Tutorial2_Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This git hub repo has been created to understand data using Python. \n",
    "\n",
    "Dataset : Happiness Ranking of countries from different region\n",
    "\n",
    "Goal of this data :-\n",
    "\n",
    "1. Understand the data \n",
    "2. Using correlation to determine the independant variables \n",
    "3. Analyze the numerical values \n",
    "4. Remove the outliers and then analyze the numerical values \n",
    "5. Analyze the categorical values \n",
    "6. How can we create dummy variables - numerical values for our model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing required Libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data (.csv file) in a data frame\n",
    "Happiness = pd.read_csv(\"Happiness_2015.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Happiness.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158, 12)\n"
     ]
    }
   ],
   "source": [
    "#By Data frame i mean a matrix with dimensions (No. of Rows X No. of Columns)\n",
    "#The result shows that the number of rows are 158 - Number of examples/Rows ; \n",
    "#12 - Number of features/Columns (That describe each example)\n",
    "print(Happiness.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Standard Error', 'Economy (GDP per Capita)', 'Family',\n",
       "       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',\n",
       "       'Generosity', 'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing the columns/features in the data frame Happiness\n",
    "Happiness.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 12 columns):\n",
      "Country                          158 non-null object\n",
      "Region                           158 non-null object\n",
      "Happiness Rank                   158 non-null int64\n",
      "Happiness Score                  158 non-null float64\n",
      "Standard Error                   158 non-null float64\n",
      "Economy (GDP per Capita)         158 non-null float64\n",
      "Family                           158 non-null float64\n",
      "Health (Life Expectancy)         158 non-null float64\n",
      "Freedom                          158 non-null float64\n",
      "Trust (Government Corruption)    158 non-null float64\n",
      "Generosity                       158 non-null float64\n",
      "Dystopia Residual                158 non-null float64\n",
      "dtypes: float64(9), int64(1), object(2)\n",
      "memory usage: 14.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Viewing the data types of each column/feature\n",
    "print(Happiness.info())\n",
    "\n",
    "# The output y can be Happiness Rank OR Happiness Score\n",
    "#There are 2 categorical values ; 'Country' + 'Region'\n",
    "#There are 7 numerical values (We are not going to include Standard error in the dataset) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country                          0\n",
      "Region                           0\n",
