.56 -0.57 -0.58   \n",
       "Elasticity_Promotion_0   -0.71 -0.73 -0.74 -0.75 -0.76 -0.78 -0.79 -0.80   \n",
       "Brand5_own_Avg           -0.20 -0.20 -0.20 -0.21 -0.21 -0.22 -0.22 -0.22   \n",
       "Brand5_Cross_Brand_4_Avg  0.54  0.54  0.54  0.54  0.54  0.55  0.55  0.55   \n",
       "\n",
       "                            64    65    66    67    68    69    70    71  \\\n",
       "Price_Point               1.14  1.15  1.16  1.17  1.18  1.19  1.20  1.21   \n",
       "Mean_PE                  -0.77 -0.79 -0.81 -0.83 -0.85 -0.87 -0.89 -0.91   \n",
       "PE_Segment_0_FO          -0.64 -0.66 -0.69 -0.71 -0.74 -0.77 -0.80 -0.83   \n",
       "PE_Segment_1_Cf          -0.64 -0.65 -0.66 -0.68 -0.69 -0.71 -0.72 -0.73   \n",
       "PE_Segment_2_Strd        -0.87 -0.88 -0.90 -0.91 -0.93 -0.94 -0.96 -0.97   \n",
       "PE_Segment_3_WO          -0.65 -0.66 -0.67 -0.68 -0.69 -0.70 -0.71 -0.72   \n",
       "Elasticity_Promotion_1   -0.59 -0.60 -0.61 -0.62 -0.63 -0.64 -0.66 -0.67   \n",
       "Elasticity_Promotion_0   -0.82 -0.83 -0.84 -0.86 -0.87 -0.89 -0.90 -0.92   \n",
       "Brand5_own_Avg           -0.23 -0.23 -0.24 -0.24 -0.25 -0.25 -0.26 -0.26   \n",
       "Brand5_Cross_Brand_4_Avg  0.55  0.55  0.55  0.56  0.56  0.56  0.56  0.56   \n",
       "\n",
       "                            72    73    74    75    76    77    78    79  \\\n",
       "Price_Point               1.22  1.23  1.24  1.25  1.26  1.27  1.28  1.29   \n",
       "Mean_PE                  -0.94 -0.96 -0.98 -1.00 -1.03 -1.05 -1.08 -1.10   \n",
       "PE_Segment_0_FO          -0.86 -0.90 -0.93 -0.96 -1.00 -1.04 -1.07 -1.11   \n",
       "PE_Segment_1_Cf          -0.75 -0.76 -0.78 -0.79 -0.81 -0.82 -0.84 -0.85   \n",
       "PE_Segment_2_Strd        -0.98 -1.00 -1.02 -1.03 -1.05 -1.06 -1.08 -1.09   \n",
       "PE_Segment_3_WO          -0.73 -0.74 -0.75 -0.76 -0.78 -0.79 -0.80 -0.81   \n",
       "Elasticity_Promotion_1   -0.68 -0.69 -0.70 -0.72 -0.73 -0.74 -0.75 -0.77   \n",
       "Elasticity_Promotion_0   -0.93 -0.94 -0.96 -0.97 -0.99 -1.00 -1.02 -1.03   \n",
       "Brand5_own_Avg           -0.27 -0.27 -0.28 -0.28 -0.29 -0.29 -0.30 -0.30   \n",
       "Brand5_Cross_Brand_4_Avg  0.56  0.56  0.56  0.56  0.57  0.57  0.57  0.57   \n",
       "\n",
       "                            80    81    82    83    84    85    86    87  \\\n",
       "Price_Point               1.30  1.31  1.32  1.33  1.34  1.35  1.36  1.37   \n",
       "Mean_PE                  -1.13 -1.15 -1.18 -1.20 -1.23 -1.26 -1.29 -1.31   \n",
       "PE_Segment_0_FO          -1.15 -1.19 -1.23 -1.28 -1.32 -1.37 -1.41 -1.46   \n",
       "PE_Segment_1_Cf          -0.87 -0.89 -0.90 -0.92 -0.93 -0.95 -0.97 -0.98   \n",
       "PE_Segment_2_Strd        -1.11 -1.12 -1.14 -1.16 -1.17 -1.19 -1.20 -1.22   \n",
       "PE_Segment_3_WO          -0.82 -0.83 -0.84 -0.86 -0.87 -0.88 -0.89 -0.91   \n",
       "Elasticity_Promotion_1   -0.78 -0.79 -0.80 -0.82 -0.83 -0.84 -0.86 -0.87   \n",
       "Elasticity_Promotion_0   -1.05 -1.06 -1.08 -1.09 -1.11 -1.13 -1.14 -1.16   \n",
       "Brand5_own_Avg           -0.31 -0.32 -0.32 -0.33 -0.33 -0.34 -0.35 -0.35   \n",
       "Brand5_Cross_Brand_4_Avg  0.57  0.57  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "\n",
       "                            88    89    90    91    92    93    94    95  \\\n",
       "Price_Point               1.38  1.39  1.40  1.41  1.42  1.43  1.44  1.45   \n",
       "Mean_PE                  -1.34 -1.37 -1.40 -1.43 -1.46 -1.49 -1.52 -1.55   \n",
       "PE_Segment_0_FO          -1.51 -1.56 -1.61 -1.66 -1.71 -1.76 -1.82 -1.88   \n",
       "PE_Segment_1_Cf          -1.00 -1.02 -1.04 -1.05 -1.07 -1.09 -1.11 -1.13   \n",
       "PE_Segment_2_Strd        -1.24 -1.25 -1.27 -1.29 -1.30 -1.32 -1.34 -1.36   \n",
       "PE_Segment_3_WO          -0.92 -0.93 -0.94 -0.95 -0.97 -0.98 -0.99 -1.01   \n",
       "Elasticity_Promotion_1   -0.89 -0.90 -0.91 -0.93 -0.94 -0.96 -0.97 -0.99   \n",
       "Elasticity_Promotion_0   -1.17 -1.19 -1.21 -1.22 -1.24 -1.26 -1.27 -1.29   \n",
       "Brand5_own_Avg           -0.36 -0.36 -0.37 -0.38 -0.38 -0.39 -0.40 -0.40   \n",
       "Brand5_Cross_Brand_4_Avg  0.57  0.57  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "\n",
       "                            96    97    98    99   100   101   102   103  \\\n",
       "Price_Point               1.46  1.47  1.48  1.49  1.50  1.51  1.52  1.53   \n",
       "Mean_PE                  -1.58 -1.61 -1.64 -1.67 -1.70 -1.73 -1.77 -1.80   \n",
       "PE_Segment_0_FO          -1.93 -1.99 -2.05 -2.11 -2.17 -2.23 -2.29 -2.36   \n",
       "PE_Segment_1_Cf          -1.15 -1.16 -1.18 -1.20 -1.22 -1.24 -1.26 -1.28   \n",
       "PE_Segment_2_Strd        -1.37 -1.39 -1.41 -1.42 -1.44 -1.46 -1.48 -1.49   \n",
       "PE_Segment_3_WO          -1.02 -1.03 -1.04 -1.06 -1.07 -1.08 -1.10 -1.11   \n",
       "Elasticity_Promotion_1   -1.00 -1.02 -1.03 -1.05 -1.06 -1.08 -1.09 -1.11   \n",
       "Elasticity_Promotion_0   -1.30 -1.32 -1.34 -1.36 -1.37 -1.39 -1.41 -1.42   \n",
       "Brand5_own_Avg           -0.41 -0.42 -0.43 -0.43 -0.44 -0.45 -0.45 -0.46   \n",
       "Brand5_Cross_Brand_4_Avg  0.57  0.57  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "\n",
       "                           104   105   106   107   108   109   110   111  \\\n",
       "Price_Point               1.54  1.55  1.56  1.57  1.58  1.59  1.60  1.61   \n",
       "Mean_PE                  -1.83 -1.87 -1.90 -1.93 -1.97 -2.00 -2.04 -2.07   \n",
       "PE_Segment_0_FO          -2.42 -2.49 -2.55 -2.62 -2.69 -2.76 -2.83 -2.90   \n",
       "PE_Segment_1_Cf          -1.30 -1.32 -1.34 -1.36 -1.38 -1.40 -1.42 -1.44   \n",
       "PE_Segment_2_Strd        -1.51 -1.53 -1.55 -1.57 -1.58 -1.60 -1.62 -1.64   \n",
       "PE_Segment_3_WO          -1.12 -1.14 -1.15 -1.17 -1.18 -1.19 -1.21 -1.22   \n",
       "Elasticity_Promotion_1   -1.13 -1.14 -1.16 -1.17 -1.19 -1.21 -1.22 -1.24   \n",
       "Elasticity_Promotion_0   -1.44 -1.46 -1.48 -1.49 -1.51 -1.53 -1.55 -1.57   \n",
       "Brand5_own_Avg           -0.47 -0.48 -0.48 -0.49 -0.50 -0.51 -0.52 -0.53   \n",
       "Brand5_Cross_Brand_4_Avg  0.57  0.57  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "\n",
       "                           112   113   114   115   116   117   118   119  \\\n",
       "Price_Point               1.62  1.63  1.64  1.65  1.66  1.67  1.68  1.69   \n",
       "Mean_PE                  -2.11 -2.14 -2.18 -2.21 -2.25 -2.28 -2.32 -2.36   \n",
       "PE_Segment_0_FO          -2.97 -3.04 -3.11 -3.18 -3.25 -3.33 -3.40 -3.48   \n",
       "PE_Segment_1_Cf          -1.46 -1.48 -1.50 -1.53 -1.55 -1.57 -1.59 -1.61   \n",
       "PE_Segment_2_Strd        -1.66 -1.67 -1.69 -1.71 -1.73 -1.75 -1.77 -1.79   \n",
       "PE_Segment_3_WO          -1.24 -1.25 -1.26 -1.28 -1.29 -1.31 -1.32 -1.34   \n",
       "Elasticity_Promotion_1   -1.26 -1.27 -1.29 -1.31 -1.32 -1.34 -1.36 -1.38   \n",
       "Elasticity_Promotion_0   -1.58 -1.60 -1.62 -1.64 -1.66 -1.67 -1.69 -1.71   \n",
       "Brand5_own_Avg           -0.53 -0.54 -0.55 -0.56 -0.57 -0.58 -0.59 -0.59   \n",
       "Brand5_Cross_Brand_4_Avg  0.57  0.57  0.57  0.56  0.56  0.56  0.56  0.56   \n",
       "\n",
       "                           120   121   122   123   124   125   126   127  \\\n",
       "Price_Point               1.70  1.71  1.72  1.73  1.74  1.75  1.76  1.77   \n",
       "Mean_PE                  -2.39 -2.43 -2.47 -2.50 -2.54 -2.58 -2.61 -2.65   \n",
       "PE_Segment_0_FO          -3.55 -3.62 -3.70 -3.78 -3.85 -3.93 -4.00 -4.08   \n",
       "PE_Segment_1_Cf          -1.64 -1.66 -1.68 -1.70 -1.72 -1.75 -1.77 -1.79   \n",
       "PE_Segment_2_Strd        -1.80 -1.82 -1.84 -1.86 -1.88 -1.90 -1.92 -1.94   \n",
       "PE_Segment_3_WO          -1.35 -1.36 -1.38 -1.39 -1.41 -1.42 -1.44 -1.45   \n",
       "Elasticity_Promotion_1   -1.39 -1.41 -1.43 -1.45 -1.46 -1.48 -1.50 -1.52   \n",
       "Elasticity_Promotion_0   -1.73 -1.75 -1.77 -1.78 -1.80 -1.82 -1.84 -1.86   \n",
       "Brand5_own_Avg           -0.60 -0.61 -0.62 -0.63 -0.64 -0.65 -0.66 -0.67   \n",
       "Brand5_Cross_Brand_4_Avg  0.56  0.56  0.56  0.56  0.55  0.55  0.55  0.55   \n",
       "\n",
       "                           128   129   130   131   132   133   134   135  \\\n",
       "Price_Point               1.78  1.79  1.80  1.81  1.82  1.83  1.84  1.85   \n",
       "Mean_PE                  -2.69 -2.73 -2.77 -2.80 -2.84 -2.88 -2.92 -2.96   \n",
       "PE_Segment_0_FO          -4.15 -4.23 -4.31 -4.38 -4.46 -4.53 -4.61 -4.69   \n",
       "PE_Segment_1_Cf          -1.82 -1.84 -1.86 -1.88 -1.91 -1.93 -1.95 -1.98   \n",
       "PE_Segment_2_Strd        -1.96 -1.97 -1.99 -2.01 -2.03 -2.05 -2.07 -2.09   \n",
       "PE_Segment_3_WO          -1.47 -1.48 -1.50 -1.51 -1.53 -1.54 -1.56 -1.57   \n",
       "Elasticity_Promotion_1   -1.54 -1.56 -1.57 -1.59 -1.61 -1.63 -1.65 -1.67   \n",
       "Elasticity_Promotion_0   -1.88 -1.90 -1.92 -1.93 -1.95 -1.97 -1.99 -2.01   \n",
       "Brand5_own_Avg           -0.68 -0.69 -0.70 -0.71 -0.72 -0.73 -0.74 -0.75   \n",
       "Brand5_Cross_Brand_4_Avg  0.55  0.55  0.55  0.55  0.54  0.54  0.54  0.54   \n",
       "\n",
       "                           136   137   138   139   140   141   142   143  \\\n",
       "Price_Point               1.86  1.87  1.88  1.89  1.90  1.91  1.92  1.93   \n",
       "Mean_PE                  -2.99 -3.03 -3.07 -3.11 -3.15 -3.18 -3.22 -3.26   \n",
       "PE_Segment_0_FO          -4.76 -4.84 -4.91 -4.99 -5.06 -5.13 -5.21 -5.28   \n",
       "PE_Segment_1_Cf          -2.00 -2.03 -2.05 -2.07 -2.10 -2.12 -2.15 -2.17   \n",
       "PE_Segment_2_Strd        -2.11 -2.13 -2.15 -2.17 -2.19 -2.20 -2.22 -2.24   \n",
       "PE_Segment_3_WO          -1.59 -1.61 -1.62 -1.64 -1.65 -1.67 -1.68 -1.70   \n",
       "Elasticity_Promotion_1   -1.69 -1.71 -1.72 -1.74 -1.76 -1.78 -1.80 -1.82   \n",
       "Elasticity_Promotion_0   -2.03 -2.05 -2.07 -2.09 -2.11 -2.13 -2.14 -2.16   \n",
       "Brand5_own_Avg           -0.76 -0.77 -0.78 -0.80 -0.81 -0.82 -0.83 -0.84   \n",
       "Brand5_Cross_Brand_4_Avg  0.54  0.54  0.53  0.53  0.53  0.53  0.53  0.53   \n",
       "\n",
       "                           144   145   146   147   148   149   150   151  \\\n",
       "Price_Point               1.94  1.95  1.96  1.97  1.98  1.99  2.00  2.01   \n",
       "Mean_PE                  -3.30 -3.34 -3.38 -3.42 -3.45 -3.49 -3.53 -3.57   \n",
       "PE_Segment_0_FO          -5.35 -5.43 -5.50 -5.57 -5.64 -5.71 -5.78 -5.85   \n",
       "PE_Segment_1_Cf          -2.19 -2.22 -2.24 -2.27 -2.29 -2.32 -2.34 -2.37   \n",
       "PE_Segment_2_Strd        -2.26 -2.28 -2.30 -2.32 -2.34 -2.36 -2.38 -2.40   \n",
       "PE_Segment_3_WO          -1.71 -1.73 -1.75 -1.76 -1.78 -1.79 -1.81 -1.83   \n",
       "Elasticity_Promotion_1   -1.84 -1.86 -1.88 -1.90 -1.92 -1.94 -1.96 -1.98   \n",
       "Elasticity_Promotion_0   -2.18 -2.20 -2.22 -2.24 -2.26 -2.28 -2.30 -2.32   \n",
       "Brand5_own_Avg           -0.85 -0.86 -0.88 -0.89 -0.90 -0.91 -0.92 -0.93   \n",
       "Brand5_Cross_Brand_4_Avg  0.52  0.52  0.52  0.52  0.52  0.51  0.51  0.51   \n",
       "\n",
       "                           152   153   154   155   156   157   158   159  \\\n",
       "Price_Point               2.02  2.03  2.04  2.05  2.06  2.07  2.08  2.09   \n",
       "Mean_PE                  -3.61 -3.64 -3.68 -3.72 -3.76 -3.80 -3.83 -3.87   \n",
       "PE_Segment_0_FO          -5.92 -5.99 -6.06 -6.13 -6.19 -6.26 -6.33 -6.39   \n",
       "PE_Segment_1_Cf          -2.39 -2.42 -2.44 -2.47 -2.49 -2.51 -2.54 -2.56   \n",
       "PE_Segment_2_Strd        -2.42 -2.44 -2.46 -2.48 -2.50 -2.52 -2.54 -2.55   \n",
       "PE_Segment_3_WO          -1.84 -1.86 -1.87 -1.89 -1.91 -1.92 -1.94 -1.95   \n",
       "Elasticity_Promotion_1   -2.00 -2.02 -2.04 -2.06 -2.08 -2.10 -2.12 -2.14   \n",
       "Elasticity_Promotion_0   -2.34 -2.36 -2.38 -2.40 -2.42 -2.44 -2.45 -2.47   \n",
       "Brand5_own_Avg           -0.95 -0.96 -0.97 -0.98 -1.00 -1.01 -1.02 -1.04   \n",
       "Brand5_Cross_Brand_4_Avg  0.51  0.51  0.50  0.50  0.50  0.50  0.50  0.49   \n",
       "\n",
       "                           160   161   162   163   164   165   166   167  \\\n",
       "Price_Point               2.10  2.11  2.12  2.13  2.14  2.15  2.16  2.17   \n",
       "Mean_PE                  -3.91 -3.95 -3.98 -4.02 -4.06 -4.10 -4.13 -4.17   \n",
       "PE_Segment_0_FO          -6.46 -6.52 -6.59 -6.65 -6.71 -6.78 -6.84 -6.90   \n",
       "PE_Segment_1_Cf          -2.59 -2.61 -2.64 -2.66 -2.69 -2.72 -2.74 -2.77   \n",
       "PE_Segment_2_Strd        -2.57 -2.59 -2.61 -2.63 -2.65 -2.67 -2.69 -2.71   \n",
       "PE_Segment_3_WO          -1.97 -1.99 -2.00 -2.02 -2.03 -2.05 -2.07 -2.08   \n",
       "Elasticity_Promotion_1   -2.16 -2.18 -2.20 -2.22 -2.24 -2.26 -2.28 -2.30   \n",
       "Elasticity_Promotion_0   -2.49 -2.51 -2.53 -2.55 -2.57 -2.59 -2.61 -2.63   \n",
       "Brand5_own_Avg           -1.05 -1.06 -1.07 -1.09 -1.10 -1.11 -1.13 -1.14   \n",
       "Brand5_Cross_Brand_4_Avg  0.49  0.49  0.49  0.48  0.48  0.48  0.48  0.48   \n",
       "\n",
       "                           168   169   170   171   172   173   174   175  \\\n",
       "Price_Point               2.18  2.19  2.20  2.21  2.22  2.23  2.24  2.25   \n",
       "Mean_PE                  -4.21 -4.24 -4.28 -4.32 -4.35 -4.39 -4.43 -4.46   \n",
       "PE_Segment_0_FO          -6.96 -7.02 -7.08 -7.14 -7.20 -7.26 -7.31 -7.37   \n",
       "PE_Segment_1_Cf          -2.79 -2.82 -2.84 -2.87 -2.89 -2.92 -2.94 -2.97   \n",
       "PE_Segment_2_Strd        -2.73 -2.75 -2.77 -2.79 -2.81 -2.83 -2.85 -2.87   \n",
       "PE_Segment_3_WO          -2.10 -2.12 -2.13 -2.15 -2.17 -2.18 -2.20 -2.22   \n",
       "Elasticity_Promotion_1   -2.32 -2.34 -2.37 -2.39 -2.41 -2.43 -2.45 -2.47   \n",
       "Elasticity_Promotion_0   -2.65 -2.67 -2.69 -2.71 -2.73 -2.75 -2.77 -2.79   \n",
       "Brand5_own_Avg           -1.16 -1.17 -1.18 -1.20 -1.21 -1.22 -1.24 -1.25   \n",
       "Brand5_Cross_Brand_4_Avg  0.47  0.47  0.47  0.47  0.46  0.46  0.46  0.46   \n",
       "\n",
       "                           176   177   178   179   180   181   182   183  \\\n",
       "Price_Point               2.26  2.27  2.28  2.29  2.30  2.31  2.32  2.33   \n",
       "Mean_PE                  -4.50 -4.54 -4.57 -4.61 -4.64 -4.68 -4.71 -4.75   \n",
       "PE_Segment_0_FO          -7.43 -7.48 -7.54 -7.59 -7.65 -7.70 -7.76 -7.81   \n",
       "PE_Segment_1_Cf          -2.99 -3.02 -3.04 -3.07 -3.09 -3.12 -3.14 -3.17   \n",
       "PE_Segment_2_Strd        -2.89 -2.91 -2.93 -2.94 -2.96 -2.98 -3.00 -3.02   \n",
       "PE_Segment_3_WO          -2.23 -2.25 -2.26 -2.28 -2.30 -2.31 -2.33 -2.35   \n",
       "Elasticity_Promotion_1   -2.49 -2.51 -2.53 -2.55 -2.57 -2.59 -2.61 -2.64   \n",
       "Elasticity_Promotion_0   -2.81 -2.83 -2.85 -2.86 -2.88 -2.90 -2.92 -2.94   \n",
       "Brand5_own_Avg           -1.27 -1.28 -1.30 -1.31 -1.33 -1.34 -1.36 -1.37   \n",
       "Brand5_Cross_Brand_4_Avg  0.45  0.45  0.45  0.45  0.44  0.44  0.44  0.44   \n",
       "\n",
       "                           184   185   186   187   188   189   190   191  \\\n",
       "Price_Point               2.34  2.35  2.36  2.37  2.38  2.39  2.40  2.41   \n",
       "Mean_PE                  -4.78 -4.82 -4.85 -4.89 -4.92 -4.96 -4.99 -5.02   \n",
       "PE_Segment_0_FO          -7.86 -7.92 -7.97 -8.02 -8.07 -8.12 -8.17 -8.22   \n",
       "PE_Segment_1_Cf          -3.19 -3.22 -3.24 -3.27 -3.29 -3.32 -3.34 -3.37   \n",
       "PE_Segment_2_Strd        -3.04 -3.06 -3.08 -3.10 -3.12 -3.14 -3.16 -3.18   \n",
       "PE_Segment_3_WO          -2.36 -2.38 -2.40 -2.41 -2.43 -2.45 -2.46 -2.48   \n",
       "Elasticity_Promotion_1   -2.66 -2.68 -2.70 -2.72 -2.74 -2.76 -2.78 -2.80   \n",
       "Elasticity_Promotion_0   -2.96 -2.98 -3.00 -3.02 -3.04 -3.06 -3.08 -3.10   \n",
       "Brand5_own_Avg           -1.39 -1.40 -1.42 -1.43 -1.45 -1.46 -1.48 -1.49   \n",
       "Brand5_Cross_Brand_4_Avg  0.43  0.43  0.43  0.43  0.42  0.42  0.42  0.42   \n",
       "\n",
       "                           192   193   194   195   196   197   198   199  \\\n",
       "Price_Point               2.42  2.43  2.44  2.45  2.46  2.47  2.48  2.49   \n",
       "Mean_PE                  -5.06 -5.09 -5.13 -5.16 -5.19 -5.23 -5.26 -5.29   \n",
       "PE_Segment_0_FO          -8.27 -8.32 -8.37 -8.42 -8.47 -8.52 -8.56 -8.61   \n",
       "PE_Segment_1_Cf          -3.39 -3.42 -3.44 -3.47 -3.49 -3.52 -3.54 -3.57   \n",
       "PE_Segment_2_Strd        -3.20 -3.21 -3.23 -3.25 -3.27 -3.29 -3.31 -3.33   \n",
       "PE_Segment_3_WO          -2.50 -2.51 -2.53 -2.55 -2.56 -2.58 -2.60 -2.61   \n",
       "Elasticity_Promotion_1   -2.82 -2.84 -2.87 -2.89 -2.91 -2.93 -2.95 -2.97   \n",
       "Elasticity_Promotion_0   -3.12 -3.14 -3.16 -3.17 -3.19 -3.21 -3.23 -3.25   \n",
       "Brand5_own_Avg           -1.51 -1.52 -1.54 -1.55 -1.57 -1.59 -1.60 -1.62   \n",
       "Brand5_Cross_Brand_4_Avg  0.41  0.41  0.41  0.41  0.40  0.40  0.40  0.39   \n",
       "\n",
       "                           200   201   202   203   204   205   206   207  \\\n",
       "Price_Point               2.50  2.51  2.52  2.53  2.54  2.55  2.56  2.57   \n",
       "Mean_PE                  -5.33 -5.36 -5.39 -5.42 -5.46 -5.49 -5.52 -5.55   \n",
       "PE_Segment_0_FO          -8.66 -8.70 -8.75 -8.80 -8.84 -8.89 -8.93 -8.98   \n",
       "PE_Segment_1_Cf          -3.59 -3.62 -3.64 -3.67 -3.69 -3.71 -3.74 -3.76   \n",
       "PE_Segment_2_Strd        -3.35 -3.37 -3.39 -3.41 -3.42 -3.44 -3.46 -3.48   \n",
       "PE_Segment_3_WO          -2.63 -2.65 -2.66 -2.68 -2.70 -2.71 -2.73 -2.75   \n",
       "Elasticity_Promotion_1   -2.99 -3.01 -3.03 -3.05 -3.07 -3.10 -3.12 -3.14   \n",
       "Elasticity_Promotion_0   -3.27 -3.29 -3.31 -3.33 -3.35 -3.37 -3.39 -3.40   \n",
       "Brand5_own_Avg           -1.63 -1.65 -1.66 -1.68 -1.70 -1.71 -1.73 -1.75   \n",
       "Brand5_Cross_Brand_4_Avg  0.39  0.39  0.39  0.38  0.38  0.38  0.38  0.37   \n",
       "\n",
       "                           208   209   210   211   212   213   214   215  \\\n",
       "Price_Point               2.58  2.59  2.60  2.61  2.62  2.63  2.64  2.65   \n",
       "Mean_PE                  -5.59 -5.62 -5.65 -5.68 -5.71 -5.74 -5.77 -5.81   \n",
       "PE_Segment_0_FO          -9.02 -9.07 -9.11 -9.16 -9.20 -9.24 -9.29 -9.33   \n",
       "PE_Segment_1_Cf          -3.79 -3.81 -3.84 -3.86 -3.88 -3.91 -3.93 -3.96   \n",
       "PE_Segment_2_Strd        -3.50 -3.52 -3.54 -3.56 -3.58 -3.59 -3.61 -3.63   \n",
       "PE_Segment_3_WO          -2.76 -2.78 -2.80 -2.81 -2.83 -2.85 -2.86 -2.88   \n",
       "Elasticity_Promotion_1   -3.16 -3.18 -3.20 -3.22 -3.24 -3.26 -3.28 -3.30   \n",
       "Elasticity_Promotion_0   -3.42 -3.44 -3.46 -3.48 -3.50 -3.52 -3.54 -3.56   \n",
       "Brand5_own_Avg           -1.76 -1.78 -1.79 -1.81 -1.83 -1.84 -1.86 -1.88   \n",
       "Brand5_Cross_Brand_4_Avg  0.37  0.37  0.37  0.36  0.36  0.36  0.35  0.35   \n",
       "\n",
       "                           216   217   218   219   220   221   222   223  \\\n",
       "Price_Point               2.66  2.67  2.68  2.69  2.70  2.71  2.72  2.73   \n",
       "Mean_PE                  -5.84 -5.87 -5.90 -5.93 -5.96 -5.99 -6.02 -6.05   \n",
       "PE_Segment_0_FO          -9.37 -9.41 -9.46 -9.50 -9.54 -9.58 -9.62 -9.67   \n",
       "PE_Segment_1_Cf          -3.98 -4.01 -4.03 -4.05 -4.08 -4.10 -4.12 -4.15   \n",
       "PE_Segment_2_Strd        -3.65 -3.67 -3.69 -3.71 -3.73 -3.74 -3.76 -3.78   \n",
       "PE_Segment_3_WO          -2.90 -2.91 -2.93 -2.95 -2.96 -2.98 -3.00 -3.01   \n",
       "Elasticity_Promotion_1   -3.32 -3.34 -3.36 -3.38 -3.41 -3.43 -3.45 -3.47   \n",
       "Elasticity_Promotion_0   -3.57 -3.59 -3.61 -3.63 -3.65 -3.67 -3.69 -3.71   \n",
       "Brand5_own_Avg           -1.89 -1.91 -1.93 -1.94 -1.96 -1.98 -1.99 -2.01   \n",
       "Brand5_Cross_Brand_4_Avg  0.35  0.35  0.34  0.34  0.34  0.34  0.33  0.33   \n",
       "\n",
       "                           224   225   226   227   228   229   230   231  \\\n",
       "Price_Point               2.74  2.75  2.76  2.77  2.78  2.79  2.80  2.81   \n",
       "Mean_PE                  -6.08 -6.11 -6.14 -6.17 -6.20 -6.23 -6.26 -6.29   \n",
       "PE_Segment_0_FO          -9.71 -9.75 -9.79 -9.83 -9.87 -9.91 -9.95 -9.99   \n",
       "PE_Segment_1_Cf          -4.17 -4.20 -4.22 -4.24 -4.27 -4.29 -4.31 -4.34   \n",
       "PE_Segment_2_Strd        -3.80 -3.82 -3.84 -3.85 -3.87 -3.89 -3.91 -3.93   \n",
       "PE_Segment_3_WO          -3.03 -3.05 -3.06 -3.08 -3.09 -3.11 -3.13 -3.14   \n",
       "Elasticity_Promotion_1   -3.49 -3.51 -3.53 -3.55 -3.57 -3.59 -3.61 -3.63   \n",
       "Elasticity_Promotion_0   -3.72 -3.74 -3.76 -3.78 -3.80 -3.82 -3.84 -3.85   \n",
       "Brand5_own_Avg           -2.03 -2.05 -2.06 -2.08 -2.10 -2.11 -2.13 -2.15   \n",
       "Brand5_Cross_Brand_4_Avg  0.33  0.33  0.32  0.32  0.32  0.31  0.31  0.31   \n",
       "\n",
       "                            232    233    234    235    236    237    238  \\\n",
       "Price_Point                2.82   2.83   2.84   2.85   2.86   2.87   2.88   \n",
       "Mean_PE                   -6.32  -6.35  -6.38  -6.40  -6.43  -6.46  -6.49   \n",
       "PE_Segment_0_FO          -10.03 -10.07 -10.11 -10.15 -10.19 -10.23 -10.27   \n",
       "PE_Segment_1_Cf           -4.36  -4.38  -4.40  -4.43  -4.45  -4.47  -4.50   \n",
       "PE_Segment_2_Strd         -3.95  -3.96  -3.98  -4.00  -4.02  -4.04  -4.06   \n",
       "PE_Segment_3_WO           -3.16  -3.18  -3.19  -3.21  -3.23  -3.24  -3.26   \n",
       "Elasticity_Promotion_1    -3.65  -3.67  -3.69  -3.71  -3.73  -3.75  -3.77   \n",
       "Elasticity_Promotion_0    -3.87  -3.89  -3.91  -3.93  -3.95  -3.96  -3.98   \n",
       "Brand5_own_Avg            -2.16  -2.18  -2.20  -2.22  -2.23  -2.25  -2.27   \n",
       "Brand5_Cross_Brand_4_Avg   0.31   0.30   0.30   0.30   0.30   0.29   0.29   \n",
       "\n",
       "                            239    240    241    242    243    244    245  \\\n",
       "Price_Point                2.89   2.90   2.91   2.92   2.93   2.94   2.95   \n",
       "Mean_PE                   -6.52  -6.55  -6.58  -6.60  -6.63  -6.66  -6.69   \n",
       "PE_Segment_0_FO          -10.31 -10.35 -10.39 -10.43 -10.47 -10.51 -10.55   \n",
       "PE_Segment_1_Cf           -4.52  -4.54  -4.57  -4.59  -4.61  -4.63  -4.66   \n",
       "PE_Segment_2_Strd         -4.07  -4.09  -4.11  -4.13  -4.15  -4.16  -4.18   \n",
       "PE_Segment_3_WO           -3.27  -3.29  -3.31  -3.32  -3.34  -3.36  -3.37   \n",
       "Elasticity_Promotion_1    -3.79  -3.81  -3.83  -3.85  -3.87  -3.89  -3.91   \n",
       "Elasticity_Promotion_0    -4.00  -4.02  -4.04  -4.06  -4.07  -4.09  -4.11   \n",
