Regresion.ipynb

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   "source": [
    "# importando modulos que se van a usar\n",
    "import os, sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling\n",
    "\n",
    "# module to do statistics\n",
    "import scipy.stats  as stats\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')\n",
    "import pandas_profiling\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "import scipy.stats as stats# Funciones adicionales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funcion de otra persona http://stackoverflow.com/questions/26266362/how-to-count-the-nan-values-in-the-column-in-panda-data-frame\"\n",
    "def missing_values_table(df): \n",
    "        mis_val = df.isnull().sum()\n",
    "        mis_val_percent = 100 * df.isnull().sum()/len(df)\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        return mis_val_table_ren_columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Escriba el PATH directorio / carpeta de su archivo de analisisC:\\Users\\lc.mojica639\\Documents\\Cientificas de Datos\\Bootcamp\\\n"
     ]
    }
   ],
   "source": [
    "# Changing directory\n",
    "pathArchivos = input(\"Escriba el PATH directorio / carpeta de su archivo de analisis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caso\n",
    "\n",
    "Caso\n",
    "Los ingenieros de control de costos están interesados en reducir la cantidad de agua utilizada en la planta de producción. \n",
    "El archivo Agua.xlsx contiene observaciones mensuales de las siguientes variables:\n",
    "\n",
    " Temperatura: Promedio mensual de temperatura en grados Fahrenheit.\n",
    "\n",
    " Produccion: Cantidad de producción en Millones de libras.\n",
    "\n",
    " Dias: Número de días al mes que la planta estuvo operando.\n",
    "\n",
    " Personas: Número de operadores en la nómina de la planta mensual.\n",
    "\n",
    " Agua: Uso de agua mensual en galones.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading dataframe\n",
    "df = pd.read_excel(pathArchivos +\"Agua.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 5)"
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     "execution_count": 5,
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   "source": [
    "df.shape"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperatura    float64\n",
       "Produccion       int64\n",
       "Dias             int64\n",
       "Personas         int64\n",
       "Agua             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperatura</th>\n",
       "      <td>2</td>\n",
       "      <td>11.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Produccion</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dias</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personas</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agua</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             Missing Values  % of Total Values\n",
       "Temperatura               2          11.764706\n",
       "Produccion                0           0.000000\n",
       "Dias                      0           0.000000\n",
       "Personas                  0           0.000000\n",
       "Agua                      0           0.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking missing values by column\n",
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>5 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>17 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>2.4% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>760.0 B </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>44.7 B </td>\n",
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       "            </tbody>\n",
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       "        <p class=\"h4\">Variables types</p>\n",
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       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>4 </td>\n",
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       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>0 </td>\n",
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       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>0 </td>\n",
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       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
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       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>1 </td>\n",
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       "            <tr>\n",
       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
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       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_Personas\"><code>Personas</code></a> is highly correlated with <a href=\"#pp_var_Produccion\"><code>Produccion</code></a> (ρ = 0.91848) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_Temperatura\"><code>Temperatura</code></a> has 2 / 11.8% missing values <span class=\"label label-default\">Missing</span></li> </ul>\n",
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       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
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       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Agua\">Agua<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
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       "    <div class=\"row\">\n",
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       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>17</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
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       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>3303.7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>2828</td>\n",
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       "                    <th>Maximum</th>\n",
       "                    <td>4488</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
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       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6402531586459656144\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAcVJREFUeJzt3EGuk1AYhuGfxiks4AZ24SLck2P35CLcBaQLoHHgwOJA60Tz5bZeD3J9nmEb8kNy3hBOU7pt27ZqbFmWmqap9VgObp7nGsex6cw3Taf90Pd9VX2/4GEY9jgFDmRd15qm6ee6aWmXQLquq6qqYRheJJC37z/efcynD%2B/%2BeC5t3dZNS6fmE%2BFABAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEgl3ei5U88o6rVnO8S%2Bv/4w4CgUAgEAgEAoFAIBAIBIJ/bpv3NWm1lXzvHNvVz%2BcOAoFAIBAIBAKBQCAQCAQCgUDgdxD%2BmtfwlwJ3EAgEAoFAIBAIBAKBQCAQ7LLNu21bVVWt6/rLd1%2B/fG59Os/2u/NNHrmWe2c8MueRGY94qeu/fXZbNy112w5Tl2WpaZpaj%2BXg5nmucRybztwlkOv1Wufzufq%2Br67rWo/nYLZtq8vlUk9PT3U6tX0q2CUQOAoP6RAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgE3wBr3mJIppL7OwAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
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       "                                                  aria-controls=\"quantiles-6402531586459656144\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6402531586459656144\" aria-controls=\"histogram-6402531586459656144\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6402531586459656144\" aria-controls=\"common-6402531586459656144\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6402531586459656144\" aria-controls=\"extreme-6402531586459656144\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
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       "\n",
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       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>2828</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>2878.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>3022</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>3125</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>3502</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>4057.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>4488</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>1660</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>480</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>446.7</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.13521</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>1.8284</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>3303.7</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>336.64</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>1.4554</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>56163</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>199540</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>216.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6402531586459656144\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xl0VOX9x/HPQMiwZYadBBIgUpQlsoYjcWUrS0Gr1J4ii%2BBCwcOOFAWqBTkSa7VFjxUFEW2rAi7Y9CgIWFl6ACEBJKhFEJQtYStkAsIQ4Pn9wS9zGBJI0IfcyZ3365w5h/vcOzff%2BXJ58uHeOzMeY4wRAAAArKngdAEAAABuQ8ACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMtinC4gGpw/f14HDhxQXFycPB6P0%2BUAABAVjDHKz89XgwYNVKFC2Z5TImCVgQMHDigpKcnpMgAAiEp79%2B5VYmJimf5MAlYZiIuLk3ThL9jn8zlcDQAA0SEQCCgpKSn0e7gsEbDKQOFlQZ/PR8ACAKCMOXF7Dje5AwAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYJXC/v37NWjQINWuXVtVq1ZV27ZtlZWV5XRZAAAgQvFJ7iU4duyYbrnlFnXp0kVLlixRvXr19O2336pGjRpOlwYAACIUAasEf/zjH5WUlKT58%2BeHxpo0aeJcQQAAIOJxibAEGRkZSk1N1a9//WvVq1dP7dq109y5c6/4nGAwqEAgEPYAAADRgzNYJdi1a5dmz56tCRMmaMqUKdqwYYPGjBkjr9er%2B%2B%2B/v9jnpKena/r06WVcaeRLnbrU6RKuSubTvZwuAQBQTnmMMcbpIiJZbGysUlNTtXbt2tDYmDFjtHHjRq1bt67Y5wSDQQWDwdByIBBQUlKS8vLy5PP5rnnNkYqABQAoS4FAQH6/35Hfv1wiLEFCQoJatmwZNtaiRQvt2bPnss/xer3y%2BXxhDwAAED0IWCW45ZZbtH379rCxb775Ro0bN3aoIgAAEOkIWCUYP3681q9fr5kzZ2rnzp16%2B%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%2Bvfvr127dl1x%2B2AwqEAgEPYAAADRg4BVgptuukl/%2B9vf9Mknn2ju3LnKzc3VzTffrKNHj172Oenp6fL7/aFHUlJSGVYMAACc5jHGGKeLKE9Onjyppk2batKkSZowYUKx2wSDQQWDwdByIBBQUlKS8vLy5PP5yqrUiJM6danTJVyVzKd7OV0CAOAnCAQC8vv9jvz%2B5R6sq1StWjXdeOON2rFjx2W38Xq98nq9ZVgVAACIJFwivErBYFBff/21EhISnC4FAABEKAJWCSZOnKhVq1Zp9%2B7d%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%2B50KQAAIMLFOF1AebBgwQJlZWUpMzOzVNsHg0EFg8HQciAQuFalAQCACMQZrBLs3btXY8eO1VtvvaXKlSuX6jnp6eny%2B/2hR1JS0jWuEgAARBKPMcY4XUQk%2B/DDD3XPPfeoYsWKobFz587J4/GoQoUKCgaDYeuk4s9gJSUlKS8vTz6fr8xqjzSpU5c6XcJVyXy6l9MlAAB%2BgkAgIL/f78jvXy4RlqBbt27Kzs4OG3vggQfUvHlzPfbYY0XClSR5vV55vd6yKhEAAEQYAlYJ4uLilJKSEjZWrVo11a5du8g4AACAxD1YAAAA1nEG60dYuXKl0yUAAIAIxhksAAAAywhYAAAAlhGwAAAALHN1wPrHP/6h06dPO10GAACIMq4OWBMmTFB8fLyGDx%2BuDRs2OF0OAACIEq4OWAcOHNDrr7%2BunJwc3XrrrWrVqpWef/55HT582OnSAACAi7k6YMXExKhfv37KyMjQnj17NGTIEL3%2B%2ButKTExUv3799NFHH4lvCgIAALa5OmBdLD4%2BXt26dVPnzp3l8XiUmZmpAQMGqFmzZlqzZo3T5QEAABdxfcA6cuSIZs2apTZt2uiWW27RoUOH9OGHH%2Br777/X/v371bdvX91///1OlwkAAFzE1Z/kfs899%2Bjjjz9WcnKyHn74YQ0ZMkR169YNra9evbomTZqkF1980cEqAQCA27g6YPl8Pq1YsUK33XbbZbdJSEjQjh07yrAqAADgdq4OWG%2B%2B%2BWaJ23g8HjVt2rQMqgEAANHC1fdgjR8/Xi%2B99FKR8b/%2B9a969NFHHagIAABEA1cHrHfffVedOnUqMp6WlqaFCxc6UBEAAIgGrg5YR44cUc2aNYuM%2B3w%2BHTlyxIGKAABANHB1wGratKk%2B%2BeSTIuOffPKJkpOTHagIAABEA1ff5D5u3DiNGzdOR48eVdeuXSVJn376qZ599lk999xzDlcHAADcytUBa9iwYTp9%2BrRmzpypP/zhD5KkxMREvfjii3rwwQcdrg4AALiVqwOWJI0ePVqjR49WTk6OqlSpoho1ajhdEgAAcDnXB6xCCQkJTpcAAACihKtvcj98%2BLAeeOABNWrUSJUrV1ZsbGzYAwAA4Fpw9RmsoUOH6ttvv9Xvfvc7JSQkyOPxOF0SAACIAq4OWKtXr9bq1avVrl07p0sBAABRxNWXCBMTEzlrBQAAypyrA9Zf/vIXTZ48Wfv27XO6FAAAEEVcfYlw8ODBys/PV%2BPGjeXz%2BVSpUqWw9YcOHXKoMgAA4GauDljPPPOM0yUAAIAo5OqA9dBDDzldAgAAiEKuvgdLkr777jtNmzZNgwcPDl0SXLZsmb7%2B%2BmuHKwMAAG7l6oC1Zs0atWrVSqtWrdKiRYt04sQJSdKmTZv05JNPOlwdAABwK1cHrMcee0zTpk3TZ599FvbJ7V27dtX69esdrAwAALiZqwPW1q1bde%2B99xYZr1evng4fPuxARQAAIBq4OmDVqFFDubm5Rca3bNmihg0bOlARAACIBq4OWP3799fjjz%2Buw4cPhz7R/fPPP9fEiRM1aNAgh6sDAABu5eqANXPmTMXHxyshIUEnTpxQy5YtdfPNN6tjx4564oknnC4PAAC4lKs/Bys2NlYLFy7UN998o02bNun8%2BfNq3769mjdv7nRpAADAxVwdsApdf/31uv76650uAwAARAlXB6zf/va3V1w/Z86cMqoEAABEE1cHrJycnLDlgoICffnll8rPz9ftt9/uUFUAAMDtXB2w/vWvfxUZO3v2rB555BG1aNHCgYoAAEA0cPW7CIsTExOjiRMn6k9/%2BlOpnzN79my1bt1aPp9PPp9PaWlpWrJkyTWsEgAAlGdRF7AkadeuXSooKCj19omJiXrmmWeUmZmpzMxMde3aVb/85S/15ZdfXsMqAQBAeeXqS4STJk0KWzbGKCcnRxkZGRo4cGCp93PnnXeGLT/99NOaPXu21q9fr1atWlmpFQAAuIerA9a6devClitUqKC6devqmWee0bBhw37UPs%2BdO6d3331XJ0%2BeVFpamo0yAQCAy7g6YK1Zs8bavrKzs5WWlqbTp0%2BrevXqWrx4sVq2bFnstsFgUMFgMLQcCASs1QEAACKfxxhjnC6iPDhz5oz27Nmj48eP6/3339drr72mVatWFRuypk2bpunTpxcZz8vLk8/ns1pX6tSlVveH8ivz6V5OlwAAESUQCMjv91%2BT378lcXXA6tixY%2BhLnkuyYcOGq9p39%2B7d1bRpU7366qtF1hV3BispKYmAhWuKgAUA4ZwMWK6%2BRNilSxe9%2Buqruv7660P3S61fv17bt2/X8OHD5fV6f/S%2BjTFhIepiXq/3J%2B0bAACUb64OWMePH9fIkSM1c%2BbMsPGpU6fq4MGDeu2110q1nylTpqh3795KSkpSfn6%2BFixYoJUrV2rpUs4eAQCAolwdsBYtWqSNGzcWGR86dKhSU1NLHbAOHjyowYMHKycnR36/X61bt9bSpUv185//3HbJAADABVwdsLxer9auXatmzZqFja9du/aqLuHNmzfPdmkAAMDFXB2wxowZoxEjRmjz5s3q1KmTpAv3YM2dO1dTpkxxuDoAAOBWrg5YU6dOVXJysl544QW9/vrrkqQWLVpo7ty5GjBggMPVAQAAt3J1wJKkAQMGEKYAAECZcv2XPQcCAb3xxht68skndezYMUnSF198oZycHIcrAwAAbuXqM1jbtm1T9%2B7dVbVqVe3du1dDhw5VzZo1tWjRIu3bt09vvvmm0yUCAAAXcvUZrPHjx2vAgAH69ttvVbly5dB4nz59tHr1agcrAwAAbubqM1gbN27U7Nmzi3xdTsOGDblECAAArhlXn8GKjY3ViRMniozv2LFDderUcaAiAAAQDVwdsO666y7NmDFDZ8%2BelSR5PB7t379fjz/%2BuPr16%2BdwdQAAwK1cHbCef/55HThwQPHx8Tp16pS6du2q6667TpUrVy7y/YQAAAC2uPoeLL/fr7Vr12r58uXatGmTzp8/r/bt26tnz55F7ssCAACwxbUBq6CgQL/4xS/08ssvq0ePHurRo4fTJQEAgCjh2kuElSpV0ubNmzlTBQAAypxrA5YkDRo0SPPnz3e6DAAAEGVce4mw0EsvvaQVK1YoNTVV1apVC1v37LPPOlQVAABwM1cHrKysLLVu3VqStHXr1rB1XDoEAADXiisD1q5du5ScnKw1a9Y4XQoAAIhCrrwHq1mzZjp8%2BHBo%2BTe/%2BY0OHjzoYEUAACCauDJgGWPClj/%2B%2BGOdPHnSoWoAAEC0cWXAAgAAcJIrA5bH4ylyEzs3tQMAgLLiypvcjTEaOnSovF6vJOn06dMaMWJEkY9p%2BOCDD5woDwAAuJwrA9aQIUPClgcNGuRQJQAAIBq5MmDx6e0AAMBJrrwHCwAAwEkELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgJWKaSnp6tjx46Ki4tTvXr1dPfdd2v79u1OlwUAACIUAasUVq1apZEjR2r9%2BvVavny5zp49qx49eujkyZNOlwYAACJQjNMFlAdLly4NW54/f77q1aunrKws3X777Q5VBQAAIhUB60fIy8uTJNWqVavY9cFgUMFgMLQcCATKpC4AABAZCFhXyRijCRMm6NZbb1VKSkqx26Snp2v69OllXBlQfqROXVryRhEk8%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%2B%2B8Uw0aNJDH49GHH37odEkAACCCEbBK4eTJk2rTpo1eeuklp0sBAADlQIzTBZQHvXv3Vu/evZ0uAwAAlBMErGsgGAwqGAyGlgOBgIPVAACAskbAugbS09M1ffp0p8sAAES51KlLnS6h1DKf7uV0CVZxD9Y1MHnyZOXl5YUee/fudbokAABQhjiDdQ14vV55vV6nywAAAA7hDBYAAIBlnMEqhRMnTmjnzp2h5d27d2vLli2qVauWGjVq5GBlAAAgEhGwSiEzM1NdunQJLU%2BYMEGSNGTIEL3xxhsOVQUAACIVAasUOnfuLGOM02UAAIBygnuwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbBK6eWXX1ZycrIqV66sDh06aM2aNU6XBAAAIhQBqxQWLlyocePGaerUqdq8ebNuu%2B029e7dW3v27HG6NAAAEIEIWKXw5z//WQ899JAefvhhtWjRQrNmzVJSUpJmz57tdGkAACACxThdQKQ7c%2BaMsrKy9Pjjj4eN9%2BjRQ2vXri32OcFgUMFgMLScl5cnSQoEAtbrOxc8aX2fKJ%2BuxfF1rZS347Y89VYqX/0tb70tb6L9WCjcpzHG%2Br5LQsAqwZEjR3Tu3DnVr18/bLx%2B/frKzc0t9jnp6emaPn16kfGkpKRrUiMgSf7nna7AvejttUNvUehaHgv5%2Bfny%2B/3X7gcUg4BVSh6PJ2zZGFNkrNDkyZM1YcKE0PL58%2Bf1/fffq23bttq7d698Pt81rTWSBQIBJSUl0Qf6QA/%2BH324gD5cQB/s9sAYo/z8fDVo0MBSdaVHwCpBnTp1VLFixSJnqw4dOlTkrFYhr9crr9cbNlahwoXb3Xw%2BX9T%2Bo7kYfbiAPtCDQvThAvpwAX2w14OyPnNViJvcSxAbG6sOHTpo%2BfLlYePLly/XzTff7FBVAAAgknEGqxQmTJigwYMHKzU1VWlpaZozZ4727NmjESNGOF0aAACIQBWnTZs2zekiIl1KSopq166tmTNn6rnnntOpU6f097//XW3atLmq/VSsWFGdO3dWTEx051r6cAF9oAeF6MMF9OEC%2BuCOHniME%2B9dBAAAcDHuwQIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBKyrkJ6ero4dOyouLk716tXT3Xffre3bt4fWf/fdd/J4PMU%2B3n333dB2xa1/5ZVXwn5Wdna27rjjDlWpUkUNGzbUU0895ch3KRVn9uzZat26dehD4NLS0rRkyZLQ%2BmAwqNGjR6tOnTqqVq2a7rrrLu3bty9sH3v27NGdd96patWqqU6dOhozZozOnDkTts2qVavUoUMHVa5cWdddd12RHjnpSj343//%2Bp9GjR%2BuGG25Q1apV1ahRI40ZMyb0nZSFyvtxIJV8LHTu3LnIa%2Bzfv3/YPo4dO6bBgwfL7/fL7/dr8ODBOn78eNg25bkP0TIvXCo9PV0ej0fjxo0LjUXD3HCpS/sQTfNDoeKOhaiYGwxKrWfPnmb%2B/Plm27ZtZsuWLaZPnz6mUaNG5sSJE8YYY86ePWtycnLCHtOnTzfVqlUz%2Bfn5of1IMvPnzw/b7ocffgitz8vLM/Xr1zf9%2B/c32dnZ5v333zdxcXHmueeeK/PXXJyMjAzz0Ucfme3bt5vt27ebKVOmmEqVKplt27YZY4wZMWKEadiwoVm%2BfLnZtGmT6dKli2nTpo05e/asMeZCn1JSUkyXLl3Mpk2bzPLly02DBg3MqFGjQj9j165dpmrVqmbs2LHmq6%2B%2BMnPnzjWVKlUy7733niOv%2BVJX6kF2drbp16%2BfycjIMDt37jSffvqpadasmfnVr34Vto/yfhwYU/KxcMcdd5hhw4aFvcbjx4%2BH7aNXr14mJSXFrF271qxdu9akpKSYvn37htaX9z5Ey7xwsQ0bNpgmTZqY1q1bm7Fjx4bGo2FuuFhxfYim%2BcGYyx8L0TA3ELB%2BgkOHDhlJZtWqVZfdpm3btubBBx8MG5NkFi9efNnnvPzyy8bv95vTp0%2BHxtLT002DBg3M%2BfPnf3rh10DNmjXNa6%2B9Zo4fP24qVapkFixYEFq3f/9%2BU6FCBbN06VJjjDEff/yxqVChgtm/f39om3feecd4vV6Tl5dnjDFm0qRJpnnz5mE/Y/jw4aZTp05l8Gp%2BnMIeFGfRokUmNjbWFBQUhMbceBwYE96HO%2B64I2xSvdRXX31lJJn169eHxtatW2ckmf/%2B97/GGHf04VJunhfy8/NNs2bNzPLly8P%2B/qNtbrhcH4rj1vnhSj2IhrmBS4Q/QeEp3Vq1ahW7PisrS1u2bNFDDz1UZN2oUaNUp04ddezYUa%2B88orOnz8fWrdu3TrdcccdYd9n2LNnTx04cEDfffed3RfxE507d04LFizQyZMnlZaWpqysLBUUFKhHjx6hbRo0aKCUlBStXbtW0oXXl5KSEvblmz179lQwGFRWVlZom4v3UbhNZmamCgoKyuCVld6lPShOXl6efD5fkQ/Nc8txIF2%2BD2%2B99Zbq1KmjVq1aaeLEicrPzw%2BtW7dunfx%2Bv2666abQWKdOneT3%2B8OOFzf0oZDb54WRI0eqT58%2B6t69e9h4tM0Nl%2BtDcdw6P5TUA7fPDeX3I1IdZozRhAkTdOuttyolJaXYbebNm6cWLVoU%2Bc7CGTNmqFu3bqpSpYo%2B/fRTPfroozpy5Ih%2B//vfS5Jyc3PVpEmTsOcUfrF0bm6ukpOT7b%2Bgq5Sdna20tDSdPn1a1atX1%2BLFi9WyZUtt2bJFsbGxqlmzZtj29evXD31hdm5ubpEvyq5Zs6ZiY2OvuE39%2BvV19uxZHTlyRAkJCdfw1ZXO5XpwqaNHj2rGjBkaPnx42LgbjgPpyn0YOHCgkpOTFR8fr23btmny5Mn64osvQt/tmZubq3r16hXZZ7169cKOhfLeh4u5eV5YsGCBsrKylJmZWWRdbm5u1MwNV%2BrDpdw6P5TUg2iYGwhYP9KoUaO0detW/ec//yl2/alTp/T222/riSeeKLKu8B%2BIJLVt21aS9NRTT4WNezyesOeY/79p79Jxp9xwww3asmWLjh8/rvfff19DhgzRqlWrLru9MSas9uJeR0nblJceXPxLNRAIqE%2BfPmrZsqX%2B8Ic/hD3fDceBdOU%2BDBs2LLRdSkqKmjVrptTUVG3atEnt27eX5I5jQSrd8eDmeWHv3r0aO3asli1bpsqVK5f6eW6bG66mD26dH0rTg2iYG7hE%2BCOMHj1aGRkZ%2Buyzz5SYmFjsNu%2B9955%2B%2BOEH3X///SXur1OnTgoEAjp48KAkKT4%2BPpTQCx06dEiSivzPzSmxsbH62c9%2BptTUVKWnp6tNmzZ64YUXFB8frzNnzujYsWNh2x86dChUe3Gv79ixYyooKLjiNocOHVJMTIxq1659DV9Z6V2uB4Xy8/PVq1ev0NmMSpUqXXF/5fE4kEruw8Xat2%2BvSpUqaceOHZIuvMbC13uxw4cPl3gsSOWvD26eF7KysnTo0CF16NBBMTExiomJ0apVq/Tiiy8qJiZG9evXj4q5oaQ%2BnDt3TpK754fS9uBibpwbCFhXwRijUaNG6YMPPtC///3vK55%2BnDdvnu666y7VrVu3xP1u3rxZlStXVo0aNSRJaWlpWr16ddhbk5ctW6YGDRoUOR0aKYwxCgaD6tChgypVqhQ6zStJOTk52rZtW%2BiSSFpamrZt26acnJzQNsuWLZPX61WHDh1C21y8j8JtUlNTS5yInFLYA%2BnC/0x79Oih2NhYZWRklOp/9G44DqTwPlzqyy%2B/VEFBQegyTlpamvLy8rRhw4bQNp9//rny8vLCjhe39MHN80K3bt2UnZ2tLVu2hB6pqan1QyJIAAAC10lEQVQaOHBg6M/RMDeU1IeKFSu6fn4oTQ8u5cq5oWzvqS/fHnnkEeP3%2B83KlSsv%2B9ZZY4zZsWOH8Xg8ZsmSJUX2kZGRYebMmWOys7PNzp07zdy5c43P5zNjxowJbXP8%2BHFTv359c99995ns7GzzwQcfGJ/PFzFvPZ08ebJZvXq12b17t9m6dauZMmWKqVChglm2bJkx5sJbsRMTE82KFSvMpk2bTNeuXYt9K3a3bt3Mpk2bzIoVK0xiYmKxb8UeP368%2Beqrr8y8efMi6q3YV%2BpBIBAwN910k7nxxhvNzp07w46Vwh644Tgw5sp92Llzp5k%2BfbrZuHGj2b17t/noo49M8%2BbNTbt27UJ9MObCW7Fbt25t1q1bZ9atW2duvPHGsLdil/c%2BFHL7vFCcS98pFg1zQ3Eu7kM0zQ8Xu7gH0TI3ELCugqRiH/Pnzw/bbvLkySYxMdGcO3euyD6WLFli2rZta6pXr26qVq1qUlJSzKxZs8LenmuMMVu3bjW33Xab8Xq9Jj4%2B3kybNi0i3nZqjDEPPvigady4sYmNjTV169Y13bp1C/tFcurUKTNq1ChTq1YtU6VKFdO3b1%2BzZ8%2BesH18//33pk%2BfPqZKlSqmVq1aZtSoUWFvtTXGmJUrV5p27dqZ2NhY06RJEzN79uwyeX2lcaUefPbZZ5c9Vnbv3m2MccdxYMyV%2B7Bnzx5z%2B%2B23m1q1apnY2FjTtGlTM2bMGHP06NGwfRw9etQMHDjQxMXFmbi4ODNw4EBz7NixsG3Kcx8KuX1eKM6lASsa5obiXNyHaJofLnZxD6JlbvAYEykfeQoAAOAO3IMFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCy/wOW5l5atCbUugAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6402531586459656144\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3295</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2994</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2922</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3950</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3082</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2891</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2828</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3502</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3060</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3211</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (7)</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">41.