      "Happiness Rank                   0\n",
      "Happiness Score                  0\n",
      "Standard Error                   0\n",
      "Economy (GDP per Capita)         0\n",
      "Family                           0\n",
      "Health (Life Expectancy)         0\n",
      "Freedom                          0\n",
      "Trust (Government Corruption)    0\n",
      "Generosity                       0\n",
      "Dystopia Residual                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Check for any missing values in the dataset\n",
    "#Checking the misssing values if any for any of the features\n",
    "print(Happiness.isnull().sum())\n",
    "#There are no missing values for this data - As the sum of all the values are showing as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.493671</td>\n",
       "      <td>5.375734</td>\n",
       "      <td>0.047885</td>\n",
       "      <td>0.846137</td>\n",
       "      <td>0.991046</td>\n",
       "      <td>0.630259</td>\n",
       "      <td>0.428615</td>\n",
       "      <td>0.143422</td>\n",
       "      <td>0.237296</td>\n",
       "      <td>2.098977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.754363</td>\n",
       "      <td>1.145010</td>\n",
       "      <td>0.017146</td>\n",
       "      <td>0.403121</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0.247078</td>\n",
       "      <td>0.150693</td>\n",
       "      <td>0.120034</td>\n",
       "      <td>0.126685</td>\n",
       "      <td>0.553550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.839000</td>\n",
       "      <td>0.018480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.328580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40.250000</td>\n",
       "      <td>4.526000</td>\n",
       "      <td>0.037268</td>\n",
       "      <td>0.545808</td>\n",
       "      <td>0.856823</td>\n",
       "      <td>0.439185</td>\n",
       "      <td>0.328330</td>\n",
       "      <td>0.061675</td>\n",
       "      <td>0.150553</td>\n",
       "      <td>1.759410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>79.500000</td>\n",
       "      <td>5.232500</td>\n",
       "      <td>0.043940</td>\n",
       "      <td>0.910245</td>\n",
       "      <td>1.029510</td>\n",
       "      <td>0.696705</td>\n",
       "      <td>0.435515</td>\n",
       "      <td>0.107220</td>\n",
       "      <td>0.216130</td>\n",
       "      <td>2.095415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>118.750000</td>\n",
       "      <td>6.243750</td>\n",
       "      <td>0.052300</td>\n",
       "      <td>1.158448</td>\n",
       "      <td>1.214405</td>\n",
       "      <td>0.811013</td>\n",
       "      <td>0.549092</td>\n",
       "      <td>0.180255</td>\n",
       "      <td>0.309883</td>\n",
       "      <td>2.462415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.587000</td>\n",
       "      <td>0.136930</td>\n",
       "      <td>1.690420</td>\n",
       "      <td>1.402230</td>\n",