       "Brand5_own_Avg            -2.28  -2.30  -2.32  -2.33  -2.35  -2.37  -2.39   \n",
       "Brand5_Cross_Brand_4_Avg   0.29   0.29   0.28   0.28   0.28   0.28   0.27   \n",
       "\n",
       "                            246    247    248    249    250    251    252  \\\n",
       "Price_Point                2.96   2.97   2.98   2.99   3.00   3.01   3.02   \n",
       "Mean_PE                   -6.72  -6.75  -6.77  -6.80  -6.83  -6.86  -6.88   \n",
       "PE_Segment_0_FO          -10.58 -10.62 -10.66 -10.70 -10.74 -10.78 -10.82   \n",
       "PE_Segment_1_Cf           -4.68  -4.70  -4.72  -4.75  -4.77  -4.79  -4.81   \n",
       "PE_Segment_2_Strd         -4.20  -4.22  -4.24  -4.25  -4.27  -4.29  -4.31   \n",
       "PE_Segment_3_WO           -3.39  -3.40  -3.42  -3.44  -3.45  -3.47  -3.49   \n",
       "Elasticity_Promotion_1    -3.93  -3.95  -3.97  -3.99  -4.01  -4.03  -4.05   \n",
       "Elasticity_Promotion_0    -4.13  -4.15  -4.16  -4.18  -4.20  -4.22  -4.24   \n",
       "Brand5_own_Avg            -2.40  -2.42  -2.44  -2.45  -2.47  -2.49  -2.51   \n",
       "Brand5_Cross_Brand_4_Avg   0.27   0.27   0.27   0.26   0.26   0.26   0.26   \n",
       "\n",
       "                            253    254    255    256    257    258    259  \\\n",
       "Price_Point                3.03   3.04   3.05   3.06   3.07   3.08   3.09   \n",
       "Mean_PE                   -6.91  -6.94  -6.97  -6.99  -7.02  -7.05  -7.07   \n",
       "PE_Segment_0_FO          -10.85 -10.89 -10.93 -10.97 -11.01 -11.04 -11.08   \n",
       "PE_Segment_1_Cf           -4.83  -4.86  -4.88  -4.90  -4.92  -4.94  -4.97   \n",
       "PE_Segment_2_Strd         -4.32  -4.34  -4.36  -4.38  -4.40  -4.41  -4.43   \n",
       "PE_Segment_3_WO           -3.50  -3.52  -3.53  -3.55  -3.57  -3.58  -3.60   \n",
       "Elasticity_Promotion_1    -4.07  -4.09  -4.11  -4.13  -4.15  -4.17  -4.19   \n",
       "Elasticity_Promotion_0    -4.25  -4.27  -4.29  -4.31  -4.32  -4.34  -4.36   \n",
       "Brand5_own_Avg            -2.52  -2.54  -2.56  -2.57  -2.59  -2.61  -2.63   \n",
       "Brand5_Cross_Brand_4_Avg   0.25   0.25   0.25   0.25   0.24   0.24   0.24   \n",
       "\n",
       "                            260    261    262    263    264    265    266  \\\n",
       "Price_Point                3.10   3.11   3.12   3.13   3.14   3.15   3.16   \n",
       "Mean_PE                   -7.10  -7.13  -7.16  -7.18  -7.21  -7.24  -7.26   \n",
       "PE_Segment_0_FO          -11.12 -11.16 -11.19 -11.23 -11.27 -11.31 -11.34   \n",
       "PE_Segment_1_Cf           -4.99  -5.01  -5.03  -5.05  -5.07  -5.10  -5.12   \n",
       "PE_Segment_2_Strd         -4.45  -4.47  -4.48  -4.50  -4.52  -4.54  -4.55   \n",
       "PE_Segment_3_WO           -3.61  -3.63  -3.65  -3.66  -3.68  -3.69  -3.71   \n",
       "Elasticity_Promotion_1    -4.20  -4.22  -4.24  -4.26  -4.28  -4.30  -4.32   \n",
       "Elasticity_Promotion_0    -4.38  -4.40  -4.41  -4.43  -4.45  -4.47  -4.48   \n",
       "Brand5_own_Avg            -2.64  -2.66  -2.68  -2.69  -2.71  -2.73  -2.74   \n",
       "Brand5_Cross_Brand_4_Avg   0.24   0.23   0.23   0.23   0.23   0.23   0.22   \n",
       "\n",
       "                            267    268    269    270    271    272    273  \\\n",
       "Price_Point                3.17   3.18   3.19   3.20   3.21   3.22   3.23   \n",
       "Mean_PE                   -7.29  -7.32  -7.34  -7.37  -7.39  -7.42  -7.45   \n",
       "PE_Segment_0_FO          -11.38 -11.42 -11.46 -11.49 -11.53 -11.57 -11.61   \n",
       "PE_Segment_1_Cf           -5.14  -5.16  -5.18  -5.20  -5.22  -5.25  -5.27   \n",
       "PE_Segment_2_Strd         -4.57  -4.59  -4.61  -4.62  -4.64  -4.66  -4.68   \n",
       "PE_Segment_3_WO           -3.72  -3.74  -3.76  -3.77  -3.79  -3.80  -3.82   \n",
       "Elasticity_Promotion_1    -4.34  -4.36  -4.38  -4.40  -4.42  -4.43  -4.45   \n",
       "Elasticity_Promotion_0    -4.50  -4.52  -4.54  -4.55  -4.57  -4.59  -4.61   \n",
       "Brand5_own_Avg            -2.76  -2.78  -2.80  -2.81  -2.83  -2.85  -2.86   \n",
       "Brand5_Cross_Brand_4_Avg   0.22   0.22   0.22   0.21   0.21   0.21   0.21   \n",
       "\n",
       "                            274    275    276    277    278    279    280  \\\n",
       "Price_Point                3.24   3.25   3.26   3.27   3.28   3.29   3.30   \n",
       "Mean_PE                   -7.47  -7.50  -7.53  -7.55  -7.58  -7.60  -7.63   \n",
       "PE_Segment_0_FO          -11.64 -11.68 -11.72 -11.75 -11.79 -11.83 -11.86   \n",
       "PE_Segment_1_Cf           -5.29  -5.31  -5.33  -5.35  -5.37  -5.39  -5.41   \n",
       "PE_Segment_2_Strd         -4.69  -4.71  -4.73  -4.74  -4.76  -4.78  -4.80   \n",
       "PE_Segment_3_WO           -3.84  -3.85  -3.87  -3.88  -3.90  -3.91  -3.93   \n",
       "Elasticity_Promotion_1    -4.47  -4.49  -4.51  -4.53  -4.55  -4.57  -4.59   \n",
       "Elasticity_Promotion_0    -4.62  -4.64  -4.66  -4.68  -4.69  -4.71  -4.73   \n",
       "Brand5_own_Avg            -2.88  -2.90  -2.91  -2.93  -2.95  -2.96  -2.98   \n",
       "Brand5_Cross_Brand_4_Avg   0.20   0.20   0.20   0.20   0.20   0.19   0.19   \n",
       "\n",
       "                            281    282    283    284    285    286    287  \\\n",
       "Price_Point                3.31   3.32   3.33   3.34   3.35   3.36   3.37   \n",
       "Mean_PE                   -7.66  -7.68  -7.71  -7.73  -7.76  -7.78  -7.81   \n",
       "PE_Segment_0_FO          -11.90 -11.94 -11.98 -12.01 -12.05 -12.09 -12.12   \n",
       "PE_Segment_1_Cf           -5.44  -5.46  -5.48  -5.50  -5.52  -5.54  -5.56   \n",
       "PE_Segment_2_Strd         -4.81  -4.83  -4.85  -4.86  -4.88  -4.90  -4.92   \n",
       "PE_Segment_3_WO           -3.94  -3.96  -3.98  -3.99  -4.01  -4.02  -4.04   \n",
       "Elasticity_Promotion_1    -4.60  -4.62  -4.64  -4.66  -4.68  -4.70  -4.72   \n",
       "Elasticity_Promotion_0    -4.74  -4.76  -4.78  -4.80  -4.81  -4.83  -4.85   \n",
       "Brand5_own_Avg            -3.00  -3.01  -3.03  -3.05  -3.06  -3.08  -3.09   \n",
       "Brand5_Cross_Brand_4_Avg   0.19   0.19   0.19   0.18   0.18   0.18   0.18   \n",
       "\n",
       "                            288    289    290    291    292    293    294  \\\n",
       "Price_Point                3.38   3.39   3.40   3.41   3.42   3.43   3.44   \n",
       "Mean_PE                   -7.83  -7.86  -7.89  -7.91  -7.94  -7.96  -7.99   \n",
       "PE_Segment_0_FO          -12.16 -12.20 -12.23 -12.27 -12.31 -12.34 -12.38   \n",
       "PE_Segment_1_Cf           -5.58  -5.60  -5.62  -5.64  -5.66  -5.68  -5.70   \n",
       "PE_Segment_2_Strd         -4.93  -4.95  -4.97  -4.98  -5.00  -5.02  -5.03   \n",
       "PE_Segment_3_WO           -4.05  -4.07  -4.08  -4.10  -4.12  -4.13  -4.15   \n",
       "Elasticity_Promotion_1    -4.73  -4.75  -4.77  -4.79  -4.81  -4.83  -4.84   \n",
       "Elasticity_Promotion_0    -4.86  -4.88  -4.90  -4.92  -4.93  -4.95  -4.97   \n",
       "Brand5_own_Avg            -3.11  -3.13  -3.14  -3.16  -3.18  -3.19  -3.21   \n",
       "Brand5_Cross_Brand_4_Avg   0.18   0.17   0.17   0.17   0.17   0.17   0.16   \n",
       "\n",
       "                            295    296    297    298    299  \n",
       "Price_Point                3.45   3.46   3.47   3.48   3.49  \n",
       "Mean_PE                   -8.01  -8.04  -8.06  -8.09  -8.11  \n",
       "PE_Segment_0_FO          -12.42 -12.45 -12.49 -12.53 -12.56  \n",
       "PE_Segment_1_Cf           -5.72  -5.74  -5.76  -5.78  -5.80  \n",
       "PE_Segment_2_Strd         -5.05  -5.07  -5.09  -5.10  -5.12  \n",
       "PE_Segment_3_WO           -4.16  -4.18  -4.19  -4.21  -4.22  \n",
       "Elasticity_Promotion_1    -4.86  -4.88  -4.90  -4.92  -4.93  \n",
       "Elasticity_Promotion_0    -4.98  -5.00  -5.02  -5.03  -5.05  \n",
       "Brand5_own_Avg            -3.23  -3.24  -3.26  -3.27  -3.29  \n",
       "Brand5_Cross_Brand_4_Avg   0.16   0.16   0.16   0.16   0.15  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update price elasticities data frame to include the cross price elasticities for brand 5 with respect to brand 4.\n",
    "pd.options.display.max_columns = None\n",
    "price_elasticities.loc['Brand5_Cross_Brand_4_Avg'] = brand5_cross_brand4_price_elasticity\n",
    "price_elasticities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From the last row in the table, we examine the cross price elasticity of purchase probability for brand 5 with respect to brand 4. We can see that the values are all are positive. This implies that as the price of the competitor brand increases, so does the probability for purchasing the own brand (Brand 5). Even though the elasticity starts to decrease from the price point 1.48, it is still positive, meaning that the increase in purchase probability for the own brand happens more slowly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(price_range, brand5_cross_brand4_price_elasticity, color = 'grey', label =\"Cross Price Elasticity\")\n",
    "plt.plot(price_range, np.abs(own_price_elasticity_brand_5), color = 'grey',  \n",
    "         linestyle = '--',label =\"Own Product Price Elasticity in Absolute Values (Brand 5)\" )\n",
    "plt.xlabel('Price Range')\n",
    "plt.ylabel('Elasticity')\n",
    "plt.title('Cross Price Elasticity of Brand 5 wrt Brand 4')\n",
    "plt.axvline(x=1.65, color='blue', linewidth=1, label = \"Strong Substitute cut-off line\")\n",
    "plt.axvspan(0.5, 1.65, facecolor='b', alpha=0.2, label = \"Strong Substitute Price Zone for own Brand\")\n",
    "# show the legend\n",
    "plt.legend( bbox_to_anchor = (1, 0.5), loc = 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">- The positive values of the elasticities across the price range indicates that if competitor brand 4 increases prices, the purchase probability for Brand 5  would increase. In other words, our competitor raises prices and they start buying our own product more, and the elasticities show us exactly how much more. \n",
    ">-If the cross price elasticity is greater than 0, the two products are considered \"Substitutes\". If, however we were looking at brand 5 cross some type of different products (as an example of products having nothing in common), the cross price elasticity would not be necessarily positive.\n",
    ">-In the above example all cross price elasticities are positive, since all brands are substitutes for one another.\n",
    "Furthermore, if the cross price elasticity at some price point is greater in absolute terms than our own price elasticity, the alternative brand is considered a **\"Strong Substitute\"**. In this sense, the Brand 4 could be strong substitute for brand 5, depending on the price point. In the graph, brand 4 is a strong substitute for brand 5 for all prices up to 1,65 (Blue-coloured Zone)\n",
    "However, note that these prices are out of the natural price domain of brand 4. Therefore, if brand 4 had a substantially lower price it would be an extraordinarily strong competitor a brand 5. It is important to mark that the observed price range of brand 4 lies between 1,76(min. price) and 2,26 (max. price). Thus, when it comes to the **\"average\"** customer, Brand 4 is an weak substitute for brand 5. \n",
    ">- Also, the elasticity is gradually decreasing. This signals that with an increase in price the purchase probability changes more slowly. Therefore, our purchase probability still increases with the increase in price of brand 4 but at a slower rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='OaCPEbySeg'></a>\n",
    "### ii-2-4. Own and Cross-Price Elasticity by Segment\n",
    "\n",
    "Until now, I analyse the Own Product Price Elasticity and Cross Price Elasticity regarding Brand 5, for the **\"average\"** customers. However, that targeting the average customer can be quite cumbersome, sometimes impossible. No brand can make everyone satisfied, but a brand can make a certain customer \"Segment\" more satisfied. For these reasons, I conducted the analysis in this section about **\"Own and Cross Price Elasticities by Customer Segments\"**.<br><br>\n",
    "\n",
    "<li><a href=\"#Segment3\">Well-Off (Segment 3)</a></li>\n",
    "<li><a href=\"#Segment0\">Standard (Segment 0)</a></li>\n",
    "<li><a href=\"#Segment1\">Career-Focused (Segment 1)</a></li>\n",
    "<li><a href=\"#Segment2\">Fewer-Opportunities (Segment 2)</a></li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Segment3'></a>\n",
    "### Well-Off (Segment 3) \n",
    "\n",
    "> From the descriptive analysis, I found out that \"Well-Off\" segment has a strong preference for brand 4. Therefore, it should be interesting to observe their behaviour with respect to the price changes in brand 4 firstly. To analyse the purchase probability for choosing brand 5 by segments, I filtered the data to contain only purchase incidences of the 3rd segment: Well-Off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef_Brand_1</th>\n",
       "      <th>Coef_Brand_2</th>\n",
       "      <th>Coef_Brand_3</th>\n",
       "      <th>Coef_Brand_4</th>\n",
       "      <th>Coef_Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_1</th>\n",
       "      <td>-2.15</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_2</th>\n",
       "      <td>-0.95</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_4</th>\n",
       "      <td>2.52</td>\n",
       "      <td>0.95</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_5</th>\n",
       "      <td>-1.00</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef_Brand_1  Coef_Brand_2  Coef_Brand_3  Coef_Brand_4  Coef_Brand_5\n",
       "Price_1         -2.15          1.33          0.78          0.13         -0.10\n",
       "Price_2         -0.95         -1.37          1.24          0.79          0.29\n",
       "Price_3          2.00         -1.66         -0.41         -0.01          0.09\n",
       "Price_4          2.52          0.95         -0.20         -1.92         -1.34\n",
       "Price_5         -1.00         -0.30          1.18          0.56         -0.44"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_choice_WO = df_pa.query('Incidence ==1 & Segment == 3')\n",
    "\n",
    "# Brand Choice Model estimation.\n",
    "output_WO = brand_choice_WO['Brand']\n",
    "brand_choice_WO = pd.get_dummies(brand_choice_WO, columns=['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "inputs_WO = brand_choice_WO[features]\n",
    "\n",
    "model_brand_choice_WO = LogisticRegression(solver = 'sag', multi_class = 'multinomial', max_iter = 300)\n",
    "model_brand_choice_WO.fit(inputs_WO, output_WO)\n",
    "\n",
    "# Coefficients table for well-off.\n",
    "bc_coef = pd.DataFrame(np.transpose(model_brand_choice_WO.coef_), columns = coefficients, index = prices)\n",
    "bc_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Well-Off) Own-Brand Price Elasticity: Brand 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price_1_WO_OBPE</th>\n",
       "      <th>Price_2_WO_OBPE</th>\n",
       "      <th>Price_3_WO_OBPE</th>\n",
       "      <th>Price_4_WO_OBPE</th>\n",
       "      <th>Price_5_WO_OBPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price_1_WO_OBPE  Price_2_WO_OBPE  Price_3_WO_OBPE  Price_4_WO_OBPE  \\\n",
       "0             1.39             1.78             2.00             2.16   \n",
       "1             1.39             1.78             2.00             2.16   \n",
       "2             1.39             1.78             2.00             2.16   \n",
       "3             1.39             1.78             2.00             2.16   \n",
       "4             1.39             1.78             2.00             2.16   \n",
       "\n",
       "   Price_5_WO_OBPE  \n",
       "0             0.50  \n",
       "1             0.51  \n",
       "2             0.52  \n",
       "3             0.53  \n",
       "4             0.54  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate own-brand price elasticity for brand 5 and the Well-off segment.\n",
    "own_brand_5_WO = pd.DataFrame({'Price_1_WO_OBPE':  brand_choice_WO['Price_1'].mean(),\n",
    "                               'Price_2_WO_OBPE':  brand_choice_WO['Price_2'].mean(), \n",
    "                               'Price_3_WO_OBPE':  brand_choice_WO['Price_3'].mean(),\n",
    "                               'Price_4_WO_OBPE':  brand_choice_WO['Price_4'].mean(),\n",
    "                               'Price_5_WO_OBPE':  price_range} , index = np.arange(price_range.size))\n",
    "\n",
    "own_brand_5_WO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_own_brand_5_WO = model_brand_choice_WO.predict_proba(own_brand_5_WO)\n",
    "pr_own_brand_5_WO = pred_own_brand_5_WO[: ][: , 4]\n",
    "own_price_elasticity_brand_5_WO =  beta5 * price_range * (1 - pr_own_brand_5_WO)\n",
    "price_elasticities.loc['Brand5_own_PE_WellOff'] = own_price_elasticity_brand_5_WO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Well-Off) Cross-Brand Price Elasticity: Brand 5 vs. 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Calculate cross-brand price elasticity for brand 5 with respect to brand 4 for the Well-off segment.\n",
    "brand5_cross_brand4_WO = pd.DataFrame({'B5CB4_Price_1':  brand_choice_WO['Price_1'].mean(),\n",
    "                                       'B5CB4_Price_2':  brand_choice_WO['Price_2'].mean(), \n",
    "                                       'B5CB4_Price_3':  brand_choice_WO['Price_3'].mean(),\n",
    "                                       'B5CB4_Price_4':  price_range,\n",
    "                                       'B5CB4_Price_5':  brand_choice_WO['Price_5'].mean()},\n",
    "                                        index = np.arange(price_range.size))\n",
    "\n",
    "pred_brand5_cross_brand4_WO = model_brand_choice_WO.predict_proba(brand5_cross_brand4_WO)\n",
    "pr_cross_brand_5_WO = pred_brand5_cross_brand4_WO[: ][: , 3]\n",
    "\n",
    "# Update master data frame to include the newly obtained cross-brand price elasticities.\n",
    "brand5_cross_brand4_price_elasticity_WO = -beta5 * price_range * pr_cross_brand_5_WO\n",
    "price_elasticities.loc['Brand5_Cross_Brand_4_WellOff'] = brand5_cross_brand4_price_elasticity_WO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Well-Off) Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the own brand and cross-brand price elasticities for brand 5 cross brand 4 side by side.\n",
    "fig, axs = plt.subplots(1, 2, figsize = (14, 4))\n",
    "axs[0].plot(price_range, own_price_elasticity_brand_5_WO, color = 'r')\n",
    "axs[0].set_title('Brand 5 Segment Well-Off')\n",
    "axs[0].set_xlabel('Price 5')\n",
    "axs[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='r', alpha=0.2, label = \"Natural Price Domain of Brand 5\")\n",
    "\n",
    "axs[1].plot(price_range, brand5_cross_brand4_price_elasticity_WO, color = 'r')\n",
    "axs[1].set_title('Cross Price Elasticity of Brand 5 wrt Brand 4 Segment Well-Off')\n",
    "axs[1].set_xlabel('Price 4')\n",
    "axs[1].axvspan(brand_choice.Price_4.min(), brand_choice.Price_4.max(), facecolor='r', alpha=0.4, label = \"Natural Price Domain of Brand 4\")\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylabel = 'Elasticity')\n",
    "    ax.legend(bbox_to_anchor = (0, 1.1), loc = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.76, 2.26)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The natural price range of Brand 4\n",
    "brand_choice.Price_4.min(), brand_choice.Price_4.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.11, 2.8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The natural price range of Brand 5\n",
    "brand_choice.Price_5.min(), brand_choice.Price_5.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Well-Off\n",
    "> Notice that while the X axes both show the same price range, the left plot refers to changes in the price of \"Brand 5\", the right plot refers to changes in the price of \"Brand 4\". Also, the red-coloured zone in both plots indicates the natural price range of Brand  5, 4, respectively, It is worth focusing on this point of the graph. \n",
    ">- **\"Own Price Elasticity\"** indicates that well-off customers is elastic to the own brand (Brand 5). This can be verified from the descriptive analysis table that indeed over 60 percent of the well-off segment purchased brand 4 at about 20 percent by brand 5.\n",
    ">- **\"Cross Price Elasticities\"**: The values of elasticity are positive indicating that for the well off's brand 4 is substitute for brand 5. \n",
    "\n",
    "> For example, assume that Brand 5 costs 2,40 dollars. Then the own price elasticity is -1,97. Moreover, when Brand 4 cost 2,00, the cross price elasticity is about 1,53. <br>\n",
    ">- If the competitor Brand 4 decrease its prices by 1%, then the cross price elasticity is 1,53, thus the purchase probability of our brand (Brand 5) will fall by 1,53%.\n",
    ">-  To strike back we can also set lower our own price by 1%, we must look at the \"own\" price elasticity of our brand (the left plot), since it is minus to a 1 percent decrease in our price would be reflected in a 1,97% increase in purchase profitability. The net effect of the two price decreases is 1,97% - 1,53% = 0,44 %. Therefore, we have reacted to the competitors price range and have actually gained some market share.\n",
    ">-  With the price elasticities, we can also react to the  competitor to keep the purchase probability constant. We establish that if brand 4 decreases their price by 1 %, the purchase probability for brand 5 would decrease by 1,53 %. Let's say, X is the decrease in price we require to reach a 1,53% increase in purchase probability. Then we can make an equation: X*2,00 = 1,53%, to match that by our own price decrease.  X is equal to 0,76%. If brand 4 decreases their price by 1 percent, we can decrease ours by 0,75% and\n",
    "theoretically we will not lose a single customer from the well-off segment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Segment0'></a>\n",
    "### Standard (Segment 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef_Brand_1</th>\n",
       "      <th>Coef_Brand_2</th>\n",
       "      <th>Coef_Brand_3</th>\n",
       "      <th>Coef_Brand_4</th>\n",
       "      <th>Coef_Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_1</th>\n",
       "      <td>-3.99</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_2</th>\n",
       "      <td>0.65</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_3</th>\n",
       "      <td>1.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_4</th>\n",
       "      <td>1.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_5</th>\n",