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6402531586459656144\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2828</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2891</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2922</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2994</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3022</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3502</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3542</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3898</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3950</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4488</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Dias\">Dias<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>41.2%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>22</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>19</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>32</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram8036832609146029176\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAbxJREFUeJzt3UGuk2AYhtGPxiks4AZ24SLck2P35CLcBaQLoHHgwOJA68SbN/c28ac05wzbNB%2B0PCEUCN22bVs1tixLTdPUeiwHN89zjePYdOaHptP%2B6Pu%2Bqn6v8DAMeywCB7Kua03T9He7aWmXQLquq6qqYRh2C%2BTj56/v/sy3L5/%2Bw5LwVrftpqVT84lwIAKBQCAQCAQCgUAgEAgEAsEu50ES5yd4JPYgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQPdz/IPe65hwTewh4EAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCARPcbl7Ky0uq3%2BmRzk8w6Ms7EEgEAgEAoFAIBAIBAKBQLDL37zbtlVV1bqu/7z388f31ovzUF77To7qnt/ytfW/vXbbblrqth2mLstS0zS1HsvBzfNc4zg2nblLINfrtc7nc/V9X13XtR7PwWzbVpfLpV5eXup0antUsEsgcBQO0iEQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIfgGjQVPGnpG8PgAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives8036832609146029176,#minihistogram8036832609146029176\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives8036832609146029176\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles8036832609146029176\"\n",
       "                                                  aria-controls=\"quantiles8036832609146029176\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram8036832609146029176\" aria-controls=\"histogram8036832609146029176\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common8036832609146029176\" aria-controls=\"common8036832609146029176\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme8036832609146029176\" aria-controls=\"extreme8036832609146029176\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles8036832609146029176\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>19</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>19.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>20</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>22</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>22</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>26.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>32</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>13</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>2.9368</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.13349</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>8.8668</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>22</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>1.6471</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>2.7013</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>374</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>8.625</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>216.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram8036832609146029176\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHv5JREFUeJzt3X%2BQVfV5%2BPFnYWURZPmlCGRXQq0aETEqjC5qgmBpqCSmNKkJStCYBDOAQWoTUWtgbFibkmodGyZqRU2bgDbB0jGSgIlABlFAKehkEMQWFBSIygLChh/n%2B0e%2BLNkA2TX97J7l7Os1c/%2B4Z8%2B9PPNxObw95%2BzdkizLsgAAIJk2eQ8AAFA0AgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASK817gNbg4MGDsXnz5ujUqVOUlJTkPQ4AtApZlsXOnTujd%2B/e0aZN855TEljNYPPmzVFZWZn3GADQKm3atCkqKiqa9c8UWM2gU6dOEfHb/8Dl5eU5TwMArUNNTU1UVlbW/TvcnARWMzh0WbC8vFxgAUAzy%2BP2HDe5AwAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgNcKbb74Z1157bXTv3j06dOgQH/3oR2PlypV5jwUAtFA%2Byb0B7777blxyySVx%2BeWXx9NPPx09evSI1157Lbp06ZL3aABACyWwGvAP//APUVlZGbNmzarb9uEPfzi/gQCAFs8lwgbMmzcvBg4cGJ/97GejR48ecf7558eDDz74B19TW1sbNTU19R4AQOvhDFYDNmzYEDNnzozJkyfHbbfdFi%2B88ELcdNNNUVZWFl/4wheO%2Bprq6uqYNm1aM09KagNvn5/3CB/Iim99Iu8RAPj/SrIsy/IeoiVr165dDBw4MJYuXVq37aabborly5fHc889d9TX1NbWRm1tbd3zmpqaqKysjB07dkR5eXmTz0waAgvg%2BFZTUxOdO3fO5d9flwgb0KtXr%2BjXr1%2B9bWeffXZs3LjxmK8pKyuL8vLyeg8AoPUQWA245JJLYu3atfW2vfrqq9GnT5%2BcJgIAWjqB1YCbb745li1bFtOnT4/169fHD37wg3jggQdi/PjxeY8GALRQAqsBgwYNirlz58YPf/jD6N%2B/f9x1111x7733xjXXXJP3aABAC%2BWnCBth5MiRMXLkyLzHAACOE85gAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgNWDq1KlRUlJS79GzZ8%2B8xwIAWrDSvAc4HpxzzjmxcOHCuudt27bNcRoAoKUTWI1QWlrqrBUA0GguETbCunXronfv3tG3b9/43Oc%2BFxs2bPiD%2B9fW1kZNTU29BwDQejiD1YCLLrooHnvssTjzzDPj7bffjr//%2B7%2BPwYMHxyuvvBLdu3c/6muqq6tj2rRpzTLfwNvnN8ufk8KKb30i7xEAoFk4g9WAESNGxF/91V/FueeeG1dccUU89dRTERHx6KOPHvM1U6ZMiR07dtQ9Nm3a1FzjAgAtgDNYH1DHjh3j3HPPjXXr1h1zn7KysigrK2vGqQCAlsQZrA%2BotrY2fvWrX0WvXr3yHgUAaKEEVgNuueWWWLRoUbz%2B%2Buvx/PPPx2c%2B85moqamJsWPH5j0aANBCuUTYgDfeeCM%2B//nPx/bt2%2BOUU06Jiy%2B%2BOJYtWxZ9%2BvTJezQAoIUSWA2YPXt23iMAAMcZlwgBABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgfUDV1dVRUlISkyZNynsUAKCFElgfwPLly%2BOBBx6IAQMG5D0KANCCCaxG2rVrV1xzzTXx4IMPRteuXfMeBwBowQRWI40fPz6uvPLKuOKKK/IeBQBo4UrzHuB4MHv27Fi5cmWsWLGiUfvX1tZGbW1t3fOampqmGg0AaIGcwWrApk2b4mtf%2B1r8%2B7//e7Rv375Rr6muro7OnTvXPSorK5t4SgCgJRFYDVi5cmVs3bo1LrzwwigtLY3S0tJYtGhR3HfffVFaWhoHDhw44jVTpkyJHTt21D02bdqUw%2BQAQF5cImzAsGHDYs2aNfW2XX/99fGRj3wkvvGNb0Tbtm2PeE1ZWVmUlZU114gAQAsjsBrQqVOn6N%2B/f71tHTt2jO7dux%2BxHQAgwiVCAIDknMH6Izz77LN5jwAAtGDOYAEAJCawAAASE1gAAIkVOrD%2B7d/%2BLfbu3Zv3GABAK1PowJo8eXL07Nkzxo0bFy%2B88ELe4wAArUShA2vz5s3x8MMPx5YtW%2BLSSy%2BNc845J77zne/Etm3b8h4NACiwQgdWaWlpjBo1KubNmxcbN26MsWPHxsMPPxwVFRUxatSoeOqppyLLsrzHBAAKptCB9bt69uwZw4YNiyFDhkRJSUmsWLEiRo8eHWeccUYsWbIk7/EAgAIpfGBt37497r333jjvvPPikksuia1bt8aTTz4Z//u//xtvvvlmjBw5Mr7whS/kPSYAUCCF/iT3v/zLv4yf/OQn0bdv3/jSl74UY8eOjVNOOaXu6yeddFJ8/etfj/vuuy/HKQGAoil0YJWXl8fChQvjsssuO%2BY%2BvXr1inXr1jXjVABA0RU6sB599NEG9ykpKYnTTz%2B9GaYBAFqLQt%2BDdfPNN8f9999/xPZ/%2BZd/ib/5m7/JYSIAoDUodGA98cQTcfHFFx%2BxvaqqKubMmZPDRABAa1DowNq%2BfXt07dr1iO3l5eWxffv2HCYCAFqDQgfW6aefHj/96U%2BP2P7Tn/40%2Bvbtm8NEAEBrUOib3CdNmhSTJk2KX//61zF06NCIiHjmmWfi29/%2BdsyYMSPn6QCAoip0YH35y1%2BOvXv3xvTp0%2BOb3/xmRERUVFTEfffdF1/84hdzng4AKKpCB1ZExMSJE2PixImxZcuWOPHEE6NLly55jwQAFFzhA%2BuQXr165T0CANBKFPom923btsX1118fp512WrRv3z7atWtX7wEA0BQKfQbruuuui9deey3%2B9m//Nnr16hUlJSV5jwQAtAKFDqzFixfH4sWL4/zzz897FACgFSn0JcKKigpnrQCAZlfowLrnnntiypQp8cYbb%2BQ9CgDQihT6EuGYMWNi586d0adPnygvL48TTjih3te3bt2a02QAQJEVOrDuvvvuvEcAAFqhQgfWDTfckPcIAEArVOh7sCIi/ud//iemTp0aY8aMqbsk%2BLOf/Sx%2B9atf5TwZAFBUhQ6sJUuWxDnnnBOLFi2Kxx9/PHbt2hURES%2B%2B%2BGLceeedOU8HABRVoQPrG9/4RkydOjV%2B8Ytf1Pvk9qFDh8ayZctynAwAKLJCB9bq1avjM5/5zBHbe/ToEdu2bcthIgCgNSh0YHXp0iXeeuutI7avWrUqPvShD%2BUwEQDQGhQ6sD73uc/FrbfeGtu2bav7RPfnn38%2Bbrnllrj22mtzng4AKKpCB9b06dOjZ8%2Be0atXr9i1a1f069cvBg8eHIMGDYq/%2B7u/y3s8AKCgCv05WO3atYs5c%2BbEq6%2B%2BGi%2B%2B%2BGIcPHgwLrjggvjIRz6S92gAQIEVOrAOOfPMM%2BPMM8/MewwAoJUodGB95Stf%2BYNff%2BCBB5ppEgCgNSl0YG3ZsqXe83379sUrr7wSO3fujI997GM5TQUAFF2hA%2Bu//uu/jti2f//%2B%2BOpXvxpnn312DhMBAK1BoX%2BK8GhKS0vjlltuiX/8x39s9GtmzpwZAwYMiPLy8igvL4%2Bqqqp4%2Bumnm3BKAOB41uoCKyJiw4YNsW/fvkbvX1FREXfffXesWLEiVqxYEUOHDo2rrroqXnnllSacEgA4XhX6EuHXv/71es%2BzLIstW7bEvHnz4pprrmn0%2B3zyk5%2Bs9/xb3/pWzJw5M5YtWxbnnHNOklkBgOIodGA999xz9Z63adMmTjnllLj77rvjy1/%2B8h/1ngcOHIgnnngidu/eHVVVVSnGBAAKptCBtWTJkmTvtWbNmqiqqoq9e/fGSSedFHPnzo1%2B/foddd/a2tqora2te15TU5NsDgCg5St0YKV01llnxapVq%2BK9996LH/3oRzF27NhYtGjRUSOruro6pk2blsOULdvA2%2BfnPQIANIuSLMuyvIdoKoMGDar7Jc8NeeGFFz7Qe19xxRVx%2Bumnx/e%2B970jvna0M1iVlZWxY8eOKC8v/0B/TkNEC4es%2BNYn8h4BoEWpqamJzp07N8m/vw0p9Bmsyy%2B/PL73ve/FmWeeWXe/1LJly2Lt2rUxbty4KCsr%2B6PfO8uyehH1u8rKyv5P7w0AHN8KHVjvvfdejB8/PqZPn15v%2B%2B233x5vv/12PPTQQ416n9tuuy1GjBgRlZWVsXPnzpg9e3Y8%2B%2ByzMX%2B%2Bs0cAwJEKHViPP/54LF%2B%2B/Ijt1113XQwcOLDRgfX222/HmDFjYsuWLdG5c%2BcYMGBAzJ8/P/7sz/4s9cgAQAEUOrDKyspi6dKlccYZZ9TbvnTp0g90Ce9f//VfU48GABRYoQPrpptuihtvvDFeeumluPjiiyPit/dgPfjgg3HbbbflPB0AUFSFDqzbb789%2BvbtG//8z/8cDz/8cEREnH322fHggw/G6NGjc54OACiqQgdWRMTo0aPFFADQrAr/y55ramrikUceiTvvvDPefffdiIj47//%2B79iyZUvOkwEARVXoM1gvv/xyXHHFFdGhQ4fYtGlTXHfdddG1a9d4/PHH44033ohHH3007xEBgAIq9Bmsm2%2B%2BOUaPHh2vvfZatG/fvm77lVdeGYsXL85xMgCgyAp9Bmv58uUxc%2BbMI35dzoc%2B9CGXCAGAJlPoM1jt2rWLXbt2HbF93bp1cfLJJ%2BcwEQDQGhQ6sD71qU/FXXfdFfv374%2BIiJKSknjzzTfj1ltvjVGjRuU8HQBQVIUOrO985zuxefPm6NmzZ%2BzZsyeGDh0af/InfxLt27c/4vcTAgCkUuh7sDp37hxLly6NBQsWxIsvvhgHDx6MCy64IP78z//8iPuyAABSKWxg7du3L/7iL/4ivvvd78bw4cNj%2BPDheY8EALQShb1EeMIJJ8RLL73kTBUA0OwKG1gREddee23MmjUr7zEAgFamsJcID7n//vtj4cKFMXDgwOjYsWO9r33729/OaSoAoMgKHVgrV66MAQMGRETE6tWr633NpUMAoKkUMrA2bNgQffv2jSVLluQ9CgDQChXyHqwzzjgjtm3bVvf86quvjrfffjvHiQCA1qSQgZVlWb3nP/nJT2L37t05TQMAtDaFDCwAgDwVMrBKSkqOuIndTe0AQHMp5E3uWZbFddddF2VlZRERsXfv3rjxxhuP%2BJiGH//4x3mMBwAUXCEDa%2BzYsfWeX3vttTlNAgC0RoUMLJ/eDgDkqZD3YAEA5ElgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwGqE6urqGDRoUHTq1Cl69OgRn/70p2Pt2rV5jwUAtFACqxEWLVoU48ePj2XLlsWCBQti//79MXz48Ni9e3feowEALVBp3gMcD%2BbPn1/v%2BaxZs6JHjx6xcuXK%2BNjHPpbTVABASyWw/gg7duyIiIhu3bod9eu1tbVRW1tb97ympqZZ5gIAWgaXCD%2BgLMti8uTJcemll0b//v2Puk91dXV07ty57lFZWdnMUwIAeRJYH9CECRNi9erV8cMf/vCY%2B0yZMiV27NhR99i0aVMzTggA5M0lwg9g4sSJMW/evFi8eHFUVFQcc7%2BysrIoKytrxskAgJZEYDVClmUxceLEmDt3bjz77LPRt2/fvEcCAFowgdUI48ePjx/84Afxn//5n9GpU6d46623IiKic%2BfOceKJJ%2BY8HQDQ0rgHqxFmzpwZO3bsiCFDhkSvXr3qHnPmzMl7NACgBXIGqxGyLMt7BADgOOIMFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEViMsXrw4PvnJT0bv3r2jpKQknnzyybxHAgBaMIHVCLt3747zzjsv7r///rxHAQCOA6V5D3A8GDFiRIwYMSLvMQCA44TAagK1tbVRW1tb97ympibHaQCA5iawmkB1dXVMmzYt7zFoZQbePj/vEQprxbc%2BkfcI8Ec5no4LRft75h6sJjBlypTYsWNH3WPTpk15jwQANCNnsJpAWVlZlJWV5T0GAJATZ7AAABJzBqsRdu3aFevXr697/vrrr8eqVauiW7ducdppp%2BU4GQDQEgmsRlixYkVcfvnldc8nT54cERFjx46NRx55JKepAICWSmA1wpAhQyLLsrzHAACOE%2B7BAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAaqTvfve70bdv32jfvn1ceOGFsWTJkrxHAgBaKIHVCHPmzIlJkybF7bffHi%2B99FJcdtllMWLEiNi4cWPeowEALZDAaoR/%2Bqd/ihtuuCG%2B9KUvxdlnnx333ntvVFZWxsyZM/MeDQBogUrzHqCl%2B81vfhMrV66MW2%2B9td724cOHx9KlS4/6mtra2qitra17vmPHjoiIqKmpST7fgdrdyd8TqK8p/u5Cczie/o1oir9nh94zy7Lk790QgdWA7du3x4EDB%2BLUU0%2Btt/3UU0%2BNt95666ivqa6ujmnTph2xvbKysklmBJpW5%2B/kPQEUX1P%2BPdu5c2d07ty56f6AoxBYjVRSUlLveZZlR2w7ZMqUKTF58uS65wcPHox33nknunfvfszXHK9qamqisrIyNm3aFOXl5XmPkzvrcZi1OMxaHGYtDrMWhzXVWmRZFjt37ozevXsne8/GElgNOPnkk6Nt27ZHnK3aunXrEWe1DikrK4uysrJ627p06dJkM7YE5eXlrf4A8busx2HW4jBrcZi1OMxaHNYUa9HcZ64OcZN7A9q1axcXXnhhLFiwoN72BQsWxODBg3OaCgBoyZzBaoTJkyfHmDFjYuDAgVFVVRUPPPBAbNy4MW688ca8RwMAWqC2U6dOnZr3EC1d//79o3v37jF9%2BvSYMWNG7NmzJ77//e/Heeedl/doLULbtm1jyJAhUVqq1yOsx%2B%2ByFodZi8OsxWHW4rCirUVJlsfPLgIAFJh7sAAAEhNYAACJCSwAgMQEFgBAYgKLRqmuro5BgwZFp06dokePHvHpT3861q5dW2%2Bf2tramDhxYpx88snRsWPH%2BNSnPhVvvPFGThM3nYbW4p133omJEyfGWWedFR06dIjTTjstbrrpprrfSVkkjfm%2BOCTLshgxYkSUlJTEk08%2B2cyTNr3GrsVzzz0XQ4cOjY4dO0aXLl1iyJAhsWfPnhwmbjqNWYu33norxowZEz179oyOHTvGBRdcEP/xH/%2BR08RNZ%2BbMmTFgwIC6D9CsqqqKp59%2Buu7rreW4GfGH16KIx02BRaMsWrQoxo8fH8uWLYsFCxbE/v37Y/jw4bF79%2BFfJDpp0qSYO3duzJ49O375y1/Grl27YuTIkXHgwIEcJ0%2BvobXYvHlzbN68OWbMmBFr1qyJRx55JObPnx833HBDzpOn15jvi0Puvffewv2qqN/VmLV47rnn4hOf%2BEQMHz48XnjhhVi%2BfHlMmDAh2rQp1qG4MWsxZsyYWLt2bcybNy/WrFkTo0aNiquvvjpeeumlHCdPr6KiIu6%2B%2B%2B5YsWJFrFixIoYOHRpXXXVVvPLKKxHReo6bEX94LQp53Mzgj7B169YsIrJFixZlWZZl7733XnbCCSdks2fPrtvnzTffzNq0aZPNnz8/rzGbxe%2BvxdE8/vjjWbt27bJ9%2B/Y142TN71hrsWrVqqyioiLbsmVLFhHZ3Llzc5qw%2BRxtLS666KLsjjvuyHGqfBxtLTp27Jg99thj9fbr1q1b9tBDDzX3eM2ua9eu2UMPPdSqj5uHHFqLoznej5vF%2Bt8mms2h07bdunWLiIiVK1fGvn37Yvjw4XX79O7dO/r37x9Lly7NZcbm8vtrcax9ysvLC/MBesdytLV4//334/Of/3zcf//90bNnz7xGa3a/vxZbt26N559/Pnr06BGDBw%2BOU089NT7%2B8Y/HL3/5yzzHbBZH%2B7649NJLY86cOfHOO%2B/EwYMHY/bs2VFbWxtDhgzJacqmd%2BDAgZg9e3bs3r07qqqqWvVx8/fX4miO9%2BPm8Tk1ucqyLCZPnhyXXnpp9O/fPyJ%2Bez9Fu3btomvXrvX2PfXUU4/4RdlFcrS1%2BH2//vWv46677opx48Y183TN61hrcfPNN8fgwYPjqquuynG65nW0tdiwYUNEREydOjVmzJgRH/3oR%2BOxxx6LYcOGxcsvvxxnnHFGniM3mWN9X8yZMyeuvvrq6N69e5SWlkaHDh1i7ty5cfrpp%2Bc4bdNYs2ZNVFVVxd69e%2BOkk06KuXPnRr9%2B/WLVqlWt7rh5rLX4fUU4bgosPrAJEybE6tWrG/V/3lmWFfq%2Bm4bWoqamJq688sro169ffPOb32zm6ZrX0dZi3rx58fOf/7xw99U05GhrcfDgwYiIGDduXFx//fUREXH%2B%2BefHM888Ew8//HBUV1fnMmtTO9bfkTvuuCPefffdWLhwYZx88snx5JNPxmc/%2B9lYsmRJnHvuuTlN2zTOOuusWLVqVbz33nvxox/9KMaOHRuLFi065v5FPm4eay1%2BN7IKc9zM8fIkx6EJEyZkFRUV2YYNG%2Bptf%2BaZZ7KIyN5555162wcMGJDdeeedzTlisznWWhxSU1OTVVVVZcOGDcv27NnTzNM1r2Otxde%2B9rWspKQka9u2bd0jIrI2bdpkH//4x/MZtokday02bNiQRUT2/e9/v972v/7rv85Gjx7dnCM2m2Otxfr167OIyF5%2B%2BeV624cNG5aNGzeuOUfMxbBhw7KvfOUrrfK4%2BfsOrcUhRTpuugeLRsmyLCZMmBA//vGP4%2Bc//3n07du33tcvvPDCOOGEE2LBggV127Zs2RIvv/xyDB48uLnHbVINrUXEb/8PbPjw4dGuXbuYN29etG/fPodJm15Da3HrrbfG6tWrY9WqVXWPiIh77rknZs2alcfITaahtfjwhz8cvXv3PuLjCl599dXo06dPc47a5Bpai/fffz8i4oifnmzbtm3dmb4iy7IsamtrW9Vx81gOrUVEAY%2BbOcYdx5GvfvWrWefOnbNnn30227JlS93j/fffr9vnxhtvzCoqKrKFCxdmL774YjZ06NDsvPPOy/bv35/j5Ok1tBY1NTXZRRddlJ177rnZ%2BvXr6%2B3T2tbiaKKgP0XYmLW45557svLy8uyJJ57I1q1bl91xxx1Z%2B/bts/Xr1%2Bc4eXoNrcVvfvOb7E//9E%2Bzyy67LHv%2B%2Beez9evXZzNmzMhKSkqyp556Kufp05oyZUq2ePHi7PXXX89Wr16d3XbbbVmbNm2yn/3sZ1mWtZ7jZpb94bUo4nFTYNEoEXHUx6xZs%2Br22bNnTzZhwoSsW7du2YknnpiNHDky27hxY35DN5GG1uIXv/jFMfd5/fXXc509tcZ8XxztNUUMrMauRXV1dVZRUZF16NAhq6qqypYsWZLPwE2oMWvx6quvZqNGjcp69OiRdejQIRswYMARH9tQBF/84hezPn36ZO3atctOOeWUbNiwYXVxlWWt57iZZX94LYp43CzJsixrmnNjAACtk3uwAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIn9P2TeKE%2BYu6McAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common8036832609146029176\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">22</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">35.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">20</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">23.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">21</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">17.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">25</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">19</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">32</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme8036832609146029176\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">19</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">20</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">23.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">21</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">17.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">35.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">21</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">17.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">35.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">23</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">25</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">32</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow ignore\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Personas\"><s>Personas</s><br/>\n",
       "            <small>Highly correlated</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <p><em>This variable is highly correlated with <a href=\"#pp_var_Produccion\"><code>Produccion</code></a> and should be ignored for analysis</em></p>\n",
       "</div>\n",
       "<div class=\"col-md-6\">\n",
       "    <table class=\"stats \">\n",
       "        <tr>\n",
       "            <th>Correlation</th>\n",
       "            <td>0.91848</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Produccion\">Produccion<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>17</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>100.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>12900</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>6373</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>18573</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram6486558553096163561\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAcJJREFUeJzt3EHO0mAUhtFb4rRdAKG7cBHuybF7chHuog0LKHHgQOpAcSJ5DX/kK43nDIH0tqRPmpLydeu6rtXYPM81jmPrsezcNE11Op2aznzXdNovfd9X1c8DHoZhi11gR5ZlqXEcf583LW0SSNd1VVU1DINA/oH3Hz8/9Pkvnz48aU%2Be63betHRoPhF2RCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAINhkXazk0TWeqva7ztM9bzl%2BnscVBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEL/e4eystHqt/1UfXX3W/ql7vrwuuIBAIBAKBQCAQCAQCgUAg2ORn3nVdq6pqWZY/3vv%2B7evD27u3nb9pMectM/53977j22u386albt1g6jzPNY5j67Hs3DRNdTqdms7cJJDr9Vrn87n6vq%2Bu61qPZ2fWda3L5VLH47EOh7Z3BZsEAnvhJh0CgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKB4AdO2Vrsp53YdAAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives6486558553096163561,#minihistogram6486558553096163561\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives6486558553096163561\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles6486558553096163561\"\n",