       "      <td>1.025250</td>\n",
       "      <td>0.669730</td>\n",
       "      <td>0.551910</td>\n",
       "      <td>0.795880</td>\n",
       "      <td>3.602140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Happiness Rank  Happiness Score  Standard Error  \\\n",
       "count      158.000000       158.000000      158.000000   \n",
       "mean        79.493671         5.375734        0.047885   \n",
       "std         45.754363         1.145010        0.017146   \n",
       "min          1.000000         2.839000        0.018480   \n",
       "25%         40.250000         4.526000        0.037268   \n",
       "50%         79.500000         5.232500        0.043940   \n",
       "75%        118.750000         6.243750        0.052300   \n",
       "max        158.000000         7.587000        0.136930   \n",
       "\n",
       "       Economy (GDP per Capita)      Family  Health (Life Expectancy)  \\\n",
       "count                158.000000  158.000000                158.000000   \n",
       "mean                   0.846137    0.991046                  0.630259   \n",
       "std                    0.403121    0.272369                  0.247078   \n",
       "min                    0.000000    0.000000                  0.000000   \n",
       "25%                    0.545808    0.856823                  0.439185   \n",
       "50%                    0.910245    1.029510                  0.696705   \n",
       "75%                    1.158448    1.214405                  0.811013   \n",
       "max                    1.690420    1.402230                  1.025250   \n",
       "\n",
       "          Freedom  Trust (Government Corruption)  Generosity  \\\n",
       "count  158.000000                     158.000000  158.000000   \n",
       "mean     0.428615                       0.143422    0.237296   \n",
       "std      0.150693                       0.120034    0.126685   \n",
       "min      0.000000                       0.000000    0.000000   \n",
       "25%      0.328330                       0.061675    0.150553   \n",
       "50%      0.435515                       0.107220    0.216130   \n",
       "75%      0.549092                       0.180255    0.309883   \n",
       "max      0.669730                       0.551910    0.795880   \n",
       "\n",
       "       Dystopia Residual  \n",
       "count         158.000000  \n",
       "mean            2.098977  \n",
       "std             0.553550  \n",
       "min             0.328580  \n",
       "25%             1.759410  \n",
       "50%             2.095415  \n",
       "75%             2.462415  \n",
       "max             3.602140  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get a sense of the max , min , mean , count etc of the numerical data\n",
    "Happiness.loc[:, (Happiness.dtypes == 'float64') | (Happiness.dtypes == 'int64')].describe()\n",
    "#We are looking at describing only the features of the data type : float64 and int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  \n",