       "      <td>0.93</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-2.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef_Brand_1  Coef_Brand_2  Coef_Brand_3  Coef_Brand_4  Coef_Brand_5\n",
       "Price_1         -3.99          1.60          1.17          0.22          0.99\n",
       "Price_2          0.65         -2.14          0.11          0.27          1.11\n",
       "Price_3          1.29          0.08          1.11         -0.28         -2.19\n",
       "Price_4          1.30         -0.31          1.53         -1.49         -1.03\n",
       "Price_5          0.93          1.26          0.18          0.58         -2.94"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_choice_Strd= df_pa.query('Incidence ==1 & Segment == 0')\n",
    "\n",
    "# Brand Choice Model estimation.\n",
    "output_Strd = brand_choice_Strd['Brand']\n",
    "brand_choice_Strd = pd.get_dummies(brand_choice_Strd, columns=['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "inputs_Strd = brand_choice_Strd[features]\n",
    "\n",
    "model_brand_choice_Strd = LogisticRegression(solver = 'sag', multi_class = 'multinomial', max_iter = 300)\n",
    "model_brand_choice_Strd.fit(inputs_Strd, output_Strd)\n",
    "\n",
    "# Coefficients table for Standard. \n",
    "bc_coef = pd.DataFrame(np.transpose(model_brand_choice_Strd.coef_), columns = coefficients, index = prices)\n",
    "bc_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Standard) Own-Brand Price Elasticity: Brand 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price1_Strd_OBPE</th>\n",
       "      <th>Price2_Strd_OBPE</th>\n",
       "      <th>Price3_Strd_OBPE</th>\n",
       "      <th>Price4_Strd_OBPE</th>\n",
       "      <th>Price5_Strd_OBPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price1_Strd_OBPE  Price2_Strd_OBPE  Price3_Strd_OBPE  Price4_Strd_OBPE  \\\n",
       "0              1.38              1.74              2.01              2.16   \n",
       "1              1.38              1.74              2.01              2.16   \n",
       "2              1.38              1.74              2.01              2.16   \n",
       "3              1.38              1.74              2.01              2.16   \n",
       "4              1.38              1.74              2.01              2.16   \n",
       "\n",
       "   Price5_Strd_OBPE  \n",
       "0              0.50  \n",
       "1              0.51  \n",
       "2              0.52  \n",
       "3              0.53  \n",
       "4              0.54  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate own-brand price elasticity for brand 5 and the Standard segment.\n",
    "own_brand_5_Strd = pd.DataFrame({'Price1_Strd_OBPE':  brand_choice_Strd['Price_1'].mean(),\n",
    "                                 'Price2_Strd_OBPE':  brand_choice_Strd['Price_2'].mean(), \n",
    "                                 'Price3_Strd_OBPE':  brand_choice_Strd['Price_3'].mean(),\n",
    "                                 'Price4_Strd_OBPE':  brand_choice_Strd['Price_4'].mean(),\n",
    "                                 'Price5_Strd_OBPE':  price_range} , index = np.arange(price_range.size))\n",
    "\n",
    "own_brand_5_Strd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_own_brand_5_Strd = model_brand_choice_Strd.predict_proba(own_brand_5_Strd)\n",
    "pr_own_brand_5_Strd = pred_own_brand_5_Strd[: ][: , 4]\n",
    "\n",
    "# Compute price elasticities and update master dataframe.\n",
    "own_price_elasticity_brand_5_Strd =  beta5 * price_range * (1 - pr_own_brand_5_Strd) \n",
    "price_elasticities.loc['Brand5_own_PE_Standard'] = own_price_elasticity_brand_5_Strd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Standard) Cross-Brand Price Elasticity: Brand 5 vs. 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate cross-brand price elasticity for brand 5 with respect to brand 4 for the Well-off segment.\n",
    "brand5_cross_brand4_Strd = pd.DataFrame({'B5CB4_Price_1':  brand_choice_Strd['Price_1'].mean(),\n",
    "                                       'B5CB4_Price_2':  brand_choice_Strd['Price_2'].mean(), \n",
    "                                       'B5CB4_Price_3':  brand_choice_Strd['Price_3'].mean(),\n",
    "                                       'B5CB4_Price_4':  price_range,\n",
    "                                       'B5CB4_Price_5':  brand_choice_Strd['Price_5'].mean()},\n",
    "                                        index = np.arange(price_range.size))\n",
    "\n",
    "pred_brand5_cross_brand4_Strd = model_brand_choice_Strd.predict_proba(brand5_cross_brand4_Strd)\n",
    "pr_cross_brand_5_Strd = pred_brand5_cross_brand4_Strd[: ][: , 3]\n",
    "\n",
    "# Update master data frame to include the newly obtained cross-brand price elasticities.\n",
    "brand5_cross_brand4_price_elasticity_Strd = -beta5 * price_range * pr_cross_brand_5_Strd\n",
    "price_elasticities.loc['Brand5_Cross_Brand_4_Standard'] = brand5_cross_brand4_price_elasticity_Strd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Standard) Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the own brand and cross-brand price elasticities for brand 5 cross brand 4 side by side.\n",
    "fig, axs = plt.subplots(1, 2, figsize = (14, 4))\n",
    "axs[0].plot(price_range, own_price_elasticity_brand_5_Strd, color = 'g')\n",
    "axs[0].set_title('Brand 5 Segment Standard')\n",
    "axs[0].set_xlabel('Price 5')\n",
    "axs[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='g', alpha=0.2, label = \"Natural Price Domain of Brand 5\")\n",
    "\n",
    "\n",
    "axs[1].plot(price_range, brand5_cross_brand4_price_elasticity_Strd, color = 'g')\n",
    "axs[1].set_title('Cross Price Elasticity of Brand 5 wrt Brand 4 Segment Standard')\n",
    "axs[1].set_xlabel('Price 4')\n",
    "axs[1].axvspan(brand_choice.Price_4.min(), brand_choice.Price_4.max(), facecolor='g', alpha=0.4, label = \"Natural Price Domain of Brand 4\")\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylabel = 'Elasticity')\n",
    "    ax.legend(bbox_to_anchor = (0, 1.1), loc = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standard Segment\n",
    "\n",
    "Like before, note that the X axes refer to the price of brand 5 and price of brand 4 respectively. The y-axis reflects the own price elasticity (on the left plot) or the cross price elasticity (on the right plot). Standard customer is more elastic when compared to the average customers. The difference becomes even more pronounced when we compared the standards to other segments.\n",
    "\n",
    "The greed-coloured area indicate the natural price domain of Brand 5 (left plot) and Brand 4 (right plot) The elasticity of the standard segment is between -1,42 and -1,27. Therefore its purchase probability for the own brand is elastic for the entire observed price range of the brand.\n",
    "\n",
    "If the company is planning to win some of the standard segment market, the appropriate marketing strategy would be to lower prices in this price range to increase the purchase probability for this segment. However, considering that this segment isn't quite homogenous and a marketing strategy based on only this segment might be risky.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Segment1'></a>\n",
    "### Career-Focused (Segment 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef_Brand_1</th>\n",
       "      <th>Coef_Brand_2</th>\n",
       "      <th>Coef_Brand_3</th>\n",
       "      <th>Coef_Brand_4</th>\n",
       "      <th>Coef_Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_1</th>\n",
       "      <td>-2.38</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_2</th>\n",
       "      <td>0.74</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_3</th>\n",
       "      <td>1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_4</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_5</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef_Brand_1  Coef_Brand_2  Coef_Brand_3  Coef_Brand_4  Coef_Brand_5\n",
       "Price_1         -2.38          1.13          1.39         -0.02         -0.11\n",
       "Price_2          0.74         -1.67          0.06          0.40          0.47\n",
       "Price_3          1.15          0.34          0.45         -1.74         -0.20\n",
       "Price_4          0.04          0.59          0.42         -1.07          0.03\n",
       "Price_5         -0.02         -0.13          0.32          0.40         -0.58"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_choice_Cf= df_pa.query('Incidence ==1 & Segment == 1')\n",
    "\n",
    "# Brand Choice Model estimation.\n",
    "output_Cf = brand_choice_Cf['Brand']\n",
    "brand_choice_Cf = pd.get_dummies(brand_choice_Cf, columns=['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "inputs_Cf = brand_choice_Cf[features]\n",
    "\n",
    "model_brand_choice_Cf = LogisticRegression(solver = 'sag', multi_class = 'multinomial', max_iter = 300)\n",
    "model_brand_choice_Cf.fit(inputs_Cf, output_Cf)\n",
    "\n",
    "# Coefficients table for Standard. \n",
    "bc_coef = pd.DataFrame(np.transpose(model_brand_choice_Cf.coef_), columns = coefficients, index = prices)\n",
    "bc_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Career-Focused) Own-Brand Price Elasticity: Brand 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price1_Cf_OBPE</th>\n",
       "      <th>Price2_Cf_OBPE</th>\n",
       "      <th>Price3_Cf_OBPE</th>\n",
       "      <th>Price4_Cf_OBPE</th>\n",
       "      <th>Price5_Cf_OBPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price1_Cf_OBPE  Price2_Cf_OBPE  Price3_Cf_OBPE  Price4_Cf_OBPE  \\\n",
       "0            1.39            1.77            2.01            2.16   \n",
       "1            1.39            1.77            2.01            2.16   \n",
       "2            1.39            1.77            2.01            2.16   \n",
       "3            1.39            1.77            2.01            2.16   \n",
       "4            1.39            1.77            2.01            2.16   \n",
       "\n",
       "   Price5_Cf_OBPE  \n",
       "0            0.50  \n",
       "1            0.51  \n",
       "2            0.52  \n",
       "3            0.53  \n",
       "4            0.54  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating own-brand price elasticity for brand 5 and the Standard segment.\n",
    "own_brand_5_Cf = pd.DataFrame({'Price1_Cf_OBPE':  brand_choice_Cf['Price_1'].mean(),\n",
    "                               'Price2_Cf_OBPE':  brand_choice_Cf['Price_2'].mean(), \n",
    "                               'Price3_Cf_OBPE':  brand_choice_Cf['Price_3'].mean(),\n",
    "                               'Price4_Cf_OBPE':  brand_choice_Cf['Price_4'].mean(),\n",
    "                               'Price5_Cf_OBPE':  price_range} , index = np.arange(price_range.size))\n",
    "\n",
    "own_brand_5_Cf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_own_brand_5_Cf = model_brand_choice_Cf.predict_proba(own_brand_5_Cf)\n",
    "pr_own_brand_5_Cf = pred_own_brand_5_Cf[: ][: , 4]\n",
    "\n",
    "# Compute price elasticities and update master dataframe.\n",
    "own_price_elasticity_brand_5_Cf =  beta5 * price_range * (1 - pr_own_brand_5_Cf) \n",
    "price_elasticities.loc['Brand5_own_PE_Career_Focused'] = own_price_elasticity_brand_5_Cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Career-Focused) Cross-Brand Price Elasticity: Brand 5 vs. 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate cross-brand price elasticity for brand 5 with respect to brand 4 for the Well-off segment.\n",
    "brand5_cross_brand4_Cf = pd.DataFrame({'B5CB4_Price_1':  brand_choice_Cf['Price_1'].mean(),\n",
    "                                       'B5CB4_Price_2':  brand_choice_Cf['Price_2'].mean(), \n",
    "                                       'B5CB4_Price_3':  brand_choice_Cf['Price_3'].mean(),\n",
    "                                       'B5CB4_Price_4':  price_range,\n",
    "                                       'B5CB4_Price_5':  brand_choice_Cf['Price_5'].mean()},\n",
    "                                        index = np.arange(price_range.size))\n",
    "\n",
    "pred_brand5_cross_brand4_Cf = model_brand_choice_Cf.predict_proba(brand5_cross_brand4_Cf)\n",
    "pr_cross_brand_5_Cf = pred_brand5_cross_brand4_Cf[: ][: , 3]\n",
    "\n",
    "# Update master data frame to include the newly obtained cross-brand price elasticities.\n",
    "brand5_cross_brand4_price_elasticity_Cf = -beta5 * price_range * pr_cross_brand_5_Cf\n",
    "price_elasticities.loc['Brand5_Cross_Brand_4_Career_Focused'] = brand5_cross_brand4_price_elasticity_Cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the own brand and cross-brand price elasticities for brand 5 cross brand 4 side by side.\n",
    "fig, axs = plt.subplots(1, 2, figsize = (14, 4))\n",
    "axs[0].plot(price_range, own_price_elasticity_brand_5_Cf, color = 'orange')\n",
    "axs[0].set_title('Brand 5 Segment Career-Focused')\n",
    "axs[0].set_xlabel('Price 5')\n",
    "axs[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='orange', alpha=0.2, label = \"Natural Price Domain of Brand 5\")\n",
    "\n",
    "\n",
    "axs[1].plot(price_range, brand5_cross_brand4_price_elasticity_Strd, color = 'orange')\n",
    "axs[1].set_title('Cross Price Elasticity of Brand 5 wrt Brand 4 Segment Career-Focused')\n",
    "axs[1].set_xlabel('Price 4')\n",
    "axs[1].axvspan(brand_choice.Price_4.min(), brand_choice.Price_4.max(), facecolor='orange', alpha=0.4, label = \"Natural Price Domain of Brand 4\")\n",
    "\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylabel = 'Elasticity')\n",
    "    ax.legend(bbox_to_anchor = (0, 1.1), loc = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Career Focused \n",
    "\n",
    "Career Focused customers are the least elastic among the rest. They seem to be inelastic throughout the whole price range.\n",
    "In other words, it means that this segment is not really affected by the increase in the price of the own brand.\n",
    "\n",
    "In addition, the values of cross price elasticity in this segment is also exceptionally low. This type of customers is unlikely to switch to the competitor brand. They are loyal to the brand and the company could increase prices of its own brand without fear of losing too much market share.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Segment2'></a>\n",
    "### Fewer-Opportunities (Segment 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef_Brand_1</th>\n",
       "      <th>Coef_Brand_2</th>\n",
       "      <th>Coef_Brand_3</th>\n",
       "      <th>Coef_Brand_4</th>\n",
       "      <th>Coef_Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_1</th>\n",
       "      <td>-3.01</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_3</th>\n",
       "      <td>1.20</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_4</th>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_5</th>\n",
       "      <td>-0.07</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef_Brand_1  Coef_Brand_2  Coef_Brand_3  Coef_Brand_4  Coef_Brand_5\n",
       "Price_1         -3.01          1.09          1.19          0.27          0.47\n",
       "Price_2          0.75         -2.06          0.46          0.37          0.48\n",
       "Price_3          1.20         -0.50          0.36         -0.06         -1.00\n",
       "Price_4          0.44         -0.28          0.99         -1.25          0.11\n",
       "Price_5         -0.07          1.40          0.68         -0.86         -1.15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_choice_FO= df_pa.query('Incidence ==1 & Segment == 2')\n",
    "\n",
    "# Brand Choice Model estimation.\n",
    "output_FO = brand_choice_FO['Brand']\n",
    "brand_choice_FO = pd.get_dummies(brand_choice_FO, columns=['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "inputs_FO = brand_choice_FO[features]\n",
    "\n",
    "model_brand_choice_FO = LogisticRegression(solver = 'sag', multi_class = 'multinomial', max_iter = 300)\n",
    "model_brand_choice_FO.fit(inputs_FO, output_FO)\n",
    "\n",
    "# Coefficients table for Standard. \n",
    "bc_coef_FO = pd.DataFrame(np.transpose(model_brand_choice_FO.coef_), columns = coefficients, index = prices)\n",
    "bc_coef_FO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Fewer-Opportunities) Own-Brand Price Elasticity: Brand 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price1_FO_OBPE</th>\n",
       "      <th>Price2_FO_OBPE</th>\n",
       "      <th>Price3_FO_OBPE</th>\n",
       "      <th>Price4_FO_OBPE</th>\n",
       "      <th>Price5_FO_OBPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price1_FO_OBPE  Price2_FO_OBPE  Price3_FO_OBPE  Price4_FO_OBPE  \\\n",
       "0            1.38            1.77            2.01            2.16   \n",
       "1            1.38            1.77            2.01            2.16   \n",
       "2            1.38            1.77            2.01            2.16   \n",
       "3            1.38            1.77            2.01            2.16   \n",
       "4            1.38            1.77            2.01            2.16   \n",
       "\n",
       "   Price5_FO_OBPE  \n",
       "0            0.50  \n",
       "1            0.51  \n",
       "2            0.52  \n",
       "3            0.53  \n",
       "4            0.54  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating own-brand price elasticity for brand 5 and the Standard segment.\n",
    "own_brand_5_FO = pd.DataFrame({'Price1_FO_OBPE':  brand_choice_FO['Price_1'].mean(),\n",
    "                               'Price2_FO_OBPE':  brand_choice_FO['Price_2'].mean(), \n",
    "                               'Price3_FO_OBPE':  brand_choice_FO['Price_3'].mean(),\n",
    "                               'Price4_FO_OBPE':  brand_choice_FO['Price_4'].mean(),\n",
    "                               'Price5_FO_OBPE':  price_range} , index = np.arange(price_range.size))\n",
    "\n",
    "own_brand_5_FO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_own_brand_5_FO = model_brand_choice_FO.predict_proba(own_brand_5_FO)\n",
    "pr_own_brand_5_FO = pred_own_brand_5_FO[: ][: , 4]\n",
    "\n",
    "# Compute price elasticities and update master dataframe.\n",
    "own_price_elasticity_brand_5_FO =  beta5 * price_range * (1 - pr_own_brand_5_FO) \n",
    "price_elasticities.loc['Brand5_own_PE_Fewer_Opportunities'] = own_price_elasticity_brand_5_FO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Fewer-Opportunities) Cross-Brand Price Elasticity: Brand 5 vs. 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
       "      <th>107</th>\n",
       "      <th>108</th>\n",
       "      <th>109</th>\n",
       "      <th>110</th>\n",
       "      <th>111</th>\n",
       "      <th>112</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
       "      <th>125</th>\n",
       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
       "      <th>133</th>\n",
       "      <th>134</th>\n",
       "      <th>135</th>\n",
       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
       "      <th>152</th>\n",
       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "      <th>216</th>\n",
       "      <th>217</th>\n",
       "      <th>218</th>\n",
       "      <th>219</th>\n",
       "      <th>220</th>\n",
       "      <th>221</th>\n",
       "      <th>222</th>\n",
       "      <th>223</th>\n",
       "      <th>224</th>\n",
       "      <th>225</th>\n",
       "      <th>226</th>\n",
       "      <th>227</th>\n",
       "      <th>228</th>\n",
       "      <th>229</th>\n",
       "      <th>230</th>\n",
       "      <th>231</th>\n",
       "      <th>232</th>\n",
       "      <th>233</th>\n",
       "      <th>234</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "      <th>245</th>\n",
       "      <th>246</th>\n",
       "      <th>247</th>\n",
       "      <th>248</th>\n",
       "      <th>249</th>\n",
       "      <th>250</th>\n",
       "      <th>251</th>\n",
       "      <th>252</th>\n",
       "      <th>253</th>\n",
       "      <th>254</th>\n",
       "      <th>255</th>\n",
       "      <th>256</th>\n",
       "      <th>257</th>\n",
       "      <th>258</th>\n",
       "      <th>259</th>\n",
       "      <th>260</th>\n",
       "      <th>261</th>\n",
       "      <th>262</th>\n",
       "      <th>263</th>\n",
       "      <th>264</th>\n",
       "      <th>265</th>\n",
       "      <th>266</th>\n",
       "      <th>267</th>\n",
       "      <th>268</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "      <th>279</th>\n",
       "      <th>280</th>\n",
       "      <th>281</th>\n",
       "      <th>282</th>\n",
       "      <th>283</th>\n",
       "      <th>284</th>\n",
       "      <th>285</th>\n",
       "      <th>286</th>\n",
       "      <th>287</th>\n",
       "      <th>288</th>\n",
       "      <th>289</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price_Point</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.87</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.34</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.73</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.02</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.09</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.17</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.47</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_PE</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.35</td>\n",
       "      <td>-4.39</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.46</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.82</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.89</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.96</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>-5.13</td>\n",
       "      <td>-5.16</td>\n",
       "      <td>-5.19</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>-5.26</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.42</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.49</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>-5.62</td>\n",
       "      <td>-5.65</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>-5.84</td>\n",
       "      <td>-5.87</td>\n",
       "      <td>-5.90</td>\n",
       "      <td>-5.93</td>\n",
       "      <td>-5.96</td>\n",
       "      <td>-5.99</td>\n",
       "      <td>-6.02</td>\n",
       "      <td>-6.05</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>-6.11</td>\n",
       "      <td>-6.14</td>\n",
       "      <td>-6.17</td>\n",
       "      <td>-6.20</td>\n",