       "                                                  aria-controls=\"quantiles6486558553096163561\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram6486558553096163561\" aria-controls=\"histogram6486558553096163561\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common6486558553096163561\" aria-controls=\"common6486558553096163561\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme6486558553096163561\" aria-controls=\"extreme6486558553096163561\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles6486558553096163561\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>6373</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>6711.4</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>11964</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>14119</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>14575</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>17067</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>18573</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>12200</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>2611</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>3526.8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.27338</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-0.20763</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>12900</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>2741</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.75325</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>219308</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>12438000</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>216.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram6486558553096163561\">\n",
       "            <img 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cLoE4FepTv/OAIQHVwesdu3aBX/kuTyrV68%2B4/rY2Fidf/75kqTMzEytWbNGTz/9tJ5//vlScydMmKDRo0cHl/1%2Bv9LS0n5B5QAAoDpzdcDq0qWLnn/%2BeTVt2jR4vdSqVav05Zdf6q677pLX6/3V2zbGhJwG/F9er/c3bRsAAFRvrg5Yhw8f1tChQzV58uSQ8YkTJ2rfvn36%2B9//XqHt3HffferZs6fS0tJ05MgRzZo1Sx999JEWLuS0AQAAKM3VAevNN9/UmjVrSo0PHjxYmZmZFQ5Y%2B/btU1ZWlgoLC%2BXz%2BdSqVSstXLhQv/vd72yXDAAAXMDVAcvr9So/P18XXHBByHh%2Bfv4vOoX3wgsv2C4NAAC4mKsD1ogRIzRkyBCtX79e7du3l/TzNVgzZszQfffd53B1AADArVwdsCZOnKj09HQ9/fTTevHFFyVJzZs314wZMzRgwACHqwMAAG7l6oAlSQMGDCBMAQCAKuX6H3v2%2B/166aWX9MADD%2BjQoUOSpE8%2B%2BUSFhYUOVwYAANzK1UewPv30U1199dWqVauWdu7cqcGDB6tu3bp68803tWvXLr388stOlwgAAFzI1UewRo0apQEDBujrr79WXFxccPzaa6/V8uXLHawMAAC4mauPYK1Zs0Z5eXmlfi6nUaNGnCIEAACVxtVHsGJjY3X06NFS41u3blWDBg0cqAgAAEQCVwesPn366JFHHtGJEyckSR6PR7t379b48ePVt29fh6sDAABu5eqA9cQTT2jPnj1KSkrSDz/8oK5du%2Bq8885TXFxcqd8nBAAAsMXV12D5fD7l5%2Bdr8eLFKigoUElJiS655BJ179691HVZAAAAtrg2YBUXF%2Buaa67RtGnT1K1bN3Xr1s3pkgAAQIRw7SnCmJgYrV%2B/niNVAACgyrk2YEnSoEGDNHPmTKfLAAAAEca1pwhPevbZZ7VkyRJlZmaqdu3aIesef/xxh6oCAABu5uqAtW7dOrVq1UqStHHjxpB1nDoEAACVxZUBa9u2bUpPT9eKFSucLgUAAEQgV16DdcEFF%2BjAgQPB5Ztvvln79u1zsCIAABBJXBmwjDEhy%2B%2B9956OHTvmUDUAACDSuDJgAQAAOMmVAcvj8ZS6iJ2L2gEAQFVx5UXuxhgNHjxYXq9XknT8%2BHENGTKk1G0a5s6d60R5AADA5VwZsLKzs0OWBw0a5FAlAAAgErkyYHH3dgAA4CRXXoMFAADgJAIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAasCcnJy1K5dO8XHxysxMVG///3v9eWXXzpdFgAACFMErApYtmyZhg4dqlWrVmnx4sU6ceKEunXrpmPHjjldGgAACEPRThdQHSxcuDBkeebMmUpMTNS6det05ZVXOlQVAAAIVwSsX6GoqEiSVK9evTLXBwIBBQKB4LLf76%2BSugAAQHjgFOEvZIzR6NGj1bFjR2VkZJQ5JycnRz6fL/hIS0ur4ioBAICTCFi/0LBhw7Rx40a98cYbp50zYcIEFRUVBR87d%2B6swgoBAIDTOEX4CwwfPlwLFizQ8uXLlZqaetp5Xq9XXq%2B3CisDAADhhIBVAcYYDR8%2BXPPmzdNHH32k9PR0p0sCAABhjIBVAUOHDtXrr7%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%2B93sBoAAFDVCFiVICcnRw899JDTZeA3ypy40OkSfpG1j/ZwuoQKq269ReXhvYCTqtNnWEVwDVYlmDBhgoqKioKPnTt3Ol0SAACoQhzBqgRer1der9fpMgAAgEM4ggUAAGAZR7Aq4OjRo/rqq6%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%2BeSTuv3223XHHXeoefPmmjp1qtLS0pSXl%2Bd0aQAAIAxFO11AuPvxxx%2B1bt06jR8/PmS8W7duys/PL/M5gUBAgUAguFxUVCRJ8vv91uv7KXDM%2BjYrS2Xsf2WqTr2Vqld/q1tvAVS%2ByvgMO7lNY4z1bZeHgFWOgwcP6qefflLDhg1Dxhs2bKi9e/eW%2BZycnBw99NBDpcbT0tIqpcbqwveE0xW4G/0FUJ1V5mfYkSNH5PP5Ku8FykDAqiCPxxOybIwpNXbShAkTNHr06OBySUmJvv/%2Be9WvX7/M5/j9fqWlpWnnzp1KSEiwW7gL0J8zoz%2BnR2/OjP6cHr05s%2BrSH2OMjhw5opSUlCp/bQJWORo0aKCoqKhSR6v2799f6qjWSV6vV16vN2SsTp065b5WQkJCWL9RnUZ/zoz%2BnB69OTP6c3r05syqQ3%2Bq%2BsjVSVzkXo7Y2Fi1bdtWixcvDhlfvHixLrvsMoeqAgAA4YwjWBUwevRoZWVlKTMzUx06dND06dO1Y8cODRkyxOnSAABAGIqaNGnSJKeLCHcZGRmqX7%2B%2BJk%2BerClTpuiHH37QP/7xD7Vu3draa0RFRalz586KjibzloX%2BnBn9OT16c2b05/TozZnRnzPzGCe%2BuwgAAOBiXIMFAABgGQELAADAMgIWAACAZQQsAAAAywhYFu3evVuDBg1S/fr1VatWLV188cVat25dcL0xRpMmTVJKSopq1qypzp0767PPPgvZxqFDh5SVlSWfzyefz6esrCwdPnw4ZM6mTZvUqVMn1axZU40aNdLDDz/syO8sVdSJEyd0//33Kz09XTVr1tR5552nhx9%2BWCUlJcE5kdSb5cuXq3fv3kpJSZHH49H8%2BfND1ldlL%2BbMmaMWLVrI6/WqRYsWmjdvXuXs9C9wpv4UFxdr3Lhxuuiii1S7dm2lpKTolltu0Z49e0K24db%2BlPfe%2BV933XWXPB6Ppk6dGjLu1t5IFevP5s2b1adPH/l8PsXHx6t9%2B/basWNHcH0gENDw4cPVoEED1a5dW3369NGuXbtCtrFjxw717t1btWvXVoMGDTRixAj9%2BOOPIXOWLVumtm3bKi4uTuedd56ee%2B65ytnpCiqvN0ePHtWwYcOUmpqqmjVrqnnz5srLywuZ49beVBoDK77//nvTuHFjM3jwYPPxxx%2Bb7du3myVLlpivvvoqOCc3N9fEx8ebOXPmmE2bNpmbb77ZJCcnG7/fH5zTo0cPk5GRYfLz801%2Bfr7JyMgwvXr1Cq4vKioyDRs2NP369TObNm0yc%2BbMMfHx8WbKlClVur%2B/xJ///GdTv359884775jt27ebt956y5x11llm6tSpwTmR1Jv33nvPTJw40cyZM8dIMvPmzQtZX1W9yM/PN1FRUWby5Mlm8%2BbNZvLkySY6OtqsWrWq8ptwBmfqz%2BHDh83VV19tZs%2Bebb744guzcuVKc%2Bmll5q2bduGbMOt/SnvvXPSvHnzTOvWrU1KSop56qmnQta5tTfGlN%2Bfr776ytSrV8%2BMGTPGFBQUmK%2B//tq88847Zt%2B%2BfcE5Q4YMMY0aNTKLFy82BQUFpkuXLqZ169bmxIkTxhhjTpw4YTIyMkyXLl1MQUGBWbx4sUlJSTHDhg0LbmPbtm2mVq1a5u677zaff/65mTFjhomJiTH//Oc/q6YRZSivN3fccYdp0qSJWbp0qdm%2Bfbt5/vnnTVRUlJk/f35wjlt7U1kIWJaMGzfOdOzY8bTrS0pKTFJSksnNzQ2OHT9%2B3Ph8PvPcc88ZY4z5/PPPjaSQD6mVK1caSeaLL74wxhgzbdo04/P5zPHjx4NzcnJyTEpKiikpKbG9W1Zce%2B215rbbbgsZ69u3rxk0aJAxJrJ7c%2BoHXVX24qabbjI9evQIqad79%2B6mX79%2B9nf0VzpTiDhp9erVRpL59ttvjTGR05/T9WbXrl2mUaNG5tNPPzWNGzcOCViR0htjyu7PzTffHPzcKcvhw4dNTEyMmTVrVnBs9%2B7dpkaNGmbhwoXGmJ%2BDSo0aNczu3buDc9544w3j9XpNUVGRMcaYsWPHmmbNmoVs%2B6677jLt27f/zftlQ1m9admypXn44YdDxi655BJz//33G2Mipzc2cYrQkgULFigzM1M33nijEhMT1aZNG82YMSO4fvv27dq7d6%2B6desWHPN6verUqZPy8/MlSStXrpTP59Oll14anNO%2BfXv5fL6QOZ06dQr5rcPu3btrz549%2Buabbyp5L3%2Bdjh076sMPP9SWLVskSZ988on%2B/e9/65prrpEU2b05VVX2YuXKlSGvc3LOyW1UF0VFRfJ4PMHf%2B4zk/pSUlCgrK0tjxoxRy5YtS62P9N68%2B%2B67atq0qbp3767ExERdeumlIafK1q1bp%2BLi4pB9S0lJUUZGRkh/MjIyQn48uHv37goEAsFLQk7Xn7Vr16q4uLgyd/NX69ixoxYsWKDdu3fLGKOlS5dqy5Yt6t69u6TI7s2vRcCyZNu2bcrLy9MFF1ygRYsWaciQIRoxYoReeeUVSQr%2BWPSpPxDdsGHD4Lq9e/cqMTGx1LYTExND5pS1jf99jXAzbtw49e/fX82aNVNMTIzatGmjkSNHqn///pIiuzenqspenG5OdemVJB0/flzjx4/XgAEDgj84G8n9eeyxxxQdHa0RI0aUuT6Se7N//34dPXpUubm56tGjhz744ANdf/316tu3r5YtWybp5/2KjY1V3bp1Q5576r%2B/U/e9bt26io2NLbc/J06c0MGDBytrF3%2BTZ555Ri1atFBqaqpiY2PVo0cPTZs2TR07dpQU2b35tbi/vSUlJSXKzMzU5MmTJUlt2rTRZ599pry8PN1yyy3BeR6PJ%2BR5xpiQsVPXV2SO%2Bf8Xn5b13HAwe/Zsvfrqq3r99dfVsmVLbdiwQSNHjlRKSoqys7OD8yKxN6dTVb0o73XCWXFxsfr166eSkhJNmzYtZF0k9mfdunV6%2BumnVVBQcMYaI7E3koJfqrnuuus0atQoSdLFF1%2Bs/Px8Pffcc%2BrUqdNpnxsJn0XPPPOMVq1apQULFqhx48Zavny5/vSnPyk5OVlXX331aZ8XCb35tTiCZUlycrJatGgRMta8efPgt1OSkpIklT6Ssn///mCaT0pK0r59%2B0pt%2B8CBAyFzytqGVPqoR7gYM2aMxo8fr379%2Bumiiy5SVlaWRo0apZycHEmR3ZtTVWUvTjenOvSquLhYN910k7Zv367FixcHj15JkdufFStWaP/%2B/TrnnHMUHR2t6Ohoffvtt7rnnnt07rnnSorc3khSgwYNFB0dXe7n9I8//qhDhw6FzDn139%2Bp%2B37o0CEVFxeX25/o6GjVr1/f6n7Z8MMPP%2Bi%2B%2B%2B7Tk08%2Bqd69e6tVq1YaNmyYbr75Zk2X5COOAAAEJ0lEQVSZMkVS5PbmtyBgWXL55Zfryy%2B/DBnbsmWLGjduLElKT09XUlKSFi9eHFz/448/atmyZbrsssskSR06dFBRUZFWr14dnPPxxx%2BrqKgoZM7y5ctDvvb6wQcfKCUlJfghGm7%2B%2B9//qkaN0LdaVFRU8P8oI7k3p6rKXnTo0CHkdU7OObmNcHUyXG3dulVLliwp9aEcqf3JysrSxo0btWHDhuAjJSVFY8aM0aJFiyRFbm8kKTY2Vu3atTvj53Tbtm0VExMTsm%2BFhYX69NNPQ/rz6aefqrCwMDjngw8%2BkNfrVdu2bYNzyupPZmamYmJiKmX/fovi4mIVFxef8XM6Unvzm1ThBfWutnr1ahMdHW0effRRs3XrVvPaa6%2BZWrVqmVdffTU4Jzc31/h8PjN37lyzadMm079//zK/ft%2BqVSuzcuVKs3LlSnPRRReFfIX68OHDpmHDhqZ///5m06ZNZu7cuSYhISHsbkXwv7Kzs02jRo2Ct2mYO3euadCggRk7dmxwTiT15siRI2b9%2BvVm/fr1RpJ58sknzfr164PfgquqXvznP/8xUVFRJjc312zevNnk5uaGxVftz9Sf4uJi06dPH5Oammo2bNhgCgsLg49AIBDchlv7U95751SnfovQGPf2xpjy%2BzN37lwTExNjpk%2BfbrZu3Wr%2B%2Bte/mqioKLNixYrgNoYMGWJSU1PNkiVLTEFBgenatWuZtyK46qqrTEFBgVmyZIlJTU0t81YEo0aNMp9//rl54YUXHL8VQXm96dSpk2nZsqVZunSp2bZtm5k5c6aJi4sz06ZNC27Drb2pLAQsi95%2B%2B22TkZFhvF6vadasmZk%2BfXrI%2BpKSEvPggw%2BapKQk4/V6zZVXXmk2bdoUMue7774zAwcONPHx8SY%2BPt4MHDjQHDp0KGTOxo0bzRVXXGG8Xq9JSkoykyZNCtvbEBhjjN/vN3fffbc555xzTFxcnDnvvPPMxIkTQ/4gRlJvli5daiSVemRnZxtjqrYXb731lrnwwgtNTEyMadasmZkzZ06l7ntFnKk/27dvL3OdJLN06dLgNtzan/LeO6cqK2C5tTfGVKw/L7zwgjn//PNNXFycad26dch9nowx5ocffjDDhg0z9erVMzVr1jS9evUyO3bsCJnz7bffmmuvvdbUrFnT1KtXzwwbNizkthbGGPPRRx%2BZNm3amNjYWHPuueeavLy8StvviiivN4WFhWbw4MEmJSXFxMXFmQsvvNA88cQTIf/d3dqbyuIxJsxucw0AAFDNcQ0WAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL/h/021wz5cQ1jgAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common6486558553096163561\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">11964</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6796</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7107</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14564</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6373</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14119</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14792</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14571</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">18573</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9208</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (7)</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">41.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme6486558553096163561\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">6373</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6796</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7107</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9208</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11964</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">14575</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14792</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15618</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">16691</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">18573</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_Temperatura\">Temperatura<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>16</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>94.1%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>11.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>64.84</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>39.5</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>81</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram9053385640232196937\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAAcRJREFUeJzt3MGNm1AUhtGLlS0UYJkuUkR6yjo9pYh0AXIBtrLIIiaLxNlk5o9sjR4wOmdpZN33EJ8QQqJblmWpxuZ5rnEcW49l56ZpqtPp1HTmh6bT/uj7vqp%2Bb3gYhjWWwI5cLpcax/HvddPSKoF0XVdVVcMwCOQd%2B/j568P/%2Bfbl06vH7tdNS4fmE2FHBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCBY5du8yVt/z/UtPbq2Vut61JbP8da4g0AgEAgEAoFAIBAIBAKBQCAQCASCzb0ofMZWX3w9s65nvKe9bI07CAQCgUAgEAgEAoFAIBAIBAKBQCBY5UXhsixVVXW5XP459vPH9yZreGn2/7Ra26Me3ctW91H18l7uv92vm5a6ZYWp8zzXOI6tx7Jz0zTV6XRqOnOVQG63W53P5%2Br7vrquaz2enVmWpa7Xax2Pxzoc2j4VrBII7IWHdAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCASCX1hIX4XtTBijAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives9053385640232196937,#minihistogram9053385640232196937\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives9053385640232196937\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles9053385640232196937\"\n",
       "                                                  aria-controls=\"quantiles9053385640232196937\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram9053385640232196937\" aria-controls=\"histogram9053385640232196937\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common9053385640232196937\" aria-controls=\"common9053385640232196937\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme9053385640232196937\" aria-controls=\"extreme9053385640232196937\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles9053385640232196937\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>39.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>42.37</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>55.25</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>70.9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>78.15</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>79.88</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>81</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>41.5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>22.9</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>14.443</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.22274</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.1552</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>64.84</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>12.411</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.54927</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>972.6</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>208.59</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>216.0 B</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram9053385640232196937\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHM9JREFUeJzt3X%2BQFvV9wPHPycEDIoeKwt0J2pNKVRBqwImgNUQNKWLSlkkm/gAhxjRkCAGJAZE2Qhs5amKaGBoSbDSmJgUzo9a0CUacCmQoEVAiQas0GEHlZ6scWD2E2/6R8aaXI%2BE037s99nm9Zp4Zn919lo%2Bzz/PwZp%2B95yqyLMsCAIBkjst7AACAohFYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkFhl3gOUg6ampnjllVeiV69eUVFRkfc4AFAWsiyL/fv3R21tbRx3XMeeUxJYHeCVV16JAQMG5D0GAJSl7du3R//%2B/Tv0zxRYHaBXr14R8esDXFVVlfM0AFAeGhoaYsCAAc1/D3ckgdUB3v5YsKqqSmABQAfL4/IcF7kDACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGAdxbx586KioqLFrbq6Ou%2BxAIBOzDe5t8HgwYNjxYoVzfe7dOmS4zQAQGcnsNqgsrLSWSsAoM18RNgGW7Zsidra2qirq4urrroqtm7d%2Bju3b2xsjIaGhhY3AKB8OIN1FO9973vju9/9bgwaNCh27doVX/ziF2PUqFGxefPm6NOnzxEfU19fH/Pnz%2B/gSQGgpRFzl%2Bc9Qputv%2B1P8x4hqYosy7K8hziWvP766zFw4MCYNWtWzJw584jbNDY2RmNjY/P9hoaGGDBgQOzbty%2Bqqqo6alQAyly5B1ZDQ0P07t07l79/ncF6h3r27BnnnXdebNmy5bduUyqVolQqdeBUAEBn4hqsd6ixsTGeffbZqKmpyXsUAKCTElhHcdNNN8XKlSvjhRdeiJ/97GfxkY98JBoaGmLSpEl5jwYAdFI%2BIjyKl156Ka6%2B%2BurYu3dvnHrqqXHhhRfG2rVr44wzzsh7NACgkxJYR7F06dK8RwAAjjE%2BIgQASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrDeofr6%2BqioqIgZM2bkPQoA0EkJrHdg3bp1sWTJkhg6dGjeowAAnZjAaqMDBw7EtddeG3fddVecdNJJeY8DAHRiAquNpk6dGuPGjYvLL7/8qNs2NjZGQ0NDixsAUD4q8x7gWLB06dLYsGFDrF%2B/vk3b19fXx/z589t5KgCgs3IG6yi2b98e06dPj%2B9973vRvXv3Nj1mzpw5sW/fvubb9u3b23lKAKAzcQbrKDZs2BC7d%2B%2BO4cOHNy87fPhwrFq1KhYtWhSNjY3RpUuXFo8plUpRKpU6elQAoJMQWEdx2WWXxaZNm1os%2B/jHPx5nn312zJ49u1VcAQAIrKPo1atXDBkypMWynj17Rp8%2BfVotBwCIcA0WAEByzmC9C48//njeIwAAnZgzWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGKFDqz77rsv3nzzzbzHAADKTKEDa%2BbMmVFdXR2f%2BtSn4oknnsh7HACgTBQ6sF555ZW4%2B%2B67Y8eOHXHxxRfH4MGD44477og9e/bkPRoAUGCFDqzKysoYP358PPzww7Ft27aYNGlS3H333dG/f/8YP358/Nu//VtkWZb3mABAwRQ6sP6/6urquOyyy2L06NFRUVER69evj2uuuSbOOuusWL16dd7jAQAFUvjA2rt3b3z1q1%2BNYcOGxUUXXRS7d%2B%2BOhx56KF588cV4%2BeWX48orr4zrrrsu7zEBgAKpzHuA9vQXf/EX8aMf/Sjq6urihhtuiEmTJsWpp57avP6EE06IWbNmxZ133pnjlABA0RT6DFZVVVWsWLEi/vM//zNuuummFnH1tpqamtiyZcvv3M/ixYtj6NChUVVVFVVVVTFy5Mj48Y9/3F5jAwDHuEKfwbr33nuPuk1FRUUMHDjwd27Tv3//WLhwYfzhH/5h837/7M/%2BLJ566qkYPHhwklkBgOIo9BmsG2%2B8MRYtWtRq%2BT/8wz/E5z73uTbv50Mf%2BlBcccUVMWjQoBg0aFDcdtttccIJJ8TatWtTjgsAFEShA%2BsHP/hBXHjhha2Wjxw5MpYtW/au9nn48OFYunRpvP766zFy5MgjbtPY2BgNDQ0tbgBA%2BSh0YO3duzdOOumkVsurqqpi796972hfmzZtihNOOCFKpVJMmTIlHnzwwTj33HOPuG19fX307t27%2BTZgwIB3NT8AcGwqdGANHDgwHnnkkVbLH3nkkairq3tH%2B/qjP/qj2LhxY6xduzY%2B/elPx6RJk%2BKZZ5454rZz5syJffv2Nd%2B2b9/%2BruYHAI5Nhb7IfcaMGTFjxoz47//%2B77j00ksjIuKxxx6L22%2B/Pb785S%2B/o31169at%2BSL3ESNGxLp16%2BJrX/tafOtb32q1balUilKp9Pv/DwAAx6RCB9YnP/nJePPNN2PBggVx6623RsSvfyLwzjvvjOuvv/732neWZdHY2JhiTACgYAodWBER06ZNi2nTpsWOHTuiR48eceKJJ77jfdxyyy0xduzYGDBgQOzfvz%2BWLl0ajz/%2BeCxfvrwdJgYAjnWFD6y31dTUvOvH7tq1KyZOnBg7duyI3r17x9ChQ2P58uXxgQ98IOGEAEBRFDqw9uzZE7NmzYrHHnssdu/eHU1NTS3WHzx4sE37%2Bfa3v90e4wEABVXowJo8eXL88pe/jM9//vNRU1MTFRUVeY8EAJSBQgfWqlWrYtWqVXH%2B%2BefnPQoAUEYK/T1Y/fv3d9YKAOhwhQ6sv//7v485c%2BbESy%2B9lPcoAEAZKfRHhBMnToz9%2B/fHGWecEVVVVdG1a9cW63fv3p3TZABAkRU6sBYuXJj3CABAGSp0YH3iE5/IewQAoAwV%2BhqsiIhf/epXMW/evJg4cWLzR4I/%2BclP4tlnn815MgCgqAodWKtXr47BgwfHypUr4/77748DBw5ERMSTTz4ZX/jCF3KeDgAoqkIH1uzZs2PevHnx7//%2B79GtW7fm5ZdeemmsXbs2x8kAgCIrdGA9/fTT8ZGPfKTV8r59%2B8aePXtymAgAKAeFDqwTTzwxdu7c2Wr5xo0b47TTTsthIgCgHBQ6sK666qq4%2BeabY8%2BePc3f6P6zn/0sbrrpppgwYULO0wEARVXowFqwYEFUV1dHTU1NHDhwIM4999wYNWpUXHDBBfHXf/3XeY8HABRUob8Hq1u3brFs2bJ4/vnn48knn4ympqZ4z3veE2effXbeowEABVbowHrboEGDYtCgQXmPAQCUiUIH1l/%2B5V/%2BzvVLlizpoEkAgHJS6MDasWNHi/tvvfVWbN68Ofbv3x%2BXXHJJTlMBAEVX6MD64Q9/2GrZoUOH4tOf/nScc845OUwEAJSDQv8U4ZFUVlbGTTfdFF/60pfyHgUAKKiyC6yIiK1bt8Zbb72V9xgAQEEV%2BiPCWbNmtbifZVns2LEjHn744bj22mtzmgoAKLpCB9Z//Md/tLh/3HHHxamnnhoLFy6MT37ykzlNBQAUXaEDa/Xq1XmPAACUobK8BgsAoD0V%2BgzWBRdc0PxLno/miSeeaOdpAIByUejAev/73x/f%2Bta3YtCgQTFy5MiIiFi7dm0899xz8alPfSpKpVLOEwIARVTowHrttddi6tSpsWDBghbL586dG7t27Yp//Md/zGkyAKDICn0N1v333x8f//jHWy2fPHly/OAHP8hhIgCgHBQ6sEqlUqxZs6bV8jVr1vh4EABoN4X%2BiPCzn/1sTJkyJZ566qm48MILI%2BLX12Ddddddccstt%2BQ8HQBQVIUOrLlz50ZdXV187Wtfi7vvvjsiIs4555y466674pprrsl5OgCgqAodWBER11xzjZgCADpUoa/BiohoaGiI73znO/GFL3whXn311YiI%2BPnPfx47duzIeTIAoKgKfQbrF7/4RVx%2B%2BeVx/PHHx/bt22Py5Mlx0kknxf333x8vvfRS3HvvvXmPCAAUUKHPYN14441xzTXXxC9/%2Bcvo3r178/Jx48bFqlWrcpwMACiyQp/BWrduXSxevLjVr8s57bTTfEQIALSbQp/B6tatWxw4cKDV8i1btsQpp5ySw0QAQDkodGB9%2BMMfjr/927%2BNQ4cORURERUVFvPzyy3HzzTfH%2BPHjc54OACiqQgfWHXfcEa%2B88kpUV1fHG2%2B8EZdeemmceeaZ0b1791a/nxAAIJVCX4PVu3fvWLNmTTz66KPx5JNPRlNTU7znPe%2BJD37wg62uywIASKWwgfXWW2/FFVdcEd/4xjdizJgxMWbMmLxHAgDKRGE/IuzatWs89dRTzlQBAB2usIEVETFhwoS455578h4DACgzhf2I8G2LFi2KFStWxIgRI6Jnz54t1t1%2B%2B%2B05TQUAFFmhA2vDhg0xdOjQiIh4%2BumnW6zz0SEA0F4KGVhbt26Nurq6WL16dd6jAABlqJDXYJ111lmxZ8%2Be5vsf%2B9jHYteuXTlOBACUk0IGVpZlLe7/6Ec/itdffz2naQCAclPIwAIAyFMhA6uioqLVRey/z0Xt9fX1ccEFF0SvXr2ib9%2B%2B8ed//ufx3HPP/b5jAgAFVciL3LMsi8mTJ0epVIqIiDfffDOmTJnS6msaHnjggTbtb%2BXKlTF16tS44IIL4tChQzF37twYM2ZMPPPMM632CQBQyMCaNGlSi/sTJkz4vfa3fPnyFvfvueee6Nu3b2zYsCEuueSS32vfAEDxFDKw2vvb2/ft2xcRESeffPIR1zc2NkZjY2Pz/YaGhnadBwDoXAoZWO0py7KYOXNmXHzxxTFkyJAjblNfXx/z58/vkHlGzF1%2B9I06ifW3/WneIxSa5wLHomPpeRvhuUvbFfIi9/b0mc98Jp5%2B%2Bun453/%2B59%2B6zZw5c2Lfvn3Nt%2B3bt3fghABA3pzBegemTZsWDz/8cKxatSr69%2B//W7crlUrNF9gDAOVHYLVBlmUxbdq0ePDBB%2BPxxx%2BPurq6vEcCADoxgdUGU6dOje9///vxL//yL9GrV6/YuXNnRET07t07evTokfN0AEBn4xqsNli8eHHs27cvRo8