       "3     0.34699            2.46531  \n",
       "4     0.45811            2.45176  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Having a sense of the data  , lets now view the first 5 rows of the data\n",
    "Happiness.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the Numerical Values\n",
    "\n",
    "From our analysis above we can see that :-\n",
    "\n",
    "1. We can consider Happiness Score / Happiness Rank as y == Output\n",
    "2. We now need to understand what the distribution of values are for each feature/column and how it highly it is correlated to the Happiness Score\n",
    "\n",
    "How do i measure the numerical values with respect to the Happiness Score?\n",
    "\n",
    "1. Correlation of the independant variable with the output / dependant variable (Happiness Score)\n",
    "2. Distribution of the numerical value for all 158 examples\n",
    "\n",
    "The 7 numberical Values (Independant Variables/X) who's correlation to the Happiness Score we are going to take are:-\n",
    "\n",
    "1. Economy (GDP per Capita)         \n",
    "2. Family                           \n",
    "3. Health (Life Expectancy)         \n",
    "4. Freedom                          \n",
    "5. Trust (Government Corruption)    \n",
    "6. Generosity                       \n",
    "7. Dystopia Residual  \n",
    "\n",
    "# 1. Correlation of Happiness Score with 'Economy (GDP Per Capita)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Helper function to perform EDA on Numerical variables.\n",
    "def analyze_numeric(data, feature_var, color_plot):\n",
    "    #plt.figure()\n",
    "    #print(data[feature_var].describe())\n",
    "    print(\"Correlation between variables\")\n",
    "    print(data[[feature_var,'Happiness Score']].corr())   #Print the correlation value.\n",
    "    fig, axs = plt.subplots(figsize= (15,8),ncols=2) #creating figure with multiple axes.\n",
    "    plt.subplots_adjust(right=1)\n",
    "    sns.boxplot(data[feature_var], color = color_plot ,ax=axs[0]) #explains the distribution of the variable\n",
    "    axs[0].set_title('Distribution of '+ feature_var)\n",
    "    sns.regplot(x=feature_var, y='Happiness Score', color = color_plot,  data=data, ax= axs[1]) #regression plot for label vs feature variable\n",
    "    axs[1].set_title(feature_var + ' Vs Happiness Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                          Economy (GDP per Capita)  Happiness Score\n",
      "Economy (GDP per Capita)                  1.000000         0.780966\n",
      "Happiness Score                           0.780966         1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ml/anaconda3/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Economy (GDP per Capita)', 'green')\n",
    "#Data is well distributed around the straight line agaisnt the Happiness Score variable\n",