       "      <td>-6.23</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>-6.29</td>\n",
       "      <td>-6.32</td>\n",
       "      <td>-6.35</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>-6.40</td>\n",
       "      <td>-6.43</td>\n",
       "      <td>-6.46</td>\n",
       "      <td>-6.49</td>\n",
       "      <td>-6.52</td>\n",
       "      <td>-6.55</td>\n",
       "      <td>-6.58</td>\n",
       "      <td>-6.60</td>\n",
       "      <td>-6.63</td>\n",
       "      <td>-6.66</td>\n",
       "      <td>-6.69</td>\n",
       "      <td>-6.72</td>\n",
       "      <td>-6.75</td>\n",
       "      <td>-6.77</td>\n",
       "      <td>-6.80</td>\n",
       "      <td>-6.83</td>\n",
       "      <td>-6.86</td>\n",
       "      <td>-6.88</td>\n",
       "      <td>-6.91</td>\n",
       "      <td>-6.94</td>\n",
       "      <td>-6.97</td>\n",
       "      <td>-6.99</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>-7.05</td>\n",
       "      <td>-7.07</td>\n",
       "      <td>-7.10</td>\n",
       "      <td>-7.13</td>\n",
       "      <td>-7.16</td>\n",
       "      <td>-7.18</td>\n",
       "      <td>-7.21</td>\n",
       "      <td>-7.24</td>\n",
       "      <td>-7.26</td>\n",
       "      <td>-7.29</td>\n",
       "      <td>-7.32</td>\n",
       "      <td>-7.34</td>\n",
       "      <td>-7.37</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>-7.42</td>\n",
       "      <td>-7.45</td>\n",
       "      <td>-7.47</td>\n",
       "      <td>-7.50</td>\n",
       "      <td>-7.53</td>\n",
       "      <td>-7.55</td>\n",
       "      <td>-7.58</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>-7.63</td>\n",
       "      <td>-7.66</td>\n",
       "      <td>-7.68</td>\n",
       "      <td>-7.71</td>\n",
       "      <td>-7.73</td>\n",
       "      <td>-7.76</td>\n",
       "      <td>-7.78</td>\n",
       "      <td>-7.81</td>\n",
       "      <td>-7.83</td>\n",
       "      <td>-7.86</td>\n",
       "      <td>-7.89</td>\n",
       "      <td>-7.91</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>-7.96</td>\n",
       "      <td>-7.99</td>\n",
       "      <td>-8.01</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-8.06</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>-8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_0_FO</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.23</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.46</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-4.91</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-5.13</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-5.28</td>\n",
       "      <td>-5.35</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>-5.57</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-5.85</td>\n",
       "      <td>-5.92</td>\n",
       "      <td>-5.99</td>\n",
       "      <td>-6.06</td>\n",
       "      <td>-6.13</td>\n",
       "      <td>-6.19</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>-6.33</td>\n",
       "      <td>-6.39</td>\n",
       "      <td>-6.46</td>\n",
       "      <td>-6.52</td>\n",
       "      <td>-6.59</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>-6.71</td>\n",
       "      <td>-6.78</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>-6.90</td>\n",
       "      <td>-6.96</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>-7.08</td>\n",
       "      <td>-7.14</td>\n",
       "      <td>-7.20</td>\n",
       "      <td>-7.26</td>\n",
       "      <td>-7.31</td>\n",
       "      <td>-7.37</td>\n",
       "      <td>-7.43</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>-7.54</td>\n",
       "      <td>-7.59</td>\n",
       "      <td>-7.65</td>\n",
       "      <td>-7.70</td>\n",
       "      <td>-7.76</td>\n",
       "      <td>-7.81</td>\n",
       "      <td>-7.86</td>\n",
       "      <td>-7.92</td>\n",
       "      <td>-7.97</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>-8.07</td>\n",
       "      <td>-8.12</td>\n",
       "      <td>-8.17</td>\n",
       "      <td>-8.22</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>-8.32</td>\n",
       "      <td>-8.37</td>\n",
       "      <td>-8.42</td>\n",
       "      <td>-8.47</td>\n",
       "      <td>-8.52</td>\n",
       "      <td>-8.56</td>\n",
       "      <td>-8.61</td>\n",
       "      <td>-8.66</td>\n",
       "      <td>-8.70</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>-8.80</td>\n",
       "      <td>-8.84</td>\n",
       "      <td>-8.89</td>\n",
       "      <td>-8.93</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>-9.02</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>-9.11</td>\n",
       "      <td>-9.16</td>\n",
       "      <td>-9.20</td>\n",
       "      <td>-9.24</td>\n",
       "      <td>-9.29</td>\n",
       "      <td>-9.33</td>\n",
       "      <td>-9.37</td>\n",
       "      <td>-9.41</td>\n",
       "      <td>-9.46</td>\n",
       "      <td>-9.50</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>-9.58</td>\n",
       "      <td>-9.62</td>\n",
       "      <td>-9.67</td>\n",
       "      <td>-9.71</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>-9.79</td>\n",
       "      <td>-9.83</td>\n",
       "      <td>-9.87</td>\n",
       "      <td>-9.91</td>\n",
       "      <td>-9.95</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-10.03</td>\n",
       "      <td>-10.07</td>\n",
       "      <td>-10.11</td>\n",
       "      <td>-10.15</td>\n",
       "      <td>-10.19</td>\n",
       "      <td>-10.23</td>\n",
       "      <td>-10.27</td>\n",
       "      <td>-10.31</td>\n",
       "      <td>-10.35</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>-10.43</td>\n",
       "      <td>-10.47</td>\n",
       "      <td>-10.51</td>\n",
       "      <td>-10.55</td>\n",
       "      <td>-10.58</td>\n",
       "      <td>-10.62</td>\n",
       "      <td>-10.66</td>\n",
       "      <td>-10.70</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>-10.78</td>\n",
       "      <td>-10.82</td>\n",
       "      <td>-10.85</td>\n",
       "      <td>-10.89</td>\n",
       "      <td>-10.93</td>\n",
       "      <td>-10.97</td>\n",
       "      <td>-11.01</td>\n",
       "      <td>-11.04</td>\n",
       "      <td>-11.08</td>\n",
       "      <td>-11.12</td>\n",
       "      <td>-11.16</td>\n",
       "      <td>-11.19</td>\n",
       "      <td>-11.23</td>\n",
       "      <td>-11.27</td>\n",
       "      <td>-11.31</td>\n",
       "      <td>-11.34</td>\n",
       "      <td>-11.38</td>\n",
       "      <td>-11.42</td>\n",
       "      <td>-11.46</td>\n",
       "      <td>-11.49</td>\n",
       "      <td>-11.53</td>\n",
       "      <td>-11.57</td>\n",
       "      <td>-11.61</td>\n",
       "      <td>-11.64</td>\n",
       "      <td>-11.68</td>\n",
       "      <td>-11.72</td>\n",
       "      <td>-11.75</td>\n",
       "      <td>-11.79</td>\n",
       "      <td>-11.83</td>\n",
       "      <td>-11.86</td>\n",
       "      <td>-11.90</td>\n",
       "      <td>-11.94</td>\n",
       "      <td>-11.98</td>\n",
       "      <td>-12.01</td>\n",
       "      <td>-12.05</td>\n",
       "      <td>-12.09</td>\n",
       "      <td>-12.12</td>\n",
       "      <td>-12.16</td>\n",
       "      <td>-12.20</td>\n",
       "      <td>-12.23</td>\n",
       "      <td>-12.27</td>\n",
       "      <td>-12.31</td>\n",
       "      <td>-12.34</td>\n",
       "      <td>-12.38</td>\n",
       "      <td>-12.42</td>\n",
       "      <td>-12.45</td>\n",
       "      <td>-12.49</td>\n",
       "      <td>-12.53</td>\n",
       "      <td>-12.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_1_Cf</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.86</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.63</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.70</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.77</td>\n",
       "      <td>-4.79</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.94</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.01</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>-5.12</td>\n",
       "      <td>-5.14</td>\n",
       "      <td>-5.16</td>\n",
       "      <td>-5.18</td>\n",
       "      <td>-5.20</td>\n",
       "      <td>-5.22</td>\n",
       "      <td>-5.25</td>\n",
       "      <td>-5.27</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-5.31</td>\n",
       "      <td>-5.33</td>\n",
       "      <td>-5.35</td>\n",
       "      <td>-5.37</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.41</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.48</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-5.54</td>\n",
       "      <td>-5.56</td>\n",
       "      <td>-5.58</td>\n",
       "      <td>-5.60</td>\n",
       "      <td>-5.62</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-5.76</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_2_Strd</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.73</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.25</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.48</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-5.07</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>-5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE_Segment_3_WO</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.94</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_1</th>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.73</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>-4.01</td>\n",
       "      <td>-4.03</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.26</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.42</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.49</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.60</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.70</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>-4.77</td>\n",
       "      <td>-4.79</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elasticity_Promotion_0</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>-3.87</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-3.95</td>\n",
       "      <td>-3.96</td>\n",
       "      <td>-3.98</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-4.09</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-4.24</td>\n",
       "      <td>-4.25</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-4.34</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-4.38</td>\n",
       "      <td>-4.40</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>-4.43</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>-4.47</td>\n",
       "      <td>-4.48</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.52</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-4.55</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-4.59</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.62</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-4.71</td>\n",
       "      <td>-4.73</td>\n",
       "      <td>-4.74</td>\n",
       "      <td>-4.76</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>-4.81</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-4.85</td>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.88</td>\n",
       "      <td>-4.90</td>\n",
       "      <td>-4.92</td>\n",
       "      <td>-4.93</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-4.98</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>-5.03</td>\n",
       "      <td>-5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_Avg</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_Avg</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_PE_WellOff</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.94</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.13</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.64</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.11</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_WellOff</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_PE_Standard</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.98</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-2.97</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>-3.64</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-3.66</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>-3.68</td>\n",
       "      <td>-3.69</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>-3.73</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-3.75</td>\n",
       "      <td>-3.76</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>-3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_Standard</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_PE_Career_Focused</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_Career_Focused</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_own_PE_Fewer_Opportunities</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.72</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-2.21</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-2.57</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-2.62</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.68</td>\n",
       "      <td>-2.70</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.77</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>-2.91</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-2.99</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-3.02</td>\n",
       "      <td>-3.04</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.09</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-3.15</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-3.24</td>\n",
       "      <td>-3.25</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-3.30</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-3.34</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-3.59</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brand5_Cross_Brand_4_Fewer_Opportunities</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0     1     2     3     4     5  \\\n",
       "Price_Point                               0.50  0.51  0.52  0.53  0.54  0.55   \n",
       "Mean_PE                                  -0.10 -0.10 -0.10 -0.11 -0.11 -0.12   \n",
       "PE_Segment_0_FO                          -0.03 -0.03 -0.04 -0.04 -0.04 -0.04   \n",
       "PE_Segment_1_Cf                          -0.12 -0.12 -0.13 -0.13 -0.14 -0.14   \n",
       "PE_Segment_2_Strd                        -0.21 -0.22 -0.22 -0.23 -0.24 -0.25   \n",
       "PE_Segment_3_WO                          -0.17 -0.17 -0.18 -0.18 -0.19 -0.19   \n",
       "Elasticity_Promotion_1                   -0.13 -0.13 -0.13 -0.14 -0.14 -0.15   \n",
       "Elasticity_Promotion_0                   -0.20 -0.20 -0.21 -0.21 -0.22 -0.23   \n",
       "Brand5_own_Avg                           -0.05 -0.05 -0.05 -0.05 -0.06 -0.06   \n",
       "Brand5_Cross_Brand_4_Avg                  0.33  0.34  0.34  0.35  0.35  0.36   \n",
       "Brand5_own_PE_WellOff                    -0.30 -0.30 -0.31 -0.32 -0.32 -0.33   \n",
       "Brand5_Cross_Brand_4_WellOff              0.48  0.49  0.50  0.51  0.52  0.53   \n",
       "Brand5_own_PE_Standard                   -0.00 -0.00 -0.00 -0.00 -0.00 -0.00   \n",
       "Brand5_Cross_Brand_4_Standard             0.17  0.17  0.18  0.18  0.18  0.18   \n",
       "Brand5_own_PE_Career_Focused             -0.04 -0.04 -0.04 -0.04 -0.04 -0.04   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.20  0.20  0.20  0.20  0.21  0.21   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.12 -0.12 -0.12 -0.12 -0.13 -0.13   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.17  0.18  0.18  0.18  0.18  0.18   \n",
       "\n",
       "                                             6     7     8     9    10    11  \\\n",
       "Price_Point                               0.56  0.57  0.58  0.59  0.60  0.61   \n",
       "Mean_PE                                  -0.12 -0.13 -0.13 -0.14 -0.14 -0.15   \n",
       "PE_Segment_0_FO                          -0.04 -0.05 -0.05 -0.05 -0.06 -0.06   \n",
       "PE_Segment_1_Cf                          -0.15 -0.15 -0.15 -0.16 -0.16 -0.17   \n",
       "PE_Segment_2_Strd                        -0.25 -0.26 -0.27 -0.27 -0.28 -0.29   \n",
       "PE_Segment_3_WO                          -0.20 -0.20 -0.21 -0.21 -0.22 -0.22   \n",
       "Elasticity_Promotion_1                   -0.15 -0.16 -0.16 -0.17 -0.17 -0.18   \n",
       "Elasticity_Promotion_0                   -0.23 -0.24 -0.25 -0.25 -0.26 -0.27   \n",
       "Brand5_own_Avg                           -0.06 -0.06 -0.06 -0.06 -0.07 -0.07   \n",
       "Brand5_Cross_Brand_4_Avg                  0.36  0.37  0.37  0.38  0.38  0.39   \n",
       "Brand5_own_PE_WellOff                    -0.33 -0.34 -0.35 -0.35 -0.36 -0.36   \n",
       "Brand5_Cross_Brand_4_WellOff              0.54  0.55  0.56  0.57  0.57  0.58   \n",
       "Brand5_own_PE_Standard                   -0.00 -0.00 -0.00 -0.00 -0.00 -0.00   \n",
       "Brand5_Cross_Brand_4_Standard             0.19  0.19  0.19  0.19  0.19  0.20   \n",
       "Brand5_own_PE_Career_Focused             -0.05 -0.05 -0.05 -0.05 -0.05 -0.05   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.21  0.21  0.21  0.22  0.22  0.22   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.13 -0.14 -0.14 -0.14 -0.15 -0.15   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.19  0.19  0.19  0.19  0.19  0.19   \n",
       "\n",
       "                                            12    13    14    15    16    17  \\\n",
       "Price_Point                               0.62  0.63  0.64  0.65  0.66  0.67   \n",
       "Mean_PE                                  -0.15 -0.16 -0.17 -0.17 -0.18 -0.19   \n",
       "PE_Segment_0_FO                          -0.06 -0.06 -0.07 -0.07 -0.07 -0.08   \n",
       "PE_Segment_1_Cf                          -0.18 -0.18 -0.19 -0.19 -0.20 -0.20   \n",
       "PE_Segment_2_Strd                        -0.30 -0.31 -0.31 -0.32 -0.33 -0.34   \n",
       "PE_Segment_3_WO                          -0.23 -0.24 -0.24 -0.25 -0.25 -0.26   \n",
       "Elasticity_Promotion_1                   -0.18 -0.19 -0.19 -0.20 -0.20 -0.21   \n",
       "Elasticity_Promotion_0                   -0.28 -0.28 -0.29 -0.30 -0.31 -0.31   \n",
       "Brand5_own_Avg                           -0.07 -0.07 -0.07 -0.08 -0.08 -0.08   \n",
       "Brand5_Cross_Brand_4_Avg                  0.39  0.40  0.40  0.40  0.41  0.41   \n",
       "Brand5_own_PE_WellOff                    -0.37 -0.38 -0.38 -0.39 -0.40 -0.40   \n",
       "Brand5_Cross_Brand_4_WellOff              0.59  0.60  0.61  0.62  0.63  0.64   \n",
       "Brand5_own_PE_Standard                   -0.00 -0.00 -0.00 -0.00 -0.00 -0.00   \n",
       "Brand5_Cross_Brand_4_Standard             0.20  0.20  0.20  0.20  0.20  0.21   \n",
       "Brand5_own_PE_Career_Focused             -0.05 -0.05 -0.05 -0.06 -0.06 -0.06   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.22  0.22  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.15 -0.16 -0.16 -0.16 -0.17 -0.17   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.20  0.20  0.20  0.20  0.20  0.20   \n",
       "\n",
       "                                            18    19    20    21    22    23  \\\n",
       "Price_Point                               0.68  0.69  0.70  0.71  0.72  0.73   \n",
       "Mean_PE                                  -0.19 -0.20 -0.21 -0.21 -0.22 -0.23   \n",
       "PE_Segment_0_FO                          -0.08 -0.09 -0.09 -0.10 -0.10 -0.11   \n",
       "PE_Segment_1_Cf                          -0.21 -0.22 -0.22 -0.23 -0.23 -0.24   \n",
       "PE_Segment_2_Strd                        -0.35 -0.36 -0.36 -0.37 -0.38 -0.39   \n",
       "PE_Segment_3_WO                          -0.27 -0.27 -0.28 -0.28 -0.29 -0.30   \n",
       "Elasticity_Promotion_1                   -0.21 -0.22 -0.22 -0.23 -0.24 -0.24   \n",
       "Elasticity_Promotion_0                   -0.32 -0.33 -0.34 -0.35 -0.36 -0.36   \n",
       "Brand5_own_Avg                           -0.08 -0.08 -0.09 -0.09 -0.09 -0.09   \n",
       "Brand5_Cross_Brand_4_Avg                  0.42  0.42  0.43  0.43  0.43  0.44   \n",
       "Brand5_own_PE_WellOff                    -0.41 -0.41 -0.42 -0.43 -0.43 -0.44   \n",
       "Brand5_Cross_Brand_4_WellOff              0.65  0.66  0.66  0.67  0.68  0.69   \n",
       "Brand5_own_PE_Standard                   -0.00 -0.00 -0.00 -0.00 -0.00 -0.00   \n",
       "Brand5_Cross_Brand_4_Standard             0.21  0.21  0.21  0.21  0.21  0.21   \n",
       "Brand5_own_PE_Career_Focused             -0.06 -0.06 -0.06 -0.06 -0.06 -0.06   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.23  0.23  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.18 -0.18 -0.18 -0.19 -0.19 -0.19   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.20  0.20  0.21  0.21  0.21  0.21   \n",
       "\n",
       "                                            24    25    26    27    28    29  \\\n",
       "Price_Point                               0.74  0.75  0.76  0.77  0.78  0.79   \n",
       "Mean_PE                                  -0.24 -0.24 -0.25 -0.26 -0.27 -0.28   \n",
       "PE_Segment_0_FO                          -0.11 -0.12 -0.12 -0.13 -0.13 -0.14   \n",
       "PE_Segment_1_Cf                          -0.25 -0.25 -0.26 -0.27 -0.28 -0.28   \n",
       "PE_Segment_2_Strd                        -0.40 -0.41 -0.42 -0.43 -0.44 -0.45   \n",
       "PE_Segment_3_WO                          -0.30 -0.31 -0.32 -0.33 -0.33 -0.34   \n",
       "Elasticity_Promotion_1                   -0.25 -0.25 -0.26 -0.27 -0.27 -0.28   \n",
       "Elasticity_Promotion_0                   -0.37 -0.38 -0.39 -0.40 -0.41 -0.42   \n",
       "Brand5_own_Avg                           -0.10 -0.10 -0.10 -0.10 -0.11 -0.11   \n",
       "Brand5_Cross_Brand_4_Avg                  0.44  0.45  0.45  0.45  0.46  0.46   \n",
       "Brand5_own_PE_WellOff                    -0.45 -0.45 -0.46 -0.47 -0.47 -0.48   \n",
       "Brand5_Cross_Brand_4_WellOff              0.70  0.71  0.72  0.73  0.74  0.74   \n",
       "Brand5_own_PE_Standard                   -0.00 -0.00 -0.00 -0.00 -0.00 -0.00   \n",
       "Brand5_Cross_Brand_4_Standard             0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "Brand5_own_PE_Career_Focused             -0.07 -0.07 -0.07 -0.07 -0.07 -0.07   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.24  0.24  0.24  0.25  0.25  0.25   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.20 -0.20 -0.21 -0.21 -0.22 -0.22   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.21  0.21  0.21  0.21  0.21  0.21   \n",
       "\n",