eHTU1Nc23ZcuW5T0aANAJOYPVBr/5uw0BAH4XZ7AAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQksAIDEBBYAQGICCwAgMYEFAJCYwAIASExgAQAkJrAAABITWAAAiQmsNli1alV86EMfitra2qioqIiHHnoo75EAgE5MYLXB66%2B/HsOGDYtFixblPQoAcAyozHuAY8HYsWNj7NixeY8BABwjBFY7aGxsjMbGxub7DQ0NOU4DAHQ0gdUO6uvrY/78%2BXmP0emMmLs87xHekfW3/WneIxTWsfZcONZ47rYfz13ayjVY7WDOnDmxb9%2B%2B5tv27dvzHgkA6EDOYLWDUqkUpVIp7zEAgJw4gwUAkJgzWG1w4MCB%2BK//%2Bq/m%2By%2B88EJs3LgxTj755Dj99NNznAwA6IwEVhusX78%2B3v/%2B9zffnzlzZkRETJo0Kb7zne/kNBUA0FkJrDYYPXp0ZFmW9xgAwDHCNVgAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxAQWAEBiAgsAIDGBBQCQmMACAEhMYAEAJCawAAASE1gAAIkJLACAxARWG33jG9%2BIurq66N69ewwfPjxWr16d90gAQCclsNpg2bJlMWPGjJg7d2489dRT8Sd/8icxduzY2LZtW96jAQCdkMBqg6985SvxiU98Im644YY455xz4qtf/WoMGDAgFi9enPdoAEAnVJn3AJ3dwYMHY8OGDXHzzTe3WD5mzJhYs2bNER/T2NgYjY2Nzff37dsXERENDQ3J5zvc%2BHryffJr7XG82pPnAm87lp67nre8rT2et2/vM8uy5Ps%2BGoF1FHv37o3Dhw9Hv379Wizv169f7Ny584iPqa%2Bvj/nz57daPmDAgHaZkfbR%2B468J4B3x3OXY1F7Pm/3798fvXv3br8/4AgEVhtVVFS0uJ9lWatlb5szZ07MnDmz%2BX5TU1P8z//8T/Tp0%2Be3PiaVhoaGGDBgQGzfvj2qqqra9c/i3XGMOj/H6NjgOHV%2BeR%2BjLMti//79UVtb2%2BF/tsA6ilNOOSW6dOnS6mzV7t27W53VelupVIpSqdRi2YknnthuMx5JVVWVN5xOzjHq/ByjY4Pj1PnleYw6%2BszV21zkfhTdunWL4cOHx6OPPtpi%2BaOPPhqjRo3KaSoAoDNzBqsNZs6cGRMnTowRI0bEyJEjY8mSJbFt27aYMmVK3qMBAJ1Ql3nz5s3Le4jObsiQIdGnT59YsGBBfPnLX4433ngj/umf/imGDRuW92hH1KVLlxg9enRUVurnzsox6vwco2OD49T5lesxqsjy%2BNlFAIACcw0WAEBiAgsAIDGBBQCQmMACAEhMYBVAfX19VFRUxIwZM5qXNTY2xrRp0%2BKUU06Jnj17xoc//OF46aWXcpyyvMybNy8qKipa3Kqrq5vXZ1kW8%2BbNi9ra2ujRo0eMHj06Nm/enOPE5evll1%2BOCRMmRJ8%2BfeL444%2BPP/7jP44NGzY0r3es8vUHf/AHrV5LFRUVMXXq1IjwXtcZHDp0KP7qr/4q6urqokePHnHmmWfG3/zN30RTU1PzNuX4OhJYx7h169bFkiVLYujQoS2Wz5gxIx588MFYunRp/PSnP40DBw7ElVdeGYcPH85p0vIzePDg2LFjR/Nt06ZNzetuv/32%2BMpXvhKLFi2KdevWRXV1dXzgAx%2BI/fv35zhx%2BXn11Vfjoosuiq5du8aPf/zjeOaZZ%2BKOO%2B5o8ZsXHKt8rVu3rsXr6O0vff7oRz8aEd7rOoO/%2B7u/i29%2B85uxaNGiePbZZ%2BP222%2BPL33pS/H1r3%2B9eZuyfB1lHLP279%2BfnXXWWdmjjz6ave9978umT5%2BeZVmWvfbaa1nXrl2zpUuXNm/78ssvZ8cdd1y2fPnyvMYtK7feems2bNiwI65ramrKqqurs4ULFzYve/PNN7PevXtn3/zmNztqRLIsmz17dnbxxRf/1vWOVeczffr0bODAgVlTU5P3uk5i3Lhx2fXXX99i2fjx47MJEyZkWVa%2BryNnsI5hU6dOjXHjxsXll1/eYvmGDRvirbfeijFjxjQvq62tjSFDhsSaNWs6esyytWXLlqitrY26urq46qqrYuvWrRER8cILL8TOnTtbHJ9SqRTve9/7HJ8O9vDDD8eIESPiox/9aPTt2zfOP//8uOuuu5rXO1ady8GDB%2BO%2B%2B%2B6L66%2B/PioqKrzXdRIXX3xxPPbYY/H8889HRMTPf/7z%2BOlPfxpXXHFFRJTv66i8vla1QJYuXRobNmyI9evXt1q3c%2BfO6NatW5x00kktlvfr16/VL62mfbz3ve%2BN7373uzFo0KDYtWtXfPGLX4xRo0bF5s2bm4/Bb/6y8H79%2BsWLL76Yx7hla%2BvWrbF48eKYOXNm3HLLLfHEE0/EZz/72SiVSnHdddc5Vp3MQw89FK%2B99lpMnjw5IrzXdRazZ8%2BOffv2xdlnnx1dunSJw4cPx2233RZXX311RETZvo4E1jFo%2B/btMX369PjJT34S3bt3b/PjsiyLioqKdpyMt40dO7b5v88777wYOXJkDBw4MO6999648MILIyJaHQvHp%2BM1NTXFiBEjYsGCBRERcf7558fmzZtj8eLFcd111zVv51h1Dt/%2B9rdj7NixUVtb%2Bzu3c3w61rJly%2BK%2B%2B%2B6L73//%2BzF48ODYuHFjzJgxI2pra2PSpEnN25Xb68hHhMegDRs2xO7du2P48OFRWVkZlZWVsXLlyrjzzjujsrIy%2BvXrFwcPHoxXX321xeN2797d6l8QdIyePXvGeeedF1u2bGn%2BacLf/Be249Pxampq4txzz22x7Jxzzolt27ZFRDhWnciLL74YK1asiBtuuKF5WXV1tfe6TuDzn/983HzzzXHVVVfFeeedFxMnTowbb7wx6uvrI6J8X0cC6xh02WWXxaZNm2Ljxo3NtxEjRsS1117b/N9du3Zt/mmbiIgdO3bEL37xixg1alSOk5evxsbGePbZZ6Ompibq6uqiurq6xfE5ePBgrFy50vHpYBdddFE899xzLZY9//zzccYZZ0REOFadyD333BN9%2B/aNcePGNS8bPny497pO4H//93/juONa5kSXLl2av6ahbF9HuV5iTzL//6cIsyzLpkyZkvXv3z9bsWJF9uSTT2aXXnppNmzYsOzQoUM5Tlk%2BPve5z2WPP/54tnXr1mzt2rXZlVdemfXq1Sv71a9%2BlWVZli1cuDDr3bt39sADD2SbNm3Krr766qympiZraGjIefLy8sQTT2SVlZXZbbfdlm3ZsiX73ve%2Blx1//PHZfffd17yNY5W/w4cPZ6effno2e/bsVuu81%2BVv0qRJ2WmnnZb967/%2Ba/bCCy9kDzzwQHbKKadks2bNat6mHF9HAqsgfjOw3njjjewzn/lMdvLJJ2c9evTIrrzyymzbtm05TlhePvaxj2U1NTVZ165ds9ra2mz8%2BPHZ5s2bm9c3NTVlt956a1ZdXZ2VSqXskksuyTZt2pTjxOXrhz/8YTZkyJCsVCplZ599drZkyZIW6x2r/D3yyCNZRGTPPfdcq3Xe6/LX0NCQTZ8%2BPTv99NOz7t27Z2eeeWY2d%2B7crLGxsXmbcnwdVWRZluV9Fg0AoEhcgwUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBITGABACQmsAAAEhNYAACJCSwAgMQEFgBAYgILACAxgQUAkJjAAgBI7P8A/obHTVZzvsQAAAAASUVORK5CYII%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common9053385640232196937\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">79.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.9</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">58.8</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">77.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">63.9</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">71.9</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">43.6</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">79.3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">78.9</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">73.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (5)</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">29.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">11.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:40%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme9053385640232196937\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">39.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">43.6</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">44.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">54.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">56.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">77.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">78.9</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">79.3</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">79.4</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">81.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">5.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAn8AAAIzCAYAAABx1H/sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XlYlPX%2B//EX%2ByIqJou7poJ13EBRLJfcytzNVMotc%2BnkgmaanuyUC2WZWCcl0zyWR8sjilqWnrSstN8pJRM9pmHuERogRIKAgMzvD3K%2BTqKgA87czfNxXXMN87mXec8nHF697/uecTKZTCYBAADAITjbugAAAADcPoQ/AAAAB0L4AwAAcCCEPwAAAAdC%2BAMAAHAghD8AAAAHQvgDAABwIIQ/AAAAB0L4AwAAcCCuti4AgDH9/PPP6tat23WXu7m5ycfHRw0aNFDnzp01fPhw%2Bfj43MYKAQAlceLr3QDciqvDX3Bw8DXBrqCgQBkZGUpOTpYk1apVS6tWrVL9%2BvVve60AgP9D%2BANwS64Of6tXr1Z4eHiJ6%2B3du1cTJkxQdna2QkNDtW7duttZJgDgDzjnD0CFCg8P19NPPy1JSkhI0Pfff2/jigDAsRH%2BAFS4%2B%2B%2B/3/zzwYMHbVgJAIALPgBUuMqVK5t/vnjxosWyb7/9VmvWrNH%2B/fuVmZmpKlWqKCQkRCNGjNA999xT4v4uXLigdevWadeuXTp%2B/Liys7Pl5eWlevXqqUuXLho5cqSqVq1qsU2TJk0kSf/973/1yiuvaOfOnXJ2dlbTpk31zjvvyNXVVQcPHtSqVat05MgRnTt3Th4eHrrzzjvVvXt3DR06tMQLVvLy8rRu3Tpt27ZNx48fV0FBgQIDA3Xvvfdq9OjRatCggcX6e/fu1ciRI9WyZUu9//77WrNmjT744AOdOXNGbm5uatq0qUaMGKHu3bvfylQDQKkIfwAq3JkzZ8w/16hRw/xzdHS0VqxYIUmqWrWqgoODlZqaqp07d2rnzp0aO3asnnnmGYt9nT59WqNGjdK5c%2Bfk6uqqevXqqXbt2kpOTtbhw4d1%2BPBhbd26VRs3blSlSpWuqSUyMlIJCQkKDg5WRkaG/P395erqqh07dmjq1KkqLCxUtWrV1LhxY128eFH/%2B9//dPDgQW3ZskXr1q2zCIC//PKLHn/8cZ08eVKS1KBBA1WqVEknTpxQbGysPvjgA73yyivq1avXNXUUFBRo3Lhx%2Buabb1StWjU1atRIp06d0p49e7Rnzx7NmTNHjz76qHUTDwAlMQHALUhKSjIFBwebgoODTXv27LnhujNmzDAFBwebmjZtakpLSzOZTCbTv//9b1NwcLApLCzM9OGHH5rXLSoqMm3dutUUEhJiCg4ONq1fv95iX8OHDzcFBwebhgwZYkpJSbHYbvPmzaa77rrLFBwcbHrvvfcstrtSa7NmzUzx8fEmk8lkunz5sunXX381Xb582dS%2BfXtTcHCwacWKFabCwkLzdt9//72pXbt2puDgYNPy5cvN44WFhab%2B/fubgoODTT169DD98MMP5mVZWVmm5557zvyaDxw4YF62Z88ecy0hISGmLVu2mJdduHDB9Nhjj5mCg4NNbdu2NRUUFNxwXgHgVnDOH4AKkZeXpyNHjmj27Nn64IMPJEmjRo2Sn5%2Bf8vPztWTJEknS/Pnz1a9fP/N2Tk5O6tWrl7njt2TJEhUWFkqS0tPTdezYMUlSVFSUAgICLLYbMGCA2rZtK0k6evRoiXX17NlTbdq0kSQ5OzvL19dXGRkZSktLkyQNGTJELi4u5vWbNm2qqVOnqnv37vL19TWPf/LJJ/rhhx/k4eGhFStW6K677jIv8/Hx0YsvvqiOHTuqoKBAr7/%2Beom1TJ48WX379jU/rly5svl1Z2Zm6tSpU9eZXQC4dRz2BWC1kSNHlrrO4MGDNWXKFEnFV/2eP39elSpVuu4HRffr109RUVFKSUnRkSNH1KJFC1WvXl179uxRXl6ePD09r9nm8uXL5sOyeXl5Je63devW14xVq1ZNVatW1W%2B//abp06dr/PjxatmypZydi///eMiQIRoyZIjFNp9//rkkqWvXrqpbt26Jz/X444/rq6%2B%2BUnx8vLKysizOfZSkLl26XLNNo0aNzD9fuHChxP0CgDUIfwCs9scPeXZycpKHh4d8fX3VpEkTde/eXY0bNzYvv9K9Kygo0LBhw667XxcXFxUVFenkyZNq0aKFedzT01Pnzp3TwYMH9dNPPykpKUknTpzQDz/8oJycHElSUVFRifv09/cv8XmmT5%2Bu559/Xrt27dKuXbtUtWpVhYeHq3379urcubPFuYqSzF25pk2bXrf%2BK8suX76sM2fOqFmzZhbLAwMDr9nm6lB7%2BfLl6%2B4bAG4V4Q%2BA1f7%2B979f90OeS5KVlSVJys/P1/79%2B0td/%2BoO2MmTJ/Xqq69q165dFgHPx8dHYWFhSk1NVWJi4nX3VVLHUCru7tWvX1/vvvuuvv76a/3222/asWOHduzYIScnJ3Xu3Flz5swxh8Ds7GxJuqabd7WrA/Efr3KWir8C70ZMfAY/gApA%2BANw23l5eUkq7oxt2rSpzNulp6dr%2BPDhSk9PV61atTRkyBD95S9/UcOGDVWnTh05OTlp2rRpNwx/NxIeHq7w8HDl5eVp3759%2Bvbbb/XVV1/p8OHD%2BuKLL3Tu3Dl98MEHcnJyMl9JfCXIluTq0FrSlccAYAuEPwC33Z133imp%2BGNbCgsL5ep67VuRyWTS3r17VaNGDdWqVUvu7u7auHGj0tPT5evrq40bN%2BqOO%2B64ZruUlJSbric/P19JSUnKzs5Wy5Yt5enpqQ4dOqhDhw6aOnWqtm7dqqefflqJiYk6evSo7rrrLjVs2FBHjhzR4cOHr7vfQ4cOSSo%2BDF6vXr2brgsAKgJX%2BwK47dq0aaPKlSvr4sWL1%2B38ffTRR3rsscfUs2dP/fLLL5KKv09YkmrVqlVi8Dt%2B/LgOHDgg6ebOl9u9e7d69eqlJ554Qvn5%2Bdcsv/fee80/X9nvlYs1Pv/8cyUlJZW439WrV0uSQkJCVKVKlTLXAwAVifAH4Lbz9vbWE088IUl66aWXtHHjRovz9z777DPNnj1bUvFHs1zpmjVs2FCSlJiYqO3bt5vXN5lM2r17t8aOHauCggJJUm5ubpnr6dSpk6pVq6bMzEzNnDlTmZmZ5mUXL17UggULJEk1a9ZUUFCQJOnBBx9UkyZNdOnSJY0bN87iUHN2draef/55/b//9//k6uqq6dOnl31yAKCCcdgXgE2MGzdOSUlJWr9%2BvWbNmqWFCxeqTp06SklJUWpqqiSpVatWevHFF83bDBo0SGvXrtWZM2c0efJk1a5dW9WqVdO5c%2BeUnp4uNzc3tW3bVvHx8Td1%2BNfd3V1vvPGGxowZo23btmnnzp2qV6%2BenJ2dlZSUpJycHHl5eemVV16Ru7u7JMnV1VVLly7VuHHjdPLkSfXv39/iGz6ufBzN3LlzFRYWVr6TBwBWIPwBsAknJydFRUWpR48eWrdunQ4cOGD%2B0OSQkBD16dNHERER5rAlFV89GxcXpxUrVuiLL77Qzz//rPPnz6tGjRrq3LmzHnvsMXl7e6t79%2B5KTEzU2bNnVatWrTLVEx4erg0bNujdd9/Vd999p9OnT8vV1VU1atRQhw4dNHr06Gv2VadOHW3cuFH//ve/9cknn%2BjEiRP65ZdfVLNmTXXs2FHDhg275rt9AcDWnEx8lgAAAIDD4Jw/AAAAB0L4AwAAcCCEPwAAAAdC%2BAMAACgnGRkZuv/%2B%2B7V3797rrrNr1y717dtXISEh6tmzp7744guL5StWrFCnTp0UEhKiESNG6OTJk%2BVaI%2BEPAACgHHz33XeKiIjQTz/9dN11Tp8%2BrcjISE2ZMkX79u1TZGSknnrqKfPHU23evFlr1qzRypUrtXfvXjVt2lSTJ08u1%2B/6JvwBAACHl5qaqsOHD1vcrnzmaFls3rxZ06dP19SpU0tdLywsTN27d5erq6t69eqlNm3aKDY2VpK0fv16DR06VEFBQfLw8NC0adN09uzZG3YSbxaf82fvnJxsXUHp7rxTOnZMCgqSTp2ydTWlupRnjE83cneXSvimMbvjEbPI1iWUTdWq0ujR0jvvSL/9Zutqbihl%2BDRbl1AmLi5S9epSerp0E9%2BmZxOBez60dQml8/aWuneXPvtMysmxdTWl69/fds9dAX8bYxcvVkxMjMXYpEmTFBkZWabtO3TooL59%2B8rV1fWGAfD48eMKDg62GGvcuLH5W4KOHz%2BucePGmZe5ubmpQYMGSkxMVLt27cr6cm6I8Afr%2BfoW/xXw9bV1JX8aTk7/d%2BOTOMuJh4fk7Fx8j3Jx9e8pyoGbW/FkurnZuhKHFBERoa5du1qM%2Bfv7l3n7sq578eJFeXl5WYx5enoq5/fAX9ry8kD4AwAADi8gIEABAQEV/jxeXl7Ky8uzGMvLy1OlSpXKtLw8cM4fAAAwFmfn8r/dJsHBwTp27JjF2PHjxxUUFCRJCgoKslheUFCg06dPX3Oo2BqEPwAAYCwGDn/9%2BvVTfHy8tm3bpsLCQm3btk3x8fHq//s5lA8//LDee%2B89JSYm6tKlS1q0aJH8/PwUFhZWbjUQ/gAAACpQaGiotmzZIklq1KiR3nzzTS1fvlxt2rTR0qVLtWTJEt15552SpEGDBmnUqFGaOHGi2rVrpyNHjmj58uVyK8dzQTnnDwAAGMtt7NTdiqNHj1o8TkhIsHjcsWNHdezYscRtnZycNHr0aI0ePbrC6rPv2QMAAEC5ovMHAACMxc47f/aO8AcAAIyF8GcVZg8AAMCB0PkDAADGQufPKsweAACAA6HzBwAAjIXOn1UIfwAAwFgIf1Zh9gAAABwInT8AAGAsdP6sQvgDAADGQvizCrMHAADgQOj8AQAAY6HzZxVmDwAAwIHQ%2BQMAAMZC588qhD8AAGAshD%2BrMHsAAAAOhM4fAAAwFjp/VmH2AAAAHAidPwAAYCx0/qxC%2BAMAAMZC%2BLMKswcAAOBA6PwBAABjofNnFWYPAADAgdD5AwAAxkLnzyqEPwAAYCyEP6swewAAAA6Ezh8AADAWOn9WYfYAAAAcCJ0/AABgLHT%2BrEL4AwAAxkL4swqz97vTp0/bugQAAIAKV%2BbO3wsvvKCPPvpIklRYWKiCggJ5eXmZl69YsUJhYWHlX%2BFt8Omnnyo6Olrbt2%2B3dSkAAKA0dP6sUubwN2/ePM2bN0%2BStGnTJsXExOjzzz%2BvsMJup8zMTBUVFdm6DAAAgApXbtG5qKhIq1atUo8ePRQWFqbhw4fr8OHD5uWdOnXSqlWr1LdvX7Vs2VLDhg3T4cOHNWbMGIWGhqp37946dOiQJGnDhg169NFH9dxzz6lVq1bq0KGDli9fLpPJJEnKz8/X66%2B/rq5duyo8PFxPPPGEkpKSJBV3JZs0aaIXX3xR4eHhmjhxokwmk5YtW6Y%2BffooLCxMbdu21TPPPKNLly7p66%2B/1rx585SUlKTQ0FClp6fr0Ucf1dKlS821nzlzRk2aNNEvv/xy0/sHAADlzNm5/G8OpNwu%2BFizZo1Wr16tt956Sw0bNtTmzZv1%2BOOP65NPPtEdd9whSYqLi9PKlSvl5eWlgQMHauTIkXrnnXd09913a/r06Xr99df1zjvvSJL279%2BvkJAQ7dmzR4mJiRo7dqwCAgL00EMPKTo6Wvv27dOaNWvk5%2Ben5cuXa/To0dq6daucf/8P%2BPPPP%2BvLL79UQUGBPv74Y73//vt67733VL9%2BfR0/flyPPvqotm3bpoceekgvvPCC3n77bX366adlfr03s/%2BySk1NVVpamsWY/513KsDXt8z7sIm77rK8t3NOTrauoHRXajRCrQoIsHUFZfP7%2B5D53o65GuRSPBcXy3u7VrWqrSsonY%2BP5T2uz8HCWnkrt7eYtWvXavz48WrSpIkkaciQIVq/fr0%2B%2BugjPfbYY5KkQYMGKTAwUJLUokULFRQUqGXLlpKk9u3bm4OfJFWvXl3Tpk2Tq6urWrRooUGDBunDDz9U//79tW7dOi1fvly1a9eWJEVGRio2Nla7d%2B9W586dJUn9%2BvWTl5eXvLy81KVLF7Vt21aBgYHKyMhQZmamfH19lZKScsuvtyL2Hxsbq5iYGIuxSVOmKHLKlFuu87Zau9bWFZSJu60LuAlubrauoAxGjLB1BTend29bV1Cq6rYu4CbZ%2B/%2BfSpJ%2B/9tgCAY9fx7GUW7hLzk5WfPnz9eCBQvMY4WFhTp79qz5se9V7xDOzs6qUqWKxeOrz7urU6eOXK/6399atWpp165dSktL06VLlzRx4kRzl0%2BSCgoKlJycbH4ccFU3oqioSNHR0dq1a5fuuOMO3X333SooKLDqPL%2BK2H9ERIS6du1qMebft6/0r3/dcp23xV13FQe/oUOlxERbV1Oq/D37bV1CqZycioNfQYH0%2B9kOdss9do2tSyibO%2B4oDn5bt0oZGbau5obSexkjULu4FAe/zEzp8mVbV3Nj1Q99aesSSufjUxz89u2TsrNtXU3pbBmo6fxZpdzCX2BgoJ555hk9%2BOCD5rGffvpJ1apVMz92uoljWKmpqTKZTOZtfv75Z9WsWVN%2Bfn5yc3PTv/71LzVv3ty8/okTJ1SzZs0S9/Xqq68qLS1Nn3/%2BuXx%2Bb6f37Nnzus/t4uKigoIC8%2BNff/31hrXe7P6vJyAgwCJUSpJOnbrp/dhMYqKUkGDrKkpl72HqaiaTAepNTbV1BTcnI8Puay4stHUFN%2BfyZQPU/Ntvtq6g7LKzjVUvDKfconNERISWLl2qU7%2BHlV27dqlXr17av//Wuiznzp3TypUrVVBQoISEBMXFxWnw4MFycXHRwIEDFR0drZSUFBUVFSkuLk79%2BvUzX/TxR1lZWfLw8JCrq6vy8vK0YsUKnTx50hzw3N3dlZubq8Lf370aNmyo3bt3KysrSxcuXNA///nPG9Za2v4BAEA54oIPq5Rb52/MmDGSpL/%2B9a9KS0tTjRo1NHfuXN133323tL8aNWro1KlT6tChg3x8fDR9%2BnT16NFDkjRr1iwtXrxYjz76qDIzM1W/fn0tWbJETZo0MQe4qz399NOaNWuW7rnnHnl7e6tNmzbq06ePfvzxR0lSu3btVKVKFbVp00ZxcXGaMGGCnnvuOXXp0kVVq1ZVZGTkDS8GKW3/AACgHDlYWCtvTiaT/R1U2rBhw01fffunZYTLPUNDpf37pVatDHHY91Ke3f3KX8PJSXJ3l/Lz7f%2Bwr0fMIluXUDYBAcUXp6xZY/eHfVOGT7N1CWXi6ipVry6lp9v/Yd/APR/auoTSVa1afB7dl18a47Bv//62e%2B577in/fX7zTfnv004Z5AMFAAAAfkfnzyqEPwAAACulp6fr%2BeefV3x8vFxcXNSvXz/NnDnT4pNLJGns2LH67rvvLMZycnIUERGhefPm6fz582rfvr28vb3Ny6tVq1au36pml%2BFv8ODBGjx4sK3LAAAA9sgOO39PPfWUAgMD9dVXX%2Bn8%2BfMaP368Vq1apbFjx1qs98eLSOPi4hQTE6NJkyZJkg4dOqTatWtX6Ffo2mX4AwAAuK4KCH8lfsuWv/%2B1H8FWgjNnzig%2BPl67d%2B%2BWl5eX6tatqwkTJmjhwoXXhL%2BrnTx5UlFRUVq5cqX5eQ4dOqRmzZpZ92JKQfgDAAAOr8Rv2Zo0SZGRkaVue%2BzYMfn6%2Bpq/xUySGjVqpLNnz%2BrChQsWX2pxtblz52rAgAEKu%2BpbXQ4dOqTffvtNffr00fnz59W8eXPNnDlTjRs3vsVXdi3CHwAAMJYK6PyV%2BC1b/v5l2vbixYvy8vKyGLvyOCcnp8Twt2/fPh08eFDR0dEW41WqVFHjxo01btw4ubu764033tDjjz%2Bubdu2qXLlyjfzkq6L8AcAABxeid%2ByVUbe3t7Kzc21GLvyuFKlSiVuExsbq549e14TMBctsvz4rGeffVYbN27Uvn371KVLl1uq74/s74xJAACAG7Gzb/gICgpSZmamzp8/bx47ceKEatSoUWK3rrCwUDt37lS/fv0sxrOzs7VgwQIlJyebxy5fvqzCwkJ5enpaVePVCH8AAMBY7Cz8NWjQQK1bt9b8%2BfOVnZ2tpKQkLV26VIMGDSpx/aNHj%2BrSpUtq1aqVxbiPj4%2B%2B/vprLViwQFlZWbp48aKioqJUp04di/MCrUX4AwAAsNLixYtVWFiobt26aciQIerYsaMmTJggSQoNDdWWLVvM6yYlJalq1ary8PC4Zj9Lly5VUVGRunfvro4dOyotLU0rVqyQm5tbudXKOX8AAMBY7PBz/vz8/LR48eISlyX84atPH3zwQT344IMlrlu7du1rrjoub4Q/AABgLHYY/oyE2QMAAHAgdP4AAICx0PmzCrMHAADgQOj8AQAAY6HzZxXCHwAAMBbCn1WYPQAAAAdC5w8AABgLnT%2BrMHsAAAAOhM4fAAAwFjp/ViH8AQAAYyH8WYXZAwAAcCB0/gAAgLHQ%2BbMKswcAAOBA6PwBAABjofNnFcIfAAAwFsKfVZg9AAAAB0LnDwAAGAudP6swewAAAA6Ezh8AADAWOn9WIfwBAABjIfxZhdkDAABwIHT%2BAACAsdD5swqzBwAA4EDo/AEAAGOh82cVwp%2Bdu5RnsnUJpXJyktwl5e/ZL5P9lysPTydbl1C60FBp/365t2slJSTYupob%2B/FHW1dQNh4exff33SddumTbWkoR2Mzf1iWUTYsW0s6dqj6km/S//9m6mhvr1cvWFZSufn2pc2fp88%2BlM2dsXU3p%2Bve33XMT/qzC7AEAADgQOn8AAMBY6PxZhdkDAABwIHT%2BAACAsdD5swrhDwAAGAvhzyrMHgAAgAOh8wcAAIyFzp9VmD0AAAAHQucPAAAYC50/qxD%2BAACAsRD%2BrMLsAQAAOBA6fwAAwFjo/FmF2QMAAHAgdP4AAICx0PmzCuEPAAAYC%2BHPKsweAACAA6HzBwAAjIXOn1WYPQAAYCzOzuV/s1J6eromTJigsLAwhYeH66WXXlJhYWGJ644dO1bNmzdXaGio%2BbZ7925J0uXLl7VgwQLde%2B%2B9Cg0N1fjx45Wammp1fVcj/AEAAFjpqaeekre3t7766ivFxcXpm2%2B%2B0apVq0pc9/vvv9fKlSuVkJBgvnXq1EmS9NZbb%2Bm///2vNm7cqK%2B%2B%2Bkqenp76%2B9//Xq61ctgXAAAYSwUc9k1NTVVaWprFmL%2B/vwICAkrd9syZM4qPj9fu3bvl5eWlunXrasKECVq4cKHGjh1rsW5SUpJ%2B%2B%2B03/eUvfylxXxs2bND06dNVs2ZNSdJzzz2nDh06KCkpSXXr1r3FV2eJ8AcAABxebGysYmJiLMYmTZqkyMjIUrc9duyYfH19FRgYaB5r1KiRzp49qwsXLqhKlSrm8UOHDqlSpUqaOnWqDh06JD8/P40aNUqDBg1SVlaWfvnlFwUHB5vX9/PzU9WqVXX06FHCHwAAcFAV0PmLiIhQ165dLcb8/f3LtO3Fixfl5eVlMXblcU5OjkX4y8/PV0hIiKZOnaqgoCDt3btXkZGRqlSpkkJDQyVJ3t7eFvvy9PTUxYsXb/o1XQ/hDwAAGEsFhL%2BAgIAyHeItibe3t3Jzcy3GrjyuVKmSxfiAAQM0YMAA8%2BMOHTpowIAB%2Bs9//qN7773XYtsr8vLyrtmPNbjgAwAAwApBQUHKzMzU%2BfPnzWMnTpxQjRo1VLlyZYt14%2BLi9J///MdiLD8/Xx4eHqpataoCAwN1/Phx87K0tDRlZmZaHAq2FuEPAAAYi5191EuDBg3UunVrzZ8/X9nZ2UpKStLSpUs1aNCga9bNzs5WVFSUjhw5oqKiIn355Zf6%2BOOPFRERIUkaOHCg3nrrLSUlJSk7O1vz589X27ZtVa9ePatqvBqHfQEAAKy0ePFizZs3T926dZOzs7MGDBigCRMmSJJCQ0M1d%2B5c9evXT4899phycnI0adIkpaenq27dulqwYIHCwsIkSRMnTlRhYaGGDRumixcvKjw8XP/4xz/KtVbCHwAAMBY7/IYPPz8/LV68uMRlCQkJ5p%2BdnJw0YcIEczD8Izc3N02fPl3Tp0%2BvkDolwh8AADAaOwx/RsLsAQAAOBA6fwAAwFjo/FmF2QMAAHAgdP4AAICx0PmzCuEPAAAYC%2BHPKsweAACAA6HzBwAAjIXOn1WYPQAAAAdC5w8AABgLnT%2BrEP4AAICxEP6sUiGz17VrVzVv3lyhoaEKDQ1VSEiIOnTooAULFqioqKhcnmPEiBFasmRJueyrJGfPnlVoaKjOnj1bYc8BAABwu1VY52/u3LkaOHCg%2BfHRo0c1atQoeXl5afLkyRX1tOWmVq1aFl/EDAAA7ASdP6vctsO%2BTZo0UZs2bXTkyBGNGDFCtWvX1t69e2UymfTxxx8rOTlZ0dHROnjwoDw9PdW1a1dNmzZNlStXliRt2LBBy5YtU0ZGhh544AHl5uaa9/23v/1NkvTKK69YPN/q1asVHh6ujIwMzZ8/X7t27ZKzs7PuvfdezZkzR1WrVlVSUpJeeuklffvtt3J3d1ePHj00a9Yspaamqlu3btq5c6fq1Kmj5ORkLVy4UHv37pWzs7PatWunmTNnKiAgQHv37tWzzz6rwYMHa%2B3atbp06ZLCw8P18ssvy8fHp8xzlJqaqrS0NIuxqlX9FRAQYM3UVzgnJ8t7uxcaausKSnfXXZb39szDw9YVlI2bm%2BW9PWvRwtYVlE1QkOW9Patf39YVlK5mTct7oILclvBXUFCg/fv3a8%2BePYqMjNSOHTv09ddfa8OGDfLy8lJBQYFGjhypgQMHasmSJcrKytL06dM1Y8YMvfXWW/rmm280b948vf3222rTpo3Wr1%2BvDz74QPfdd1%2BZnn/KlCmqVKmSduzYITc3N02ZMkVz587Vq6%2B%2BqjFjxig8PFy7d%2B9WXl6exowZoyVLligiIsKi/tGjR6tZs2basWOHTCaT5s6dqyeffFLr16%2BXJCUnJyslJUWffvqpUlJSNGzYMK1du1ZPPPFEmecpNjZWMTExFmMTJ07S5MmRZd6HLRnhb6okaf9%2BW1dQdmvX2rqCPx8j/GHdudPWFdycZctsXcGfy/jxtq7A/tH5s0qFHvadP3%2B%2B%2BXGNGjX0%2BOOPa/jw4doKItosAAAgAElEQVSxY4c6deqkwMBASVJcXJzc3Nw0ffp0ubi4yNPTU88//7x69%2B6ttLQ0bdmyRQ888IDuueceSdLQoUO1YcOGMtWRnJys%2BPh4ffLJJ6pWrZqk4g5hZmam9u/fr%2BTkZM2aNUteXl6qVKmSYmJirjkvcd%2B%2BfUpKStLGjRvNnby5c%2Beqbdu2%2Bv77783rTZw4UZ6enqpfv77Cw8N16tSpm5qziIgIde3a1WKsalV/5eff1G5uOyen4uBXUCCZTLaupnTu7VrZuoTS3XVXcfAbOlRKTLR1NTf2wQe2rqBs3NyKg9%2B5c8W/rPbs8cdtXUHZBAUVB78nn5SOHbN1NTfWvr2tKyhdzZrFwe%2Btt4p/T%2B3dvHm2e27Cn1UqLPzNnj3b4py/P7r6UGZ6erpq1aolFxcX81idOnUk/V9HrWnTphbb161bt0x1XDmMWrt2bfOYv7%2B//P39tXXrVlWrVk1eXl7XPO/PP/9sUV%2B1atUsDuH6%2BPjI19dXycnJ8vPzM%2B/3Cjc3N5luMgkFBARcc4j30iVjBCqpuE5D1GqkczkTE%2B2/3kuXbF3BzSkosP%2Ba//c/W1dwc44ds/%2Baf39vN4Rz56QzZ2xdBf7EbBadna46Qax27do6e/asLl%2B%2BbB776aefJBUHqho1aigpKcli%2B19%2B%2BcX8s7Ozswqu%2Bj/5jIwM8881fz/Ec/VVu8ePH9c//vEP1ahRQ7/%2B%2BqvF%2BYP79u3TqlWrLJ6rdu3a%2BvXXX5WdnW0ey8rK0q%2B//moR%2BAAAwG3g7Fz%2BNwdiF6/2yrl70dHRysvLU1paml566SW1a9dOtWvX1sMPP6zPPvtMX3zxhQoLC7V582YdPHjQvH2jRo20b98%2BpaSkKC8vT2%2B%2B%2BaY5XAYGBqp9%2B/Z69dVXdeHCBWVnZ2vhwoVKSkpSixYt1KBBAy1YsEC5ubk6f/68Xn75ZYvwKEnNmzdX48aNNXv2bGVlZSkrK0tz5sxRvXr11KqVAQ4hAgAA/M4uwl/lypX17rvv6scff9R9992nPn36qHbt2nrjjTckSa1bt9arr76qV155RWFhYdq%2BfbvaX3X%2BRkREhEJDQ9WvXz/df//9qlmzpmrVqmVeHh0dLR8fH/Xs2VPdunXTHXfcoblz58rNzU3Lli1TSkqKOnfurP79%2B6tNmzbXfBSNq6urli9frsLCQvXo0UNdunRRQUGB3n33Xbm68jnZAADcVnT%2BrOJkutkT03Bb2fupSVLxBR/u7lJ%2BvjHO%2BfPwNMBn0oSGFl%2BV3KqV/Z/z9%2BOPtq6gbDw8pHr1pJ9%2Bsv9/WPfea%2BsKyqZFi%2BIrk7t1s/9z/nr1snUFpatfv/giihdeMMY5f//615/ruR97rPz3aaccK%2BoCAAA4OI5ZAgAAY3Gww7TljdkDAABwIHT%2BAACAsdD5swrhDwAAGAvhzyrMHgAAgAOh8wcAAIyFzp9VmD0AAAAHQucPAAAYC50/qxD%2BAACAsRD%2BrMLsAQAAOBA6fwAAwFjo/FmF2QMAAHAgdP4AAICx0PmzCuEPAAAYC%2BHPKsweAACAA6HzBwAAjIXOn1UIfwAAwFgIf1Zh9gAAABwInT8AAGAsdP6swuwBAAA4EDp/AADAWOj8WYXwBwAAjMUOw196erqef/55xcfHy8XFRf369dPMmTPl6npt1Pr3v/%2BtVatWKTU1VQEBARo5cqSGDRsmSSoqKlLr1q1lMpnk5ORk3ua///2vvL29y6VWwh8AAICVnnrqKQUGBuqrr77S%2BfPnNX78eK1atUpjx461WO%2Bzzz7Ta6%2B9phUrVqhly5Y6cOCAnnjiCfn5%2BalHjx46fvy4CgoKtH//frm7u1dIrYQ/AABgLBXQ%2BUtNTVVaWprFmL%2B/vwICAkrd9syZM4qPj9fu3bvl5eWlunXrasKECVq4cOE14S8lJUXjxo1TSEiIJCk0NFTh4eH69ttv1aNHDx06dEhNmjSpsOAnEf4AAAAUGxurmJgYi7FJkyYpMjKy1G2PHTsmX19fBQYGmscaNWqks2fP6sKFC6pSpYp5/Mrh3SvS09P17bff6tlnn5UkHTp0SJcuXdLDDz%2Bs5ORkNWrUSNOmTVOrVq2seXkWCH8AAMBYKqDzFxERoa5du1qM%2Bfv7l2nbixcvysvLy2LsyuOcnByL8He1tLQ0/fWvf1WzZs3Up08fSZKnp6datGihKVOmqGrVqnr//fc1ZswYbdmyRXXr1r3Zl1Uiwh8AADCWCgh/AQEBZTrEWxJvb2/l5uZajF15XKlSpRK3OXDggKZMmaKwsDC9/PLL5gtD/va3v1msN2bMGG3atEm7du3S8OHDb6m%2BP7K/y2UAAAAMJCgoSJmZmTp//rx57MSJE6pRo4YqV658zfpxcXEaNWqUHnvsMS1atMji/L7XX39dR44csVg/Pz9fHh4e5VYv4Q8AABiLs3P536zQoEEDtW7dWvPnz1d2draSkpK0dOlSDRo06Jp1t2/frjlz5mjJkiUaPXr0Nct//PFHvfTSS0pLS1N%2Bfr5iYmKUnZ2t%2B%2B%2B/36oar0b4AwAAsNLixYtVWFiobt26aciQIerYsaMmTJggqfiK3i1btkiSYmJidPnyZU2ePFmhoaHm2wsvvCBJevnll1WvXj31799f4eHhio%2BP17vvvitfX99yq5Vz/gAAgLHY4Yc8%2B/n5afHixSUuS0hIMP/80Ucf3XA/vr6%2Bevnll8u1tj8i/AEAAGOxw/BnJMweAACAA6HzBwAAjIXOn1UIf3bOI2aRrUsoXUCANGKE3GPXSKmptq6mdD/%2BaOsKSnflkv4PPpAuXbJtLaUJDrZ1BWUTGirt3y8NGCBddf6NXTLCvyNJuvKF9XFxUmGhbWspzenTtq6gdN7exfcREVJOjm1rwZ8a4Q8AABgLnT%2BrEP4AAICxEP6swuwBAAA4EDp/AADAWOj8WYXZAwAAcCB0/gAAgLHQ%2BbMK4Q8AABgL4c8qzB4AAIADofMHAACMhc6fVZg9AAAAB0LnDwAAGAudP6sQ/gAAgLEQ/qzC7AEAADgQOn8AAMBY6PxZhdkDAABwIHT%2BAACAsdD5swrhDwAAGAvhzyrMHgAAgAOh8wcAAIyFzp9VmD0AAAAHQucPAAAYC50/qxD%2BAACAsRD%2BrMLsAQAAOBA6fwAAwFjo/FmF8AcAAIyF8GcVZg8AAMCB0PkDAADGQufPKsweAACAA6HzBwAAjIXOn1UIfwAAwFgIf1Zh9gAAABwInT8AAGAsdP6swuwBAAA4EDp/AADAWOj8WYXwBwAAjIXwZxVmDwAAwIHQ%2BQMAAMZC588qzB4AAIADofMHAACMhc6fVRx69rp27armzZsrNDRUoaGhCgkJUf/%2B/bVhwwbzOqGhodq3b58NqwQAABacncv/ZqX09HRNmDBBYWFhCg8P10svvaTCwsIS1921a5f69u2rkJAQ9ezZU1988YXF8hUrVqhTp04KCQnRiBEjdPLkSavru5pDhz9Jmjt3rhISEpSQkKD4%2BHhNnDhRr7zyit5%2B%2B21JUkJCgsLCwmxcJQAAsGdPPfWUvL299dVXXykuLk7ffPONVq1adc16p0%2BfVmRkpKZMmaJ9%2B/YpMjJSTz31lFJSUiRJmzdv1po1a7Ry5Urt3btXTZs21eTJk2UymcqtVg77XsXd3V0PPPCAMjMz9eKLL2ro0KFq3bq1Vq9erfDwcJ04cUKvvvqqjh49qoyMDNWpU0fPPPOMunTpIklasmSJ4uLilJubq7p162rChAnq1q1bmZ8/NTVVaWlpFmP%2BLi4KqF69XF9nubvjDst7e%2BfhYesKSufmZnlvz0JDbV1B2dx1l%2BW9PXM1yFuzi4vlvT3z9rZ1BaXz9LS8x/VVwGHfEv8G%2B/srICCg1G3PnDmj%2BPh47d69W15eXuYMsHDhQo0dO9Zi3c2bNyssLEzdu3eXJPXq1UubNm1SbGysJk%2BerPXr12vo0KEKCgqSJE2bNk3r16/X3r171a5du3J5rQZ5h7m9OnfurOeff1779%2B%2B3GI%2BMjFS3bt0UExMjk8mk6OhozZkzR126dNGePXsUGxurTZs2yd/fX7GxsXruuefUqVMnuZXxD3hsbKxiYmIsxiZNnKjIESPK7bVVqN69bV3Bn0/NmrauoHR/%2BHdi99autXUFfz5Vqti6gtJVq2brCsquUSNbV%2BCQSvwbPGmSIiMjS9322LFj8vX1VWBgoHmsUaNGOnv2rC5cuKAqV/0bOX78uIKDgy22b9y4sRITE83Lx40bZ17m5uamBg0aKDExkfBXkar9/iaRmZlpMb58%2BXIFBgbKZDIpOTlZVapUMbdpPTw89Ntvv2n9%2BvXq0qWLBg8erIiICDk5OZX5eSMiItS1a1eLMf9PP5XWrLHyFVWwO%2B4oDn5bt0oZGbaupnT33WfrCkrn5lYc/M6dkwoKbF3NjQ0YYOsKyuauu4qD39Ch0u9vsnZr505bV1A2Li7Fwe/CBenyZVtXc2Nnz9q6gtJ5ehYHvxMnpLw8W1dTuqZNbfbUJpX9b2tZlfg32N%2B/TNtevHhRXl5eFmNXHufk5FiEv5LW9fT0VE5OTpmWlwfCXwkyfg8w1f9wuDUxMVETJkxQWlqaGjVqpDvuuMN8DD40NFRLlizRmjVr9M9//lOenp4aMWKExo8fL%2BcytqcDAgKubS9/8omUmmr9i7odMjKMUeulS7auoOwKCuy/3oQEW1dwcxIT7b/m65wkbrcuX7b/msvxD2eFy8szVr02UFRU/vss8W9wGXl7eys3N9di7MrjSpUqWYx7eXkp7w/hPi8vz7xeacvLg8Nf8FGSzz//XN7e3mrZsqV5LCUlRVOmTNHUqVO1Z88evf/%2B%2B%2BrTp495%2BdmzZ1W9enWtXLlS8fHxWrBggZYtW6bdu3fb4iUAAIDbJCgoSJmZmTp//rx57MSJE6pRo4YqV65ssW5wcLCOHTtmMXb8%2BHHzOX5BQUEWywsKCnT69OlrDhVbg/B3lfz8fG3btk2vvfaapk6dKh8fH/Oyixcv6vLly%2BZW7PHjx/Xmm2%2Batzt06JDGjh2rxMREubu7m7uG1Yx0ngkAAAZQVFT%2BN2s0aNBArVu31vz585Wdna2kpCQtXbpUgwYNumbdfv36KT4%2BXtu2bVNhYaG2bdum%2BPh49e/fX5L08MMP67333lNiYqIuXbqkRYsWyc/Pr1w/ecThD/vOnj1bUVFRkorP22vYsKHmzp2rXr16WazXsGFDzZgxQ88884xyc3NVo0YNDRkyRAsXLtSPP/6oHj166PTp0xo/frx%2B/fVXVa9eXbNmzbLoHgIAgD%2BnxYsXa968eerWrZucnZ01YMAATZgwQVLxqWFz585Vv3791KhRI7355puKjo7Wc889p9q1a2vJkiW68847JUmDBg1SVlaWJk6cqIyMDDVv3lzLly8v88WjZeFkKs8PjkH5W7TI1hWULiBAGjGi%2BMIUI5zz16%2BfrSsonYeHVK%2Be9NNP9n/OXzkeiqhQoaHFVya3amX/5/wZ4d%2BRVPyRNNWqSb/%2Bav/n/J0%2BbesKSuftXXwRxeHDxjjnr00bmz11RbwtGuFTwMqLw3f%2BAACAsVTEBR%2BOhHP%2BAAAAHAidPwAAYCh0/qxD5w8AAMCB0PkDAACGQufPOoQ/AABgKIQ/63DYFwAAwIHQ%2BQMAAIZC5886dP4AAAAcCJ0/AABgKHT%2BrEP4AwAAhkL4sw6HfQEAABwInT8AAGAodP6sQ%2BcPAADAgdD5AwAAhkLnzzqEPwAAYCiEP%2Btw2BcAAMCB0PkDAACGQufPOoQ/AABgKIQ/63DYFwAAwIHQ%2BQMAAIZC5886dP4AAAAcCJ0/AABgKHT%2BrEP4AwAAhkL4sw6HfQEAABwInT8AAGAodP6sQ%2BcPAADAgdD5AwAAhkLnzzqEPwAAYCiEP%2Btw2BcAAMCB0PkDAACGQufPOnT%2BAAAAHAidPwAAYCh0/qxD%2BAMAAIZC%2BLMOh30BAAAcCJ0/O5cyfJqtSyiVq6tUXVJ6rxEqLLR1NaULbOZv6xJK16KFtHOn9Pjj0v/%2BZ%2Btqbiw11dYVlI3r7293O3fK7n9RAwJsXUHZhIZK%2B/dL3bpJCQm2rubGli61dQWl8/OTmjaVjhyRzp%2B3dTWla9PGZk9N5886dP4AAAAcCJ0/AABgKHT%2BrEP4AwAAhkL4sw6HfQEAABwInT8AAGAodP6sQ%2BcPAADAgdD5AwAAhkLnzzqEPwAAYChGC385OTmKiorS559/rsLCQnXr1k2zZ89WpUqVSlx/%2B/btWrp0qZKSkuTr66uBAwdqwoQJcnYuPmDbs2dPnT171vxYkuLi4tSoUaMy1UP4AwAAqEBRUVE6d%2B6ctm/frsuXL%2Bupp55SdHS0Zs%2Befc2633//vWbMmKF//OMfuu%2B%2B%2B3Tq1CmNGzdO3t7eGj16tLKzs3Xq1Cnt3LlTtWvXvqV6OOcPAAAYSlFR%2Bd9SU1N1%2BPBhi1tqOXyDUW5urj766CNNnjxZvr6%2Bql69uqZPn65NmzYpNzf3mvWTk5P1yCOPqEuXLnJ2dlajRo10//3369tvv5VUHA59fX1vOfhJdP4AAAAUGxurmJgYi7FJkyYpMjKy1G3z8vKUkpJS4rLc3FwVFBQoODjYPNaoUSPl5eXp9OnTuvvuuy3W79Gjh3r06GGx7y%2B//FJ9%2B/aVJB06dEheXl4aPny4jh07ptq1aysyMlJdunQp82sl/AEAAEOpiHP%2BIiIi1LVrV4sxf/%2ByfRf8wYMHNXLkyBKXTZkyRZLk7e1tHvPy8pIkXbx48Yb7zc7O1pQpU%2BTp6alRo0ZJkpycnNS8eXM9/fTTqlWrlj755BNFRkbqvffeU0hISJnqJfwBAABDqYjwFxAQoICAgFvaNjw8XEePHi1x2ZEjR/TGG28oNzfXfIHHlcO9Pj4%2B193nyZMnNXnyZFWvXl2rV682rzt27FiL9fr166ePP/5Y27dvL3P445w/AACACnLnnXfKzc1Nx48fN4%2BdOHFCbm5uatCgQYnb7Nq1S4MHD1bHjh21cuVKVa1a1bxs5cqV%2BuabbyzWz8/Pl4eHR5lrIvwBAABDqYgLPiqKl5eXevbsqejoaGVkZCgjI0PR0dHq06ePPD09r1n/wIEDmjhxop599lnNnDlTrq6WB2nPnTunuXPnKikpSYWFhYqLi1NCQoIeeuihMtdE%2BAMAAKhAs2fPVoMGDdS3b189%2BOCDqlOnjl544QXz8t69e2vZsmWSpGXLlqmwsFAvvfSSQkNDzbcrh3tnzJihTp06aejQoQoLC9O6dev09ttvq379%2BmWuh3P%2BAACAoRjtQ559fHwUFRWlqKioEpdv3brV/POVEHg97u7umjVrlmbNmnXL9RD%2BAACAoRgt/NkbDvsCAAA4EDp/AADAUOj8WYfOHwAAgAOh8wcAAAyFzp91CH8AAMBQCH/W4bAvAACAA6HzBwAADIXOn3Xo/AEAADgQOn8AAMBQ6PxZh/AHAAAMhfBnHQ77AgAAOBA6fwAAwFDo/FmH8AcAAAyF8GcdDvsCAAA4EDp/AADAUOj8WYfOHwAAgAOh8wcAAAyFzp91CH8AAMBQCH/W4bAvAACAA6HzBwAADIXOn3VsHv66du2qtLQ0uboWl2IymeTj46O%2BffvqmWeekbMzzUkAAIDyYvPwJ0lz587VwIEDzY%2BPHj2qUaNGycvLS5MnT7ZhZQAAwN7Q%2BbOOXbbVmjRpojZt2ujIkSPKz8/XG2%2B8oW7duqlt27YaN26czpw5Y7Huiy%2B%2BqPDwcD355JPKzs7W1KlTFR4ervbt22vMmDE6ceKEef0NGzaod%2B/eatWqlfr27astW7aYl40YMUKLFi3SsGHDFBoaqp49e2rbtm3m5fv379fIkSPVoUMHNW/eXAMHDtSBAwckSYWFhZozZ47at2%2Bv8PBwDR06VN99991tmC0AABxLUVH53xyJXXT%2BrlZQUKD9%2B/drz549ioyM1Ouvv649e/Zo1apVCggI0IoVKzR69Ght27ZNHh4ekqSffvpJX375pQoKCvTOO%2B8oOztbu3btkrOzs1544QVFR0frrbfe0qZNm/TKK68oJiZGbdu2VXx8vCZNmiQvLy/df//9kqT169fr3XffVePGjfXmm2/qhRdeULdu3WQymTR%2B/HhNnjxZjz76qPLy8jRr1iy9%2BuqrWrt2rT788EMlJCToP//5jypVqqTFixdr7ty5FuGyNKmpqUpLS7MYc3Hxl79/QPlNcAVwcbG8t3stWti6gtIFBVne2zNXu3sbKZmRflFDQ21dQdncdZflvT3z87N1BaXz9bW8ByqIXbxrz507V/Pnzzc/rlGjhh5//HENHz5crVq10uLFi1W3bl1J0sSJE7V%2B/Xp9%2BeWX6tGjhySpT58%2B8vLykpeXlzw9PZWYmKgPPvhA7du31/z5883nDW7cuFERERG65557JEn33HOPIiIitG7dOnP469Gjh/7yl79Ikh566CEtW7ZM6enpCgwMVGxsrOrXr69Lly4pOTlZvr6%2BOnTokCTJ09NTP//8s%2BLi4tSpUydNmTJFU6dOval5iI2NVUxMjMXYxImTNHly5M1OqU0Y5v1q505bV1B2y5bZuoI/nypVbF1B6fbvt3UFN2ftWltX8Ofy%2B98jXJ%2BjderKm12Ev9mzZ1uc83dFenq6cnJyNGXKFIsLPwoKCpScnGx%2BHBDwf52xcePGyd3dXXFxcZo3b57q1q2radOm6YEHHtD58%2BfNIfKKOnXq6PPPPzc/9vf3N/985SKUoqIiubi4aO/evRo3bpxycnLUuHFjubq6ymQySZJ69%2B6tgoICbdiwQa%2B99pqqV6%2BuJ598Uo8%2B%2BmiZ5yEiIkJdu3a1GHNx8Vd6epl3YRMuLsXBLzNTunzZ1tWUrvqQbrYuoXRBQcXB78knpWPHbF3NjcXF2bqCsnFxKQ5%2BFy7Y/y9qNwP8jkrFHb%2B1a6WhQ6XERFtXc2PPPmvrCkrn61sc/D79tPgN1d4NHmzrCnCL7CL8XU%2B1atXk4eGhd955RyEhIebxkydPKjAw0PzYycnJ/PPRo0fVtWtXjRo1SllZWVq7dq2mTp2qPXv2qE6dOvrpp58sniMpKcki8F3PwYMHFRUVpXXr1qlZs2aSpHfeeUenTp2SJJ06dUpNmzbVgAEDlJeXp08%2B%2BUQzZ85UWFiYgsp46C4gIMAiyEpSSopUWFimzW3u8mWD1Pq//9m6grI7dsz%2B6zXEf/SrGOEXNSHB1hXcnMRE%2B6/5/HlbV1B2mZnGqtcG6PxZxy4v%2BLjC2dlZgwYN0qJFi/TLL7%2BoqKhImzdvVp8%2BfSwu%2Brjahg0bNGPGDKWnp8vHx0c%2BPj7y9vaWu7u7Bg0apNjYWH3zzTe6fPmy9uzZo9jYWD388MOl1pKVlSVnZ2d5enpKkg4cOKDVq1crPz9fkvTFF19o0qRJ%2Bvnnn%2BXp6SlfX1%2B5urqqcuXK5TchAACACz6sZNedP0maOXOmlixZoqFDhyozM1N169bV4sWLzefl/dHTTz%2BtefPmqXfv3rp06ZIaNmyopUuXysPDQz179lR2drZefPFFnT17VoGBgZoxY4YGDBhQah3t27fX0KFDNWzYMBUVFalOnTrmq4PPnz%2BvkSNHKiUlRY888oiys7NVu3Ztvf7666pRo0Z5TwkAAMAtczJdOWkNdiklxdYVlM7VVapeXUpPt/%2BjaZIU2Kz0w/w216JF8YUp3brZ/2HfI0dsXUHZuLpK1apJv/5q/7%2BoAfZ9hb9ZaGjxxSmtWtn/Yd%2BlS21dQen8/IrPo9uwwRiHfcePt9lTL1lS/vuMNMa1leXCrg/7AgAAoHzZ/WFfAACAqznaOXrljfAHAAAMhfBnHQ77AgAAOBA6fwAAwFDo/FmHzh8AAIADofMHAAAMhc6fdQh/AADAUAh/1uGwLwAAgAOh8wcAAAyFzp916PwBAAA4EMIfAAAwlKKi8r9VpJycHD377LMKDw9X69atNWPGDF28ePG668%2BePVvNmjVTaGio%2BRYbG2tevmLFCnXq1EkhISEaMWKETp48eVP1EP4AAIChGC38RUVF6dy5c9q%2Bfbt27Nihc%2BfOKTo6%2BrrrHzp0SFFRUUpISDDfIiIiJEmbN2/WmjVrtHLlSu3du1dNmzbV5MmTZTKZylwP4Q8AAKCC5Obm6qOPPtLkyZPl6%2Bur6tWra/r06dq0aZNyc3OvWT8/P18//vijmjVrVuL%2B1q9fr6FDhyooKEgeHh6aNm2azp49q71795a5Ji74AAAAhlIRnbrU1FSlpaVZjPn7%2BysgIKDUbfPy8pSSklListzcXBUUFCg4ONg81qhRI%2BXl5en06dO6%2B%2B67LdZPTExUYWGhFi9erO%2B%2B%2B06VK1fWww8/rLFjx8rZ2VnHjx/XuHHjzOu7ubmpQYMGSkxMVLt27cr0Wgl/AADA4cXGxiomJsZibNKkSYqMjCx124MHD2rkyJElLpsyZYokydvb2zzm5eUlSSWe95eVlaW2bdtqxIgReu211/TDDz9o4sSJcnZ21tixY3Xx4kXz9ld4enoqJyen1DqvIPwBAABDqYjOX0REhLp27Wox5u/vX6Ztw8PDdfTo0RKXHTlyRG%2B88YZyc3NVqVIlSTIf7vXx8blm/fbt26t9%2B/bmxy1atNBjjz2mbdu2aezYsfLy8lJeXp7FNnl5eeZ9lwXhDwAAGEpFhL%2BAgIAyHeK9WXfeeafc3Nx0/PhxtWzZUpJ04sQJ8%2BHaP/rss890/vx5PfLII%2Bax/Px8eXp6SpKCgoJ07NgxdenSRZJUUFCg06dPWxxWLg0XfAAAAFQQLy8v9ezZU9HR0crIyFBGRoaio6PVp08fc6C7mslk0ssvv6xvvvlGJpNJCQkJWr16tflq34cffljvvfeeEhMTdenSJS1atEh%2Bfn4KCwsrc010/gAAgKEY7Rs%2BZs%2BerQULFqhv374qKChQt27d9Pzzz5uX9%2B7dW3379tWTTz6p%2B%2B%2B/X88%2B%2B6zmzJmjlJQU%2Bfn5KTIyUv3795ckDRo0SFlZWZo4caIyMjLUvDEAsA0AACAASURBVHlzLV%2B%2BXG5ubmWuh/AHAABQgXx8fBQVFaWoqKgSl2/dutXi8SOPPGJx2PdqTk5OGj16tEaPHn3L9RD%2BAACAoRit82dvCH8AAMBQCH/W4YIPAAAAB0LnDwAAGAqdP%2BsQ/gAAgKEQ/qzDYV8AAAAHQucPAAAYCp0/69D5AwAAcCB0/gAAgKHQ%2BbMO4Q8AABgK4c86HPYFAABwIHT%2BAACAodD5sw6dPwAAAAdC5w8AABgKnT/rEP4AAIChEP6sQ/izc4F7PrR1CaWrWlXq3FnVD30p/fabraspXa9etq6gdPXrF9%2B3by/VqWPbWkpz%2BrStKygbb2%2BpWjXp7FkpJ8fW1dzY0qW2rqBs/PyK7599Vjp/3ra1lGbCBFtXULrQUGnwYOnll6WEBFtXU7rx421dAW4R4Q8AABjK/2/vzuOiqv7/gb%2BGHSEdFBBUyg3X3MDdPkqoYSq4IilppeWSqClkmpYoKopbKm7xdUPFfGBokLmkuKdI4paGu8miiIALyDLDzO%2BPfk5NuAwzyPEyr%2Bfj4SPn3Jnh5X0EvOd97jmXnT/DcMEHERERkRFh54%2BIiIgkhZ0/w7D4IyIiIklh8WcYTvsSERERGRF2/oiIiEhS2PkzDDt/REREREaEnT8iIiKSFHb%2BDMPij4iIiCSFxZ9hOO1LREREZETY%2BSMiIiJJYefPMOz8ERERERkRdv6IiIhIUtj5MwyLPyIiIpIUFn%2BG4bQvERERkRFh54%2BIiIgkhZ0/w7DzR0RERGRE2PkjIiIiSWHnzzAs/oiIiEhSWPwZhtO%2BREREREaEnT8iIiKSFHb%2BDMPOHxEREZERYeePiIiIJIWdP8Ow%2BCMiIiJJYfFnGE77EhERERkRdv6IiIhIUtj5Mww7f0RERERGhJ0/IiIikhSpdf6ePHmCkJAQxMfHQ6lUomvXrpgxYwZsbGxKPPfbb79FXFyc1lhBQQE6duyItWvXQqVSwd3dHWq1GjKZTPOc48ePo1KlSjrlYfFHREREkiK14i8kJAR37tzB3r17UVxcjC%2B%2B%2BAILFy7EjBkzSjx31qxZmDVrlubxsWPHEBgYiClTpgAArl27BoVCgaSkJFhYWOiVh9O%2BRERERK9Ifn4%2B4uLiMH78eMjlclSrVg1BQUGIiYlBfn7%2BC1%2BbnZ2NoKAgTJs2Da6urgCACxcuoGHDhnoXfgA7f0RERCQxr6Lzd%2B/ePWRmZmqNOTg4wNHR8aWvLSgoQEZGxjOP5efnQ6FQoEGDBpqxevXqoaCgALdu3ULjxo2f%2B74LFy7E22%2B/DR8fH83YhQsXUFhYiAEDBiAtLQ316tVDYGAg3NzcXprzKRZ/REREJCmvovjbtm0bwsPDtcYCAgIwbty4l7723LlzGDZs2DOPTZgwAQC0rseztrYGAOTl5T33PVNSUhAbG4vo6GitcSsrKzRv3hwTJkxAlSpVsGXLFowYMQKxsbFwcXF5aVaAxR8RERER/Pz84OnpqTXm4OCg02vbtWuHy5cvP/PYpUuXsHTpUuTn52sWeDyd7rW1tX3ue/74449o1apVic7g02v/nhoxYgRiYmJw%2BPBhfPjhhzrlZfFHREREkvIqOn%2BOjo46TfGWVp06dWBubo5r166hRYsWAIDr16/D3NwctWvXfu7r9u3bh%2BHDh5cYX7JkCby8vNCkSRPNWFFRESwtLXXOxAUfRERERK%2BItbU13n//fSxcuBDZ2dnIzs7GwoUL0bt3b1hZWT3zNTk5Obh%2B/TratGlT4tiVK1cwZ84cZGZmoqioCOHh4cjNzUX37t11zsTij4iIiCRFpSr7P6/SjBkzULt2bXh7e6NHjx6oVasWvv32W83xXr16YfXq1ZrHqampAIDq1auXeK/Q0FC8%2Beab6NOnD9q1a4dTp05h/fr1kMvlOufhtC8RERFJitT2%2BbO1tUVISAhCQkKeeXzXrl1aj5s1a/bcawjlcjlCQ0MNysPOHxEREZERMcrib8uWLWjYsCE2bNggOgoRERGVktSmfV83Rlv8DR48GJGRkVAqlaLjEBEREZUboyv%2BTpw4gaysLEyZMgUqlQp79%2B7VHMvJycHEiRPh7u6Orl27YtOmTWjSpAlSU1ORmpqKhg0bai7CBIDly5dj6NChAAC1Wo3vv/8e3t7eaN26Ndq0aYPAwEAUFBSU%2B7%2BRiIioImPnzzBGt%2BBj06ZNGDRoEKysrDBkyBCsW7cOvXr1AgAEBQVBJpPhwIEDUKlUCAoKQnFxsU7vu3v3bkRGRmLz5s2oXbs2rl%2B/jiFDhiAuLg6%2Bvr46vcczby2jUMDR3r50/8jy9nSTyhdsVvlaeest0QleztlZ%2B7%2Bvs3/tWv9ae7qlwnO2VnitvO7f8089XV1YilWGwrRqJTrByzVqpP1fei5jK9bKmlEVf2lpaTh69KhmefWgQYOwYsUKnDp1Cm%2B99RaOHTuG3bt3a5ZLf/3115rC8GU6d%2B4MNzc3ODk5ITs7Gzk5OZDL5c%2B919%2BzPPPWMmPHYtzAgTq/h1CtW4tOoBsPD9EJdDdmjOgEFU%2B9eqITvFzTpqITlE4p9hcTRscP4a%2BFqCjRCaiCM6riLyoqCkqlEn369NGMKZVKrFu3DqNHjwYA1KpVS3NM13vkAX9P%2By5ZsgQHDx5E1apV0bhxYygUCqjVap3f45m3lvnzT%2BDQIZ3fQwhb278Lv99/B3JzRad5ufh40Qleztn578Jv1Srgzh3RaV7Mz090At1YWf1d%2BF2/Drzul2NcuiQ6gW7k8r8Lv19/BR48EJ3mxQzcGqNcNGr0d%2BE3ZAiQnCw6zcslJQn70uz8GcZoir/CwkJs374dc%2BbMQceOHTXjV65cwciRIzFq1CgAf3cH69Spo/n7U6ampgAAhUKhGcvJydH8feHChUhPT0d8fLzmXn3e3t6lyvjMW8tcuwY8fFiq9xEmN1caWf/6S3QC3d258/rnffJEdILSKSh4/TPfvy86Qek8ePD6Zz5zRnQC3SUnSysvSY7RLPiIi4uDTCaDt7c3nJycNH86d%2B6MBg0aICYmBu%2B%2B%2By4WLFiAhw8f4uHDhwgLC9O8vlq1aqhSpQp27doFtVqNixcvYs%2BePZrjubm5sLS0hKmpKQoLC7Fu3TpcuXJFq1gkIiIiw3HBh2GMpviLioqCt7c3zM3NSxzz8/PDTz/9hDlz5kAmk8HDwwP9%2BvXT3DTZ3NwcFhYWCAkJwe7du%2BHm5oZ58%2BZh0KBBmvf44osvUFBQgI4dO8LT0xNnz55Fnz59cOXKlXL7NxIRERkDFn%2BGMZpp35iYmOce8/f3h7%2B/P44fP45FixZpbrR8%2BfJlrFq1SrMAxMvLC15eXs98DxcXF2zevLnsgxMRERGVIaPp/Oli/vz5WLVqFZRKJXJzc7Fq1Sp07NgRlpaWoqMRERHR/8fOn2FY/P3LokWLcPbsWbRv3x6enp4wNTXVuu6PiIiISOqMZtpXF66urti4caPoGERERPQCxtapK2ss/oiIiEhSWPwZhtO%2BREREREaEnT8iIiKSFHb%2BDMPOHxEREZERYeePiIiIJIWdP8Ow%2BCMiIiJJYfFnGE77EhERERkRdv6IiIhIUtj5Mww7f0RERERGhJ0/IiIikhR2/gzD4o%2BIiIgkhcWfYTjtS0RERGRE2PkjIiIiSWHnzzDs/BEREREZEXb%2BiIiISFLY%2BTMMiz8iIiKSFBZ/huG0LxEREZERYeePiIiIJIWdP8Ow80dERERkRNj5IyIiIklh588wLP6IiIhIUlj8GYbTvkRERERGhJ0/IiIikhR2/gzDzh8RERGREWHnj4iIiCSFnT/DsPgjIiIiSWHxZxhO%2BxIREREZEXb%2BiIiISFLY%2BTMMiz8iIiKSFBZ/huG0LxEREVE5yM/Ph5%2BfH2JiYl74vHPnzsHX1xetWrWCp6cnoqOjtY7v2LED3bt3R8uWLdG/f3%2BcOXOmVDlY/BEREZGkqFRl/%2BdVu3r1Kvz9/XH27NkXPu/hw4cYOXIk%2Bvbti8TERMyZMwehoaE4f/48ACAhIQEhISGYN28eEhMT4ePjgzFjxiA/P1/nLCz%2BiIiIiF6hEydO4KOPPkK/fv1Qo0aNFz533759kMvl8Pf3h5mZGTp06ABvb29s2bIFABAdHY1evXrB3d0d5ubm%2BPjjj2FnZ4dffvlF5zy85o%2BIiIgk5VV06u7du4fMzEytMQcHBzg6Or70tQUFBcjIyHjmMQcHBzRq1AgHDx6EpaUl1q9f/8L3unr1Kho0aKA1Vr9%2BfWzfvh0AcO3aNQwYMKDE8eTk5JfmfIrF3%2BuuTx/RCV7q3r172LZ8Ofz8/HT6JhFOSuc0IEAa51QCJPX/aZs2ohPoRFLndMwY0QleSnM%2B9%2Bx5/c%2BnYGp12b/n8uXbEB4erjUWEBCAcePGvfS1586dw7Bhw555bMWKFejWrZvOOfLy8mBtba01ZmVlhSdPnuh0XBcs/shgmZmZCA8Ph6enJ39glRGe07LHc1r2eE7LFs%2BnWH5%2BfvD09NQac3Bw0Om17dq1w%2BXLl8skh7W1NR4/fqw1VlBQABsbG83xgoKCEsft7Ox0/hos/oiIiMjoOTo6vhZFd4MGDXD8%2BHGtsWvXrsHV1RUA4OrqiqtXr5Y43rlzZ52/Bhd8EBEREb0munfvjvv372PDhg1QKBQ4efIk4uLiNNf5DRw4EHFxcTh58iQUCgU2bNiArKwsdO/eXeevweKPiIiISKBevXph9erVAAA7OzusW7cOe/bsQbt27TB9%2BnRMnz4d7du3BwB06NABM2bMQHBwMNq2bYtdu3YhIiICcrlc568nU6tfxWWTZEzu3buHbdu2SeOib4ngOS17PKdlj%2Be0bPF8Unlh8UdERERkRDjtS0RERGREWPwRERERGREWf0RERERGhMUfERERkRFh8UdERERkRFj8ERERERkRFn9ERERERoTFHxEREZERYfFHREREZERY/BEREREZETPRAYiIiIxRUVER4uLikJGRAZVKBQBQKBS4cuUKVq1aJTgdVWQs/kgvRUVF%2BOWXX3Dv3r0SP7SWL18uOB3RP4qLi2FqagoAOHz4MOzs7NC8eXPBqSqO69evw9bWFtWrVxcdRXK%2B/vprHD16FHZ2dlAoFKhUqRKuXr2Kvn37io5GFRyLP9LL9OnTcejQIcjlciiVSlhZWeHGjRvw9vYWHU2y8vLyEBUVhVu3bmkK6qdCQ0MFpZK2%2BPh4TJ8%2BHb/99htWrlyJ1atXQyaTYdq0aRg0aJDoeJKUlJSEWbNmYefOnfjhhx8QHBwMMzMzfPfdd%2BjWrZvoeJJy9OhRbN26FdnZ2di6dSsWLVqEdevW4fz586KjUQXHa/5IL4cPH8bWrVsxd%2B5ctGrVCr/88gsCAwNRXFwsOppkTZ06FZGRkSgsLBQdpcJYtWoVvvjiC6hUKmzevBnLly/Hli1bEBERITqaZC1atAgeHh5Qq9VYs2YN5s2bh/DwcCxdulR0NMlRqVSoW7cu6tatiz///BMA4O/vj99//11wMqro2PkjvahUKtSrVw/Z2dmaH1pDhw7lJ38DJCQkYPv27XBxcREdpcK4ffs2Bg0ahEuXLiE/Px%2BdOnWCmZkZ7t%2B/LzqaZN24cQObN2/GjRs3cP/%2BffTs2RMWFhaYOHGi6GiS4%2BTkhJSUFLi4uCArKwtPnjyBiYkJ8vLyREejCo7FH%2Bnlvz%2B08vPzYWJigidPnoiOJlmWlpa8bqqMWVtbIysrC/Hx8XB3d4eZmRmSk5NhZ2cnOppkmZqaIi8vD0eOHEHLli1hYWGBtLQ02Nraio4mOd7e3hgyZAi2b98ODw8PjBkzBpaWlnj77bdFR6MKzjQ4ODhYdAiSnkePHmHWrFno1asX0tLSsH37dvz666%2BoUqUK%2BvfvLzqeJBUWFmLv3r1o1qwZrK2tRcepEB4/foxvvvkGJ06cwJQpU5Cbm4vhw4dj6NChcHd3Fx1Pkm7fvo2VK1ciPj4eo0ePhpmZGUaNGgUvLy%2B88847ouNJiru7O5ydndGgQQN07twZly5d0lyTWqVKFdHxqAKTqdVqtegQJE0///wzPD09UVxcjPnz5yM3NxeTJk3Cm2%2B%2BKTqaJHl6eiI9PR0ymazEsadT61R6CQkJsLS0RMuWLXHnzh1cuHAB7733nuhYklVcXIyffvoJVlZW6NmzJ27duoWDBw9i2LBhmlXVRPR6Y/FHehk3bhxCQ0M51VOGTp069dxjbdu2LcckFZtSqcSVK1fQpEkT0VEqlOzsbFStWlV0DEkZOnToMz/sAUBkZGQ5pyFjwmv%2BSC%2BJiYmwsLAQHaNCadu2LVQqFf744w%2BkpqbC0dERbm5uMDHhonx9HTp0CDNnzkRGRgb%2B/TnXzMwMFy5cEJhMus6dO4cFCxaU2Jg4Ozsbf/zxh%2BB00tKuXTutxzk5OdizZw/8/PwEJSJjwc4f6WX27NlIT0%2BHj48PHBwctD69urm5CUwmXZmZmRg9ejSSk5Mhl8uRk5OD2rVrY926dXBychIdT5J69%2B6NTp06oXLlyrh8%2BTJ69%2B6NFStWYODAgRg6dKjoeJI0cOBAuLi4QC6XIyUlBZ06dUJkZCSGDRuGTz75RHQ8ybt48SLCwsKwceNG0VGoAmPxR3pp1KjRM8dlMhmvT9NTUFAQ1Go1Zs2aBRsbGzx%2B/BjBwcFQKpXcQ01PLVq0wOnTp5GamopvvvkGmzZtwrVr1zBx4kTExcWJjidJLVq0QEJCAlJTUzFnzhysX78eZ8%2BexaxZsxATEyM6XoXg7u6O06dPi45BFRinfUkvFy9eFB2hwjl58iT27NkDGxsbAMAbb7yB4OBgdO3aVXAy6apatSpMTExQo0YNXL9%2BHQBQv3593L17V3Ay6apcuTKsrKzg4uKCq1evAgBatmyJtLQ0wcmkJz09XeuxQqHArl274OzsLCgRGQsWf6SXF22Sy73q9KNSqUpc/C2TyWBubi4okfQ1bNgQS5cuxdixY1GtWjUcPnwYVlZWsLS0FB1NsurWrYutW7di8ODBqFSpEv78809YWFg8d%2BECPZ%2Bnp6fWeVOr1ahSpQpmz54tMBUZA077kl4aNWr03B/2nPbVz8SJE2FmZoaZM2eiUqVKyMvLw4wZM1BcXIwlS5aIjidJ169fx/jx4/H999/j0qVLmlu9TZ48mden6SkpKQljxoxBdHQ0Tpw4gdmzZ8PU1BSDBw/GV199JTqepPy3W2pqaopq1arxAx%2B9ciz%2BSC%2B3b9/WepydnY21a9eiW7du6NOnj6BU0paeno5PPvkEaWlpkMvlePDgAerXr481a9awm1pG7t27h7y8PNSpU0d0FEkrLCyEubk5TExMcP78eTx%2B/BidOnUSHUty/jvt%2B5S5uTmqVKnCHRXolWHxR2Xm0aNHGDBgAH799VfRUSRLqVQiMTER2dnZqFmzJpo1a8aNc/Vw%2BvRpuLu7IzEx8bnPadOmTTkmqliKioqQnZ2t2erlqRo1aghKJE1NmzbVnEO1Wq01m2JiYoKOHTti/vz53D%2BRyhyLPyozubm5ePfdd1/4C5dKunv3LpycnJ7bBQD4S7W03NzckJSUpLk84b8/5rgqXX%2B7d%2B/Gt99%2Bi9zcXM3Y08KF57R0Nm/ejIMHD%2BLrr7%2BGi4sLUlNTERYWhrfffhvvvfceVq1aBTMzMyxYsEB0VKpgWPyRXlavXq31WKFQ4MiRI5DL5YiIiBCUSpr%2BW6j8G3%2BpGiY3NxdnzpzBgwcPYG9vjxYtWqBSpUqiY0laz5498d5776Ffv34wM9NeM1izZk1BqaSpe/fuiI6Ohlwu14w9fPgQAwYMwP79%2B5Gbm4uuXbsiISFBYEqqiLjal/Ry5MgRrcempqZo0qQJPv/8c0GJpGvXrl0AgAMHDghOUrH83//9H8LDw1FYWKgpoitVqoRJkybB399fdDzJunPnDgICAkoUflR6OTk5JS7rkMlkyMrKAgBYW1uXmFonKgv87iW9hIeHP/M6lBs3bghII21P9/RydHREeHi45g4KGzduRE5ODsaPHy84ofRER0dj9erVmDZtGjw8PGBnZ4esrCzEx