    "#correlation is 0.780"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Correlation of Happiness Score with 'Family'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                   Family  Happiness Score\n",
      "Family           1.000000         0.740605\n",
      "Happiness Score  0.740605         1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ml/anaconda3/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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8t6O1le1NTdzf2Mj2pqZpC6YVGIuIiIiIiMi4DbS14Y9EhmzzRyIMtLWNed5MGmlWYCwiIiIiIiIFGz7K61+wgHQsltuf7OvjxN69xLu6xhwFnshIc6kpMBYREREREZGCjDTKm+jpIdnbSyoaJXH8ONGDB3HJJOG6ujFHgcc70jwZFBiLiIiIiIhIQUYa5Q0uXEi4poZIbS3xjg4sEKCsoYHQwoVjjgKXNzQMGWkGSMdilDc0TNXLyVFgLCIiIiIiIgUZbZQ3deIEL7zzTsK1tVSsWkWwsnLI/pFGgVc1N+MSCVLRKM4573siwarm5kl/HcMpMBYREREREZGCnGqUt5hR4LrGRta2tBCprSXZ20uktpa1LS3Tsv5xYMqvKCIiIiIiIrPSquZmdm7eTApvJDgdiw0Z5T3V/uHqGhunJRAeToGxiIiIiIiIjKmjtZW9W7cy0NZGoKIC5xzJ3l7KGxpY1dycC27rGhuhpSV37PD9M5UCYxERERERERnVYCVqC4UILlqUGwV+5kc/OmLAO1NGgYuhOcYiIiIiIiIyqkdvuolYVxcD+/fTv28fLpWatvWGJ4sCYxERERERERlRR2srJ/bswWUymM+HSyaJHjpEJpmclvWGJ4tSqUVEREREROah/HnDo80F3rt1K75gEJzDzMAMMhninZ0sueCCaep56WnEWEREREREZJ4ZnDcc6+oCn4+jDz/MA+96F7+4+GI6Wltzxw20tRFeuhQyGZxzOPC+kslpWW94sigwFhERERERmWf2bt2KhUK4VIrY4cOQyYDfT/++fezcvDkXHJc3NOALBoksW4YFArh0GvP5qFi9etYV2BqLAmMREREREZF5ZqCtDX8kQry7GzPDfD5vDnE6PaSw1qrmZlwigQUCLDjjDMpXriRSW8uaTZum+RWUlgJjERERERGReaa8oYF0LEYmkfDmDQM4hy8Uwh+J5Apr1TU2sralhUhtLcneXiK1taxtaZlTo8Wg4lsiIiIiIiLzzqrmZm9tYr8fl8l4GzMZwjU1pGMxyhsacsfOxnWJi6XAWEREREREZBYqpKr0aOoaG6GlhUdvuokTe/ZgwSDh007z5hEnEqxqbs6137dnD5lEAgsGWbh6dVHXmS2USi0iIiIiIjLL5FeVDi5aRKyra0jRrELUNTbykh//mHVf/rK39FImk0uVBti5eTMn9u0jeewY6WiU1PHjnBhWnGuu0IixiIiIiIjILDNYVTpQVgZAoKyMVHZ7saO5I6VKb29qwkIhUj09mN+PmeEyGVJ9fQTq68d1nZlMI8YiIiIiIiKzzGBV6Xz5RbNK1f6Q4lxmZBKJkl5nplBgLCIiIiIiMssMVpXOFz9yhOTx49zf2Mj2pqYJpTsPtu8LhcA5b2O2avXw4lxzgVKpRaRgn//859mzZ890d2PcDh48CMDy5cunuSelcfbZZ/Pe9753urshIiIi02CwqnQKb6Q4fuQIia4uQrW1Q+YcU8TSSvnFvAIVFSR7ewlUVpLo7sZlR42DlZW54lxziQJjESnYnj17ePThh6kaLOk/y/T6vCQZf0fHNPdk4np8SvgREZHZZSIVlOVkg1WlB9/T9MAAodpaIjU1QPFzjgeLeVkoRHDRIm802ozgokW4VCpXlbrijDPm5O9OgbGIFKUqk+HV0eh0d2Nc7ssWp5it/c83+FpERERmg+FB13hGM+Vk+UWz7m9sJLho0ZD9xcwFHqmYF0B4yRJe8uMfl7DXM5OGHEREREREZFLlB11m5n0Phdi7det0d23OGGnOcTFzgSe7mNdMp8BYREREREQm1XwPuqbCquZmXCJBKhrFOed9L2Iu8EQD65lo+OsZiwJjERERERGZVHMx6Jpp6hobWdvSQqS2lmh7O/GuLlLRKHu3bi2oOvVEA+uZxDlHvLubxLFjBZ+jwFhERERERCbVXAq6ZrK6xkZWNTcTLC8nXFtLpK4uN5/7VMFxfmCd7O0lUlvL2lk4B9xlMsTa20n29RV1nopviYiIiIjIpBpeQVlVqSfPSEW0Cq1OnV/MazbKpFLEOjrIJBJFn6vAWEREREREJt1sD7pmi4G2tglVp56t0vE4sY4OXDo9rvOVSi0iIiIiIjJHzMf53KlolNjhw+MOikGBsYiIiIiIyJwx3+Zzp06cINbejnNuQu0oMBYREREREZkj5koRrUIkenuJdXWVpC3NMRYREZnHzOwcYFveprOA65xzt0xTl0REZILm+nxu5xyJI0eKrjw9FgXGIiIi85hz7gngAgAz8wMHgXuntVMiIiKjcJkMsY6Ok+ZRT5QCYxERERn0MmCvc+6p6e6IiIgUp6O1dc4vh5VJJr3lmJLJkretOcYiIiIy6E3AXdPdCRERKU5Hays7N28m1tVFcNEiYl1d7Ny8mY7W1unuWsmk43Gihw5NSlAMCoxFREQEMLMQ8PfAd0bY12xmD5rZg10lKnIiIiKls3frViwUIlBWhpl530Mh9m7dOt1dK4nUiRNEDx3CZTKTdg2lUouIiAjAq4HfO+c6hu9wzm0FtgJcdNFFE1sPQ0RESiI/dTre1UW4rg7KynL7/ZEIA21tRbVT6hTsUrSdOHaMxNGjJenPWDRiLCIiIgBvRmnUIiKzwvDUacyIHTo0pEpzOhajvKGhqHZKmYI90badc8S7u6ckKAYFxiIiIvOemS0A/hb43nT3RURETm146nSkrg6cI9bRgXOOVDSKSyRY1dxcVDulTMGeSNsukyHW3l7S5ZhORanUIiIi85xzrh+onu5+iIjMBVNRHXqgrc0bKc4KVlbili8n3tFBsre34OsObwcKT8Euto+Ftj2ZlafHohFjERERERGREpiq6tDlDQ0nrePrCwZZcuGFvLy1lRfeeWdBwfhI7RSSgj3ePp6q7VJWns6k0+z9ylcKPl6BsYiIiIiISAlMVXXoVc3NuETCS5kuInV6pHaSvb307dnD8SeeoG/PHpK9vUW3U4o+lrLydKyzk4f/4z948qtfLfgcBcYiIiIiIiKj6GhtZXtTE/c3NrK9qWnM0d+Btjb8kciQbaVKTc5X19jI2pYWIrW1JHt7idTWsralZXwp22aAV+wq/+ep7GPi2DFiJVoOsOt//5cHLruM3kceKeo8zTEWEREREREZwWBqtIVCQ1KjGSXAK29oINbVRSBv2aRSpSYPV9fYOOG5y3u3biW4cCFldXW5balolL1bt5ZkXvSp+jhYeTp14sSEr5VOJNh7220c/MEPAAguWsT5114Lb3hDQedrxFhERERERGQExaZGlyrFuZTGGvGeqhHukbh0mtjhwyUJivufeoqH3vveXFC8+IILeN5tt1HzwhcW3IYCYxERERERmZOKSYMeSbGBY0lTnEvgVMXAJrP41lgyiQTRQ4dIx+MTasc5x+H77uPBK66g/y9/wXw+znzHO7jgppsI19QU1ZZSqUVEREREZM4pNg16JONJjS5FinO+iSz/lD/iDRAoKyOV3V7X2Miq5mZ2bt5MCi/gT8dikz7CnYpGiWfXW55QO/39PHHLLXT+z/8AEF66lPOuvprFa9eOqz2NGIuIiIiIyJxTigrR050aPdHln0414j3VI9zJvj5i7e0TDoqPP/44D1x2WS4orn3Ri3jebbeNOygGjRiLiIiIiMgcNNDWRnDRoiHbip0/W9fYCC0t4x6xnahTjfieSiEj3sNHuAfTz0v9euNHjpA8fnxCbbhMhv3f+Q5PfvWruHQaXzDI2e95D8te8xpsgtW0FRiLiIiIiMicU6oK0aVOjS7GRIP7YlOlS5F+PpzLZIh1dpKORsd1/qDE0aM8+olPcPShhwAoP/10zr/mGirOOmtC7Q5SKrWIiIiIiMw5050GXQoTLY5VbKp0KdLP82WSSa/I1gSD4p4HH2THu96VC4qXXXwxF33hCyULikEjxiIiIiIiMgdNdxp0KZSiOFYxI96lSD8flIpGiXd24jKZos8dlEmlePI//5P927YBEFiwgHOuvJKlk/A7VGAsIiIiIiJz0nSmQZfCVAf3pUo/T/b1Ee/unlBfoocOsevGG+l7/HEAFq5Zw3nXXENZff2E2h2NAmMREREREZEZaiqD+4mOUDvnSBw5QrKvb0L96Pif/+GJ//t/SQ8MgBkr3/Qmznz72/EFJi98VWAsIiIiIiJSIhNZd3i6TWSE2mUyxDo6TpoTXYx0NMruL36Rw/fdB0Coqoo1H/wgVc997rjbLJQCYxERERERkRKYjKrOU208I9SZRIJYZyeZZHLc1z3xl7+w6/rrGdi/H4Cq5z2PNZs2EVqyZNxtFkOBsYiIiIiISAlMdN3h2SgVjRLv6MA5N67znXMc+uEP2fOlL5FJJjG/n7P+9V9Z8cY3Yr6pW0RJgbGIiIiIiMgIik2LLmVV59kgefw48SNHJnT+45/5DN2//jUAZcuWcd7VV7Pw3HNL1cWCaR1jERERERGRYQbTomNdXUPSojtaW0c9Z6LrDs8Wzjni3d0TCoqPPfIID1x2WS4ornvpS7no1ltLGhSbWcHHKjAWEREREREZJj8t2sy876EQe7duHfWcVc3NuESCVDSKc877XuS6wzOdS6eJtbePu/K0S6d58hvf4A8bNxLv6sIXiXDuBz7AmquuIrBgQcn6GaysJFxbW/DxSqUWEREREREZZjxp0VO97vBY8tPAAxUVOOdI9/dPqE+ZRIJYRweZVGpcfYp1dfHYxz/OsT/9CYCKVas4/9prKV+xYlztjcQXChGuqcEfDhd1ngJjERERERGRYcobGoh1deUKaUFhadFTue7waPKrY+PzcWL3bjAjsmzZuCtlpwYGiHd2jrvIVvdvfsPjN99M8vhxABpe9zrOete78IdC42pvJKGqKoILFxaVQj1IqdQiIiIiIiLDzOa06Pw08MSRI1gggPl8JI4cKSglfLhEby+xcVaeziQS7P7CF3jkwx8mefw4gcpKnvnRj7L6iitKFhQHysspb2ggtGjRuIJi0IixiIiIiIjISWZSWnSx8tPAM4kE5vfn/g2FV8p2zpE4cmTc84kH2trYdf31nNizB4BFz3oW5111FZEi5v6Oxfx+wtXVJZmbrMBYRERERERkBDMhLXpQMUtH5aeB+0IhXCoFzuHLjtAWkhLu0mlinZ0nVdkuVPvPfsaft2zxzvf5OOOSSzjjLW/JBekTFVy4kNCSJSVb61ip1CIiIiIiIjNYsUtH5aeBh6qrcakULpMhVF1dUEp4JpEgevjwuILi1MAAj37iEzz2yU+SjsUI19Zy4ac+xZmXXFKSoNgXDlO2bBnh6uqSBcWgEWMREREREZEZbXDOsEul6N+3L5ce/ehNN500ajw4spyKRsn09mLBIBWrV+eqUkdqa8ccbU5Fo16RrUym6H72/fnP7LrhBqIHDwJQ88IXcu7GjSdV9x4PM8sV15oMCoxFRERERERmsIG2NvD5iB0+jJlhPh8uk+HEnj10tLbmgtz8atSRujrSsRgukWDNpk0AuVTswcJbw4PjZF8f8e7uovvnMhkOfO97/OX223GpFL5gkFXvfjfL//7vx10MK1+gvJxQdTW+wOSFrwqMRUREREREZrDyhgaOPvxwLigeZMEge7duzQW4+