       "                                            30    31    32    33    34    35  \\\n",
       "Price_Point                               0.80  0.81  0.82  0.83  0.84  0.85   \n",
       "Mean_PE                                  -0.29 -0.30 -0.31 -0.32 -0.33 -0.34   \n",
       "PE_Segment_0_FO                          -0.15 -0.15 -0.16 -0.17 -0.18 -0.19   \n",
       "PE_Segment_1_Cf                          -0.29 -0.30 -0.31 -0.31 -0.32 -0.33   \n",
       "PE_Segment_2_Strd                        -0.46 -0.47 -0.48 -0.49 -0.50 -0.51   \n",
       "PE_Segment_3_WO                          -0.35 -0.35 -0.36 -0.37 -0.38 -0.38   \n",
       "Elasticity_Promotion_1                   -0.29 -0.29 -0.30 -0.31 -0.32 -0.32   \n",
       "Elasticity_Promotion_0                   -0.43 -0.44 -0.45 -0.46 -0.47 -0.48   \n",
       "Brand5_own_Avg                           -0.11 -0.11 -0.12 -0.12 -0.12 -0.12   \n",
       "Brand5_Cross_Brand_4_Avg                  0.46  0.47  0.47  0.47  0.48  0.48   \n",
       "Brand5_own_PE_WellOff                    -0.49 -0.49 -0.50 -0.50 -0.51 -0.52   \n",
       "Brand5_Cross_Brand_4_WellOff              0.75  0.76  0.77  0.78  0.79  0.80   \n",
       "Brand5_own_PE_Standard                   -0.01 -0.01 -0.01 -0.01 -0.01 -0.01   \n",
       "Brand5_Cross_Brand_4_Standard             0.22  0.22  0.22  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Career_Focused             -0.07 -0.08 -0.08 -0.08 -0.08 -0.08   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.25  0.25  0.25  0.25  0.25  0.25   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.22 -0.23 -0.23 -0.24 -0.24 -0.25   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.21  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            36    37    38    39    40    41  \\\n",
       "Price_Point                               0.86  0.87  0.88  0.89  0.90  0.91   \n",
       "Mean_PE                                  -0.35 -0.36 -0.37 -0.38 -0.39 -0.41   \n",
       "PE_Segment_0_FO                          -0.19 -0.20 -0.21 -0.22 -0.23 -0.24   \n",
       "PE_Segment_1_Cf                          -0.34 -0.35 -0.36 -0.37 -0.37 -0.38   \n",
       "PE_Segment_2_Strd                        -0.52 -0.53 -0.54 -0.55 -0.56 -0.58   \n",
       "PE_Segment_3_WO                          -0.39 -0.40 -0.41 -0.42 -0.42 -0.43   \n",
       "Elasticity_Promotion_1                   -0.33 -0.34 -0.35 -0.35 -0.36 -0.37   \n",
       "Elasticity_Promotion_0                   -0.49 -0.50 -0.51 -0.52 -0.53 -0.54   \n",
       "Brand5_own_Avg                           -0.13 -0.13 -0.13 -0.14 -0.14 -0.14   \n",
       "Brand5_Cross_Brand_4_Avg                  0.48  0.49  0.49  0.49  0.50  0.50   \n",
       "Brand5_own_PE_WellOff                    -0.52 -0.53 -0.54 -0.54 -0.55 -0.56   \n",
       "Brand5_Cross_Brand_4_WellOff              0.81  0.81  0.82  0.83  0.84  0.85   \n",
       "Brand5_own_PE_Standard                   -0.01 -0.01 -0.01 -0.01 -0.01 -0.01   \n",
       "Brand5_Cross_Brand_4_Standard             0.23  0.23  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Career_Focused             -0.08 -0.08 -0.08 -0.09 -0.09 -0.09   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.25 -0.25 -0.26 -0.26 -0.27 -0.27   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            42    43    44    45    46    47  \\\n",
       "Price_Point                               0.92  0.93  0.94  0.95  0.96  0.97   \n",
       "Mean_PE                                  -0.42 -0.43 -0.44 -0.46 -0.47 -0.48   \n",
       "PE_Segment_0_FO                          -0.25 -0.27 -0.28 -0.29 -0.30 -0.32   \n",
       "PE_Segment_1_Cf                          -0.39 -0.40 -0.41 -0.42 -0.43 -0.44   \n",
       "PE_Segment_2_Strd                        -0.59 -0.60 -0.61 -0.62 -0.63 -0.65   \n",
       "PE_Segment_3_WO                          -0.44 -0.45 -0.46 -0.47 -0.47 -0.48   \n",
       "Elasticity_Promotion_1                   -0.38 -0.39 -0.39 -0.40 -0.41 -0.42   \n",
       "Elasticity_Promotion_0                   -0.55 -0.56 -0.57 -0.58 -0.59 -0.60   \n",
       "Brand5_own_Avg                           -0.15 -0.15 -0.15 -0.16 -0.16 -0.16   \n",
       "Brand5_Cross_Brand_4_Avg                  0.50  0.51  0.51  0.51  0.51  0.52   \n",
       "Brand5_own_PE_WellOff                    -0.56 -0.57 -0.58 -0.59 -0.59 -0.60   \n",
       "Brand5_Cross_Brand_4_WellOff              0.86  0.87  0.87  0.88  0.89  0.90   \n",
       "Brand5_own_PE_Standard                   -0.01 -0.01 -0.01 -0.01 -0.01 -0.01   \n",
       "Brand5_Cross_Brand_4_Standard             0.23  0.23  0.23  0.23  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.09 -0.09 -0.09 -0.10 -0.10 -0.10   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.28 -0.28 -0.29 -0.29 -0.30 -0.30   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            48    49    50    51    52    53  \\\n",
       "Price_Point                               0.98  0.99  1.00  1.01  1.02  1.03   \n",
       "Mean_PE                                  -0.50 -0.51 -0.53 -0.54 -0.56 -0.57   \n",
       "PE_Segment_0_FO                          -0.33 -0.34 -0.36 -0.37 -0.39 -0.41   \n",
       "PE_Segment_1_Cf                          -0.45 -0.46 -0.47 -0.48 -0.49 -0.51   \n",
       "PE_Segment_2_Strd                        -0.66 -0.67 -0.68 -0.70 -0.71 -0.72   \n",
       "PE_Segment_3_WO                          -0.49 -0.50 -0.51 -0.52 -0.53 -0.54   \n",
       "Elasticity_Promotion_1                   -0.43 -0.44 -0.45 -0.46 -0.47 -0.48   \n",
       "Elasticity_Promotion_0                   -0.62 -0.63 -0.64 -0.65 -0.66 -0.68   \n",
       "Brand5_own_Avg                           -0.17 -0.17 -0.17 -0.18 -0.18 -0.18   \n",
       "Brand5_Cross_Brand_4_Avg                  0.52  0.52  0.52  0.53  0.53  0.53   \n",
       "Brand5_own_PE_WellOff                    -0.61 -0.61 -0.62 -0.63 -0.63 -0.64   \n",
       "Brand5_Cross_Brand_4_WellOff              0.91  0.92  0.92  0.93  0.94  0.95   \n",
       "Brand5_own_PE_Standard                   -0.01 -0.01 -0.01 -0.01 -0.02 -0.02   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.10 -0.10 -0.10 -0.10 -0.11 -0.11   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.27  0.27   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.31 -0.31 -0.32 -0.32 -0.33 -0.34   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            54    55    56    57    58    59  \\\n",
       "Price_Point                               1.04  1.05  1.06  1.07  1.08  1.09   \n",
       "Mean_PE                                  -0.59 -0.61 -0.62 -0.64 -0.66 -0.67   \n",
       "PE_Segment_0_FO                          -0.42 -0.44 -0.46 -0.48 -0.50 -0.52   \n",
       "PE_Segment_1_Cf                          -0.52 -0.53 -0.54 -0.55 -0.56 -0.58   \n",
       "PE_Segment_2_Strd                        -0.73 -0.75 -0.76 -0.77 -0.79 -0.80   \n",
       "PE_Segment_3_WO                          -0.55 -0.56 -0.57 -0.58 -0.59 -0.60   \n",
       "Elasticity_Promotion_1                   -0.48 -0.49 -0.50 -0.51 -0.52 -0.53   \n",
       "Elasticity_Promotion_0                   -0.69 -0.70 -0.71 -0.73 -0.74 -0.75   \n",
       "Brand5_own_Avg                           -0.19 -0.19 -0.20 -0.20 -0.20 -0.21   \n",
       "Brand5_Cross_Brand_4_Avg                  0.53  0.53  0.54  0.54  0.54  0.54   \n",
       "Brand5_own_PE_WellOff                    -0.65 -0.66 -0.66 -0.67 -0.68 -0.68   \n",
       "Brand5_Cross_Brand_4_WellOff              0.96  0.97  0.97  0.98  0.99  1.00   \n",
       "Brand5_own_PE_Standard                   -0.02 -0.02 -0.02 -0.02 -0.02 -0.02   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.11 -0.11 -0.11 -0.11 -0.12 -0.12   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.27  0.27  0.27  0.27  0.27  0.27   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.34 -0.35 -0.35 -0.36 -0.36 -0.37   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            60    61    62    63    64    65  \\\n",
       "Price_Point                               1.10  1.11  1.12  1.13  1.14  1.15   \n",
       "Mean_PE                                  -0.69 -0.71 -0.73 -0.75 -0.77 -0.79   \n",
       "PE_Segment_0_FO                          -0.54 -0.56 -0.59 -0.61 -0.64 -0.66   \n",
       "PE_Segment_1_Cf                          -0.59 -0.60 -0.61 -0.63 -0.64 -0.65   \n",
       "PE_Segment_2_Strd                        -0.81 -0.83 -0.84 -0.85 -0.87 -0.88   \n",
       "PE_Segment_3_WO                          -0.60 -0.61 -0.63 -0.64 -0.65 -0.66   \n",
       "Elasticity_Promotion_1                   -0.54 -0.56 -0.57 -0.58 -0.59 -0.60   \n",
       "Elasticity_Promotion_0                   -0.76 -0.78 -0.79 -0.80 -0.82 -0.83   \n",
       "Brand5_own_Avg                           -0.21 -0.22 -0.22 -0.22 -0.23 -0.23   \n",
       "Brand5_Cross_Brand_4_Avg                  0.54  0.55  0.55  0.55  0.55  0.55   \n",
       "Brand5_own_PE_WellOff                    -0.69 -0.70 -0.71 -0.71 -0.72 -0.73   \n",
       "Brand5_Cross_Brand_4_WellOff              1.01  1.01  1.02  1.03  1.04  1.05   \n",
       "Brand5_own_PE_Standard                   -0.02 -0.02 -0.03 -0.03 -0.03 -0.03   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.12 -0.12 -0.12 -0.12 -0.13 -0.13   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.27  0.27  0.27  0.27  0.27  0.27   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.38 -0.38 -0.39 -0.39 -0.40 -0.41   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            66    67    68    69    70    71  \\\n",
       "Price_Point                               1.16  1.17  1.18  1.19  1.20  1.21   \n",
       "Mean_PE                                  -0.81 -0.83 -0.85 -0.87 -0.89 -0.91   \n",
       "PE_Segment_0_FO                          -0.69 -0.71 -0.74 -0.77 -0.80 -0.83   \n",
       "PE_Segment_1_Cf                          -0.66 -0.68 -0.69 -0.71 -0.72 -0.73   \n",
       "PE_Segment_2_Strd                        -0.90 -0.91 -0.93 -0.94 -0.96 -0.97   \n",
       "PE_Segment_3_WO                          -0.67 -0.68 -0.69 -0.70 -0.71 -0.72   \n",
       "Elasticity_Promotion_1                   -0.61 -0.62 -0.63 -0.64 -0.66 -0.67   \n",
       "Elasticity_Promotion_0                   -0.84 -0.86 -0.87 -0.89 -0.90 -0.92   \n",
       "Brand5_own_Avg                           -0.24 -0.24 -0.25 -0.25 -0.26 -0.26   \n",
       "Brand5_Cross_Brand_4_Avg                  0.55  0.56  0.56  0.56  0.56  0.56   \n",
       "Brand5_own_PE_WellOff                    -0.74 -0.74 -0.75 -0.76 -0.77 -0.77   \n",
       "Brand5_Cross_Brand_4_WellOff              1.05  1.06  1.07  1.08  1.08  1.09   \n",
       "Brand5_own_PE_Standard                   -0.03 -0.03 -0.03 -0.03 -0.04 -0.04   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.13 -0.13 -0.13 -0.14 -0.14 -0.14   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.27  0.27  0.27  0.27  0.27  0.27   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.41 -0.42 -0.43 -0.43 -0.44 -0.45   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "\n",
       "                                            72    73    74    75    76    77  \\\n",
       "Price_Point                               1.22  1.23  1.24  1.25  1.26  1.27   \n",
       "Mean_PE                                  -0.94 -0.96 -0.98 -1.00 -1.03 -1.05   \n",
       "PE_Segment_0_FO                          -0.86 -0.90 -0.93 -0.96 -1.00 -1.04   \n",
       "PE_Segment_1_Cf                          -0.75 -0.76 -0.78 -0.79 -0.81 -0.82   \n",
       "PE_Segment_2_Strd                        -0.98 -1.00 -1.02 -1.03 -1.05 -1.06   \n",
       "PE_Segment_3_WO                          -0.73 -0.74 -0.75 -0.76 -0.78 -0.79   \n",
       "Elasticity_Promotion_1                   -0.68 -0.69 -0.70 -0.72 -0.73 -0.74   \n",
       "Elasticity_Promotion_0                   -0.93 -0.94 -0.96 -0.97 -0.99 -1.00   \n",
       "Brand5_own_Avg                           -0.27 -0.27 -0.28 -0.28 -0.29 -0.29   \n",
       "Brand5_Cross_Brand_4_Avg                  0.56  0.56  0.56  0.56  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -0.78 -0.79 -0.80 -0.80 -0.81 -0.82   \n",
       "Brand5_Cross_Brand_4_WellOff              1.10  1.11  1.12  1.12  1.13  1.14   \n",
       "Brand5_own_PE_Standard                   -0.04 -0.04 -0.04 -0.05 -0.05 -0.05   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.14 -0.14 -0.15 -0.15 -0.15 -0.15   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.27  0.27  0.27  0.27  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.45 -0.46 -0.47 -0.48 -0.48 -0.49   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.22  0.21  0.21  0.21  0.21  0.21   \n",
       "\n",
       "                                            78    79    80    81    82    83  \\\n",
       "Price_Point                               1.28  1.29  1.30  1.31  1.32  1.33   \n",
       "Mean_PE                                  -1.08 -1.10 -1.13 -1.15 -1.18 -1.20   \n",
       "PE_Segment_0_FO                          -1.07 -1.11 -1.15 -1.19 -1.23 -1.28   \n",
       "PE_Segment_1_Cf                          -0.84 -0.85 -0.87 -0.89 -0.90 -0.92   \n",
       "PE_Segment_2_Strd                        -1.08 -1.09 -1.11 -1.12 -1.14 -1.16   \n",
       "PE_Segment_3_WO                          -0.80 -0.81 -0.82 -0.83 -0.84 -0.86   \n",
       "Elasticity_Promotion_1                   -0.75 -0.77 -0.78 -0.79 -0.80 -0.82   \n",
       "Elasticity_Promotion_0                   -1.02 -1.03 -1.05 -1.06 -1.08 -1.09   \n",
       "Brand5_own_Avg                           -0.30 -0.30 -0.31 -0.32 -0.32 -0.33   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -0.83 -0.84 -0.84 -0.85 -0.86 -0.87   \n",
       "Brand5_Cross_Brand_4_WellOff              1.15  1.15  1.16  1.17  1.18  1.18   \n",
       "Brand5_own_PE_Standard                   -0.05 -0.06 -0.06 -0.06 -0.06 -0.07   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Career_Focused             -0.15 -0.16 -0.16 -0.16 -0.16 -0.16   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.50 -0.50 -0.51 -0.52 -0.53 -0.53   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.21  0.21  0.21  0.21  0.21  0.21   \n",
       "\n",
       "                                            84    85    86    87    88    89  \\\n",
       "Price_Point                               1.34  1.35  1.36  1.37  1.38  1.39   \n",
       "Mean_PE                                  -1.23 -1.26 -1.29 -1.31 -1.34 -1.37   \n",
       "PE_Segment_0_FO                          -1.32 -1.37 -1.41 -1.46 -1.51 -1.56   \n",
       "PE_Segment_1_Cf                          -0.93 -0.95 -0.97 -0.98 -1.00 -1.02   \n",
       "PE_Segment_2_Strd                        -1.17 -1.19 -1.20 -1.22 -1.24 -1.25   \n",
       "PE_Segment_3_WO                          -0.87 -0.88 -0.89 -0.91 -0.92 -0.93   \n",
       "Elasticity_Promotion_1                   -0.83 -0.84 -0.86 -0.87 -0.89 -0.90   \n",
       "Elasticity_Promotion_0                   -1.11 -1.13 -1.14 -1.16 -1.17 -1.19   \n",
       "Brand5_own_Avg                           -0.33 -0.34 -0.35 -0.35 -0.36 -0.36   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -0.88 -0.88 -0.89 -0.90 -0.91 -0.92   \n",
       "Brand5_Cross_Brand_4_WellOff              1.19  1.20  1.20  1.21  1.22  1.23   \n",
       "Brand5_own_PE_Standard                   -0.07 -0.07 -0.08 -0.08 -0.08 -0.09   \n",
       "Brand5_Cross_Brand_4_Standard             0.24  0.23  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Career_Focused             -0.17 -0.17 -0.17 -0.17 -0.17 -0.18   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.54 -0.55 -0.56 -0.57 -0.57 -0.58   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.21  0.21  0.21  0.20  0.20  0.20   \n",
       "\n",
       "                                            90    91    92    93    94    95  \\\n",
       "Price_Point                               1.40  1.41  1.42  1.43  1.44  1.45   \n",
       "Mean_PE                                  -1.40 -1.43 -1.46 -1.49 -1.52 -1.55   \n",
       "PE_Segment_0_FO                          -1.61 -1.66 -1.71 -1.76 -1.82 -1.88   \n",
       "PE_Segment_1_Cf                          -1.04 -1.05 -1.07 -1.09 -1.11 -1.13   \n",
       "PE_Segment_2_Strd                        -1.27 -1.29 -1.30 -1.32 -1.34 -1.36   \n",
       "PE_Segment_3_WO                          -0.94 -0.95 -0.97 -0.98 -0.99 -1.01   \n",
       "Elasticity_Promotion_1                   -0.91 -0.93 -0.94 -0.96 -0.97 -0.99   \n",
       "Elasticity_Promotion_0                   -1.21 -1.22 -1.24 -1.26 -1.27 -1.29   \n",
       "Brand5_own_Avg                           -0.37 -0.38 -0.38 -0.39 -0.40 -0.40   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -0.93 -0.93 -0.94 -0.95 -0.96 -0.97   \n",
       "Brand5_Cross_Brand_4_WellOff              1.23  1.24  1.25  1.25  1.26  1.27   \n",
       "Brand5_own_PE_Standard                   -0.09 -0.09 -0.10 -0.10 -0.11 -0.11   \n",
       "Brand5_Cross_Brand_4_Standard             0.23  0.23  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Career_Focused             -0.18 -0.18 -0.18 -0.19 -0.19 -0.19   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.26  0.26  0.26  0.26  0.26  0.26   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.59 -0.60 -0.61 -0.62 -0.63 -0.63   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.20  0.20  0.20  0.20  0.20  0.20   \n",
       "\n",
       "                                            96    97    98    99   100   101  \\\n",
       "Price_Point                               1.46  1.47  1.48  1.49  1.50  1.51   \n",
       "Mean_PE                                  -1.58 -1.61 -1.64 -1.67 -1.70 -1.73   \n",
       "PE_Segment_0_FO                          -1.93 -1.99 -2.05 -2.11 -2.17 -2.23   \n",
       "PE_Segment_1_Cf                          -1.15 -1.16 -1.18 -1.20 -1.22 -1.24   \n",
       "PE_Segment_2_Strd                        -1.37 -1.39 -1.41 -1.42 -1.44 -1.46   \n",
       "PE_Segment_3_WO                          -1.02 -1.03 -1.04 -1.06 -1.07 -1.08   \n",
       "Elasticity_Promotion_1                   -1.00 -1.02 -1.03 -1.05 -1.06 -1.08   \n",
       "Elasticity_Promotion_0                   -1.30 -1.32 -1.34 -1.36 -1.37 -1.39   \n",
       "Brand5_own_Avg                           -0.41 -0.42 -0.43 -0.43 -0.44 -0.45   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -0.98 -0.99 -0.99 -1.00 -1.01 -1.02   \n",
       "Brand5_Cross_Brand_4_WellOff              1.27  1.28  1.29  1.29  1.30  1.31   \n",
       "Brand5_own_PE_Standard                   -0.12 -0.12 -0.13 -0.13 -0.14 -0.15   \n",
       "Brand5_Cross_Brand_4_Standard             0.23  0.23  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Career_Focused             -0.19 -0.20 -0.20 -0.20 -0.20 -0.21   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.25  0.25  0.25  0.25  0.25  0.25   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.64 -0.65 -0.66 -0.67 -0.68 -0.69   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.20  0.20  0.19  0.19  0.19  0.19   \n",
       "\n",
       "                                           102   103   104   105   106   107  \\\n",
       "Price_Point                               1.52  1.53  1.54  1.55  1.56  1.57   \n",
       "Mean_PE                                  -1.77 -1.80 -1.83 -1.87 -1.90 -1.93   \n",
       "PE_Segment_0_FO                          -2.29 -2.36 -2.42 -2.49 -2.55 -2.62   \n",
       "PE_Segment_1_Cf                          -1.26 -1.28 -1.30 -1.32 -1.34 -1.36   \n",
       "PE_Segment_2_Strd                        -1.48 -1.49 -1.51 -1.53 -1.55 -1.57   \n",
       "PE_Segment_3_WO                          -1.10 -1.11 -1.12 -1.14 -1.15 -1.17   \n",
       "Elasticity_Promotion_1                   -1.09 -1.11 -1.13 -1.14 -1.16 -1.17   \n",
       "Elasticity_Promotion_0                   -1.41 -1.42 -1.44 -1.46 -1.48 -1.49   \n",
       "Brand5_own_Avg                           -0.45 -0.46 -0.47 -0.48 -0.48 -0.49   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -1.03 -1.04 -1.05 -1.06 -1.06 -1.07   \n",
       "Brand5_Cross_Brand_4_WellOff              1.31  1.32  1.32  1.33  1.34  1.34   \n",
       "Brand5_own_PE_Standard                   -0.15 -0.16 -0.17 -0.17 -0.18 -0.19   \n",
       "Brand5_Cross_Brand_4_Standard             0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "Brand5_own_PE_Career_Focused             -0.21 -0.21 -0.21 -0.22 -0.22 -0.22   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.25  0.25  0.25  0.25  0.25  0.25   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.70 -0.71 -0.72 -0.73 -0.74 -0.75   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.19  0.19  0.19  0.19  0.19  0.19   \n",
       "\n",
       "                                           108   109   110   111   112   113  \\\n",
       "Price_Point                               1.58  1.59  1.60  1.61  1.62  1.63   \n",
       "Mean_PE                                  -1.97 -2.00 -2.04 -2.07 -2.11 -2.14   \n",
       "PE_Segment_0_FO                          -2.69 -2.76 -2.83 -2.90 -2.97 -3.04   \n",
       "PE_Segment_1_Cf                          -1.38 -1.40 -1.42 -1.44 -1.46 -1.48   \n",
       "PE_Segment_2_Strd                        -1.58 -1.60 -1.62 -1.64 -1.66 -1.67   \n",
       "PE_Segment_3_WO                          -1.18 -1.19 -1.21 -1.22 -1.24 -1.25   \n",
       "Elasticity_Promotion_1                   -1.19 -1.21 -1.22 -1.24 -1.26 -1.27   \n",
       "Elasticity_Promotion_0                   -1.51 -1.53 -1.55 -1.57 -1.58 -1.60   \n",
       "Brand5_own_Avg                           -0.50 -0.51 -0.52 -0.53 -0.53 -0.54   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.57  0.57  0.57  0.57  0.57   \n",
       "Brand5_own_PE_WellOff                    -1.08 -1.09 -1.10 -1.11 -1.12 -1.13   \n",
       "Brand5_Cross_Brand_4_WellOff              1.35  1.35  1.36  1.37  1.37  1.38   \n",
       "Brand5_own_PE_Standard                   -0.20 -0.21 -0.21 -0.22 -0.23 -0.24   \n",
       "Brand5_Cross_Brand_4_Standard             0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "Brand5_own_PE_Career_Focused             -0.22 -0.23 -0.23 -0.23 -0.23 -0.24   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.25  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.76 -0.77 -0.78 -0.79 -0.80 -0.81   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.18  0.18  0.18  0.18  0.18  0.18   \n",
       "\n",
       "                                           114   115   116   117   118   119  \\\n",
       "Price_Point                               1.64  1.65  1.66  1.67  1.68  1.69   \n",
       "Mean_PE                                  -2.18 -2.21 -2.25 -2.28 -2.32 -2.36   \n",