8djyZIlsLe3h5eXl%2BiYktS0aVNcu3btuRu9k%2B7%2B97//ITAwENOmTUONGjWQnp6OsLAwdOrUCUVFRVixYgWaNm0qOiZVQCz%2BSC/dunVDUlKS1lhxcTF8fX25M72e5s6di7Nnz2ru69m0aVPMmzcPRUVFmDx5suB00hIVFYXQ0FB0795dM1a9enUMHjwYVapUwaZNm1j86cnNzQ0ff/wxevToAXt7e61jAQEBglJJ04wZMxAYGAgvLy/NJR8eHh6YM2cOfv/9dxw6dAiLFy8WnJIqIl7zRzr766%2B/MHLkSAB/b/Xy5ptvah1/8uQJKleurJnGpNLp1KkT4uLitDqq9%2B/fR9%2B%2BfXHs2DGByaSnVatWSExMfObUZFFRETw8PPDbb78JSCZ9z7snskwmQ2RkZDmnqRgyMjJw9%2B5dqNVqxMTEIDY2FmfPnhUdiyowdv5IZ2%2B99Ra%2B/PJL5OTkICQkBJ9%2B%2BqnWcUtLS7Rr105QOukrLCwssRjB1tYWSqVSUCLpkslkz70mzcLCAgUFBeWcqOLYtGmT6AgVTkpKCtauXYvDhw/D1dUVX375pehIVMGx%2BKNS6datGwCgVq1a6NChg%2BA0FUvr1q0RGhqKadOmwcLCAoWFhQgLC4Obm5voaERa9u/fj23btiEtLQ0ODg4YOHAgvL29RceSFJVKhT179mD9%2BvW4evUqlEol1qxZg//973%2Bio5ERYPFHenF3d8fOnTtx7949zWo0hUKBK1euYPny5YLTSdO0adMwYsQIuLm5wc7ODjk5OahTp06JbXXo5ZRKJXbu3Pnc48XFxeWYpmKJi4vDzJkz4efnB09PT9y%2BfRvBwcEoKCiAr6%2Bv6HiSsHHjRkRGRkKlUmHw4MGIiIhAjx490KBBA9HRyEiw%2BCO9TJ8%2BHYcOHYJcLodSqYSVlRVu3LjBT/8GcHFxwe7du5GUlITMzEw4OTmhefPm3FJDD/b29li2bNlzj1erVq0c01QsERERCA8PR/v27TVjXbp0waxZs1j86Sg0NBRDhgzBlClTeAs3EoK/VUgvhw8fxtatW5GTk4OtW7di0aJFiIiI4GbEBnj06BFmzpyJzz//HG3atMHSpUuxdetWBAcHw8bGRnQ8SYmPjxcdocJKT08vcW1v27ZtcffuXUGJpOebb75BVFQUunTpgkGDBmHIkCElNngnepVMRAcgaVKpVKhXrx7q1q2rKfiGDh2KU6dOCU4mXcHBwXj48KFmt//evXvj8ePHmDt3ruBkRP9wcnIqcQvHxMRE3oKwFPz9/bFr1y4sXrwY165dQ/fu3fHo0SOcOHGClyRQuWDnj/Ti5OSElJQUuLi4ICsrC/n5%2BTAxMcGTJ09ER5Os3377DQcOHNB0%2BerVq4eFCxdq7VVHJNpHH32EsWPHws/PDy4uLrh9%2Bza2bduGqVOnio4mOR06dECHDh2QlpaGqKgozJs3D2FhYfDx8cGUKVNEx6MKjMUf6aVXr1748MMPER0djc6dO2Ps2LGwsLBAkyZNREeTLJVKVeJTv1qtLnH7JyKRfH19YWpqipiYGOzfvx81a9bE7Nmz0aNHD9HRJKtmzZr48ssvMWHCBMTGxiIqKkp0JKrguMkz6e3nn3%2BGp6cniouLMX/%2BfOTm5mLSpEklNn8m3QQFBSEvLw9Tp06Fs7Mz7ty5g7CwMNja2mLevHmi4xE9U25uLiwsLLhwgUhCWPyRXsaNG4fQ0FDY2tqKjlJhZGdnY8KECUhMTNRc/N2xY0csXLgQdnZ2gtMR/e369etYvHgxVqxYgV9//RUTJ06EjY0NVq5cCXd3d9HxiEgHLP5IL%2B3bt8eRI0f4af8VSE9P12z1Ur16ddFxiLSMGDECjo6OmDt3Lt5//330798fNjY22LlzJ6Kjo0XHIyIdsPgjvcyePRvp6enw8fGBg4OD1jYFvCOFfv67gvLf2rRpU45JiJ7vnXfewcGDB5GRkQEvLy8kJCTAxsYG7u7uSEpKEh2PiHTABR%2Bkl82bNwMouZ%2BaTCbjXn96Gjp0aIkxExMTODs748CBAwISEZWkVCqhVqtx/PhxNG3aFLa2tsjOzoalpaXoaESkIxZ/pJeLFy%2BKjlDhJCcnaz3Ozs7GihUrULNmTUGJiErq0KEDxo0bh%2BTkZIwYMQIpKSmYPHkyPDw8REcjIh1x2pf0plAocPToUaSnp2PgwIFISUmBq6ur6FgVSkFBAby8vHD48GHRUYgAAHl5eVi3bh0sLS0xcuRIJCcnY/v27QgMDIS1tbXoeESkAxZ/pJeUlBSMGDECT548QV5eHn788Uf4%2BPhgxYoV6NKli%2Bh4FUZGRgb69OmDkydPio5CBAAYM2YMFixYwJX%2BRBLGaV/Sy5w5c%2BDj44OxY8eibdu2qFu3LoKDg7F06VIWf3r67x0SFAoFTp8%2BjY4dOwpKRFTSmTNnuMqfSOJY/JFezp49i2XLlkEmk2lW%2Bvbv35%2BbEZchS0tLDB06FH5%2BfqKjEGn07t0b48ePh7e3d4mV/lyVTiQNLP5ILzY2NsjOzoaTk5NmLDMzE5UrVxaYStpCQ0NFRyB6qacr/Q8dOqQ1zpX%2BRNLB4o/00rt3bwQEBCAoKAgqlQoXL17EggUL0LNnT9HRJCc8PPylzwkICCiHJEQv999V6UQkPSz%2BSC9jx45Ffn4%2BRo0ahcLCQnzwwQfo378/xo8fLzqa5CQkJAD4e2XvhQsX0KRJE9SqVQsZGRk4d%2B4cOnXqJDghkbaioiIcPnwYaWlp8PPzw19//YVGjRqJjkVEOuJqXzKIWq1GZmYm7O3tYWJiIjqOpE2ZMgUtWrTA4MGDNWM7duzAvn37sGrVKoHJiP5x%2B/ZtDB8%2BHAqFAo8ePUJMTAx69%2B6N8PBwvPvuu6LjEZEOWPyR3nbu3InY2FhkZmbC2dkZgwYNQrdu3UTHkiw3Nzf8/vvvWkV0cXExlRoAXQAABglJREFUWrdujTNnzghMRvSPUaNGoUWLFhgzZgzatm2LxMRE7NixA5GRkdixY4foeESkA7ZqSC/ff/89QkND0bhxY3zwwQeoW7cupk6dyhu7G6Bq1aol7u977NgxODo6CkpEVNLZs2fx6aefaq3079OnD1JSUgQnIyJd8Zo/0svmzZsRERGB5s2ba8a8vLwwefJk%2BPr6CkwmXaNGjcJnn30GLy8v1KhRAykpKdi/fz/mz58vOhqRxhtvvIH79%2B%2BjRo0amrHMzExUqVJFYCoiKg12/kgveXl5aNy4sdZYs2bN8PjxY0GJpM/X1xdr1qyBmZkZ/vjjD1hbW2PDhg14//33RUcj0vD29kZAQACOHz8OlUqF8%2BfPIygoCL169RIdjYh0xGv%2BSC/Tp0%2BHXC7HpEmTNNeorVu3Djdv3kRISIjgdNKWlZWFtLQ0ODg4wNnZWXQcIi0KhQJLlizB1q1bkZ%2BfDysrKwwcOBCTJ0/mnT%2BIJILFH%2BllyJAhSEpKgr29PWrVqoV79%2B4hPT0dzs7OWr8A9u7dKzCltOTm5uKrr75CfHy8ZqxDhw747rvvuHk2vRbCw8Nx8eJFvPPOO/D390d2djbs7Oy07vJBRK8/XvNHeunXrx/69esnOkaFsmjRIuTl5eHnn39GrVq18Ndff2Hu3LlYsGABu6kkXFhYGHbu3InWrVtj2bJlyMvLw8iRI0XHIiI9sPNH9Jrw8PDAjz/%2BiGrVqmnGMjMz4ePjgxMnTghMRgR07twZa9euhaurKxISEjB79mzExcWJjkVEemDnj/Ry8eJFLF68GGlpafjv5wdO9eonPz8fb7zxhtZY5cqVoVKpBCUi%2Bsfjx4/h6uoKAHB3d0dGRobgRESkLxZ/pJcpU6agTp06GD58OK/3KSMtWrTA0qVLERQUBJlMBrVajaVLl6JZs2aioxFpbT5uZsZfHURSxu9g0ktqaipiYmJgbm4uOkqFERgYiGHDhiE2NhY1a9ZEWloaZDIZ1q9fLzoaUYkOPxFJF4s/0ou7uzuSk5PZlSpDLi4u2Lt3Lw4cOICsrCzUrFkTXbp0ga2trehoRFAqldi5c6fmsUKh0HoMAH379i3vWESkBy74IL1cunQJH330ETp06FBiZ3%2BuTNWPp6cnYmNjWezRa8nT0/OFx2UyGQ4cOFBOaYjIEOz8kV5CQ0Mhl8thbm6OgoIC0XEqjPz8fBZ/9Fr69/6TRCRtLP5ILxcuXMCxY8dYqJShdu3awdfXF507d4ajo6PWsYCAAEGpiIioomHxR3pxcXFhl6qMpaamwsXFBTdv3sTNmzc141xNTUREZYnFH%2Bll4MCBGD58OHx9fSGXy7UKFG9vb4HJpCk8PBy2traa22YRERG9KlzwQXrp0qXLM8dlMhkOHTpUvmEk7t%2B3zUpISMCIESN42ywiInplWPwRCcbbZhERUXkyeflTiJ7twYMH2LRpE%2BbPn4/c3FwcOXJEdCRJ4m2ziIioPLH4I71cunQJXl5eiI2NxQ8//ICcnByMHTu2xKav9HK8bRYREZUnFn%2Bkl9DQUAQFBSE6OhpmZmZwcXFBeHg4IiIiREeTHF55QURE5YltBtLL5cuX0b9/fwD/bEXSpUsXTJo0SWQsSeJts4iIqDyx%2BCO92NnZ4ebNm6hfv75m7NatW7C3txeYSprs7e2xbNkyzWM7OzutxzKZjMUfERGVGRZ/pJfBgwdj9OjRGDNmDJRKJfbt24eVK1fC19dXdDTJ4W2ziIioPHGrFyqV06dPw93dHWq1GpGRkYiKikJ6ejqcnJzg6%2BuLzz77jHekICIieo2x%2BKNScXNzQ1JSkugYREREpCeu9qVS4WcFIiIiaWPxR6XCKV0iIiJp47QvlUrjxo1Ro0aNFz7nwIED5ZSGiIiISourfalUzM3NERAQIDoGERER6YnFH5WKmZkZ%2BvXrJzoGERER6YnX/FGp8CoBIiIiaWPxR6Xi4%2BMjOgIREREZgAs%2BiIiIiIwIO39ERERERoTFHxEREZERYfFHREREZERY/BEREREZERZ/REREREaExR8RERGREWHxR0RERGRE/h/OsEy7vfeXGAAAAABJRU5ErkJggg%3D%3D\" class=\"center-img\">\n",
       "    <img 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HhoQkTJuj06dPX7STeKN7nr6ozwpu93nyzdPiwFBwsHT9u72rKtmePvSsom7u71LKl9OOP0sWL9q7m%2Bj76yN4VlE/NmtK4cdKiRdJvv9m7muubOtXeFZSfs7N02aeyVFnffmvvCsrm6SmFhRX/jsrPt3c1ZevUyX7PXQl/N8YuWqQlS5ZYjY0dO1ZRUVHlOr5jx47q1auXXF1drxsAjxw5opCQEKuxpk2bWj77/MiRIxo5cqRlm5ubmxo3bqykpCTdcccd5X0510X4g%2B18fSUXl%2BLvqBguLsW/3Fxc7F3JX4enZ3FQ8fS0dyV/LUb4B6pR8P%2B9XUVGRlo%2Bd7yEn59fuY8v777nz5%2BXyWSyGvP09FRubm65tlcEwh8AAHB4/v7%2B8vf3r/TnMZlMyr%2Bis5ufn2/5nPSytlcErvkDAADG4uxc8V9/kpCQEB0%2BfNhq7MiRIwoODpYkBQcHW20vKCjQiRMnrloqtgXhDwAAGIuBw1/v3r0VHx%2BvrVu3qrCwUFu3blV8fLz69OkjSXr44Ye1du1aJSUl6cKFC1qwYIHq1KmjsLCwCquB8AcAAFCJQkND9dF/b44LCgrSG2%2B8oeXLl6tdu3ZaunSpFi9erJtvvlmS1L9/fw0fPlxjxozRHXfcoYMHD2r58uVyc3OrsHq45g8AABjLn9ip%2ByMOHTpk9TgxMdHqcadOndTpGndLOzk56YknntATTzxRafVV7dkDAABAhaLzBwAAjKWKd/6qOsIfAAAwFsKfTZg9AAAAB0LnDwAAGAudP5swewAAAA6Ezh8AADAWOn82IfwBAABjIfzZhNkDAABwIHT%2BAACAsdD5swnhDwAAGAvhzybMHgAAgAOh8wcAAIyFzp9NmD0AAAAHQucPAAAYC50/mxD%2BAACAsRD%2BbMLsAQAAOBA6fwAAwFjo/NmE2QMAAHAgdP4AAICx0PmzCeEPAAAYC%2BHPJsweAACAA6HzBwAAjIXOn02YPQAAAAdC5w8AABgLnT%2BbEP4AAICxEP5swuwBAAA4EDp/AADAWOj82YTZAwAAcCB0/gAAgLHQ%2BbMJ4Q8AABgL4c8mzN5/nThxwt4lAAAAVLpyd/6mTp2qLVu2SJIKCwtVUFAgk8lk2b5ixQqFhYVVfIV/gi%2B%2B%2BEIxMTHatm2bvUsBAABlofNnk3KHvxkzZmjGjBmSpE2bNmnJkiX68ssvK62wP1N2draKiorsXQYAAEClq7DoXFRUpFWrVql79%2B4KCwvT4MGDdeDAAcv2zp07a9WqVerVq5duv/12DRo0SAcOHNCTTz6p0NBQPfjgg9q/f78kacOGDRowYIBeeOEFtWnTRh07dtTy5ctlNpslSRcvXtTChQsVERGh8PBwPfXUU0pOTpZU3JVs1qyZXn75ZYWHh2vMmDEym81atmyZevbsqbCwMLVv317PPvusLly4oG%2B//VYzZsxQcnKyQkNDlZmZqQEDBmjp0qWW2n/55Rc1a9ZMv/766w2fHwAAVDBn54r/ciAVdsPHmjVrtHr1ar355ptq0qSJNm/erMcff1yfffaZatWqJUmKi4vTypUrZTKZ1K9fPw0dOlRvv/22brnlFk2cOFELFy7U22%2B/LUlKSEhQ69attWvXLiUlJWnEiBHy9/fXQw89pJiYGO3Zs0dr1qxRnTp1tHz5cj3xxBP65JNP5Pzf/4CnTp3S119/rYKCAn388cd67733tHbtWt100006cuSIBgwYoK1bt%2Bqhhx7S1KlT9dZbb%2BmLL74o9%2Bu9kfOXV1pamtLT063G/G6%2BWf6%2BvuU%2Bh100b279vaq77HKFKsvT0/p7VVa3rr0rKJ86day/w7F4e9u7grKV/G4ywu8oe3OwsFbRKiz8rVu3TqNGjVKzZs0kSY8%2B%2BqjWr1%2BvLVu2aNiwYZKk/v37KyAgQJJ02223qaCgQLfffrskqUOHDpbgJ0m1a9fWhAkT5Orqqttuu039%2B/fXhx9%2BqD59%2Buj999/X8uXLVb9%2BfUlSVFSUYmNjtXPnTt1zzz2SpN69e8tkMslkMqlLly5q3769AgIClJWVpezsbPn6%2Bio1NfUPv97KOH9sbKyWLFliNTY2OlpR0dF/uM4/1bp19q7gr6dJE3tXULZbb7V3BTemf397V/DX4%2BJi7wrK1qaNvSsov1tusXcF%2BIursPCXkpKi2bNna%2B7cuZaxwsJCnT592vLY97IOlrOzs6pXr271%2BPLr7ho0aCBX1/%2BVV69ePe3YsUPp6em6cOGCxowZY%2BnySVJBQYFSUlIsj/39/S1/LioqUkxMjHbs2KFatWrplltuUUFBgU3X%2BVXG%2BSMjIxUREWE15terl/Tuu3%2B4zj9F8%2BbFwW/gQCkpyd7VlG3tWntXUDZPz%2BLgd%2ByYlJ9v72qub%2BdOe1dQPnXqFAe/uDgpI8Pe1VzfyJH2rqD8XFykS5fsXUXZ9u2zdwVlM5mKg99PP0l5efaupmz2DNR0/mxSYeEvICBAzz77rO6//37L2MmTJ1WzZk3LYycnp3KfLy0tTWaz2XLMqVOnVLduXdWpU0dubm5699131apVK8v%2BR48eVd1rLD/NmzdP6enp%2BvLLL%2BXj4yNJ6tGjxzWf28XFRQUFBZbHv/3223VrvdHzX4u/v79VqJQkHT9%2Bw%2Bexm6QkKTHR3lWUzQi/VEvk51f9es%2BcsXcFNyYjw3g1w3bnz9u7gvLLyzNWvTCcCovOkZGRWrp0qY7/N6zs2LFDDzzwgBISEv7Q%2Bc6cOaOVK1eqoKBAiYmJiouL0yOPPCIXFxf169dPMTExSk1NVVFRkeLi4tS7d2/LTR9XOnfunDw8POTq6qr8/HytWLFCx44dswQ8d3d35eXlqbCwUJLUpEkT7dy5U%2BfOndPZs2f1z3/%2B87q1lnV%2BAABQgbjhwyYV1vl78sknJUl/%2B9vflJ6ersDAQE2fPl133333HzpfYGCgjh8/ro4dO8rHx0cTJ05U9%2B7dJUlTpkzRokWLNGDAAGVnZ%2Bumm27S4sWL1axZM0uAu9zf//53TZkyRXfeeae8vLzUrl079ezZUz///LMk6Y477lD16tXVrl07xcXFafTo0XrhhRfUpUsX1ahRQ1FRUde9GaSs8wMAgArkYGGtojmZS94/pQrZsGHDDd99%2B5d1A0vldhMaKiUkFF//YYRl3z177F1B2Uym4hspDh6s%2Bsu%2BH31k7wrKp25d6emnpWXLqv6y79Sp9q6g/Ixyzd%2B339q7grJ5exf/Hk1IMMayb6dO9nvuO%2B%2Bs%2BHN%2B913Fn7OK4rN9AQCAsdD5swnhDwAAwEaZmZl68cUXFR8fLxcXF/Xu3VuTJk2yeucSSRoxYoR%2B%2BOEHq7Hc3FxFRkZqxowZysjIUIcOHeTl5WXZXrNmzQr9VLUqGf4eeeQRPfLII/YuAwAAVEVVsPM3fvx4BQQE6JtvvlFGRoZGjRqlVatWacSIEVb7XXkTaVxcnJYsWaKxY8dKkvbv36/69etX6kfoVsnwBwAAcE2VEP5K/ZQtP7%2Br34KtFL/88ovi4%2BO1c%2BdOmUwmNWzYUKNHj9b8%2BfOvCn%2BXO3bsmGbOnKmVK1danmf//v1q2bKlbS%2BmDIQ/AADg8Er9lK2xYxUVFVXmsYcPH5avr6/lU8wkKSgoSKdPn9bZs2etPtTictOnT1ffvn0VFhZmGdu/f79%2B//139ezZUxkZGWrVqpUmTZqkpk2b/sFXdjXCHwAAMJZK6PyV%2Bilbfn7lOvb8%2BfMyXfGZzCWPc3NzSw1/e/bs0b59%2BxQTE2M1Xr16dTVt2lQjR46Uu7u7Xn/9dT3%2B%2BOPaunWrqlWrdiMv6ZoIfwAAwOGV%2Bilb5eTl5aW8K96Wq%2BSxt7d3qcfExsaqR48eVwXMBQsWWD2ePHmyNm7cqD179qhLly5/qL4rVb0rJgEAAK6nin3CR3BwsLKzs5Vx2eeGHz16VIGBgaV26woLC/Xvf/9bvXv3thrPycnR3LlzlZKSYhm7dOmSCgsL5enpaVONlyP8AQAAY6li4a9x48Zq27atZs%2BerZycHCUnJ2vp0qXq379/qfsfOnRIFy5cUJs2bazGfXx89O2332ru3Lk6d%2B6czp8/r5kzZ6pBgwZW1wXaivAHAABgo0WLFqmwsFBdu3bVo48%2Bqk6dOmn06NGSpNDQUH102achJScnq0aNGvLw8LjqPEuXLlVRUZHuvfdederUSenp6VqxYoXc3NwqrFau%2BQMAAMZSBd/nr06dOlq0aFGp2xKv%2BOjT%2B%2B%2B/X/fff3%2Bp%2B9avX/%2Bqu44rGuEPAAAYSxUMf0bC7AEAADgQOn8AAMBY6PzZhNkDAABwIHT%2BAACAsdD5swnhDwAAGAvhzybMHgAAgAOh8wcAAIyFzp9NmD0AAAAHQucPAAAYC50/mxD%2BAACAsRD%2BbMLsAQAAOBA6fwAAwFjo/NmE2QMAAHAgdP4AAICx0PmzCeEPAAAYC%2BHPJsweAACAA6HzBwAAjIXOn02YPQAAAAdC5w8AABgLnT%2BbEP4AAICxEP5swuwBAAA4EDp/AADAWOj82YTZAwAAcCB0/gAAgLHQ%2BbMJ4a%2Bq27PH3hWUzWQq/r52rZSXZ99ayiMszN4VlC00VEpIkAYPlhIT7V3N9RnhZ1T6389p585V/%2Bc0ONjeFZRPixbSli1S377SgQP2rub6una1dwVla9RIatNG%2BuQT6eRJe1dTtk6d7PfchD%2BbMHsAAAAOhM4fAAAwFjp/NmH2AAAAHAidPwAAYCx0/mxC%2BAMAAMZC%2BLMJswcAAOBA6PwBAABjofNnE2YPAADAgdD5AwAAxkLnzyaEPwAAYCyEP5swewAAAA6Ezh8AADAWOn82YfYAAAAcCJ0/AABgLHT%2BbEL4AwAAxkL4swmzBwAA4EDo/AEAAGOh82cTZg8AABiLs3PFf9koMzNTo0ePVlhYmMLDwzVr1iwVFhaWuu%2BIESPUqlUrhYaGWr527twpSbp06ZLmzp2ru%2B66S6GhoRo1apTS0tJsru9yhD8AAAAbjR8/Xl5eXvrmm28UFxen7777TqtWrSp13x9//FErV65UYmKi5atz586SpDfffFP/%2Bc9/tHHjRn3zzTfy9PTUP/7xjwqtlWVfAABgLJWw7JuWlqb09HSrMT8/P/n7%2B5d57C%2B//KL4%2BHjt3LlTJpNJDRs21OjRozV//nyNGDHCat/k5GT9/vvvuvXWW0s914YNGzRx4kTVrVtXkvTCCy%2BoY8eOSk5OVsOGDf/gq7NG%2BAMAAA4vNjZWS5YssRobO3asoqKiyjz28OHD8vX1VUBAgGUsKChIp0%2Bf1tmzZ1W9enXL%2BP79%2B%2BXt7a1nnnlG%2B/fvV506dTR8%2BHD1799f586d06%2B//qqQkBDL/nXq1FGNGjV06NAhwh8AAHBQldD5i4yMVEREhNWYn59fuY49f/68TCaT1VjJ49zcXKvwd/HiRbVu3VrPPPOMgoODtXv3bkVFRcnb21uhoaGSJC8vL6tzeXp66vz58zf8mq6F8AcAAIylEsKfv79/uZZ4S%2BPl5aW8vDyrsZLH3t7eVuN9%2B/ZV3759LY87duyovn376tNPP9Vdd91ldWyJ/Pz8q85jC274AAAAsEFwcLCys7OVkZFhGTt69KgCAwNVrVo1q33j4uL06aefWo1dvHhRHh4eqlGjhgICAnTkyBHLtvT0dGVnZ1stBduK8AcAAIylir3VS%2BPGjdW2bVvNnj1bOTk5Sk5O1tKlS9W/f/%2Br9s3JydHMmTN18OBBFRUV6euvv9bHH3%2BsyMhISVK/fv305ptvKjk5WTk5OZo9e7bat2%2BvRo0a2VTj5Vj2BQAAsNGiRYs0Y8YMde3aVc7Ozurbt69Gjx4tSQoNDdX06dPVu3dvDRs2TLm5uRo7dqwyMzPVsGFDzZ07V2FhYZKkMWPGqLCwUIMGDdL58%2BcVHh6u1157rUJrJfwBAABjqYKf8FGnTh0tWrSo1G2JiYmWPzs5OWn06NGWYHglNzc3TZw4URMnTqyUOiXCHwAAMJoqGP6MhNkDAABwIHT%2BAACAsdD5swmzBwAA4EDo/AEAAGOh82cTwh8AADAWwp9NmD0AAAAHQucPAAAYC50/mzB7AAAADoTOHwAAMBY6fzYh/AEAAGMh/NmkUmYvIiJCrVq1UmhoqEJDQ9W6dWt17NhRc%2BfOVVFRUYU8x5AhQ7R48eIKOVdpTp8%2BrdDQUJ0%2BfbrSngMAAODPVmmdv%2BnTp6tfv36Wx4cOHdLw4cNlMpk0bty4ynraClOvXj2rD2IGAABVBJ0/m/xpy77NmjVTu3btdPDgQQ0ZMkT169fX7t27ZTab9fHHHyslJUUxMTHat2%2BfPD09FRERoQkTJqhatWqSpA0bNmjZsmXKysrSfffdp7y8PMu5n3/%2BeUnSnDlzrJ5v9erVCg8PV1ZWlmZIuA4iAAAgAElEQVTPnq0dO3bI2dlZd911l1566SXVqFFDycnJmjVrlr7//nu5u7ure/fumjJlitLS0tS1a1f9%2B9//VoMGDZSSkqL58%2Bdr9%2B7dcnZ21h133KFJkybJ399fu3fv1uTJk/XII49o3bp1unDhgsLDw/XKK6/Ix8en3HOUlpam9PR0qzG/8%2BflX6eOLVNf%2BTw9rb9XdaGh9q6gbM2bW3%2Bvykwme1dQPkb6OW3Rwt4VlE9QkPX3qqxRI3tXULbAQOvvQCX5U8JfQUGBEhIStGvXLkVFRenzzz/Xt99%2Bqw0bNshkMqmgoEBDhw5Vv379tHjxYp07d04TJ07Uc889pzfffFPfffedZsyYobfeekvt2rXT%2BvXr9cEHH%2Bjuu%2B8u1/NHR0fL29tbn3/%2Budzc3BQdHa3p06dr3rx5evLJJxUeHq6dO3cqPz9fTz75pBYvXqzIyEir%2Bp944gm1bNlSn3/%2Bucxms6ZPn66nn35a69evlySlpKQoNTVVX3zxhVJTUzVo0CCtW7dOTz31VLnnKTY2VkuWLLEaGztmjKIM0CmVJDVpYu8Kyichwd4VlN%2B6dfau4K/HCD%2BnW7bYu4Ib89pr9q7gr2XkSHtXUPXR%2BbNJpS77zp492/I4MDBQjz/%2BuAYPHqzPP/9cnTt3VkBAgCQpLi5Obm5umjhxolxcXOTp6akXX3xRDz74oNLT0/XRRx/pvvvu05133ilJGjhwoDZs2FCuOlJSUhQfH6/PPvtMNWvWlFTcIczOzlZCQoJSUlI0ZcoUmUwmeXt7a8mSJVddl7hnzx4lJydr48aNlk7e9OnT1b59e/3444%2BW/caMGSNPT0/ddNNNCg8P1/Hjx29oziIjIxUREWE15peZKR08eEPn%2BdN5ehb/hXrsmJSfb%2B9qyjZ4sL0rKFvz5sXBb%2BBAKSnJ3tVc39q19q6gfIz0czppkr0rKJ%2BgoOLgN368dPSovau5vvbt7V1B2QIDi4PfihXSr7/au5qyvfii/Z6b8GeTSgt/06ZNs7rm70r%2B/v6WP2dmZqpevXpycXGxjDVo0EDS/zpqLa5YBmnYsGG56ihZRq1fv75lzM/PT35%2Bfvrkk09Us2ZNmS5btip53lOnTlnVV7NmTaslXB8fH/n6%2BiolJUV1/rss6%2BfnZ9nu5uYms9lcrhpL%2BPv7W82LJOmHH6TLlrirtPx8Y9RqpGs5k5Kqfr1G%2BG9%2BOSP8nB44YO8KbszRo1W/ZiMtpf76q3TypL2rwF%2BY3aKzk5OT5c/169fX6dOndenSJcvYyf/%2B4Pv5%2BSkwMFDJyclWx/962b%2BKnJ2dVVBQYHmclZVl%2BXPdunUlyequ3SNHjui1115TYGCgfvvtN6vrB/fs2aNVq1ZZPVf9%2BvX122%2B/KScnxzJ27tw5/fbbb1aBDwAA/AmcnSv%2By4FUiVdbcu1eTEyM8vPzlZ6erlmzZumOO%2B5Q/fr19fDDD2v79u366quvVFhYqM2bN2vfvn2W44OCgrRnzx6lpqYqPz9fb7zxhiVcBgQEqEOHDpo3b57Onj2rnJwczZ8/X8nJybrtttvUuHFjzZ07V3l5ecrIyNArr7xiFR4lqVWrVmratKmmTZumc%2BfO6dy5c3rppZfUqFEjtWnT5s%2BbKAAAABtVifBXrVo1vfPOO/r555919913q2fPnqpfv75ef/11SVLbtm01b948zZkzR2FhYdq2bZs6dOhgOT4yMlKhoaHq3bu3unXrprp166pevXqW7TExMfLx8VGPHj3UtWtX1apVS9OnT5ebm5uWLVum1NRU3XPPPerTp4/atWt31VvRuLq6avny5SosLFT37t3VpUsXFRQU6J133pGrK%2B%2BTDQDAn4rOn02czDd6YRr%2BXD/8YO8KymYySbfeWnxjSlW/lkqSwsLsXUHZQkOL70pu06bqX/O3Z4%2B9KygfI/2cPvKIvSsonxYtiu9M7tWr6l/z17WrvSsoW6NGxTdRzJxpjGv%2BVqyw33O/%2B27Fn3PYsIo/ZxXlWFEXAADAwbFmCQAAjMXBlmkrGrMHAADgQOj8AQAAY6HzZxPCHwAAMBbCn02YPQAAAAdC5w8AABgLnT%2BbMHsAAAAOhM4fAAAwFjp/NiH8AQAAYyH82YTZAwAAcCB0/gAAgLHQ%2BbMJswcAAOBA6PwBAABjofNnE8IfAAAwFsKfTZg9AAAAB0LnDwAAGAudP5sQ/gAAgLEQ/mzC7AEAADgQOn8AAMBY6PzZhNkDAABwIHT%2BAACAsdD5swnhDwAAGEsVDH%2BZmZl68cUXFR8fLxcXF/Xu3VuTJk2Sq%2BvVUetf//qXVq1apbS0NPn7%2B2vo0KEaNGiQJKmoqEht27aV2WyWk5OT5Zj//Oc/8vLyqpBaCX8AAAA2Gj9%2BvAICAvTNN98oIyNDo0aN0qpVqzRixAir/bZv365XX31VK1as0O233669e/fqqaeeUp06ddS9e3cdOXJEBQUFSkhIkLu7e6XUSvgDAADGUgmdv7S0NKWnp1uN%2Bfn5yd/fv8xjf/nlF8XHx2vnzp0ymUxq2LChRo8erfnz518V/lJTUzVy5Ei1bt1akhQaGqrw8HB9//336t69u/bv369mzZpVWvCTCH8AAACKjY3VkiVLrMbGjh2rqKioMo89fPiwfH19FRAQYBkLCgrS6dOndfbsWVWvXt0yXrK8WyIzM1Pff/%2B9Jk%2BeLEnav3%2B/Lly4oIcfflgpKSkKCgrShAkT1KZNG1tenhXCHwAAMJZK6PxFRkYqIiLCaszPz69cx54/f14mk8lqrORxbm6uVfi7XHp6uv72t7%2BpZcuW6tmzpyTJ09NTt912m6Kjo1WjRg299957evLJJ/XRRx%2BpYcOGN/qySkX4AwAAxlIJ4c/f379cS7yl8fLyUl5entVYyWNvb%2B9Sj9m7d6%2Bio6MVFhamV155xXJjyPPPP2%2B135NPPqlNmzZpx44dGjx48B%2Bq70pV73YZAAAAAwkODlZ2drYyMjIsY0ePHlVgYKCqVat21f5xcXEaPny4hg0bpgULFlhd37dw4UIdPHjQav%2BLFy/Kw8Ojwuol/AEAAGNxdq74Lxs0btxYbdu21ezZs5WTk6Pk5GQtXbpU/fv3v2rfbdu26aWXXtLixYv1xBNPXLX9559/1qxZs5Senq6LFy9qyZIlysnJUbdu3Wyq8XKEPwAAABstWrRIhYWF6tq1qx599FF16tRJo0ePllR8R%2B9HH30kSVqyZIkuXbqkcePGKTQ01PI1depUSdIrr7yiRo0aqU%2BfPgoPD1d8fLzeeecd%2Bfr6VlitXPMHAACMpQq%2ByXOdOnW0aNGiUrclJiZa/rxly5brnsfX11evvPJKhdZ2JcIfAAAwlioY/oyE2QMAAHAgdP4AAICx0PmzCeGvqvvvBaJVWt260q23Sjt3SmfO2Luasu3ZY%2B8KylbyZqFr10pXvHdUlRMWZu8Kyic0VEpIkAYPli67/qZKKiy0dwU35oMP7F1BmQ4fc7F3CWXy8JAaSTo57EVduGDvasoWbO8C8IcR/gAAgLHQ%2BbMJ4Q8AABgL4c8mzB4AAIADofMHAACMhc6fTZg9AAAAB0LnDwAAGAudP5sQ/gAAgLEQ/mzC7AEAADgQOn8AAMBY6PzZhNkDAABwIHT%2BAACAsdD5swnhDwAAGAvhzybMHgAAgAOh8wcAAIyFzp9NmD0AAAAHQucPAAAYC50/mxD%2BAACAsRD%2BbMLsAQAAOBA6fwAAwFjo/NmE2QMAAHAgdP4AAICx0PmzCeEPAAAYC%2BHPJsweAACAA6HzBwAAjIXOn00IfwAAwFgIfzZh9gAAABwInT8AAGAsdP5swuwBAAA4EDp/AADAWOj82YTwBwAAjIXwZxNmDwAAwIHQ%2BQMAAMZC588mzB4AAIADofMHAACMhc6fTQh/AADAWAh/NmH2AAAAHAidPwAAYCx0/mzC7AEAADgQOn8AAMBY6PzZxKFnLyIiQq1atVJoaKhCQ0PVunVr9enTRxs2bLDsExoaqj179tixSgAAYMXZueK/bJSZmanRo0crLCxM4eHhmjVrlgoLC0vdd8eOHerVq5dat26tHj166KuvvrLavmLFCnXu3FmtW7fWkCFDdOzYMZvru5xDhz9Jmj59uhITE5WYmKj4%2BHiNGTNGc%2BbM0VtvvSVJSkxMVFhYmJ2rBAAAVdn48ePl5eWlb775RnFxcfruu%2B%2B0atWqq/Y7ceKEoqKiFB0drT179igqKkrjx49XamqqJGnz5s1as2aNVq5cqd27d6tFixYaN26czGZzhdXKsu9l3N3ddd999yk7O1svv/yyBg4cqLZt22r16tUKDw/X0aNHNW/ePB06dEhZWVlq0KCBnn32WXXp0kWStHjxYsXFxSkvL08NGzbU6NGj1bVr13I/f1pamtLT063G/Nzd5V%2B7doW%2BzgpXp47196rOZLJ3BWXz9LT%2BXpWFhtq7gvJp3tz6OxyKh4e9Kyibm5v1d1xHJSz7lvp3sJ%2Bf/P39yzz2l19%2BUXx8vHbu3CmTyWTJAPPnz9eIESOs9t28ebPCwsJ07733SpIeeOABbdq0SbGxsRo3bpzWr1%2BvgQMHKjg4WJI0YcIErV%2B/Xrt379Ydd9xRIa%2BV8FeKe%2B65Ry%2B%2B%2BKISEhKsxqOiotS1a1ctWbJEZrNZMTExeumll9SlSxft2rVLsbGx2rRpk/z8/BQbG6sXXnhBnTt3lls5/0%2BOjY3VkiVLrMbGjhmjqKefrrDXVqn697d3BX89TZrYu4KyXfH/SZW3bp29K/jrcXGxdwVlatTI3hWUX9269q7AMZX6d/DYsYqKiirz2MOHD8vX11cBAQGWsaCgIJ0%2BfVpnz55V9erVLeNHjhxRSEiI1fFNmzZVUlKSZfvIkSMt29zc3NS4cWMlJSUR/ipTzZo1JUnZ2dlW48uXL1dAQIDMZrNSUlJUvXp1S5vWw8NDv//%2Bu9avX68uXbrokUceUWRkpJycnMr9vJGRkYqIiLAa8/vgA2nZMhtfUSWrU6c4%2BMXFSRkZ9q6mbJ0727uCsnl6Fge/Y8ek/Hx7V3N9gwfbu4Lyad68OPgNHCj995dslfX99/auoPxcXKRLl%2BxdRZlOplT9gOrmVhz8zpyRCgrsXU3Z7BmozSr/363lVerfwX5%2B5Tr2/PnzMl2xqlTyODc31yr8lbavp6encnNzy7W9IhD%2BSpGVlSVJqn3FcmtSUpJGjx6t9PR0BQUFqVatWpY1%2BNDQUC1evFhr1qzRP//5T3l6emrIkCEaNWqUnMvZnvb397%2B6vbx%2BffFvAiPIyDBGrXl59q6g/PLzq369iYn2ruDGJCUZr2bY7MIFe1dQfgUFxqrXHoqKKv6cpf4dXE5eXl7Ku%2BJ3dcljb29vq3GTyaT8K/5Rn5%2Bfb9mvrO0VweFv%2BCjNl19%2BKS8vL91%2B%2B%2B2WsdTUVEVHR%2BuZZ57Rrl279N5776lnz56W7adPn1bt2rW1cuVKxcfHa%2B7cuVq2bJl27txpj5cAAAD%2BJMHBwcrOzlbGZatfR48eVWBgoKpVq2a1b0hIiA4fPmw1duTIEcs1fsHBwVbbCwoKdOLEiauWim1B%2BLvMxYsXtXXrVr366qt65pln5OPjY9l2/vx5Xbp0ydKKPXLkiN544w3Lcfv379eIESOUlJQkd3d3S9ewZAkZAABUjKKiiv%2ByRePGjdW2bVvNnj1bOTk5Sk5O1tKlS9W/lGvhe/furfj4eG3dulWFhYXaunWr4uPj1adPH0nSww8/rLVr1yopKUkXLlzQggULVKdOnQp95xGHX/adNm2aZs6cKan4ur0mTZpo%2BvTpeuCBB6z2a9KkiZ577jk9%2B%2ByzysvLU2BgoB599FHNnz9fP//8s7p3764TJ05o1KhR%2Bu2331S7dm1NmTLFqnsIAAD%2BmhYtWqQZM2aoa9eucnZ2Vt%2B%2BfTV69GhJxZeGTZ8%2BXb1791ZQUJDeeOMNxcTE6IUXXlD9%2BvW1ePFi3XzzzZKk/v3769y5cxozZoyysrLUqlUrLV%2B%2BvNw3j5aHk7ki3zgGFW/aNHtXULa6daWnny6%2BMcUI1/z17m3vCspmMkm33iodPFj1r/kzyvtghoYW35ncpk3Vv%2BbvGm8MWyUZ5IaPw8eq/g0fHh7FN1GcPGmMa/7%2Bu0ppF5UxP0Z4O6CK4vCdPwAAYCyVccOHI%2BGaPwAAAAdC5w8AABgKnT/b0PkDAABwIHT%2BAACAodD5sw3hDwAAGArhzzYs%2BwIAADgQOn8AAMBQ6PzZhs4fAACAA6HzBwAADIXOn20IfwAAwFAIf7Zh2RcAAMCB0PkDAACGQufPNnT%2BAAAAHAidPwAAYCh0/mxD%2BAMAAIZC%2BLMNy74AAAAOhM4fAAAwFDp/tiH8AQAAQyH82YZlXwAAAAdC5w8AABgKnT/b0PkDAABwIHT%2BAACAodD5sw3hDwAAGArhzzYs%2BwIAADgQOn8AAMBQ6PzZhs4fAACAA6HzBwAADIXOn20IfwAAwFAIf7Zh2RcAAMCB0PkDAACGQufPNnT%2BAAAAHAidPwAAYCh0/mxD%2BAMAAIZC%2BLMNy74AAAAOxMlsNpvtXQSu49Ile1dQPi4uxqk1ONjeFZStRQtpyxapVy/pwAF7V3N9hw/bu4LyM8rPqatBFmVCQ6WEBKlNGykx0d7VXN%2B8efauoGz%2B/tKwYdK770ppafaupmzPPmu3p/7hh4o/Z9u2FX/OqorOHwAAgAMxyD8vAQAAinHNn20IfwAAwFAIf7Zh2RcAAMCB0PkDAACGQufPNnT%2BAAAAHAidPwAAYCh0/mxD%2BAMAAIZitPCXm5urmTNn6ssvv1RhYaG6du2qadOmydvbu9T9t23bpqVLlyo5OVm%2Bvr7q16%2BfRo8eLWfn4gXbHj166PTp05bHkhQXF6egoKBy1UP4AwAAqEQzZ87UmTNntG3bNl26dEnjx49XTEyMpk2bdtW%2BP/74o5577jm99tpruvvuu3X8%2BHGNHDlSXl5eeuKJJ5STk6Pjx4/r3//%2Bt%2BrXr/%2BH6uGaPwAAYChFRRX/lZaWpgMHDlh9pVXAJ63k5eVpy5YtGjdunHx9fVW7dm1NnDhRmzZtUl5e3lX7p6Sk6LHHHlOXLl3k7OysoKAgdevWTd9//72k4nDo6%2Bv7h4OfROcPAABAsbGxWrJkidXY2LFjFRUVVeax%2Bfn5Sk1NLXVbXl6eCgoKFBISYhkLCgpSfn6%2BTpw4oVtuucVq/%2B7du6t79%2B5W5/7666/Vq1cvSdL%2B/ftlMpk0ePBgHT58WPXr11dUVJS6dOlS7tdK%2BAMAAIZSGdf8RUZGKiIiwmrMz8%2BvXMfu27dPQ4cOLXVbdHS0JMnLy8syZjKZJEnnz5%2B/7nlzcnIUHR0tT09PDR8%2BXJLk5OSkVq1a6e9//7vq1aunzz77TFFRUVq7dq1at25drnoJfwAAwFAqI/z5%2B/vL39//Dx0bHh6uQ4cOlbrt4MGDev3115WXl2e5waNkudfHx%2Bea5zx27JjGjRun2rVra/Xq1ZZ9R4wYYbVf79699fHHH2vbtm3lDn9c8wcAAFBJbr75Zrm5uenIkSOWsaNHj8rNzU2NGzcu9ZgdO3bokUceUadOnbRy5UrVqFHDsm3lypX67rvvrPa/ePGiPDw8yl0T4Q8AABhKZdzwUVlMJpN69OihmJgYZWVlKSsrSzExMerZs6c8PT2v2n/v3r0aM2aMJk%2BerEmTJsnV1XqR9syZM5o%2BfbqSk5NVWFiouLg4JSYm6qGHHip3TYQ/AACASjRt2jQ1btxYvXr10v33368GDRpo6tSplu0PPvigli1bJklatmyZCgsLNWvWLIWGhlq%2BSpZ7n3vuOXXu3FkDBw5UWFiY3n//fb311lu66aabyl0P1/wBAABDMdqbPPv4%2BGjmzJmaOXNmqds/%2BeQTy59LQuC1uLu7a8qUKZoyZcofrofwBwAADMVo4a%2BqYdkXAADAgdD5AwAAhkLnzzZ0/gAAABwInT8AAGAodP5sQ/gDAACGQvizDcu%2BAAAADoTOHwAAMBQ6f7ah8wcAAOBA6PwBAABDofNnG8IfAAAwFMKfbVj2BQAAcCB0/gAAgKHQ%2BbMN4Q8AABgK4c82LPsCAAA4EDp/AADAUOj82YbOHwAAgAOh8wcAAAyFzp9tCH8AAMBQCH%2B2YdkXAADAgdD5AwAAhkLnzzZ2D38RERFKT0%2BXq2txKWazWT4%2BPurVq5eeffZZOTvTnAQAAKgodg9/kjR9%2BnT169fP8vjQoUMaPny4TCaTxo0bZ8fKAABAVUPnzzZVsq3WrFkztWvXTgcPHtTFixf1%2Buuvq2vXrmrfvr1GjhypX375xWrfl19%2BWeHh4Xr66aeVk5OjZ555RuHh4erQoYOefPJJHT161LL/hg0b9OCDD6pNmzbq1auXPvroI8u2IUOGaMGCBRo0aJBCQ0PVo0cPbd261bI9ISFBQ4cOVceOHdWqVSv169dPe/fulSQVFhbqpZdeUocOHRQeHq6BAwfqhx9%2B%2BBNmCwAAx1JUVPFfjqRKdP4uV1BQoISEBO3atUtRUVFauHChdu3apVWrVsnf318rVqzQE088oa1bt8rDw0OSdPLkSX399dcqKCjQ22%2B/rZycHO3YsUPOzs6aOnWqYmJi9Oabb2rTpk2aM2eOlixZovbt2ys%2BPl5jx46VyWRSt27dJEnr16/XO%2B%2B8o6ZNm%2BqNN97Q1KlT1bVrV5nNZo0aNUrjxo3TgAEDlJ%2BfrylTpmjevHlat26dPvzwQyUmJurTTz%2BVt7e3Fi1apOnTp1uFy7KkpaUpPT3dasyvdm35%2B/lV3ARDatHC3hWULSjI%2BjscS2iovSson%2BbNrb9XZf7%2B9q6gbLVqWX8HKkmVCH/Tp0/X7NmzLY8DAwP1%2BOOPa/DgwWrTpo0WLVqkhg0bSpLGjBmj9evX6%2Buvv1b37t0lST179pTJZJLJZJKnp6eSkpL0wQcfqEOHDpo9e7blusGNGzcqMjJSd955pyTpzjvvVGRkpN5//31L%2BOvevbtuvfVWSdJDDz2kZcuWKTMzUwEBAYqNjdVNN92kCxcuKCUlRb6%2Bvtq/f78kydPTU6dOnVJcXJw6d%2B6s6OhoPfPMMzc0D7GxsVqyZInV2NgxYxRllKVvFxd7V1A%2BW7bYu4Lye%2B01e1fw12OEn9OEBHtXcGPWrbN3BX8tvXrZu4Iqz9E6dRWtSoS/adOmWV3zVyIzM1O5ubmKjo62uvGjoKBAKSkplsf%2Bl/2LbuTIkXJ3d1dcXJxmzJihhg0basKECbrvvvuUkZFhCZElGjRooC%2B//NLy2O%2ByLlvJTShFRUVycXHR7t27NXLkSOXm5qpp06ZydXWV2WyWJD344IMqKCjQhg0b9Oqrr6p27dp6%2BumnNWDAgHLPQ2RkpCIiIqzG/GrXli5dKvc57MbFxRh1SlLfvvauoGxBQcXBb/x46bLLFqqkDz6wdwXlZ5Sf03bt7F1B%2BTRvXhz8Bg6UkpLsXc31RUfbu4Ky1apVHPy2bJGysuxdTdmGDbN3BfiDqkT4u5aaNWvKw8NDb7/9tlq3bm0ZP3bsmAICAiyPnZycLH8%2BdOiQIiIiNHz4cJ07d07r1q3TM888o127dqlBgwY6efKk1XMkJydbBb5r2bdvn2bOnKn3339fLVu2lCS9/fbbOn78uCTp%2BPHjatGihfr27av8/Hx99tlnmjRpksLCwhQcHFyu1%2Bvv728VZCUZ4y8qozlwwN4VlN/Ro8aqFxUjMdHeFdyYpKSqX3Namr0rKL%2BsLGPVawd0/mxTJW/4KOHs7Kz%2B/ftrwYIF%2BvXXX1VUVKTNmzerZ8%2BeVjd9XG7Dhg167rnnlJmZKR8fH/n4%2BMjLy0vu7u7q37%2B/YmNj9d133%2BnSpUvatWuXYmNj9fDDD5dZy7lz5%2BTs7CxPT09J0t69e7V69WpdvHhRkvTVV19p7NixOnXqlDw9PeXr6ytXV1dVq1at4iYEAABww4eNqnTnT5ImTZqkxYsXa%2BDAgcrOzlbDhg21aNEiy3V5V/r73/%2BuGTNm6MEHH9SFCxfUpEkTLV26VB4eHurRo4dycnL08ssv6/Tp0woICNBzzz2nvuVYBuzQoYMGDhyoQYMGqaioSA0aNLDcHZyRkaGhQ4cqNTVVjz32mHJyclS/fn0tXLhQgYGBFT0lAAAAf5iTueSiNVRNRln2Ncq1VJJUzmV4u2rRovi6n169qv6y7%2BHD9q6g/Izyc%2Bpa5f9dXiw0tPjmlDZtqv6y77x59q6gbP7%2BxdfRvfuuMZZ9n33Wbk%2B9eHHFnzMqquLPWVVV6WVfAAAAVCyD/PMSAACgmKNdo1fRCH8AAMBQCH%2B2YdkXAADAgdD5AwAAhkLnzzZ0/gAAABwInT8AAGAodP5sQ/gDAACGQvizDcu%2BAAAADoTOHwAAMBQ6f7ah8wcAAOBACH8AAMBQiooq/qsy5ebmavLkyQoPD1fbtm313HPP6fz589fcf9q0aWrZsqVCQ0MtX7GxsZbtK1asUOfOndW6dWsNGTJEx44du6F6CH8AAMBQjBb%2BZs6cqTNnzmjbtm36/PPPdebMGcXExFxz//3792vmzJlKTEy0fEVGRkqSNm/erDVr1mjlypXavXu3WrRooXHjxslsNpe7HsIfAABAJcnLy9OWLVs0btw4%2Bfr6qnbt2po4caI2bdqkvLy8q/a/ePGifv75Z7Vs2bLU861fv14DBw5UcHCwPDw8NGHCBJ0%2BfVq7d%2B8ud03c8AEAAAylMjp1aWlpSk9Ptxrz8/OTv79/mcfm5%2BcrNTW11G15eXkqKChQSEiIZSwoKEj5%2Bfk6ceKEbrnlFqv9k5KSVFhYqEWLFumHH35QtWrV9PDDD2vEiBFydnbWkSNHNHLkSMv%2Bbm5uaty4sZKSknTHHXeU67US/gAAgMOLjY3VkiVLrMbGjh2rqKioMo/dt2%2Bfhg4dWuq26OhoSZKXl5dlzGQySVKp1/2dO3dO7du315AhQ/Tqq6/qp59%2B0pgxY%2BTs7KwRI0bo/PnzluNLeHp6Kjc3t8w6S9txqXgAACAASURBVBD%2BAACAoVRG5y8yMlIRERFWY35%2BfuU6Njw8XIcOHSp128GDB/X6668rLy9P3t7ekmRZ7vXx8blq/w4dOqhDhw6Wx7fddpuGDRumrVu3asSIETKZTMrPz7c6Jj8/33Lu8iD8AQAAQ6mM8Ofv71%2BuJd4bdfPNN8vNzU1HjhzR7bffLkk6evSoZbn2Stu3b1dGRoYee%2Bwxy9jFixfl6ekpSQoODtbhw4fVpUsXSVJBQYFOnDhhtaxcFm74AAAAqCQmk0k9evRQTEyMsrKylJWVpZiYGPXs2dMS6C5nNpv1yiuv6LvvvpPZbFZiYqJWr15tudv34Ycf1tq1a5WUlKQLFy5owYIFqlOnjsLCwspdE50/AABgKEb7hI9p06Zp7ty56tWrlwoKCtS1a1e9%2BOKLlu0PPvigevXqpaefflrdunXT5MmT/3979x6W8/3/Afx5d06NopJD5pTjnMrZhoVlKMc0DBubw4QhxthEiJwWOa2vEGquLKaZw5wPI01OYzmbDlS6G0qHu%2B7794efe7sXurvv9O7T/Xxclyv3%2B3Pfd0%2Bf61Kv%2B/X%2BvN8f%2BPn5ISUlBXZ2dpg4cSL69u0LABg0aBCePn2KCRMmQC6Xo1mzZtiwYQNMTU21zsPij4iIiOgNsra2hr%2B/P/z9/V96fO/evRqPP/roI41p33%2BTyWQYNWoURo0apXMeFn9EREQkKVLr/JU1LP6IiIhIUlj86YcLPoiIiIgMCDt/REREJCns/OmHxR8RERFJCos//XDal4iIiMiAsPNHREREksLOn37Y%2BSMiIiIyIOz8ERERkaSw86cfFn9EREQkKSz%2B9MNpXyIiIiIDws4fERERSQo7f/ph54%2BIiIjIgLDzR0RERJLCzp9%2BWPwRERGRpLD40w%2BLv7Lut99EJyialRXg4gJcugRkZYlOU7Ru3UQnKFqtWs%2B/tm0LODqKzVKEm3eMRUfQirn589N6P8kYubmi07yec2Cg6AjacXB4/nXyZCA1VWyWosyYITpB0Vq1AkaOBIKCgAsXRKcp2vTpohOQjlj8ERERkaSw86cfLvggIiIiMiDs/BEREZGksPOnHxZ/REREJCks/vTDaV8iIiIiA8LOHxEREUkKO3/6YeePiIiIyICw80dERESSws6fflj8ERERkaSw%2BNMPp32JiIiIDAg7f0RERCQp7Pzph50/IiIiIgPCzh8RERFJCjt/%2BmHxR0RERJLC4k8/nPYlIiIiMiDs/BEREZGksPOnH3b%2BiIiIiAwIO39EREQkKez86YfFHxEREUkKiz/9cNqXiIiIyICw80dERESSws6fftj5IyIiIjIg7PwRERGRpLDzpx8Wf0RERCQpLP70w2lfIiIiIgPCzh8RERFJCjt/%2BmHnj4iIiMiAsPNHREREkiK1zt%2BzZ8/g7%2B%2BPI0eOID8/H926dcPcuXNhZWVV6LnffvstoqOjNcZycnLQsWNHbNy4EUqlEq6urlCpVJDJZOrnnD59GhUqVNAqD4s/IiIikhSpFX/%2B/v548OABDhw4gIKCAnz55ZdYtmwZ5s6dW%2Bi58%2BfPx/z589WPT506hWnTpmHmzJkAgFu3bkGhUCAuLg5mZmY65eG0LxEREdEbkp2djejoaEyaNAk2NjaoUqUKfH19ERUVhezs7Ne%2BVi6Xw9fXF7Nnz4azszMA4MqVK2jYsKHOhR/Azh8RERFJzJvo/KWmpiItLU1jzN7eHg4ODkW%2BNicnBykpKS89lp2dDYVCgQYNGqjH6tWrh5ycHNy7dw%2BNGzd%2B5fsuW7YM77zzDjw9PdVjV65cQW5uLgYOHIikpCTUq1cP06ZNg4uLS5E5X2DxR0RERJLyJoq/HTt2IDg4WGPMx8cHEydOLPK1ly5dwogRI156bPLkyQCgcT2epaUlACArK%2BuV75mQkIA9e/YgMjJSY9zCwgLNmzfH5MmTUalSJWzfvh2jR4/Gnj174OTkVGRWgMUfEREREby9veHm5qYxZm9vr9Vr27Vrh%2BvXr7/02LVr1xAUFITs7Gz1Ao8X073W1tavfM8ff/wRrVq1KtQZfHHt3wujR49GVFQUjh8/jo8//lirvCz%2BiIiISFLeROfPwcFBqyne4qpTpw5MTU1x69YttGjRAgBw%2B/ZtmJqaonbt2q983cGDBzFq1KhC4ytXroS7uzuaNGmiHsvLy4O5ubnWmbjgg4iIiOgNsbS0xIcffohly5ZBLpdDLpdj2bJl6NOnDywsLF76moyMDNy%2BfRtt2rQpdOzGjRtYuHAh0tLSkJeXh%2BDgYGRmZqJHjx5aZ2LxR0RERJKiVJb8nzdp7ty5qF27Njw8PNCzZ0/UrFkT3377rfp47969sX79evXjxMREAEDVqlULvVdAQABq1aqFvn37ol27djh37hw2bdoEGxsbrfNw2peIiIgkRWr7/FlbW8Pf3x/%2B/v4vPb53716Nx82aNXvlNYQ2NjYICAjQKw87f0REREQGxCCLv%2B3bt6Nhw4bYvHmz6ChERERUTFKb9i1rDLb4GzJkCMLCwpCfny86DhEREVGpMbji78yZM0hPT8fMmTOhVCpx4MAB9bGMjAxMmTIFrq6u6NatG7Zu3YomTZogMTERiYmJaNiwofoiTABYvXo1hg8fDgBQqVT4/vvv4eHhgdatW6NNmzaYNm0acnJySv3fSEREVJ6x86cfg1vwsXXrVgwePBgWFhYYOnQoQkND0bt3bwCAr68vZDIZDh8%2BDKVSCV9fXxQUFGj1vvv27UNYWBi2bduG2rVr4/bt2xg6dCiio6Ph5eWl1Xu89NYy2dlwsLMr3j%2BytP3/TuXqr2VdrVqiExTN0VHzaxlWjK2lhDI11fxapr2BvcbeiMqVNb%2BWZa1aiU5QtEaNNL/SKxlasVbSDKr4S0pKwsmTJ9XLqwcPHow1a9bg3LlzePvtt3Hq1Cns27dPvVz666%2B/VheGRencuTNcXFzg6OgIuVyOjIwM2NjYvPJefy/z0lvLTJiAiZMmaf0eQr3m/oRlSjHufyjc55%2BLTlAkCZTSGqpVE51ACyNHik5QPB4eohMUTUrnNDxcdAIq5wyq%2BAsPD0d%2Bfj769u2rHsvPz0doaCjGjRsHAKhZs6b6mLb3yAOeT/uuXLkSR48eReXKldG4cWMoFAqoVCqt3%2BOlt5ZJSgLi4rR%2BDyEsLZ8Xfn/%2BCfz/LWvKtP8sqS%2BTHB2fF34hIcDDh6LTvNb9kd%2BIjqAVU9Pnhd%2BDB4BCITrN69U6ukV0BO1Urvy88IuOBuRy0WleLyhIdIKiNWr0vPAbOhSIjxedpmgCfzex86cfgyn%2BcnNzsXPnTixcuBAdO3ZUj9%2B4cQNjxozB2LFjATzvDtapU0f99xeMjY0BAIp//dbIyMhQ/33ZsmVITk7GkSNH1Pfq8yjmp%2BGX3lpGLgdec%2BPnMiU7WxpZ798XnUB7Dx%2BW%2Bby5uaITFI9CIYHMqamiExSPXF72M1%2B4IDqB9uLjpZWXJMdgFnxER0dDJpPBw8MDjo6O6j%2BdO3dGgwYNEBUVhffffx9Lly7F48eP8fjxYwQGBqpfX6VKFVSqVAl79%2B6FSqXC1atXsX//fvXxzMxMmJubw9jYGLm5uQgNDcWNGzc0ikUiIiLSHxd86Mdgir/w8HB4eHjA9CVXe3t7e%2BOnn37CwoULIZPJ0LVrV/Tv319902RTU1OYmZnB398f%2B/btg4uLCxYvXozBgwer3%2BPLL79ETk4OOnbsCDc3N1y8eBF9%2B/bFjRs3Su3fSEREZAhY/OnHYKZ9o6KiXnls2LBhGDZsGE6fPo3ly5erb7R8/fp1rFu3Tr0AxN3dHe7u7i99DycnJ2zbtq3kgxMRERGVIIPp/GljyZIlWLduHfLz85GZmYl169ahY8eOMJfKXhZEREQGgJ0//bD4%2B5fly5fj4sWLaN%2B%2BPdzc3GBsbKxx3R8RERGR1BnMtK82nJ2dsWWLRLZYICIiMlCG1qkraSz%2BiIiISFJY/OmH075EREREBoSdPyIiIpIUdv70w84fERERkQFh54%2BIiIgkhZ0//bD4IyIiIklh8acfTvsSERERGRB2/oiIiEhS2PnTDzt/RERERAaEnT8iIiKSFHb%2B9MPij4iIiCSFxZ9%2BOO1LREREZEDY%2BSMiIiJJYedPP%2Bz8ERERERkQdv6IiIhIUtj50w%2BLPyIiIpIUFn/64bQvERERkQFh54%2BIiIgkhZ0//bDzR0RERGRA2PkjIiIiSWHnTz8s/oiIiEhSWPzph9O%2BRERERAaEnT8iIiKSFHb%2B9MPOHxEREZEBYeePiIiIJIWdP/2w%2BCMiIiJJYfGnH077EhERERkQdv6IiIhIUtj50w%2BLPyIiIpIUFn/64bQvERERUSnIzs6Gt7c3oqKiXvu8S5cuwcvLC61atYKbmxsiIyM1ju/atQs9evRAy5YtMWDAAFy4cKFYOVj8ERERkaQolSX/5027efMmhg0bhosXL772eY8fP8aYMWPQr18/xMbGYuHChQgICMDly5cBADExMfD398fixYsRGxsLT09PjB8/HtnZ2VpnYfFHRERE9AadOXMGI0eORP/%2B/VG9evXXPvfgwYOwsbHBsGHDYGJigg4dOsDDwwPbt28HAERGRqJ3795wdXWFqakpPvnkE9ja2uKXX37ROg%2Bv%2BSMiIiJJeROdutTUVKSlpWmM2dvbw8HBocjX5uTkICUl5aXH7O3t0ahRIxw9ehTm5ubYtGnTa9/r5s2baNCggcZY/fr1sXPnTgDArVu3MHDgwELH4%2BPji8z5Aou/su6990QnKFJqaip2rF4Nb29vrf6TCCelczp2bJk/p86iA2gpNTUVq1fvgLe3N2rVKtvnFNOni06gFUn935fAOVWfz/37y/75FEylKvn3XL16B4KDgzXGfHx8MHHixCJfe%2BnSJYwYMeKlx9asWYPu3btrnSMrKwuWlpYaYxYWFnj27JlWx7XB4o/0lpaWhuDgYLi5ufEHVgnhOS15PKclj%2Be0ZPF8iuXt7Q03NzeNMXt7e61e265dO1y/fr1EclhaWuLp06caYzk5ObCyslIfz8nJKXTc1tZW6%2B/B4o%2BIiIgMnoODQ5kouhs0aIDTp09rjN26dQvOzs/nWZydnXHz5s1Cxzt37qz19%2BCCDyIiIqIyokePHnj06BE2b94MhUKBs2fPIjo6Wn2d36BBgxAdHY2zZ89CoVBg8%2BbNSE9PR48ePbT%2BHiz%2BiIiIiATq3bs31q9fDwCwtbVFaGgo9u/fj3bt2mHOnDmYM2cO2rdvDwDo0KED5s6dCz8/P7Rt2xZ79%2B5FSEgIbGxstP5%2BMpXqTVw2SYYkNTUVO3bskMZF3xLBc1ryeE5LHs9pyeL5pNLC4o%2BIiIjIgHDal4iIiMiAsPgjIiIiMiAs/oiIiIgMCIs/IiIiIgPC4o%2BIiIjIgLD4IyIiIjIgLP6IiIiIDAiLPyIiIiIDwuKPiIiIyICw%2BCMiIiIyICaiAxARERmivLw8REdHIyUlBUqlEgCgUChw48YNrFu3TnA6Ks9Y/JFO8vLy8MsvvyA1NbXQD63Vq1cLTkf0j4KCAhgbGwMAjh8/DltbWzRv3lxwqvLj9u3bsLa2RtWqVUVHkZyvv/4aJ0%2BehK2tLRQKBSpUqICbN2%2BiX79%2BoqNROcfij3QyZ84cHDt2DDY2NsjPz4eFhQXu3LkDDw8P0dEkKysrC%2BHh4bh37566oH4hICBAUCppO3LkCObMmYPffvsNa9euxfr16yGTyTB79mwMHjxYdDxJiouLw/z587F792788MMP8PPzg4mJCb777jt0795ddDxJOXnyJCIiIiCXyxEREYHly5cjNDQUly9fFh2Nyjle80c6OX78OCIiIrBo0SK0atUKv/zyC6ZNm4aCggLR0SRr1qxZCAsLQ25urugo5ca6devw5ZdfQqlUYtu2bVi9ejW2b9%2BOkJAQ0dEka/ny5ejatStUKhU2bNiAxYsXIzg4GEFBQaKjSY5SqUTdunVRt25d/PnnnwCAYcOG4ffffxecjMo7dv5IJ0qlEvXq1YNcLlf/0Bo%2BfDg/%2BeshJiYGO3fuhJOTk%2Bgo5cb9%2B/cxePBgXLt2DdnZ2ejUqRNMTEzw6NEj0dEk686dO9i2bRvu3LmDR48eoVevXjAzM8OUKVNER5McR0dHJCQkwMnJCenp6Xj27BmMjIyQlZUlOhqVcyz%2BSCf//aGVnZ0NIyMjPHv2THQ0yTI3N%2Bd1UyXM0tIS6enpOHLkCFxdXWFiYoL4%2BHjY2tqKjiZZxsbGyMrKwokTJ9CyZUuYmZkhKSkJ1tbWoqNJjoeHB4YOHYqdO3eia9euGD9%2BPMzNzfHOO%2B%2BIjkblnLGfn5%2Bf6BAkPU%2BePMH8%2BfPRu3dvJCUlYefOnfj1119RqVIlDBgwQHQ8ScrNzcWBAwfQrFkzWFpaio5TLjx9%2BhTffPMNzpw5g5kzZyIzMxOjRo3C8OHD4erqKjqeJN2/fx9r167FkSNHMG7cOJiYmGDs2LFwd3fHu%2B%2B%2BKzqepLi6uqJatWpo0KABOnfujGvXrqmvSa1UqZLoeFSOyVQqlUp0CJKmn3/%2BGW5ubigoKMCSJUuQmZmJqVOnolatWqKjSZKbmxuSk5Mhk8kKHXsxtU7FFxMTA3Nzc7Rs2RIPHjzAlStX8MEHH4iOJVkFBQX46aefYGFhgV69euHevXs4evQoRowYoV5VTURlG4s/0snEiRMREBDAqZ4SdO7cuVcea9u2bSkmKd/y8/Nx48YNNGnSRHSUckUul6Ny5cqiY0jK8OHDX/phDwDCwsJKOQ0ZEl7zRzqJjY2FmZmZ6BjlStu2baFUKvHHH38gMTERDg4OcHFxgZERF%2BXr6tixY5g3bx5SUlLw78%2B5JiYmuHLlisBk0nXp0iUsXbq00MbEcrkcf/zxh%2BB00tKuXTuNxxkZGdi/fz%2B8vb0FJSJDwc4f6WTBggVITk6Gp6cn7O3tNT69uri4CEwmXWlpaRg3bhzi4%2BNhY2ODjIwM1K5dG6GhoXB0dBQdT5L69OmDTp06oWLFirh%2B/Tr69OmDNWvWYNCgQRg%2BfLjoeJI0aNAgODk5wcbGBgkJCejUqRPCwsIwYsQIfPrpp6LjSd7Vq1cRGBiILVu2iI5C5RiLP9JJo0aNXjouk8l4fZqOfH19oVKpMH/%2BfFhZWeHp06fw8/NDfn4%2B91DTUYsWLXD%2B/HkkJibim2%2B%2BwdatW3Hr1i1MmTIF0dHRouNJUosWLRATE4PExEQsXLgQmzZtwsWLFzF//nxERUWJjlcuuLq64vz586JjUDnGaV/SydWrV0VHKHfOnj2L/fv3w8rKCgDw1ltvwc/PD926dROcTLoqV64MIyMjVK9eHbdv3wYA1K9fHw8fPhScTLoqVqwICwsLODk54ebNmwCAli1bIikpSXAy6UlOTtZ4rFAosHfvXlSrVk1QIjIULP5IJ6/bJJd71elGqVQWuvhbJpPB1NRUUCLpa9iwIYKCgjBhwgRUqVIFx48fh4WFBczNzUVHk6y6desiIiICQ4YMQYUKFfDnn3/CzMzslQsX6NXc3Nw0zptKpUKlSpWwYMECganIEHDal3TSqFGjV/6w57SvbqZMmQITExPMmzcPFSpUQFZWFubOnYuCggKsXLlSdDxJun37NiZNmoTvv/8e165dU9/qbcaMGbw%2BTUdxcXEYP348IiMjcebMGSxYsADGxsYYMmQIvvrqK9HxJOW/3VJjY2NUqVKFH/jojWPxRzq5f/%2B%2BxmO5XI6NGzeie/fu6Nu3r6BU0pacnIxPP/0USUlJsLGxwd9//4369etjw4YN7KaWkNTUVGRlZaFOnTqio0habm4uTE1NYWRkhMuXL%2BPp06fo1KmT6FiS899p3xdMTU1RqVIl7qhAbwyLPyoxT548wcCBA/Hrr7%2BKjiJZ%2Bfn5iI2NhVwuR40aNdCsWTNunKuD8%2BfPw9XVFbGxsa98Tps2bUoxUfmSl5cHuVyu3urlherVqwtKJE1NmzZVn0OVSqUxm2JkZISOHTtiyZIl3D%2BRShyLPyoxmZmZeP/991/7C5cKe/jwIRwdHV/ZBQD4S7W4XFxcEBcXp7484b8/5rgqXXf79u3Dt99%2Bi8zMTPXYi8KF57R4tm3bhqNHj%2BLrr7%2BGk5MTEhMTERgYiHfeeQcffPAB1q1bBxMTEyxdulR0VCpnWPyRTtavX6/xWKFQ4MSJE7CxsUFISIigVNL030Ll3/hLVT%2BZmZm4cOEC/v77b9jZ2aFFixaoUKGC6FiS1qtXL3zwwQfo378/TEw01wzWqFFDUCpp6tGjByIjI2FjY6Mee/z4MQYOHIhDhw4hMzMT3bp1Q0xMjMCUVB5xtS/p5MSJExqPjY2N0aRJE3zxxReCEknX3r17AQCHDx8WnKR8%2Bd///ofg4GDk5uaqi%2BgKFSpg6tSpGDZsmOh4kvXgwQP4%2BPgUKvyo%2BDIyMgpd1iGTyZCeng4AsLS0LDS1TlQS%2BL%2BXdBIcHPzS61Du3LkjII20vdjTy8HBAcHBweo7KGzZsgUZGRmYNGmS4ITSExkZifXr12P27Nno2rUrbG1tkZ6ejiNHjmDlypWws7ODu7u76JiS1LRpU9y6deuVG72T9t577z1MmzYNs2fPRvXq1ZGcnIzAwEB06tQJeXl5WLNmDZo2bSo6JpVDLP5IJ927d0dcXJzGWEFBAby8vLgzvY4WLVqEixcvqu/r2bRpUyxevBh5eXmYMWOG4HTSEh4ejoCAAPTo0UM9VrVqVQwZMgSVKlXC1q1bWfzpyMXFBZ988gl69uwJOzs7jWM%2BPj6CUknT3LlzMW3aNLi7u6sv%2BejatSsWLlyI33//HceOHcOKFSsEp6TyiNf8kdb%2B%2BusvjBkzBsDzrV5q1aqlcfzZs2eoWLGiehqTiqdTp06Ijo7W6Kg%2BevQI/fr1w6lTpwQmk55WrVohNjb2pVOTeXl56Nq1K3777TcByaTvVfdElslkCAsLK%2BU05UNKSgoePnwIlUqFqKgo7NmzBxcvXhQdi8oxdv5Ia2%2B//TamT5%2BOjIwM%2BPv747PPPtM4bm5ujnbt2glKJ325ubmFFiNYW1sjPz9fUCLpkslkr7wmzczMDDk5OaWcqPzYunWr6AjlTkJCAjZu3Ijjx4/D2dkZ06dPFx2JyjkWf1Qs3bt3BwDUrFkTHTp0EJymfGndujUCAgIwe/ZsmJmZITc3F4GBgXBxcREdjUjDoUOHsGPHDiQlJcHe3h6DBg2Ch4eH6FiSolQqsX//fmzatAk3b95Efn4%2BNmzYgPfee090NDIALP5IJ66urti9ezdSU1PVq9EUCgVu3LiB1atXC04nTbNnz8bo0aPh4uICW1tbZGRkoE6dOoW21aGi5efnY/fu3a88XlBQUIppypfo6GjMmzcP3t7ecHNzw/379%2BHn54ecnBx4eXmJjicJW7ZsQVhYGJRKJYYMGYKQkBD07NkTDRo0EB2NDASLP9LJnDlzcOzYMdjY2CA/Px8WFha4c%2BcOP/3rwcnJCfv27UNcXBzS0tLg6OiI5s2bc0sNHdjZ2WHVqlWvPF6lSpVSTFO%2BhISEIDg4GO3bt1ePdenSBfPnz2fxp6WAgAAMHToUM2fO5C3cSAj%2BViGdHD9%2BHBEREcjIyEBERASWL1%2BOkJAQbkashydPnmDevHn44osv0KZNGwQFBSEiIgJ%2Bfn6wsrISHU9Sjhw5IjpCuZWcnFzo2t62bdvi4cOHghJJzzfffIPw8HB06dIFgwcPxtChQwtt8E70JhmJDkDSpFQqUa9ePdStW1dd8A0fPhznzp0TnEy6/Pz88PjxY/Vu/3369MHTp0%2BxaNEiwcmI/uHo6FjoFo6xsbG8BWExDBs2DHv37sWKFStw69Yt9OjRA0%2BePMGZM2d4SQKVCnb%2BSCeOjo5ISEiAk5MT0tPTkZ2dDSMjIzx79kx0NMn67bffcPjwYXWXr169eli2bJnGXnVEoo0cORITJkyAt7c3nJyccP/%2BfezYsQOzZs0SHU1yOnTogA4dOiApKQnh4eFYvHgxAgMD4enpiZkzZ4qOR%2BUYiz/SSe/evfHxxx8jMjISnTt3xoQJE2BmZoYmTZqIjiZZSqWy0Kd%2BlUpV6PZPRCJ5eXnB2NgYUVFROHToEGrUqIEFCxagZ8%2BeoqNJVo0aNTB9%2BnRMnjwZe/bsQXh4uOhIVM5xk2fS2c8//ww3NzcUFBRgyZIlyMzMxNSpUwtt/kza8fX1RVZWFmbNmoVq1arhwYMHCAwMhLW1NRYvXiw6HtFLZWZmwszMjAsXiCSExR/pZOLEiQgICIC1tbXoKOWGXC7H5MmTERsbq774u2PHjli2bBlsbW0FpyN67vbt21ixYgXWrFmDX3/9FVOmTIGVlRXWrl0LV1dX0fGISAss/kgn7du3x4kTJ/hp/w1ITk5Wb/VStWpV0XGINIwePRoODg5YtGgRPvzwQwwYMABWVlbYvXs3IiMjRccjIi2w%2BCOdLFiwAMnJyfD09IS9vb3GNgW8I4Vu/ruC8t/atGlTikmIXu3dd9/F0aNHkZKSAnd3d8TExMDKygqurq6Ii4sTHY%2BItMAFH6STbdu2ASi8n5pMJuNefzoaPnx4oTEjIyNUq1YNhw8fFpCIqLD8/HyoVCqcPn0aTZs2hbW1NeRyOczNzUVHIyItsfgjnVy9elV0hHInPj5e47FcLseaNWtQo0YNQYmICuvQoQMmTpyI%2BPh4jB49GgkJCZgxYwa6du0qOhoRaYnTvqQzhUKBkydPIjk5GYMGDUJCQgKcnZ1FxypXcnJy4O7ujuPHj4uOQgQAyMrKQmhoKMzNzTFmzBjEx8dj586dmDZtGiwtLUXHIyItsPgjnSQkJGD06NF49uwZsrKy8OOPP8LT0xNr1qxBly5dRMcrN1JSUtC3b1%2BcPXtWdBQiAMD48eOxdOlSrvQnkjBO%2B5JOFi5cCE9PT0yYMAFt27ZF3bp14efnh6CgIBZ/OvrvHRIUCgXOnz%2BPjh07CkpEVNiFCxe4yp9Ijmy%2B0gAAB4RJREFU4lj8kU4uXryIVatWQSaTqVf6DhgwgJsRlyBzc3MMHz4c3t7eoqMQqfXp0weTJk2Ch4dHoZX%2BXJVOJA0s/kgnVlZWkMvlcHR0VI%2BlpaWhYsWKAlNJW0BAgOgIREV6sdL/2LFjGuNc6U8kHSz%2BSCd9%2BvSBj48PfH19oVQqcfXqVSxduhS9evUSHU1ygoODi3yOj49PKSQhKtp/V6UTkfSw%2BCOdTJgwAdnZ2Rg7dixyc3Px0UcfYcCAAZg0aZLoaJITExMD4PnK3itXrqBJkyaoWbMmUlJScOnSJXTq1ElwQiJNeXl5OH78OJKSkuDt7Y2//voLjRo1Eh2LiLTE1b6kF5VKhbS0NNjZ2cHIyEh0HEmbOXMmWrRogSFDhqjHdu3ahYMHD2LdunUCkxH94/79%2Bxg1ahQUCgWePHmCqKgo9OnTB8HBwXj//fdFxyMiLbD4I53t3r0be/bsQVpaGqpVq4bBgweje/fuomNJlouLC37//XeNIrqgoACtW7fGhQsXBCYj%2BsfYsWPRokULjB8/Hm3btkVsbCx27dqFsLAw7Nq1S3Q8ItICWzWkk%2B%2B//x4BAQFo3LgxPvroI9StWxezZs3ijd31ULly5UL39z116hQcHBwEJSIq7OLFi/jss880Vvr37dsXCQkJgpMRkbZ4zR/pZNu2bQgJCUHz5s3VY%2B7u7pgxYwa8vLwEJpOusWPH4vPPP4e7uzuqV6%2BOhIQEHDp0CEuWLBEdjUjtrbfewqNHj1C9enX1WFpaGipVqiQwFREVBzt/pJOsrCw0btxYY6xZs2Z4%2BvSpoETS5%2BXlhQ0bNsDExAR//PEHLC0tsXnzZnz44YeioxGpeXh4wMfHB6dPn4ZSqcTly5fh6%2BuL3r17i45GRFriNX%2Bkkzlz5sDGxgZTp05VX6MWGhqKu3fvwt/fX3A6aUtPT0dSUhLs7e1RrVo10XGINCgUCqxcuRIRERHIzs6GhYUFBg0ahBkzZvDOH0QSweKPdDJ06FDExcXBzs4ONWvWRGpqKpKTk1GtWjWNXwAHDhwQmFJaMjMz8dVXX%2BHIkSPqsQ4dOuC7777j5tlUJgQHB%2BPq1at49913MWzYMMjlctja2mrc5YOIyj5e80c66d%2B/P/r37y86RrmyfPlyZGVl4eeff0bNmjXx119/YdGiRVi6dCm7qSRcYGAgdu/ejdatW2PVqlXIysrCmDFjRMciIh2w80dURnTt2hU//vgjqlSpoh5LS0uDp6cnzpw5IzAZEdC5c2ds3LgRzs7OiImJwYIFCxAdHS06FhHpgJ0/0snVq1exYsUKJCUl4b%2BfHzjVq5vs7Gy89dZbGmMVK1aEUqkUlIjoH0%2BfPoWzszMAwNXVFSkpKYITEZGuWPyRTmbOnIk6depg1KhRvN6nhLRo0QJBQUHw9fWFTCaDSqVCUFAQmjVrJjoakcbm4yYm/NVBJGX8H0w6SUxMRFRUFExNTUVHKTemTZuGESNGYM%2BePahRowaSkpIgk8mwadMm0dGICnX4iUi6WPyRTlxdXREfH8%2BuVAlycnLCgQMHcPjwYaSnp6NGjRro0qULrK2tRUcjQn5%2BPnbv3q1%2BrFAoNB4DQL9%2B/Uo7FhHpgAs%2BSCfXrl3DyJEj0aFDh0I7%2B3Nlqm7c3NywZ88eFntUJrm5ub32uEwmw%2BHDh0spDRHpg50/0klAQABsbGxgamqKnJwc0XHKjezsbBZ/VCb9e/9JIpI2Fn%2BkkytXruDUqVMsVEpQu3bt4OXlhc6dO8PBwUHjmI%2BPj6BURERU3rD4I504OTmxS1XCEhMT4eTkhLt37%2BLu3bvqca6mJiKiksTij3QyaNAgjBo1Cl5eXrCxsdEoUDw8PAQmk6bg4GBYW1urb5tFRET0pnDBB%2BmkS5cuLx2XyWQ4duxY6YaRuH/fNismJgajR4/mbbOIiOiNYfFHJBhvm0VERKXJqOinEL3c33//ja1bt2LJkiXIzMzEiRMnREeSJN42i4iIShOLP9LJtWvX4O7ujj179uCHH35ARkYGJkyYUGjTVyoab5tFRESlicUf6SQgIAC%2Bvr6IjIyEiYkJnJycEBwcjJCQENHRJIdXXhARUWlim4F0cv36dQwYMADAP1uRdOnSBVOnThUZS5J42ywiIipNLP5IJ7a2trh79y7q16%2BvHrt37x7s7OwEppImOzs7rFq1Sv3Y1tZW47FMJmPxR0REJYbFH%2BlkyJAhGDduHMaPH4/8/HwcPHgQa9euhZeXl%2BhoksPbZhERUWniVi9ULOfPn4erqytUKhXCwsIQHh6O5ORkODo6wsvLC59//jnvSEFERFSGsfijYnFxcUFcXJzoGERERKQjrvalYuFnBSIiImlj8UfFwildIiIiaeO0LxVL48aNUb169dc%2B5/Dhw6WUhoiIiIqLq32pWExNTeHj4yM6BhEREemIxR8Vi4mJCfr37y86BhEREemI1/xRsfAqASIiImlj8UfF4unpKToCERER6YELPoiIiIgMCDt/RERERAaExR8RERGRAWHxR0RERGRAWPwRERERGRAWf0REREQGhMUfERERkQFh8UdERERkQP4PVjHiuDKOl0sAAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Produccion</th>\n",