       "PE_Segment_0_FO                          -3.11 -3.18 -3.25 -3.33 -3.40 -3.48   \n",
       "PE_Segment_1_Cf                          -1.50 -1.53 -1.55 -1.57 -1.59 -1.61   \n",
       "PE_Segment_2_Strd                        -1.69 -1.71 -1.73 -1.75 -1.77 -1.79   \n",
       "PE_Segment_3_WO                          -1.26 -1.28 -1.29 -1.31 -1.32 -1.34   \n",
       "Elasticity_Promotion_1                   -1.29 -1.31 -1.32 -1.34 -1.36 -1.38   \n",
       "Elasticity_Promotion_0                   -1.62 -1.64 -1.66 -1.67 -1.69 -1.71   \n",
       "Brand5_own_Avg                           -0.55 -0.56 -0.57 -0.58 -0.59 -0.59   \n",
       "Brand5_Cross_Brand_4_Avg                  0.57  0.56  0.56  0.56  0.56  0.56   \n",
       "Brand5_own_PE_WellOff                    -1.14 -1.15 -1.16 -1.17 -1.18 -1.19   \n",
       "Brand5_Cross_Brand_4_WellOff              1.38  1.39  1.39  1.40  1.41  1.41   \n",
       "Brand5_own_PE_Standard                   -0.25 -0.26 -0.27 -0.28 -0.30 -0.31   \n",
       "Brand5_Cross_Brand_4_Standard             0.21  0.21  0.21  0.21  0.21  0.21   \n",
       "Brand5_own_PE_Career_Focused             -0.24 -0.24 -0.25 -0.25 -0.25 -0.25   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.24  0.24  0.24  0.24  0.24  0.24   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.82 -0.83 -0.84 -0.85 -0.86 -0.88   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.18  0.18  0.18  0.17  0.17  0.17   \n",
       "\n",
       "                                           120   121   122   123   124   125  \\\n",
       "Price_Point                               1.70  1.71  1.72  1.73  1.74  1.75   \n",
       "Mean_PE                                  -2.39 -2.43 -2.47 -2.50 -2.54 -2.58   \n",
       "PE_Segment_0_FO                          -3.55 -3.62 -3.70 -3.78 -3.85 -3.93   \n",
       "PE_Segment_1_Cf                          -1.64 -1.66 -1.68 -1.70 -1.72 -1.75   \n",
       "PE_Segment_2_Strd                        -1.80 -1.82 -1.84 -1.86 -1.88 -1.90   \n",
       "PE_Segment_3_WO                          -1.35 -1.36 -1.38 -1.39 -1.41 -1.42   \n",
       "Elasticity_Promotion_1                   -1.39 -1.41 -1.43 -1.45 -1.46 -1.48   \n",
       "Elasticity_Promotion_0                   -1.73 -1.75 -1.77 -1.78 -1.80 -1.82   \n",
       "Brand5_own_Avg                           -0.60 -0.61 -0.62 -0.63 -0.64 -0.65   \n",
       "Brand5_Cross_Brand_4_Avg                  0.56  0.56  0.56  0.56  0.55  0.55   \n",
       "Brand5_own_PE_WellOff                    -1.20 -1.20 -1.21 -1.22 -1.23 -1.24   \n",
       "Brand5_Cross_Brand_4_WellOff              1.42  1.42  1.43  1.43  1.44  1.44   \n",
       "Brand5_own_PE_Standard                   -0.32 -0.33 -0.35 -0.36 -0.37 -0.39   \n",
       "Brand5_Cross_Brand_4_Standard             0.21  0.21  0.21  0.21  0.20  0.20   \n",
       "Brand5_own_PE_Career_Focused             -0.26 -0.26 -0.26 -0.27 -0.27 -0.27   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.23  0.23  0.23  0.23  0.23  0.23   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.89 -0.90 -0.91 -0.92 -0.93 -0.94   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.17  0.17  0.17  0.17  0.17  0.17   \n",
       "\n",
       "                                           126   127   128   129   130   131  \\\n",
       "Price_Point                               1.76  1.77  1.78  1.79  1.80  1.81   \n",
       "Mean_PE                                  -2.61 -2.65 -2.69 -2.73 -2.77 -2.80   \n",
       "PE_Segment_0_FO                          -4.00 -4.08 -4.15 -4.23 -4.31 -4.38   \n",
       "PE_Segment_1_Cf                          -1.77 -1.79 -1.82 -1.84 -1.86 -1.88   \n",
       "PE_Segment_2_Strd                        -1.92 -1.94 -1.96 -1.97 -1.99 -2.01   \n",
       "PE_Segment_3_WO                          -1.44 -1.45 -1.47 -1.48 -1.50 -1.51   \n",
       "Elasticity_Promotion_1                   -1.50 -1.52 -1.54 -1.56 -1.57 -1.59   \n",
       "Elasticity_Promotion_0                   -1.84 -1.86 -1.88 -1.90 -1.92 -1.93   \n",
       "Brand5_own_Avg                           -0.66 -0.67 -0.68 -0.69 -0.70 -0.71   \n",
       "Brand5_Cross_Brand_4_Avg                  0.55  0.55  0.55  0.55  0.55  0.55   \n",
       "Brand5_own_PE_WellOff                    -1.25 -1.26 -1.27 -1.28 -1.29 -1.30   \n",
       "Brand5_Cross_Brand_4_WellOff              1.44  1.45  1.45  1.46  1.46  1.47   \n",
       "Brand5_own_PE_Standard                   -0.40 -0.42 -0.43 -0.45 -0.47 -0.48   \n",
       "Brand5_Cross_Brand_4_Standard             0.20  0.20  0.20  0.20  0.20  0.20   \n",
       "Brand5_own_PE_Career_Focused             -0.28 -0.28 -0.28 -0.28 -0.29 -0.29   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.23  0.23  0.23  0.23  0.22  0.22   \n",
       "Brand5_own_PE_Fewer_Opportunities        -0.96 -0.97 -0.98 -0.99 -1.00 -1.02   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.16  0.16  0.16  0.16  0.16  0.16   \n",
       "\n",
       "                                           132   133   134   135   136   137  \\\n",
       "Price_Point                               1.82  1.83  1.84  1.85  1.86  1.87   \n",
       "Mean_PE                                  -2.84 -2.88 -2.92 -2.96 -2.99 -3.03   \n",
       "PE_Segment_0_FO                          -4.46 -4.53 -4.61 -4.69 -4.76 -4.84   \n",
       "PE_Segment_1_Cf                          -1.91 -1.93 -1.95 -1.98 -2.00 -2.03   \n",
       "PE_Segment_2_Strd                        -2.03 -2.05 -2.07 -2.09 -2.11 -2.13   \n",
       "PE_Segment_3_WO                          -1.53 -1.54 -1.56 -1.57 -1.59 -1.61   \n",
       "Elasticity_Promotion_1                   -1.61 -1.63 -1.65 -1.67 -1.69 -1.71   \n",
       "Elasticity_Promotion_0                   -1.95 -1.97 -1.99 -2.01 -2.03 -2.05   \n",
       "Brand5_own_Avg                           -0.72 -0.73 -0.74 -0.75 -0.76 -0.77   \n",
       "Brand5_Cross_Brand_4_Avg                  0.54  0.54  0.54  0.54  0.54  0.54   \n",
       "Brand5_own_PE_WellOff                    -1.31 -1.32 -1.33 -1.34 -1.35 -1.36   \n",
       "Brand5_Cross_Brand_4_WellOff              1.47  1.47  1.48  1.48  1.49  1.49   \n",
       "Brand5_own_PE_Standard                   -0.50 -0.52 -0.54 -0.56 -0.57 -0.59   \n",
       "Brand5_Cross_Brand_4_Standard             0.20  0.19  0.19  0.19  0.19  0.19   \n",
       "Brand5_own_PE_Career_Focused             -0.29 -0.30 -0.30 -0.30 -0.31 -0.31   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.22  0.22  0.22  0.22  0.22  0.22   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.03 -1.04 -1.05 -1.07 -1.08 -1.09   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.16  0.16  0.16  0.15  0.15  0.15   \n",
       "\n",
       "                                           138   139   140   141   142   143  \\\n",
       "Price_Point                               1.88  1.89  1.90  1.91  1.92  1.93   \n",
       "Mean_PE                                  -3.07 -3.11 -3.15 -3.18 -3.22 -3.26   \n",
       "PE_Segment_0_FO                          -4.91 -4.99 -5.06 -5.13 -5.21 -5.28   \n",
       "PE_Segment_1_Cf                          -2.05 -2.07 -2.10 -2.12 -2.15 -2.17   \n",
       "PE_Segment_2_Strd                        -2.15 -2.17 -2.19 -2.20 -2.22 -2.24   \n",
       "PE_Segment_3_WO                          -1.62 -1.64 -1.65 -1.67 -1.68 -1.70   \n",
       "Elasticity_Promotion_1                   -1.72 -1.74 -1.76 -1.78 -1.80 -1.82   \n",
       "Elasticity_Promotion_0                   -2.07 -2.09 -2.11 -2.13 -2.14 -2.16   \n",
       "Brand5_own_Avg                           -0.78 -0.80 -0.81 -0.82 -0.83 -0.84   \n",
       "Brand5_Cross_Brand_4_Avg                  0.53  0.53  0.53  0.53  0.53  0.53   \n",
       "Brand5_own_PE_WellOff                    -1.37 -1.38 -1.40 -1.41 -1.42 -1.43   \n",
       "Brand5_Cross_Brand_4_WellOff              1.49  1.50  1.50  1.50  1.50  1.51   \n",
       "Brand5_own_PE_Standard                   -0.62 -0.64 -0.66 -0.68 -0.70 -0.72   \n",
       "Brand5_Cross_Brand_4_Standard             0.19  0.19  0.19  0.19  0.18  0.18   \n",
       "Brand5_own_PE_Career_Focused             -0.31 -0.32 -0.32 -0.32 -0.33 -0.33   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.22  0.21  0.21  0.21  0.21  0.21   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.11 -1.12 -1.13 -1.15 -1.16 -1.17   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.15  0.15  0.15  0.15  0.15  0.14   \n",
       "\n",
       "                                           144   145   146   147   148   149  \\\n",
       "Price_Point                               1.94  1.95  1.96  1.97  1.98  1.99   \n",
       "Mean_PE                                  -3.30 -3.34 -3.38 -3.42 -3.45 -3.49   \n",
       "PE_Segment_0_FO                          -5.35 -5.43 -5.50 -5.57 -5.64 -5.71   \n",
       "PE_Segment_1_Cf                          -2.19 -2.22 -2.24 -2.27 -2.29 -2.32   \n",
       "PE_Segment_2_Strd                        -2.26 -2.28 -2.30 -2.32 -2.34 -2.36   \n",
       "PE_Segment_3_WO                          -1.71 -1.73 -1.75 -1.76 -1.78 -1.79   \n",
       "Elasticity_Promotion_1                   -1.84 -1.86 -1.88 -1.90 -1.92 -1.94   \n",
       "Elasticity_Promotion_0                   -2.18 -2.20 -2.22 -2.24 -2.26 -2.28   \n",
       "Brand5_own_Avg                           -0.85 -0.86 -0.88 -0.89 -0.90 -0.91   \n",
       "Brand5_Cross_Brand_4_Avg                  0.52  0.52  0.52  0.52  0.52  0.51   \n",
       "Brand5_own_PE_WellOff                    -1.44 -1.45 -1.46 -1.47 -1.48 -1.49   \n",
       "Brand5_Cross_Brand_4_WellOff              1.51  1.51  1.51  1.52  1.52  1.52   \n",
       "Brand5_own_PE_Standard                   -0.75 -0.77 -0.79 -0.82 -0.84 -0.87   \n",
       "Brand5_Cross_Brand_4_Standard             0.18  0.18  0.18  0.18  0.18  0.18   \n",
       "Brand5_own_PE_Career_Focused             -0.33 -0.34 -0.34 -0.35 -0.35 -0.35   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.21  0.21  0.21  0.21  0.20  0.20   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.19 -1.20 -1.21 -1.23 -1.24 -1.26   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.14  0.14  0.14  0.14  0.14  0.14   \n",
       "\n",
       "                                           150   151   152   153   154   155  \\\n",
       "Price_Point                               2.00  2.01  2.02  2.03  2.04  2.05   \n",
       "Mean_PE                                  -3.53 -3.57 -3.61 -3.64 -3.68 -3.72   \n",
       "PE_Segment_0_FO                          -5.78 -5.85 -5.92 -5.99 -6.06 -6.13   \n",
       "PE_Segment_1_Cf                          -2.34 -2.37 -2.39 -2.42 -2.44 -2.47   \n",
       "PE_Segment_2_Strd                        -2.38 -2.40 -2.42 -2.44 -2.46 -2.48   \n",
       "PE_Segment_3_WO                          -1.81 -1.83 -1.84 -1.86 -1.87 -1.89   \n",
       "Elasticity_Promotion_1                   -1.96 -1.98 -2.00 -2.02 -2.04 -2.06   \n",
       "Elasticity_Promotion_0                   -2.30 -2.32 -2.34 -2.36 -2.38 -2.40   \n",
       "Brand5_own_Avg                           -0.92 -0.93 -0.95 -0.96 -0.97 -0.98   \n",
       "Brand5_Cross_Brand_4_Avg                  0.51  0.51  0.51  0.51  0.50  0.50   \n",
       "Brand5_own_PE_WellOff                    -1.50 -1.51 -1.52 -1.53 -1.54 -1.56   \n",
       "Brand5_Cross_Brand_4_WellOff              1.52  1.52  1.53  1.53  1.53  1.53   \n",
       "Brand5_own_PE_Standard                   -0.89 -0.92 -0.95 -0.97 -1.00 -1.03   \n",
       "Brand5_Cross_Brand_4_Standard             0.18  0.17  0.17  0.17  0.17  0.17   \n",
       "Brand5_own_PE_Career_Focused             -0.36 -0.36 -0.36 -0.37 -0.37 -0.38   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.20  0.20  0.20  0.20  0.20  0.20   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.27 -1.29 -1.30 -1.31 -1.33 -1.34   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.14  0.14  0.13  0.13  0.13  0.13   \n",
       "\n",
       "                                           156   157   158   159   160   161  \\\n",
       "Price_Point                               2.06  2.07  2.08  2.09  2.10  2.11   \n",
       "Mean_PE                                  -3.76 -3.80 -3.83 -3.87 -3.91 -3.95   \n",
       "PE_Segment_0_FO                          -6.19 -6.26 -6.33 -6.39 -6.46 -6.52   \n",
       "PE_Segment_1_Cf                          -2.49 -2.51 -2.54 -2.56 -2.59 -2.61   \n",
       "PE_Segment_2_Strd                        -2.50 -2.52 -2.54 -2.55 -2.57 -2.59   \n",
       "PE_Segment_3_WO                          -1.91 -1.92 -1.94 -1.95 -1.97 -1.99   \n",
       "Elasticity_Promotion_1                   -2.08 -2.10 -2.12 -2.14 -2.16 -2.18   \n",
       "Elasticity_Promotion_0                   -2.42 -2.44 -2.45 -2.47 -2.49 -2.51   \n",
       "Brand5_own_Avg                           -1.00 -1.01 -1.02 -1.04 -1.05 -1.06   \n",
       "Brand5_Cross_Brand_4_Avg                  0.50  0.50  0.50  0.49  0.49  0.49   \n",
       "Brand5_own_PE_WellOff                    -1.57 -1.58 -1.59 -1.60 -1.61 -1.62   \n",
       "Brand5_Cross_Brand_4_WellOff              1.53  1.53  1.53  1.53  1.53  1.53   \n",
       "Brand5_own_PE_Standard                   -1.05 -1.08 -1.11 -1.14 -1.17 -1.19   \n",
       "Brand5_Cross_Brand_4_Standard             0.17  0.17  0.17  0.16  0.16  0.16   \n",
       "Brand5_own_PE_Career_Focused             -0.38 -0.38 -0.39 -0.39 -0.40 -0.40   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.20  0.19  0.19  0.19  0.19  0.19   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.36 -1.37 -1.39 -1.40 -1.42 -1.43   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.13  0.13  0.13  0.13  0.13  0.12   \n",
       "\n",
       "                                           162   163   164   165   166   167  \\\n",
       "Price_Point                               2.12  2.13  2.14  2.15  2.16  2.17   \n",
       "Mean_PE                                  -3.98 -4.02 -4.06 -4.10 -4.13 -4.17   \n",
       "PE_Segment_0_FO                          -6.59 -6.65 -6.71 -6.78 -6.84 -6.90   \n",
       "PE_Segment_1_Cf                          -2.64 -2.66 -2.69 -2.72 -2.74 -2.77   \n",
       "PE_Segment_2_Strd                        -2.61 -2.63 -2.65 -2.67 -2.69 -2.71   \n",
       "PE_Segment_3_WO                          -2.00 -2.02 -2.03 -2.05 -2.07 -2.08   \n",
       "Elasticity_Promotion_1                   -2.20 -2.22 -2.24 -2.26 -2.28 -2.30   \n",
       "Elasticity_Promotion_0                   -2.53 -2.55 -2.57 -2.59 -2.61 -2.63   \n",
       "Brand5_own_Avg                           -1.07 -1.09 -1.10 -1.11 -1.13 -1.14   \n",
       "Brand5_Cross_Brand_4_Avg                  0.49  0.48  0.48  0.48  0.48  0.48   \n",
       "Brand5_own_PE_WellOff                    -1.63 -1.64 -1.66 -1.67 -1.68 -1.69   \n",
       "Brand5_Cross_Brand_4_WellOff              1.53  1.53  1.53  1.53  1.53  1.52   \n",
       "Brand5_own_PE_Standard                   -1.22 -1.25 -1.28 -1.31 -1.34 -1.37   \n",
       "Brand5_Cross_Brand_4_Standard             0.16  0.16  0.16  0.16  0.16  0.15   \n",
       "Brand5_own_PE_Career_Focused             -0.40 -0.41 -0.41 -0.42 -0.42 -0.42   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.19  0.19  0.19  0.19  0.18  0.18   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.45 -1.46 -1.48 -1.49 -1.51 -1.53   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.12  0.12  0.12  0.12  0.12  0.12   \n",
       "\n",
       "                                           168   169   170   171   172   173  \\\n",
       "Price_Point                               2.18  2.19  2.20  2.21  2.22  2.23   \n",
       "Mean_PE                                  -4.21 -4.24 -4.28 -4.32 -4.35 -4.39   \n",
       "PE_Segment_0_FO                          -6.96 -7.02 -7.08 -7.14 -7.20 -7.26   \n",
       "PE_Segment_1_Cf                          -2.79 -2.82 -2.84 -2.87 -2.89 -2.92   \n",
       "PE_Segment_2_Strd                        -2.73 -2.75 -2.77 -2.79 -2.81 -2.83   \n",
       "PE_Segment_3_WO                          -2.10 -2.12 -2.13 -2.15 -2.17 -2.18   \n",
       "Elasticity_Promotion_1                   -2.32 -2.34 -2.37 -2.39 -2.41 -2.43   \n",
       "Elasticity_Promotion_0                   -2.65 -2.67 -2.69 -2.71 -2.73 -2.75   \n",
       "Brand5_own_Avg                           -1.16 -1.17 -1.18 -1.20 -1.21 -1.22   \n",
       "Brand5_Cross_Brand_4_Avg                  0.47  0.47  0.47  0.47  0.46  0.46   \n",
       "Brand5_own_PE_WellOff                    -1.70 -1.71 -1.72 -1.74 -1.75 -1.76   \n",
       "Brand5_Cross_Brand_4_WellOff              1.52  1.52  1.52  1.52  1.51  1.51   \n",
       "Brand5_own_PE_Standard                   -1.40 -1.43 -1.46 -1.49 -1.52 -1.55   \n",
       "Brand5_Cross_Brand_4_Standard             0.15  0.15  0.15  0.15  0.15  0.15   \n",
       "Brand5_own_PE_Career_Focused             -0.43 -0.43 -0.44 -0.44 -0.45 -0.45   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.18  0.18  0.18  0.18  0.18  0.18   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.54 -1.56 -1.57 -1.59 -1.60 -1.62   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.12  0.12  0.11  0.11  0.11  0.11   \n",
       "\n",
       "                                           174   175   176   177   178   179  \\\n",
       "Price_Point                               2.24  2.25  2.26  2.27  2.28  2.29   \n",
       "Mean_PE                                  -4.43 -4.46 -4.50 -4.54 -4.57 -4.61   \n",
       "PE_Segment_0_FO                          -7.31 -7.37 -7.43 -7.48 -7.54 -7.59   \n",
       "PE_Segment_1_Cf                          -2.94 -2.97 -2.99 -3.02 -3.04 -3.07   \n",
       "PE_Segment_2_Strd                        -2.85 -2.87 -2.89 -2.91 -2.93 -2.94   \n",
       "PE_Segment_3_WO                          -2.20 -2.22 -2.23 -2.25 -2.26 -2.28   \n",
       "Elasticity_Promotion_1                   -2.45 -2.47 -2.49 -2.51 -2.53 -2.55   \n",
       "Elasticity_Promotion_0                   -2.77 -2.79 -2.81 -2.83 -2.85 -2.86   \n",
       "Brand5_own_Avg                           -1.24 -1.25 -1.27 -1.28 -1.30 -1.31   \n",
       "Brand5_Cross_Brand_4_Avg                  0.46  0.46  0.45  0.45  0.45  0.45   \n",
       "Brand5_own_PE_WellOff                    -1.77 -1.78 -1.79 -1.81 -1.82 -1.83   \n",
       "Brand5_Cross_Brand_4_WellOff              1.51  1.50  1.50  1.50  1.49  1.49   \n",
       "Brand5_own_PE_Standard                   -1.58 -1.61 -1.64 -1.67 -1.70 -1.72   \n",
       "Brand5_Cross_Brand_4_Standard             0.15  0.14  0.14  0.14  0.14  0.14   \n",
       "Brand5_own_PE_Career_Focused             -0.46 -0.46 -0.46 -0.47 -0.47 -0.48   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.18  0.17  0.17  0.17  0.17  0.17   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.64 -1.65 -1.67 -1.69 -1.70 -1.72   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.11  0.11  0.11  0.11  0.11  0.10   \n",
       "\n",
       "                                           180   181   182   183   184   185  \\\n",
       "Price_Point                               2.30  2.31  2.32  2.33  2.34  2.35   \n",
       "Mean_PE                                  -4.64 -4.68 -4.71 -4.75 -4.78 -4.82   \n",
       "PE_Segment_0_FO                          -7.65 -7.70 -7.76 -7.81 -7.86 -7.92   \n",
       "PE_Segment_1_Cf                          -3.09 -3.12 -3.14 -3.17 -3.19 -3.22   \n",
       "PE_Segment_2_Strd                        -2.96 -2.98 -3.00 -3.02 -3.04 -3.06   \n",
       "PE_Segment_3_WO                          -2.30 -2.31 -2.33 -2.35 -2.36 -2.38   \n",
       "Elasticity_Promotion_1                   -2.57 -2.59 -2.61 -2.64 -2.66 -2.68   \n",
       "Elasticity_Promotion_0                   -2.88 -2.90 -2.92 -2.94 -2.96 -2.98   \n",
       "Brand5_own_Avg                           -1.33 -1.34 -1.36 -1.37 -1.39 -1.40   \n",
       "Brand5_Cross_Brand_4_Avg                  0.44  0.44  0.44  0.44  0.43  0.43   \n",
       "Brand5_own_PE_WellOff                    -1.84 -1.85 -1.87 -1.88 -1.89 -1.90   \n",
       "Brand5_Cross_Brand_4_WellOff              1.48  1.48  1.47  1.47  1.46  1.46   \n",
       "Brand5_own_PE_Standard                   -1.75 -1.78 -1.81 -1.84 -1.87 -1.90   \n",
       "Brand5_Cross_Brand_4_Standard             0.14  0.14  0.14  0.13  0.13  0.13   \n",
       "Brand5_own_PE_Career_Focused             -0.48 -0.49 -0.49 -0.50 -0.50 -0.51   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.17  0.17  0.17  0.17  0.16  0.16   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.73 -1.75 -1.77 -1.78 -1.80 -1.82   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.10  0.10  0.10  0.10  0.10  0.10   \n",
       "\n",
       "                                           186   187   188   189   190   191  \\\n",
       "Price_Point                               2.36  2.37  2.38  2.39  2.40  2.41   \n",
       "Mean_PE                                  -4.85 -4.89 -4.92 -4.96 -4.99 -5.02   \n",
       "PE_Segment_0_FO                          -7.97 -8.02 -8.07 -8.12 -8.17 -8.22   \n",
       "PE_Segment_1_Cf                          -3.24 -3.27 -3.29 -3.32 -3.34 -3.37   \n",
       "PE_Segment_2_Strd                        -3.08 -3.10 -3.12 -3.14 -3.16 -3.18   \n",
       "PE_Segment_3_WO                          -2.40 -2.41 -2.43 -2.45 -2.46 -2.48   \n",
       "Elasticity_Promotion_1                   -2.70 -2.72 -2.74 -2.76 -2.78 -2.80   \n",
       "Elasticity_Promotion_0                   -3.00 -3.02 -3.04 -3.06 -3.08 -3.10   \n",
       "Brand5_own_Avg                           -1.42 -1.43 -1.45 -1.46 -1.48 -1.49   \n",
       "Brand5_Cross_Brand_4_Avg                  0.43  0.43  0.42  0.42  0.42  0.42   \n",
       "Brand5_own_PE_WellOff                    -1.91 -1.92 -1.94 -1.95 -1.96 -1.97   \n",
       "Brand5_Cross_Brand_4_WellOff              1.45  1.44  1.44  1.43  1.42  1.42   \n",
       "Brand5_own_PE_Standard                   -1.92 -1.95 -1.98 -2.01 -2.03 -2.06   \n",
       "Brand5_Cross_Brand_4_Standard             0.13  0.13  0.13  0.13  0.13  0.12   \n",
       "Brand5_own_PE_Career_Focused             -0.51 -0.52 -0.52 -0.53 -0.53 -0.54   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.16  0.16  0.16  0.16  0.16  0.16   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.83 -1.85 -1.87 -1.88 -1.90 -1.92   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.10  0.10  0.10  0.09  0.09  0.09   \n",
       "\n",
       "                                           192   193   194   195   196   197  \\\n",
       "Price_Point                               2.42  2.43  2.44  2.45  2.46  2.47   \n",
       "Mean_PE                                  -5.06 -5.09 -5.13 -5.16 -5.19 -5.23   \n",
       "PE_Segment_0_FO                          -8.27 -8.32 -8.37 -8.42 -8.47 -8.52   \n",
       "PE_Segment_1_Cf                          -3.39 -3.42 -3.44 -3.47 -3.49 -3.52   \n",
       "PE_Segment_2_Strd                        -3.20 -3.21 -3.23 -3.25 -3.27 -3.29   \n",
       "PE_Segment_3_WO                          -2.50 -2.51 -2.53 -2.55 -2.56 -2.58   \n",
       "Elasticity_Promotion_1                   -2.82 -2.84 -2.87 -2.89 -2.91 -2.93   \n",
       "Elasticity_Promotion_0                   -3.12 -3.14 -3.16 -3.17 -3.19 -3.21   \n",
       "Brand5_own_Avg                           -1.51 -1.52 -1.54 -1.55 -1.57 -1.59   \n",
       "Brand5_Cross_Brand_4_Avg                  0.41  0.41  0.41  0.41  0.40  0.40   \n",
       "Brand5_own_PE_WellOff                    -1.99 -2.00 -2.01 -2.02 -2.03 -2.05   \n",
       "Brand5_Cross_Brand_4_WellOff              1.41  1.40  1.39  1.38  1.37  1.36   \n",
       "Brand5_own_PE_Standard                   -2.09 -2.11 -2.14 -2.16 -2.19 -2.21   \n",
       "Brand5_Cross_Brand_4_Standard             0.12  0.12  0.12  0.12  0.12  0.12   \n",
       "Brand5_own_PE_Career_Focused             -0.54 -0.55 -0.55 -0.56 -0.56 -0.57   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.16  0.16  0.15  0.15  0.15  0.15   \n",
       "Brand5_own_PE_Fewer_Opportunities        -1.93 -1.95 -1.97 -1.99 -2.00 -2.02   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.09  0.09  0.09  0.09  0.09  0.09   \n",
       "\n",
       "                                           198   199   200   201   202   203  \\\n",
       "Price_Point                               2.48  2.49  2.50  2.51  2.52  2.53   \n",
       "Mean_PE                                  -5.26 -5.29 -5.33 -5.36 -5.39 -5.42   \n",
       "PE_Segment_0_FO                          -8.56 -8.61 -8.66 -8.70 -8.75 -8.80   \n",
       "PE_Segment_1_Cf                          -3.54 -3.57 -3.59 -3.62 -3.64 -3.67   \n",
       "PE_Segment_2_Strd                        -3.31 -3.33 -3.35 -3.37 -3.39 -3.41   \n",
       "PE_Segment_3_WO                          -2.60 -2.61 -2.63 -2.65 -2.66 -2.68   \n",
       "Elasticity_Promotion_1                   -2.95 -2.97 -2.99 -3.01 -3.03 -3.05   \n",
       "Elasticity_Promotion_0                   -3.23 -3.25 -3.27 -3.29 -3.31 -3.33   \n",
       "Brand5_own_Avg                           -1.60 -1.62 -1.63 -1.65 -1.66 -1.68   \n",
       "Brand5_Cross_Brand_4_Avg                  0.40  0.39  0.39  0.39  0.39  0.38   \n",
       "Brand5_own_PE_WellOff                    -2.06 -2.07 -2.08 -2.10 -2.11 -2.12   \n",
       "Brand5_Cross_Brand_4_WellOff              1.35  1.34  1.33  1.32  1.31  1.30   \n",
       "Brand5_own_PE_Standard                   -2.24 -2.26 -2.29 -2.31 -2.33 -2.36   \n",
       "Brand5_Cross_Brand_4_Standard             0.12  0.11  0.11  0.11  0.11  0.11   \n",
       "Brand5_own_PE_Career_Focused             -0.57 -0.58 -0.59 -0.59 -0.60 -0.60   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.15  0.15  0.15  0.15  0.15  0.14   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.04 -2.05 -2.07 -2.09 -2.10 -2.12   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.09  0.08  0.08  0.08  0.08  0.08   \n",
       "\n",
       "                                           204   205   206   207   208   209  \\\n",
       "Price_Point                               2.54  2.55  2.56  2.57  2.58  2.59   \n",
       "Mean_PE                                  -5.46 -5.49 -5.52 -5.55 -5.59 -5.62   \n",
       "PE_Segment_0_FO                          -8.84 -8.89 -8.93 -8.98 -9.02 -9.07   \n",
       "PE_Segment_1_Cf                          -3.69 -3.71 -3.74 -3.76 -3.79 -3.81   \n",
       "PE_Segment_2_Strd                        -3.42 -3.44 -3.46 -3.48 -3.50 -3.52   \n",
       "PE_Segment_3_WO                          -2.70 -2.71 -2.73 -2.75 -2.76 -2.78   \n",
       "Elasticity_Promotion_1                   -3.07 -3.10 -3.12 -3.14 -3.16 -3.18   \n",
       "Elasticity_Promotion_0                   -3.35 -3.37 -3.39 -3.40 -3.42 -3.44   \n",
       "Brand5_own_Avg                           -1.70 -1.71 -1.73 -1.75 -1.76 -1.78   \n",
       "Brand5_Cross_Brand_4_Avg                  0.38  0.38  0.38  0.37  0.37  0.37   \n",
       "Brand5_own_PE_WellOff                    -2.13 -2.15 -2.16 -2.17 -2.18 -2.20   \n",
       "Brand5_Cross_Brand_4_WellOff              1.29  1.28  1.27  1.25  1.24  1.23   \n",
       "Brand5_own_PE_Standard                   -2.38 -2.40 -2.43 -2.45 -2.47 -2.49   \n",
       "Brand5_Cross_Brand_4_Standard             0.11  0.11  0.11  0.11  0.10  0.10   \n",
       "Brand5_own_PE_Career_Focused             -0.61 -0.61 -0.62 -0.62 -0.63 -0.64   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.14  0.14  0.14  0.14  0.14  0.14   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.14 -2.16 -2.17 -2.19 -2.21 -2.22   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.08  0.08  0.08  0.08  0.08  0.08   \n",
       "\n",
       "                                           210   211   212   213   214   215  \\\n",
       "Price_Point                               2.60  2.61  2.62  2.63  2.64  2.65   \n",
       "Mean_PE                                  -5.65 -5.68 -5.71 -5.74 -5.77 -5.81   \n",
       "PE_Segment_0_FO                          -9.11 -9.16 -9.20 -9.24 -9.29 -9.33   \n",
       "PE_Segment_1_Cf                          -3.84 -3.86 -3.88 -3.91 -3.93 -3.96   \n",
       "PE_Segment_2_Strd                        -3.54 -3.56 -3.58 -3.59 -3.61 -3.63   \n",
       "PE_Segment_3_WO                          -2.80 -2.81 -2.83 -2.85 -2.86 -2.88   \n",
       "Elasticity_Promotion_1                   -3.20 -3.22 -3.24 -3.26 -3.28 -3.30   \n",
       "Elasticity_Promotion_0                   -3.46 -3.48 -3.50 -3.52 -3.54 -3.56   \n",
       "Brand5_own_Avg                           -1.79 -1.81 -1.83 -1.84 -1.86 -1.88   \n",
       "Brand5_Cross_Brand_4_Avg                  0.37  0.36  0.36  0.36  0.35  0.35   \n",
       "Brand5_own_PE_WellOff                    -2.21 -2.22 -2.23 -2.25 -2.26 -2.27   \n",
       "Brand5_Cross_Brand_4_WellOff              1.22  1.20  1.19  1.18  1.16  1.15   \n",
       "Brand5_own_PE_Standard                   -2.51 -2.53 -2.55 -2.57 -2.59 -2.61   \n",
       "Brand5_Cross_Brand_4_Standard             0.10  0.10  0.10  0.10  0.10  0.10   \n",
       "Brand5_own_PE_Career_Focused             -0.64 -0.65 -0.65 -0.66 -0.67 -0.67   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.14  0.14  0.14  0.13  0.13  0.13   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.24 -2.26 -2.28 -2.29 -2.31 -2.33   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.07  0.07  0.07  0.07  0.07  0.07   \n",
       "\n",
       "                                           216   217   218   219   220   221  \\\n",
       "Price_Point                               2.66  2.67  2.68  2.69  2.70  2.71   \n",
       "Mean_PE                                  -5.84 -5.87 -5.90 -5.93 -5.96 -5.99   \n",
       "PE_Segment_0_FO                          -9.37 -9.41 -9.46 -9.50 -9.54 -9.58   \n",
       "PE_Segment_1_Cf                          -3.98 -4.01 -4.03 -4.05 -4.08 -4.10   \n",
       "PE_Segment_2_Strd                        -3.65 -3.67 -3.69 -3.71 -3.73 -3.74   \n",
       "PE_Segment_3_WO                          -2.90 -2.91 -2.93 -2.95 -2.96 -2.98   \n",
       "Elasticity_Promotion_1                   -3.32 -3.34 -3.36 -3.38 -3.41 -3.43   \n",
       "Elasticity_Promotion_0                   -3.57 -3.59 -3.61 -3.63 -3.65 -3.67   \n",
       "Brand5_own_Avg                           -1.89 -1.91 -1.93 -1.94 -1.96 -1.98   \n",
       "Brand5_Cross_Brand_4_Avg                  0.35  0.35  0.34  0.34  0.34  0.34   \n",
       "Brand5_own_PE_WellOff                    -2.29 -2.30 -2.31 -2.32 -2.34 -2.35   \n",
       "Brand5_Cross_Brand_4_WellOff              1.13  1.12  1.11  1.09  1.08  1.06   \n",
       "Brand5_own_PE_Standard                   -2.63 -2.65 -2.67 -2.69 -2.71 -2.73   \n",
       "Brand5_Cross_Brand_4_Standard             0.10  0.09  0.09  0.09  0.09  0.09   \n",
       "Brand5_own_PE_Career_Focused             -0.68 -0.68 -0.69 -0.70 -0.70 -0.71   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.13  0.13  0.13  0.13  0.13  0.13   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.34 -2.36 -2.38 -2.40 -2.41 -2.43   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.07  0.07  0.07  0.07  0.07  0.07   \n",
       "\n",
       "                                           222   223   224   225   226   227  \\\n",
       "Price_Point                               2.72  2.73  2.74  2.75  2.76  2.77   \n",
       "Mean_PE                                  -6.02 -6.05 -6.08 -6.11 -6.14 -6.17   \n",
       "PE_Segment_0_FO                          -9.62 -9.67 -9.71 -9.75 -9.79 -9.83   \n",
       "PE_Segment_1_Cf                          -4.12 -4.15 -4.17 -4.20 -4.22 -4.24   \n",
       "PE_Segment_2_Strd                        -3.76 -3.78 -3.80 -3.82 -3.84 -3.85   \n",
       "PE_Segment_3_WO                          -3.00 -3.01 -3.03 -3.05 -3.06 -3.08   \n",
       "Elasticity_Promotion_1                   -3.45 -3.47 -3.49 -3.51 -3.53 -3.55   \n",
       "Elasticity_Promotion_0                   -3.69 -3.71 -3.72 -3.74 -3.76 -3.78   \n",
       "Brand5_own_Avg                           -1.99 -2.01 -2.03 -2.05 -2.06 -2.08   \n",
       "Brand5_Cross_Brand_4_Avg                  0.33  0.33  0.33  0.33  0.32  0.32   \n",
       "Brand5_own_PE_WellOff                    -2.36 -2.37 -2.39 -2.40 -2.41 -2.43   \n",
       "Brand5_Cross_Brand_4_WellOff              1.05  1.03  1.01  1.00  0.98  0.97   \n",
       "Brand5_own_PE_Standard                   -2.75 -2.76 -2.78 -2.80 -2.82 -2.83   \n",
       "Brand5_Cross_Brand_4_Standard             0.09  0.09  0.09  0.09  0.08  0.08   \n",
       "Brand5_own_PE_Career_Focused             -0.72 -0.72 -0.73 -0.74 -0.74 -0.75   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.13  0.12  0.12  0.12  0.12  0.12   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.45 -2.46 -2.48 -2.50 -2.51 -2.53   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.06  0.06  0.06  0.06  0.06  0.06   \n",
       "\n",
       "                                           228   229   230   231    232  \\\n",
       "Price_Point                               2.78  2.79  2.80  2.81   2.82   \n",
       "Mean_PE                                  -6.20 -6.23 -6.26 -6.29  -6.32   \n",
       "PE_Segment_0_FO                          -9.87 -9.91 -9.95 -9.99 -10.03   \n",
       "PE_Segment_1_Cf                          -4.27 -4.29 -4.31 -4.34  -4.36   \n",
       "PE_Segment_2_Strd                        -3.87 -3.89 -3.91 -3.93  -3.95   \n",
       "PE_Segment_3_WO                          -3.09 -3.11 -3.13 -3.14  -3.16   \n",
       "Elasticity_Promotion_1                   -3.57 -3.59 -3.61 -3.63  -3.65   \n",
       "Elasticity_Promotion_0                   -3.80 -3.82 -3.84 -3.85  -3.87   \n",
       "Brand5_own_Avg                           -2.10 -2.11 -2.13 -2.15  -2.16   \n",
       "Brand5_Cross_Brand_4_Avg                  0.32  0.31  0.31  0.31   0.31   \n",
       "Brand5_own_PE_WellOff                    -2.44 -2.45 -2.47 -2.48  -2.49   \n",
       "Brand5_Cross_Brand_4_WellOff              0.95  0.94  0.92  0.90   0.89   \n",
       "Brand5_own_PE_Standard                   -2.85 -2.87 -2.89 -2.90  -2.92   \n",
       "Brand5_Cross_Brand_4_Standard             0.08  0.08  0.08  0.08   0.08   \n",
       "Brand5_own_PE_Career_Focused             -0.76 -0.76 -0.77 -0.78  -0.78   \n",
       "Brand5_Cross_Brand_4_Career_Focused       0.12  0.12  0.12  0.12   0.12   \n",
       "Brand5_own_PE_Fewer_Opportunities        -2.55 -2.57 -2.58 -2.60  -2.62   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities  0.06  0.06  0.06  0.06   0.06   \n",
       "\n",
       "                                            233    234    235    236    237  \\\n",
       "Price_Point                                2.83   2.84   2.85   2.86   2.87   \n",
       "Mean_PE                                   -6.35  -6.38  -6.40  -6.43  -6.46   \n",
       "PE_Segment_0_FO                          -10.07 -10.11 -10.15 -10.19 -10.23   \n",
       "PE_Segment_1_Cf                           -4.38  -4.40  -4.43  -4.45  -4.47   \n",
       "PE_Segment_2_Strd                         -3.96  -3.98  -4.00  -4.02  -4.04   \n",
       "PE_Segment_3_WO                           -3.18  -3.19  -3.21  -3.23  -3.24   \n",
       "Elasticity_Promotion_1                    -3.67  -3.69  -3.71  -3.73  -3.75   \n",
       "Elasticity_Promotion_0                    -3.89  -3.91  -3.93  -3.95  -3.96   \n",
       "Brand5_own_Avg                            -2.18  -2.20  -2.22  -2.23  -2.25   \n",
       "Brand5_Cross_Brand_4_Avg                   0.30   0.30   0.30   0.30   0.29   \n",
       "Brand5_own_PE_WellOff                     -2.50  -2.52  -2.53  -2.54  -2.56   \n",
       "Brand5_Cross_Brand_4_WellOff               0.87   0.85   0.84   0.82   0.81   \n",
       "Brand5_own_PE_Standard                    -2.93  -2.95  -2.97  -2.98  -3.00   \n",
       "Brand5_Cross_Brand_4_Standard              0.08   0.08   0.07   0.07   0.07   \n",
       "Brand5_own_PE_Career_Focused              -0.79  -0.80  -0.80  -0.81  -0.82   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.12   0.11   0.11   0.11   0.11   \n",
       "Brand5_own_PE_Fewer_Opportunities         -2.63  -2.65  -2.67  -2.68  -2.70   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.06   0.06   0.06   0.05   0.05   \n",
       "\n",
       "                                            238    239    240    241    242  \\\n",
       "Price_Point                                2.88   2.89   2.90   2.91   2.92   \n",
       "Mean_PE                                   -6.49  -6.52  -6.55  -6.58  -6.60   \n",
       "PE_Segment_0_FO                          -10.27 -10.31 -10.35 -10.39 -10.43   \n",
       "PE_Segment_1_Cf                           -4.50  -4.52  -4.54  -4.57  -4.59   \n",
       "PE_Segment_2_Strd                         -4.06  -4.07  -4.09  -4.11  -4.13   \n",
       "PE_Segment_3_WO                           -3.26  -3.27  -3.29  -3.31  -3.32   \n",
       "Elasticity_Promotion_1                    -3.77  -3.79  -3.81  -3.83  -3.85   \n",
       "Elasticity_Promotion_0                    -3.98  -4.00  -4.02  -4.04  -4.06   \n",
       "Brand5_own_Avg                            -2.27  -2.28  -2.30  -2.32  -2.33   \n",
       "Brand5_Cross_Brand_4_Avg                   0.29   0.29   0.29   0.28   0.28   \n",
       "Brand5_own_PE_WellOff                     -2.57  -2.58  -2.60  -2.61  -2.62   \n",
       "Brand5_Cross_Brand_4_WellOff               0.79   0.77   0.76   0.74   0.73   \n",
       "Brand5_own_PE_Standard                    -3.01  -3.03  -3.04  -3.06  -3.07   \n",
       "Brand5_Cross_Brand_4_Standard              0.07   0.07   0.07   0.07   0.07   \n",
       "Brand5_own_PE_Career_Focused              -0.82  -0.83  -0.84  -0.85  -0.85   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.11   0.11   0.11   0.11   0.11   \n",
       "Brand5_own_PE_Fewer_Opportunities         -2.72  -2.73  -2.75  -2.77  -2.78   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.05   0.05   0.05   0.05   0.05   \n",
       "\n",
       "                                            243    244    245    246    247  \\\n",
       "Price_Point                                2.93   2.94   2.95   2.96   2.97   \n",
       "Mean_PE                                   -6.63  -6.66  -6.69  -6.72  -6.75   \n",
       "PE_Segment_0_FO                          -10.47 -10.51 -10.55 -10.58 -10.62   \n",
       "PE_Segment_1_Cf                           -4.61  -4.63  -4.66  -4.68  -4.70   \n",
       "PE_Segment_2_Strd                         -4.15  -4.16  -4.18  -4.20  -4.22   \n",
       "PE_Segment_3_WO                           -3.34  -3.36  -3.37  -3.39  -3.40   \n",
       "Elasticity_Promotion_1                    -3.87  -3.89  -3.91  -3.93  -3.95   \n",
       "Elasticity_Promotion_0                    -4.07  -4.09  -4.11  -4.13  -4.15   \n",
       "Brand5_own_Avg                            -2.35  -2.37  -2.39  -2.40  -2.42   \n",
       "Brand5_Cross_Brand_4_Avg                   0.28   0.28   0.27   0.27   0.27   \n",
       "Brand5_own_PE_WellOff                     -2.64  -2.65  -2.66  -2.67  -2.69   \n",
       "Brand5_Cross_Brand_4_WellOff               0.71   0.69   0.68   0.66   0.65   \n",
       "Brand5_own_PE_Standard                    -3.09  -3.10  -3.12  -3.13  -3.14   \n",
       "Brand5_Cross_Brand_4_Standard              0.07   0.07   0.07   0.06   0.06   \n",
       "Brand5_own_PE_Career_Focused              -0.86  -0.87  -0.88  -0.88  -0.89   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.11   0.11   0.11   0.10   0.10   \n",
       "Brand5_own_PE_Fewer_Opportunities         -2.80  -2.81  -2.83  -2.85  -2.86   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.05   0.05   0.05   0.05   0.05   \n",
       "\n",
       "                                            248    249    250    251    252  \\\n",
       "Price_Point                                2.98   2.99   3.00   3.01   3.02   \n",
       "Mean_PE                                   -6.77  -6.80  -6.83  -6.86  -6.88   \n",
       "PE_Segment_0_FO                          -10.66 -10.70 -10.74 -10.78 -10.82   \n",
       "PE_Segment_1_Cf                           -4.72  -4.75  -4.77  -4.79  -4.81   \n",
       "PE_Segment_2_Strd                         -4.24  -4.25  -4.27  -4.29  -4.31   \n",
       "PE_Segment_3_WO                           -3.42  -3.44  -3.45  -3.47  -3.49   \n",
       "Elasticity_Promotion_1                    -3.97  -3.99  -4.01  -4.03  -4.05   \n",
       "Elasticity_Promotion_0                    -4.16  -4.18  -4.20  -4.22  -4.24   \n",
       "Brand5_own_Avg                            -2.44  -2.45  -2.47  -2.49  -2.51   \n",
       "Brand5_Cross_Brand_4_Avg                   0.27   0.26   0.26   0.26   0.26   \n",
       "Brand5_own_PE_WellOff                     -2.70  -2.71  -2.73  -2.74  -2.75   \n",
       "Brand5_Cross_Brand_4_WellOff               0.63   0.62   0.60   0.59   0.57   \n",
       "Brand5_own_PE_Standard                    -3.16  -3.17  -3.19  -3.20  -3.21   \n",
       "Brand5_Cross_Brand_4_Standard              0.06   0.06   0.06   0.06   0.06   \n",
       "Brand5_own_PE_Career_Focused              -0.90  -0.91  -0.91  -0.92  -0.93   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.10   0.10   0.10   0.10   0.10   \n",
       "Brand5_own_PE_Fewer_Opportunities         -2.88  -2.90  -2.91  -2.93  -2.94   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.05   0.05   0.05   0.04   0.04   \n",
       "\n",
       "                                            253    254    255    256    257  \\\n",
       "Price_Point                                3.03   3.04   3.05   3.06   3.07   \n",
       "Mean_PE                                   -6.91  -6.94  -6.97  -6.99  -7.02   \n",
       "PE_Segment_0_FO                          -10.85 -10.89 -10.93 -10.97 -11.01   \n",
       "PE_Segment_1_Cf                           -4.83  -4.86  -4.88  -4.90  -4.92   \n",
       "PE_Segment_2_Strd                         -4.32  -4.34  -4.36  -4.38  -4.40   \n",
       "PE_Segment_3_WO                           -3.50  -3.52  -3.53  -3.55  -3.57   \n",
       "Elasticity_Promotion_1                    -4.07  -4.09  -4.11  -4.13  -4.15   \n",
       "Elasticity_Promotion_0                    -4.25  -4.27  -4.29  -4.31  -4.32   \n",
       "Brand5_own_Avg                            -2.52  -2.54  -2.56  -2.57  -2.59   \n",
       "Brand5_Cross_Brand_4_Avg                   0.25   0.25   0.25   0.25   0.24   \n",
       "Brand5_own_PE_WellOff                     -2.77  -2.78  -2.79  -2.81  -2.82   \n",
       "Brand5_Cross_Brand_4_WellOff               0.56   0.55   0.53   0.52   0.50   \n",
       "Brand5_own_PE_Standard                    -3.23  -3.24  -3.25  -3.27  -3.28   \n",
       "Brand5_Cross_Brand_4_Standard              0.06   0.06   0.06   0.06   0.05   \n",
       "Brand5_own_PE_Career_Focused              -0.94  -0.95  -0.95  -0.96  -0.97   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.10   0.10   0.10   0.10   0.10   \n",
       "Brand5_own_PE_Fewer_Opportunities         -2.96  -2.98  -2.99  -3.01  -3.02   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.04   0.04   0.04   0.04   0.04   \n",
       "\n",
       "                                            258    259    260    261    262  \\\n",
       "Price_Point                                3.08   3.09   3.10   3.11   3.12   \n",
       "Mean_PE                                   -7.05  -7.07  -7.10  -7.13  -7.16   \n",
       "PE_Segment_0_FO                          -11.04 -11.08 -11.12 -11.16 -11.19   \n",
       "PE_Segment_1_Cf                           -4.94  -4.97  -4.99  -5.01  -5.03   \n",
       "PE_Segment_2_Strd                         -4.41  -4.43  -4.45  -4.47  -4.48   \n",
       "PE_Segment_3_WO                           -3.58  -3.60  -3.61  -3.63  -3.65   \n",
       "Elasticity_Promotion_1                    -4.17  -4.19  -4.20  -4.22  -4.24   \n",
       "Elasticity_Promotion_0                    -4.34  -4.36  -4.38  -4.40  -4.41   \n",
       "Brand5_own_Avg                            -2.61  -2.63  -2.64  -2.66  -2.68   \n",
       "Brand5_Cross_Brand_4_Avg                   0.24   0.24   0.24   0.23   0.23   \n",
       "Brand5_own_PE_WellOff                     -2.83  -2.85  -2.86  -2.87  -2.89   \n",
       "Brand5_Cross_Brand_4_WellOff               0.49   0.48   0.47   0.45   0.44   \n",
       "Brand5_own_PE_Standard                    -3.29  -3.31  -3.32  -3.33  -3.34   \n",
       "Brand5_Cross_Brand_4_Standard              0.05   0.05   0.05   0.05   0.05   \n",
       "Brand5_own_PE_Career_Focused              -0.98  -0.99  -0.99  -1.00  -1.01   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.09   0.09   0.09   0.09   0.09   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.04  -3.05  -3.07  -3.09  -3.10   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.04   0.04   0.04   0.04   0.04   \n",
       "\n",
       "                                            263    264    265    266    267  \\\n",
       "Price_Point                                3.13   3.14   3.15   3.16   3.17   \n",
       "Mean_PE                                   -7.18  -7.21  -7.24  -7.26  -7.29   \n",
       "PE_Segment_0_FO                          -11.23 -11.27 -11.31 -11.34 -11.38   \n",