       "      <th>Dias</th>\n",
       "      <th>Personas</th>\n",
       "      <th>Agua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.8</td>\n",
       "      <td>7107</td>\n",
       "      <td>21</td>\n",
       "      <td>129</td>\n",
       "      <td>3067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6373</td>\n",
       "      <td>22</td>\n",
       "      <td>141</td>\n",
       "      <td>2828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.9</td>\n",
       "      <td>6796</td>\n",
       "      <td>22</td>\n",
       "      <td>153</td>\n",
       "      <td>2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.4</td>\n",
       "      <td>9208</td>\n",
       "      <td>20</td>\n",
       "      <td>166</td>\n",
       "      <td>2994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.3</td>\n",
       "      <td>14792</td>\n",
       "      <td>25</td>\n",
       "      <td>193</td>\n",
       "      <td>3082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x1f5be2d65f8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using pandas profiling\n",
    "pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Realice la depuración de datos que haga falta y exponga de manera detallada la manera en que realizó la corrección."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Temperatura</th>\n",
       "      <th>Produccion</th>\n",
       "      <th>Dias</th>\n",
       "      <th>Personas</th>\n",
       "      <th>Agua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.840000</td>\n",
       "      <td>12900.470588</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>181.823529</td>\n",
       "      <td>3303.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.442536</td>\n",
       "      <td>3526.786606</td>\n",
       "      <td>2.936835</td>\n",
       "      <td>21.994986</td>\n",
       "      <td>446.698411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>39.500000</td>\n",
       "      <td>6373.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>2828.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.250000</td>\n",
       "      <td>11964.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>3022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>70.900000</td>\n",
       "      <td>14119.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>3125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.150000</td>\n",
       "      <td>14575.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>3502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>18573.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>4488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperatura    Produccion       Dias    Personas         Agua\n",
       "count    15.000000     17.000000  17.000000   17.000000    17.000000\n",
       "mean     64.840000  12900.470588  22.000000  181.823529  3303.705882\n",
       "std      14.442536   3526.786606   2.936835   21.994986   446.698411\n",
       "min      39.500000   6373.000000  19.000000  129.000000  2828.000000\n",
       "25%      55.250000  11964.000000  20.000000  175.000000  3022.000000\n",
       "50%      70.900000  14119.000000  22.000000  190.000000  3125.000000\n",
       "75%      78.150000  14575.000000  22.000000  195.000000  3502.000000\n",
       "max      81.000000  18573.000000  32.000000  206.000000  4488.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambio de filas con datos NAN, errados"
   ]
  },
  {
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "      <th></th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Produccion</th>\n",
       "      <th>Dias</th>\n",
       "      <th>Personas</th>\n",
       "      <th>Agua</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81.0</td>\n",
       "      <td>14564</td>\n",
       "      <td>32</td>\n",
       "      <td>189</td>\n",
       "      <td>3898</td>\n",
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      ],
      "text/plain": [
       "   Temperatura  Produccion  Dias  Personas  Agua\n",
       "5         81.0       14564    32       189  3898"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conociendo la fila con este valor\n",
    "df[df.Dias==32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lc.mojica639\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "      <td>43.6</td>\n",
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      ],
      "text/plain": [
       "    Temperatura  Produccion  Dias  Personas  Agua\n",
       "0          58.8        7107    21       129  3067\n",
       "1           NaN        6373    22       141  2828\n",
       "2          70.9        6796    22       153  2891\n",
       "3          77.4        9208    20       166  2994\n",
       "4          79.3       14792    25       193  3082\n",
       "5          81.0       14564    31       189  3898\n",
       "6          71.9       11964    20       175  3502\n",
       "7          63.9       13526    23       186  3060\n",
       "8          54.5       12656    20       190  3211\n",
       "9          39.5       14119    20       187  3286\n",
       "10         44.5       16691    22       195  3542\n",
       "11         43.6       14571    19       206  3125\n",
       "12         56.0       13619    22       198  3022\n",
       "13          NaN       14575    22       192  2922\n",
       "14         73.0       14556    21       191  3950\n",
       "15         78.9       18573    21       200  4488\n",
       "16         79.4       15618    22       200  3295"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_value(5,'Dias',31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mirar datos faltantes\n",
    "dataNullDf = df[pd.isnull(df).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pearson=df.corr(method='pearson')\n",
    "spearman=df.corr(method='spearman')"
   ]
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   "execution_count": 16,
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       "             Temperatura  Produccion      Dias  Personas      Agua\n",
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       "Agua            0.308011    0.630749  0.196323  0.413246  1.000000"
      ]
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     "execution_count": 16,
     "metadata": {},
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   "source": [
    "pearson"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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   "source": [
    "spearman"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "Agua            0.072297    0.022906  0.339158 -0.041690  0.000000"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearson - spearman"
   ]
  },
  {
   "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>Temperatura</th>\n",
       "      <th>Produccion</th>\n",
       "      <th>Dias</th>\n",
       "      <th>Personas</th>\n",
       "      <th>Agua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperatura</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.223704</td>\n",
       "      <td>0.051623</td>\n",
       "      <td>0.100772</td>\n",
       "      <td>-0.072297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Produccion</th>\n",
       "      <td>0.223704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052700</td>\n",
       "      <td>-0.052753</td>\n",
       "      <td>-0.022906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dias</th>\n",
       "      <td>0.051623</td>\n",
       "      <td>0.052700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.067453</td>\n",
       "      <td>-0.339158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personas</th>\n",
       "      <td>0.100772</td>\n",
       "      <td>-0.052753</td>\n",
       "      <td>-0.067453</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agua</th>\n",
       "      <td>-0.072297</td>\n",
       "      <td>-0.022906</td>\n",
       "      <td>-0.339158</td>\n",
       "      <td>0.041690</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Temperatura  Produccion      Dias  Personas      Agua\n",
       "Temperatura     0.000000    0.223704  0.051623  0.100772 -0.072297\n",
       "Produccion      0.223704    0.000000  0.052700 -0.052753 -0.022906\n",
       "Dias            0.051623    0.052700  0.000000 -0.067453 -0.339158\n",
       "Personas        0.100772   -0.052753 -0.067453  0.000000  0.041690\n",
       "Agua           -0.072297   -0.022906 -0.339158  0.041690  0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearman - pearson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots\n",
    "\n",
    "### Cual es la variable dependiente (endogena), cual es la variable independiente (exogena) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperatura    float64\n",
       "Produccion       int64\n",
       "Dias             int64\n",
       "Personas         int64\n",
       "Agua             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d213909a20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agua dado produccion\n",
    "df.plot(x='Produccion',y='Agua',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2138f1550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agua data temperatura\n",
    "df.plot(x='Temperatura',y='Agua',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d213a23390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agua dado Dias\n",
    "df.plot(x='Dias',y='Agua',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21399f198>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agua dado Personas\n",
    "df.plot(x='Personas',y='Agua',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresion con variable dependiente Agua y los otras variables independientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Regresion = ols(formula=\"Agua ~ Temperatura+Produccion+Dias+Personas\", data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept      4041.129832\n",
       "Temperatura       9.229256\n",
       "Produccion        0.199320\n",
       "Dias            -12.993125\n",
       "Personas        -19.739891\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Regresion.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tiene sentido que haya intercepto ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lc.mojica639\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Agua</td>       <th>  R-squared:         </th> <td>   0.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 08 Mar 2019</td> <th>  Prob (F-statistic):</th>  <td>0.0146</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:24:29</td>     <th>  Log-Likelihood:    </th> <td> -103.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    15</td>      <th>  AIC:               </th> <td>   217.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    10</td>      <th>  BIC:               </th> <td>   220.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td> 4041.1298</td> <td> 1236.491</td> <td>    3.268</td> <td> 0.008</td> <td> 1286.055</td> <td> 6796.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Temperatura</th> <td>    9.2293</td> <td>    6.254</td> <td>    1.476</td> <td> 0.171</td> <td>   -4.705</td> <td>   23.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Produccion</th>  <td>    0.1993</td> <td>    0.055</td> <td>    3.642</td> <td> 0.005</td> <td>    0.077</td> <td>    0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dias</th>        <td>  -12.9931</td> <td>   31.316</td> <td>   -0.415</td> <td> 0.687</td> <td>  -82.769</td> <td>   56.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Personas</th>    <td>  -19.7399</td> <td>    8.734</td> <td>   -2.260</td> <td> 0.047</td> <td>  -39.200</td> <td>   -0.280</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.669</td> <th>  Durbin-Watson:     </th> <td>   2.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.716</td> <th>  Jarque-Bera (JB):  </th> <td>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.407</td> <th>  Prob(JB):          </th> <td>   0.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.440</td> <th>  Cond. No.          </th> <td>2.19e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.19e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   Agua   R-squared:                       0.681\n",
       "Model:                            OLS   Adj. R-squared:                  0.553\n",
       "Method:                 Least Squares   F-statistic:                     5.327\n",
       "Date:                Fri, 08 Mar 2019   Prob (F-statistic):             0.0146\n",
       "Time:                        20:24:29   Log-Likelihood:                -103.68\n",
       "No. Observations:                  15   AIC:                             217.4\n",
       "Df Residuals:                      10   BIC:                             220.9\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept    4041.1298   1236.491      3.268      0.008    1286.055    6796.205\n",
       "Temperatura     9.2293      6.254      1.476      0.171      -4.705      23.164\n",
       "Produccion      0.1993      0.055      3.642      0.005       0.077       0.321\n",
       "Dias          -12.9931     31.316     -0.415      0.687     -82.769      56.783\n",
       "Personas      -19.7399      8.734     -2.260      0.047     -39.200      -0.280\n",
       "==============================================================================\n",
       "Omnibus:                        0.669   Durbin-Watson:                   2.376\n",
       "Prob(Omnibus):                  0.716   Jarque-Bera (JB):                0.610\n",
       "Skew:                          -0.407   Prob(JB):                        0.737\n",
       "Kurtosis:                       2.440   Cond. No.                     2.19e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.19e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Regresion.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cual es la ecuacion de la recta?\n",
    "\n",
    "### Agua = 4600.81 + 9.2293*Temperatura+0.1993*Produccion -12.9931*Dias -19.7399*Personas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of R2 y adjusted R2\n",
    "\n",
    "Link1: http://www.statisticshowto.com/adjusted-r2/\n",
    "\n",
    "Link2: https://www.youtube.com/watch?v=KjRrdb2x6dA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prueba de Jarque-Bera (Test de Normalidad sin usar mean y sd)\n",
    "\n",
    "Link: http://www.statisticshowto.com/jarque-bera-test/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estudio de Residuos\n",
    "\n",
    "#### ¿Los residuos tienen distribución normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obteniendo fitted values\n",
    "Yest=Regresion.fittedvalues;\n",
    "# Obteniendo residuos\n",
    "Residuos=Regresion.resid;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_de_punto_7 = sm.qqplot(Residuos, stats.t, fit=True, line='45')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graficas relacionados a la regresion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Haciendo nuevo dataframe\n",
    "df1=pd.DataFrame({'y':df.Agua,'yest':Regresion.fittedvalues, 'Residuos':Regresion.resid})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resultados para Produccion\n",
    "fig = plt.figure(figsize=(18,18))\n",
    "fig = sm.graphics.plot_regress_exog(Regresion,exog_idx='Produccion',fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estudio de Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Haciendo nuevo dataframe\n",
    "df1=pd.DataFrame({'y':df.Agua,'yest':Regresion.fittedvalues, 'Residuos':Regresion.resid})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d214687080>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(x='yest',y='Residuos',kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leer los siguientes links:\n",
    "\n",
    "Link1: https://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions\n",
    "\n",
    "Link2: https://www.youtube.com/watch?v=_NkWG1M69CI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograma de Residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d214663e10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.Residuos.plot(kind='hist',bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resultados para Produccion\n",
    "fig = plt.figure(figsize=(18,18))\n",
    "fig = sm.graphics.plot_regress_exog(Regresion,exog_idx='Personas',fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Influence plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inf =sm.graphics.influence_plot(Regresion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Que cree que esta pasando con estos valores ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Temperatura</th>\n",
       "      <th>Produccion</th>\n",
       "      <th>Dias</th>\n",
       "      <th>Personas</th>\n",
       "      <th>Agua</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.8</td>\n",
       "      <td>7107</td>\n",
       "      <td>21</td>\n",
       "      <td>129</td>\n",
       "      <td>3067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>78.9</td>\n",
       "      <td>18573</td>\n",
       "      <td>21</td>\n",
       "      <td>200</td>\n",
       "      <td>4488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperatura  Produccion  Dias  Personas  Agua\n",
       "0          58.8        7107    21       129  3067\n",
       "15         78.9       18573    21       200  4488"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[0,15],]"
   ]
  }
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