       "PE_Segment_1_Cf                           -5.05  -5.07  -5.10  -5.12  -5.14   \n",
       "PE_Segment_2_Strd                         -4.50  -4.52  -4.54  -4.55  -4.57   \n",
       "PE_Segment_3_WO                           -3.66  -3.68  -3.69  -3.71  -3.72   \n",
       "Elasticity_Promotion_1                    -4.26  -4.28  -4.30  -4.32  -4.34   \n",
       "Elasticity_Promotion_0                    -4.43  -4.45  -4.47  -4.48  -4.50   \n",
       "Brand5_own_Avg                            -2.69  -2.71  -2.73  -2.74  -2.76   \n",
       "Brand5_Cross_Brand_4_Avg                   0.23   0.23   0.23   0.22   0.22   \n",
       "Brand5_own_PE_WellOff                     -2.90  -2.91  -2.93  -2.94  -2.95   \n",
       "Brand5_Cross_Brand_4_WellOff               0.43   0.42   0.40   0.39   0.38   \n",
       "Brand5_own_PE_Standard                    -3.36  -3.37  -3.38  -3.39  -3.41   \n",
       "Brand5_Cross_Brand_4_Standard              0.05   0.05   0.05   0.05   0.05   \n",
       "Brand5_own_PE_Career_Focused              -1.02  -1.03  -1.04  -1.05  -1.05   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.09   0.09   0.09   0.09   0.09   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.12  -3.13  -3.15  -3.16  -3.18   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.04   0.04   0.04   0.04   0.04   \n",
       "\n",
       "                                            268    269    270    271    272  \\\n",
       "Price_Point                                3.18   3.19   3.20   3.21   3.22   \n",
       "Mean_PE                                   -7.32  -7.34  -7.37  -7.39  -7.42   \n",
       "PE_Segment_0_FO                          -11.42 -11.46 -11.49 -11.53 -11.57   \n",
       "PE_Segment_1_Cf                           -5.16  -5.18  -5.20  -5.22  -5.25   \n",
       "PE_Segment_2_Strd                         -4.59  -4.61  -4.62  -4.64  -4.66   \n",
       "PE_Segment_3_WO                           -3.74  -3.76  -3.77  -3.79  -3.80   \n",
       "Elasticity_Promotion_1                    -4.36  -4.38  -4.40  -4.42  -4.43   \n",
       "Elasticity_Promotion_0                    -4.52  -4.54  -4.55  -4.57  -4.59   \n",
       "Brand5_own_Avg                            -2.78  -2.80  -2.81  -2.83  -2.85   \n",
       "Brand5_Cross_Brand_4_Avg                   0.22   0.22   0.21   0.21   0.21   \n",
       "Brand5_own_PE_WellOff                     -2.97  -2.98  -2.99  -3.01  -3.02   \n",
       "Brand5_Cross_Brand_4_WellOff               0.37   0.36   0.35   0.34   0.33   \n",
       "Brand5_own_PE_Standard                    -3.42  -3.43  -3.44  -3.46  -3.47   \n",
       "Brand5_Cross_Brand_4_Standard              0.05   0.04   0.04   0.04   0.04   \n",
       "Brand5_own_PE_Career_Focused              -1.06  -1.07  -1.08  -1.09  -1.10   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.09   0.09   0.09   0.08   0.08   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.19  -3.21  -3.22  -3.24  -3.25   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.04   0.03   0.03   0.03   0.03   \n",
       "\n",
       "                                            273    274    275    276    277  \\\n",
       "Price_Point                                3.23   3.24   3.25   3.26   3.27   \n",
       "Mean_PE                                   -7.45  -7.47  -7.50  -7.53  -7.55   \n",
       "PE_Segment_0_FO                          -11.61 -11.64 -11.68 -11.72 -11.75   \n",
       "PE_Segment_1_Cf                           -5.27  -5.29  -5.31  -5.33  -5.35   \n",
       "PE_Segment_2_Strd                         -4.68  -4.69  -4.71  -4.73  -4.74   \n",
       "PE_Segment_3_WO                           -3.82  -3.84  -3.85  -3.87  -3.88   \n",
       "Elasticity_Promotion_1                    -4.45  -4.47  -4.49  -4.51  -4.53   \n",
       "Elasticity_Promotion_0                    -4.61  -4.62  -4.64  -4.66  -4.68   \n",
       "Brand5_own_Avg                            -2.86  -2.88  -2.90  -2.91  -2.93   \n",
       "Brand5_Cross_Brand_4_Avg                   0.21   0.20   0.20   0.20   0.20   \n",
       "Brand5_own_PE_WellOff                     -3.03  -3.05  -3.06  -3.07  -3.09   \n",
       "Brand5_Cross_Brand_4_WellOff               0.32   0.31   0.30   0.29   0.28   \n",
       "Brand5_own_PE_Standard                    -3.48  -3.49  -3.50  -3.52  -3.53   \n",
       "Brand5_Cross_Brand_4_Standard              0.04   0.04   0.04   0.04   0.04   \n",
       "Brand5_own_PE_Career_Focused              -1.11  -1.12  -1.12  -1.13  -1.14   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.08   0.08   0.08   0.08   0.08   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.27  -3.28  -3.30  -3.31  -3.33   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.03   0.03   0.03   0.03   0.03   \n",
       "\n",
       "                                            278    279    280    281    282  \\\n",
       "Price_Point                                3.28   3.29   3.30   3.31   3.32   \n",
       "Mean_PE                                   -7.58  -7.60  -7.63  -7.66  -7.68   \n",
       "PE_Segment_0_FO                          -11.79 -11.83 -11.86 -11.90 -11.94   \n",
       "PE_Segment_1_Cf                           -5.37  -5.39  -5.41  -5.44  -5.46   \n",
       "PE_Segment_2_Strd                         -4.76  -4.78  -4.80  -4.81  -4.83   \n",
       "PE_Segment_3_WO                           -3.90  -3.91  -3.93  -3.94  -3.96   \n",
       "Elasticity_Promotion_1                    -4.55  -4.57  -4.59  -4.60  -4.62   \n",
       "Elasticity_Promotion_0                    -4.69  -4.71  -4.73  -4.74  -4.76   \n",
       "Brand5_own_Avg                            -2.95  -2.96  -2.98  -3.00  -3.01   \n",
       "Brand5_Cross_Brand_4_Avg                   0.20   0.19   0.19   0.19   0.19   \n",
       "Brand5_own_PE_WellOff                     -3.10  -3.11  -3.13  -3.14  -3.15   \n",
       "Brand5_Cross_Brand_4_WellOff               0.27   0.26   0.25   0.25   0.24   \n",
       "Brand5_own_PE_Standard                    -3.54  -3.55  -3.56  -3.58  -3.59   \n",
       "Brand5_Cross_Brand_4_Standard              0.04   0.04   0.04   0.04   0.04   \n",
       "Brand5_own_PE_Career_Focused              -1.15  -1.16  -1.17  -1.18  -1.19   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.08   0.08   0.08   0.08   0.08   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.34  -3.36  -3.37  -3.39  -3.40   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.03   0.03   0.03   0.03   0.03   \n",
       "\n",
       "                                            283    284    285    286    287  \\\n",
       "Price_Point                                3.33   3.34   3.35   3.36   3.37   \n",
       "Mean_PE                                   -7.71  -7.73  -7.76  -7.78  -7.81   \n",
       "PE_Segment_0_FO                          -11.98 -12.01 -12.05 -12.09 -12.12   \n",
       "PE_Segment_1_Cf                           -5.48  -5.50  -5.52  -5.54  -5.56   \n",
       "PE_Segment_2_Strd                         -4.85  -4.86  -4.88  -4.90  -4.92   \n",
       "PE_Segment_3_WO                           -3.98  -3.99  -4.01  -4.02  -4.04   \n",
       "Elasticity_Promotion_1                    -4.64  -4.66  -4.68  -4.70  -4.72   \n",
       "Elasticity_Promotion_0                    -4.78  -4.80  -4.81  -4.83  -4.85   \n",
       "Brand5_own_Avg                            -3.03  -3.05  -3.06  -3.08  -3.09   \n",
       "Brand5_Cross_Brand_4_Avg                   0.19   0.18   0.18   0.18   0.18   \n",
       "Brand5_own_PE_WellOff                     -3.17  -3.18  -3.19  -3.21  -3.22   \n",
       "Brand5_Cross_Brand_4_WellOff               0.23   0.22   0.22   0.21   0.20   \n",
       "Brand5_own_PE_Standard                    -3.60  -3.61  -3.62  -3.64  -3.65   \n",
       "Brand5_Cross_Brand_4_Standard              0.04   0.03   0.03   0.03   0.03   \n",
       "Brand5_own_PE_Career_Focused              -1.20  -1.21  -1.22  -1.23  -1.24   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.08   0.08   0.07   0.07   0.07   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.42  -3.43  -3.44  -3.46  -3.47   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.03   0.03   0.03   0.03   0.03   \n",
       "\n",
       "                                            288    289    290    291    292  \\\n",
       "Price_Point                                3.38   3.39   3.40   3.41   3.42   \n",
       "Mean_PE                                   -7.83  -7.86  -7.89  -7.91  -7.94   \n",
       "PE_Segment_0_FO                          -12.16 -12.20 -12.23 -12.27 -12.31   \n",
       "PE_Segment_1_Cf                           -5.58  -5.60  -5.62  -5.64  -5.66   \n",
       "PE_Segment_2_Strd                         -4.93  -4.95  -4.97  -4.98  -5.00   \n",
       "PE_Segment_3_WO                           -4.05  -4.07  -4.08  -4.10  -4.12   \n",
       "Elasticity_Promotion_1                    -4.73  -4.75  -4.77  -4.79  -4.81   \n",
       "Elasticity_Promotion_0                    -4.86  -4.88  -4.90  -4.92  -4.93   \n",
       "Brand5_own_Avg                            -3.11  -3.13  -3.14  -3.16  -3.18   \n",
       "Brand5_Cross_Brand_4_Avg                   0.18   0.17   0.17   0.17   0.17   \n",
       "Brand5_own_PE_WellOff                     -3.23  -3.25  -3.26  -3.27  -3.29   \n",
       "Brand5_Cross_Brand_4_WellOff               0.20   0.19   0.18   0.18   0.17   \n",
       "Brand5_own_PE_Standard                    -3.66  -3.67  -3.68  -3.69  -3.70   \n",
       "Brand5_Cross_Brand_4_Standard              0.03   0.03   0.03   0.03   0.03   \n",
       "Brand5_own_PE_Career_Focused              -1.25  -1.26  -1.27  -1.28  -1.29   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.07   0.07   0.07   0.07   0.07   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.49  -3.50  -3.52  -3.53  -3.54   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.03   0.03   0.03   0.03   0.03   \n",
       "\n",
       "                                            293    294    295    296    297  \\\n",
       "Price_Point                                3.43   3.44   3.45   3.46   3.47   \n",
       "Mean_PE                                   -7.96  -7.99  -8.01  -8.04  -8.06   \n",
       "PE_Segment_0_FO                          -12.34 -12.38 -12.42 -12.45 -12.49   \n",
       "PE_Segment_1_Cf                           -5.68  -5.70  -5.72  -5.74  -5.76   \n",
       "PE_Segment_2_Strd                         -5.02  -5.03  -5.05  -5.07  -5.09   \n",
       "PE_Segment_3_WO                           -4.13  -4.15  -4.16  -4.18  -4.19   \n",
       "Elasticity_Promotion_1                    -4.83  -4.84  -4.86  -4.88  -4.90   \n",
       "Elasticity_Promotion_0                    -4.95  -4.97  -4.98  -5.00  -5.02   \n",
       "Brand5_own_Avg                            -3.19  -3.21  -3.23  -3.24  -3.26   \n",
       "Brand5_Cross_Brand_4_Avg                   0.17   0.16   0.16   0.16   0.16   \n",
       "Brand5_own_PE_WellOff                     -3.30  -3.31  -3.33  -3.34  -3.35   \n",
       "Brand5_Cross_Brand_4_WellOff               0.16   0.16   0.15   0.15   0.14   \n",
       "Brand5_own_PE_Standard                    -3.72  -3.73  -3.74  -3.75  -3.76   \n",
       "Brand5_Cross_Brand_4_Standard              0.03   0.03   0.03   0.03   0.03   \n",
       "Brand5_own_PE_Career_Focused              -1.30  -1.31  -1.32  -1.33  -1.34   \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.07   0.07   0.07   0.07   0.07   \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.56  -3.57  -3.59  -3.60  -3.61   \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.02   0.02   0.02   0.02   0.02   \n",
       "\n",
       "                                            298    299  \n",
       "Price_Point                                3.48   3.49  \n",
       "Mean_PE                                   -8.09  -8.11  \n",
       "PE_Segment_0_FO                          -12.53 -12.56  \n",
       "PE_Segment_1_Cf                           -5.78  -5.80  \n",
       "PE_Segment_2_Strd                         -5.10  -5.12  \n",
       "PE_Segment_3_WO                           -4.21  -4.22  \n",
       "Elasticity_Promotion_1                    -4.92  -4.93  \n",
       "Elasticity_Promotion_0                    -5.03  -5.05  \n",
       "Brand5_own_Avg                            -3.27  -3.29  \n",
       "Brand5_Cross_Brand_4_Avg                   0.16   0.15  \n",
       "Brand5_own_PE_WellOff                     -3.37  -3.38  \n",
       "Brand5_Cross_Brand_4_WellOff               0.14   0.13  \n",
       "Brand5_own_PE_Standard                    -3.77  -3.78  \n",
       "Brand5_Cross_Brand_4_Standard              0.03   0.03  \n",
       "Brand5_own_PE_Career_Focused              -1.35  -1.36  \n",
       "Brand5_Cross_Brand_4_Career_Focused        0.07   0.07  \n",
       "Brand5_own_PE_Fewer_Opportunities         -3.63  -3.64  \n",
       "Brand5_Cross_Brand_4_Fewer_Opportunities   0.02   0.02  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate cross-brand price elasticity for brand 5 with respect to brand 4 for the Fewer_Opportunities. \n",
    "brand5_cross_brand4_FO = pd.DataFrame({'B5CB4_Price_1':  brand_choice_FO['Price_1'].mean(),\n",
    "                                       'B5CB4_Price_2':  brand_choice_FO['Price_2'].mean(), \n",
    "                                       'B5CB4_Price_3':  brand_choice_FO['Price_3'].mean(),\n",
    "                                       'B5CB4_Price_4':  price_range,\n",
    "                                       'B5CB4_Price_5':  brand_choice_FO['Price_5'].mean()},\n",
    "                                        index = np.arange(price_range.size))\n",
    "\n",
    "pred_brand5_cross_brand4_FO = model_brand_choice_FO.predict_proba(brand5_cross_brand4_FO)\n",
    "pr_cross_brand_5_FO = pred_brand5_cross_brand4_FO[: ][: , 3]\n",
    "\n",
    "# Update master data frame to include the newly obtained cross-brand price elasticities.\n",
    "brand5_cross_brand4_price_elasticity_FO = -beta5 * price_range * pr_cross_brand_5_FO\n",
    "price_elasticities.loc['Brand5_Cross_Brand_4_Fewer_Opportunities'] = brand5_cross_brand4_price_elasticity_FO\n",
    "pd.options.display.max_columns = None\n",
    "price_elasticities.to_csv('price_elasticities_2.csv')\n",
    "price_elasticities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the own brand and cross-brand price elasticities for brand 5 cross brand 4 side by side.\n",
    "fig, axs = plt.subplots(1, 2, figsize = (14, 4))\n",
    "axs[0].plot(price_range, own_price_elasticity_brand_5_FO, color = 'b')\n",
    "axs[0].set_title('Brand 5 Segment Fewer-Opportunities')\n",
    "axs[0].set_xlabel('Price 5')\n",
    "axs[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='b', alpha=0.2, label = \"Natural Price Domain of Brand 5\")\n",
    "\n",
    "\n",
    "axs[1].plot(price_range, brand5_cross_brand4_price_elasticity_Strd, color = 'b')\n",
    "axs[1].set_title('Cross Price Elasticity of Brand 5 wrt Brand 4 Segment Fewer-Opportunities')\n",
    "axs[1].set_xlabel('Price 4')\n",
    "axs[1].axvspan(brand_choice.Price_4.min(), brand_choice.Price_4.max(), facecolor='b', alpha=0.4, label = \"Natural Price Domain of Brand 4\")\n",
    "\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylabel = 'Elasticity')\n",
    "    ax.legend(bbox_to_anchor = (0, 1.1), loc = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fewer Opportunities segment\n",
    "\n",
    "The own price elasticity (left plot) has a more pronounced shape. This segment seems to be inelastic at lower price points and then customers become rapidly elastic at higher prices. Considering the natural price range (blue-coloured area) of the brand_5, this segment customers are rather elastic. As for Career-Focused segment, Fewer Opportunities seem loyal to brand 5, comparing to brand 4, according to the Cross price elasticity. \n",
    "\n",
    "As shown in descriptive analysis (Part 2-1), Fewer-Opportunities segment almost never buys brand 5 nor brand 4. The correlations are 0.066 and 0.098, respectively. This means we don't have enough observations to obtain an accurate model and that could be the reason why both curves look so out of character. Therefore, to target this segment in particular brand 5, more purchase data especially from this segment of people are necessary. However, if a product may be too pricey for a segment, we may never obtain more data about their behaviour. These people are simply not the target group for Brand 5.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations of All Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4, -3, -2, -1,  0,  1])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minor_ticks = np.arange(-4, 2, 1)\n",
    "minor_ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the own and cross brand price elasticities for the average customer and each of the four segments.\n",
    "minor_ticks = np.arange(-4, 2, 1)\n",
    "custom_ylim = (-4.5, 2.0)\n",
    "fig1, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 2, figsize = (15, 12), sharex = True )\n",
    "\n",
    "# Average\n",
    "ax1[0].plot(price_range, own_price_elasticity_brand_5, 'tab:grey')\n",
    "ax1[0].set_title('Brand 5 Average Customer')\n",
    "ax1[0].set_ylabel('Elasticity')\n",
    "ax1[0].set_ylim([-4.5, 2.0])\n",
    "ax1[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='grey', alpha=0.2)\n",
    "ax1[0].set_yticks(minor_ticks, minor=True)\n",
    "ax1[0].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "ax1[1].plot(price_range, brand5_cross_brand4_price_elasticity, 'tab:grey')\n",
    "ax1[1].set_title('Cross Brand 4 Average Customer')\n",
    "ax1[1].set_ylim([-4.5, 2.0])\n",
    "ax1[1].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='grey', alpha=0.2)\n",
    "ax1[1].set_yticks(minor_ticks, minor=True)\n",
    "ax1[1].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "# Well-off\n",
    "ax2[0].plot(price_range, own_price_elasticity_brand_5_WO, 'tab:red')\n",
    "ax2[0].set_title('Brand 5 Segment Well-off')\n",
    "ax2[0].set_ylabel('Elasticity')\n",
    "ax2[0].set_ylim([-4.5, 2.0])\n",
    "ax2[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='r', alpha=0.2)\n",
    "ax2[0].set_yticks(minor_ticks, minor=True)\n",
    "ax2[0].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "ax2[1].plot(price_range, brand5_cross_brand4_price_elasticity_WO, 'tab:red')\n",
    "ax2[1].set_title('Cross Brand 4 Segment Well-off')\n",
    "ax2[1].set_ylim([-4.5, 2.0])\n",
    "ax2[1].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='r', alpha=0.2)\n",
    "ax2[1].set_yticks(minor_ticks, minor=True)\n",
    "ax2[1].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "\n",
    "# Standard Segment\n",
    "ax3[0].plot(price_range, own_price_elasticity_brand_5_Strd, 'tab:green')\n",
    "ax3[0].set_title('Brand 5 Segment Standard')\n",
    "ax3[0].set_ylabel('Elasticity')\n",
    "ax3[0].set_ylim([-4.5, 2.0])\n",
    "ax3[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='g', alpha=0.2)\n",
    "ax3[0].set_yticks(minor_ticks, minor=True)\n",
    "ax3[0].grid(  which = 'minor', axis = 'y' )\n",
    "ax3[1].plot(price_range, brand5_cross_brand4_price_elasticity_Strd, 'tab:green')\n",
    "ax3[1].set_title('Cross Brand 4 Segment Standard')\n",
    "ax3[1].set_ylim([-4.5, 2.0])\n",
    "ax3[1].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='g', alpha=0.2)\n",
    "ax3[1].set_yticks(minor_ticks, minor=True)\n",
    "ax3[1].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "# Career-Focused\n",
    "ax4[0].plot(price_range, own_price_elasticity_brand_5_Cf, 'tab:orange')\n",
    "ax4[0].set_title('Brand 5 Segment Career-Focused')\n",
    "ax4[0].set_ylabel('Elasticity')\n",
    "ax4[0].set_ylim([-4.5, 2.0])\n",
    "ax4[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='orange', alpha=0.2)\n",
    "ax4[0].set_yticks(minor_ticks, minor=True)\n",
    "ax4[0].grid(  which = 'minor', axis = 'y' )\n",
    "ax4[1].plot(price_range, brand5_cross_brand4_price_elasticity_Cf, 'tab:orange')\n",
    "ax4[1].set_title('Cross Brand 4 Segment Career-Focused')\n",
    "ax4[1].set_ylim([-4.5, 2.0])\n",
    "ax4[1].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='orange', alpha=0.2)\n",
    "ax4[1].set_yticks(minor_ticks, minor=True)\n",
    "ax4[1].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "# Fewer-Opportunities\n",
    "ax5[0].plot(price_range, own_price_elasticity_brand_5_FO, 'tab:blue')\n",
    "ax5[0].set_title('Brand 5 Segment Fewer-Opportunities')\n",
    "ax5[0].set_xlabel('Price 5')\n",
    "ax5[0].set_ylabel('Elasticity')\n",
    "ax5[0].set_ylim([-4.5, 2.0])\n",
    "ax5[0].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='b', alpha=0.2)\n",
    "ax5[0].set_yticks(minor_ticks, minor=True)\n",
    "ax5[0].grid(  which = 'minor', axis = 'y' )\n",
    "\n",
    "ax5[1].plot(price_range, brand5_cross_brand4_price_elasticity_FO, 'tab:blue')\n",
    "ax5[1].set_title('Cross Brand 4 Segment Fewer-Opportunities')\n",
    "ax5[1].set_xlabel('Price 4')\n",
    "ax5[1].set_ylim([-4.5, 2.0])\n",
    "ax5[1].axvspan(brand_choice.Price_5.min(), brand_choice.Price_5.max(), facecolor='b', alpha=0.2)\n",
    "ax5[1].set_yticks(minor_ticks, minor=True)\n",
    "ax5[1].grid(  which = 'minor', axis = 'y' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get more insights, all own price elasticities and cross price elasticities are presented side by side. The two plots in the first row represents the average customer, while the following lines represent each segment.  Also, the plots are presented with shared y and x axis. It is better to compare the plots with the findings of descriptive analysis (Part 2-1). \n",
    "\n",
    "It seems that Career-Focused and Well-Off segments require the most attention as both groups are actually the people that purchase brand 5. Well-off segment is much more elastic than the Career-Focused. In other words, increasing the prices would barely affect the Career-Focused segment but would seriously damage Well-Off segment sales. \n",
    "\n",
    "If brand 4 were to decrease their price, then it would affect the Well-Off segment, but not the career focused one. A tiny decrease in our pricing would compensate such a competitor move. If prices of chocolates were to drop, we would have space to decrease our price offering while gaining solid market share from the Well-Off segment and practically retaining the Career-Focused customer base.\n"
   ]
  }
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