dWoAQJlZaSAR2+6iczAABYKDUnFHlyyaSJFthLHjvHYJz9Jz44dXl9XrOD8a6+lYtWqCb9u8/sJ19QQKC+fcFunojnGIiIiIiIiM9iq5mZcMokD78s5yGQIL106pLr0QFsb/khkyLn+SISBfftyAbOZDVmyyaXTxA4fHldQ3PP73/NAc3MuKD7tVa/ioi9+sSRBcXDhQsobGqYkKAaNGIuIiIiIiIyqmGrQk6WusZGK1avp37cPl07jC4UI19RggcCQpY/yq1EPGiygNVLA3H/gAAMHD+LS6aL6k0ml2HfHHTx1113gHP7ycs55//upe+lLJ/AqPb5wmHB1Nf5weMJtFXXdKb2aiIiIiIjILFFsNejJtGbTJiK1tZSvXMmCM87AAoGTqkvnV6N2zuUqUC8488yTKkynBgaI1NQUHRRH29v5w5VX8tS3vgXOUXnOOTzvS1+acFBsZoSrqylftmzKg2JQYCwiIiIiIjKi/Dm7w1OQp1pdYyNrW1qI1NaS7O1UcqSRAAAgAElEQVQlUlvL2uwc4VMds2bTpiEBc/LECTLRKCvWry+qD52//CUPXnYZxx99FICV69fznFtuoWzZsgm9tkB5OWUNDZNWcbqgPkzblUVERERERGawgba2k5Ya8kciQ+b1TqW6xsZTpnGPekxLC3tuu42B/fuJLF3KiuZmatatK+i66XicPbfeyqEf/QiA4OLFrPngB6l+3vOKfg35zOcjXF1NoKJiQu2UggJjERERERGREYw2Z7e8oWEaezU+tX/1V1SuWkUmmSzqvP59+9h1/fX079sHwJLnPpc1H/wg4aqqCfUnsGAB4epqzO+fUDulolRqERERERGREYw2Zzd/Xu9skOrvJ3roUFFBsXOOgz/6EQ++5z3079uH+f2c9c538uyPf3xCQbH5fESWLiWydOmMCYpBI8YiIiIiIiIjqmtshJaWaa9KPRGJo0dJHDtW1DnJvj6e+Mxn6PrVrwDwhUL4KyroeeABFpx1VsEp2MMFKioIV1XNqIB4kAJjERERERGRURQyr3cmcpkMsc5O0tFoUef17trFI5s3kxwMps3wVVQQqqoi3tPD7i1bYMOGooJj8/sJ19RM2ZrE46HAWEREREREZJipWL94sq6RSSSIdXYWlzqdTvPUtm08+bWvQSYDZuD3g3Okjh8nEIkQWLCANHBg27aCA+NgZSWhqirMN7Nn8c7s3omIiIiIiOAFkdubmri/sZHtTU2TupbwVKxfPFnXSA0MFD2fON7dzcMf+hBPfvWrkMlgwSDlK1cC3mivmZE4ehQAXzhMtL39lG36AgEi9fWEa2pmfFAMCoxFRERERGSGm4pANd9UrF88GddIHDtGrKMD51zB5xz53e944LLLOPaHPwDgKyujbMUKfKEQvmAQsm0NBtqZeJyy+vox2wxWVlK2fPmQat4znQJjERERERGZ0aYiUM030NaGPxIZsq3U6xeX8hrOOWKdnblR3UJkkkn2fOlL/Omaa0j29hKorGTtRz7Cwmc8A5cNgkNLloBzOOfwBYOkYzFcMsmK9etHbNMXDFJ22mmzZpQ4n+YYi4iIiIjIjDbQ1kZw0aIh20odqOabivWLS3WNTCpFrKODTCJR8DkDBw/y6A030PfnPwOwaO1azrvqKiJ1dVgoxO4tW0gD/vJyAosXk+rtxRcOE66qYsX69SPOLw4tXkxw8WLMrKj+zxQKjEVEREREZEabikA136rmZnZu3kwKLwBPx2IlX7+4FNdIx2Je6nQmU/A57fffz58/+1mvWrUZZ7z1rZz+1rfiyy6hVLNuHWzYwIFt24i2t7Ng+XJWXHnlqMW2/OEw4ZoafKFQwX2YiRQYi4iIiIjIjDYVgWq+qVi/eKLXSPb1Ee/uLvh6qWiU3Z/7HO0/+xkAoepqzrvqKpZccMFJx9asW3fKqtNmRqiqiuDChQX3YSZTYCwiIiIiIjPaVASqI11zstcvHs81nHMkjhwh2ddX8Dl9u3ez64YbiGZTz6tf8ALO/cAHCA1LTy+Uv6zMGyUOzJ1wcu68EhERERERmbOmIlCdCcZa2ziTSnnrE8fjBbXlnKPt3nvZ++Uv45JJLBhk1bveRcPrXjeuucDm9xOuqiJQUVH0uTOdAmMREREREZEZYHBZKguFhixLRUsL1evWEe/sLHg+caK3l8dvvpkjv/0tAGXLl3P+tddSuXr1uPoWrKwkVFU166pNF0qBsYiIiIiIzFljjcDONPnLUgEEyspIAbu/+EUWrFxZcDtHH36YRz/+cRJHjgBQ/4pXsPq97yVQXl50n3zBIOGampOWlpprFBiLiIiIiMicNNoI7LE3vIEjv/3tjAuWhy9L5ZzD/H4GDhwo6PxMOs2+b3yDp+68E5zDX1bGM973Pupf/vJx9We2L8FUjLk5Di4iIiIiIvNe/gismREoKyOdTLLn1luJdXUNCZY7Wlunu7uUNzSQjsUALyh2ySTpaJSy+vpTnhvr6ODhjRt56pvfBOeofMYzuOjWW8cVFPvCYcqXLye0ZMm8CIpBgbGIiIiIiMxRA21tJ6UAp/r6yKRSQ4JlC4XYu3XrNPXyaauam3GJBMn+fjKJBKloFJdMsmL9+jHP6/r1r3ng3e+md+dOAFa88Y0857OfLXqd58ElmMqXLZv16xIXS6nUIiIiIiIyJ5U3NBDr6srN2QXIxOP4wuEhx/kjEQaySxlNp7rGRlIf/CB/+fKXiba3U1Zfz4r160ddUzgdj7P3tts4+MMfAhBcvJg1H/gA1c9/ftHXDixYQKiqak4twVSM+fmqRURERERkzlvV3MzOzZtJ4QW/6VgM8/sJLlw45Lh0LFb06GqpDa5PvOjcc7nw058+5fH9Tz3Fruuvp//JJwFYfOGFnPehDxGuri7qur5QiFBV1ZCHB/ORUqlFRERERGROqmtsZG1LC5HaWpK9vURqazn78svxB4NemrJz3vdEglXNzdPWT5dOEzt8mGRf36mPdY5D993Hg1dcQf+TT2I+H2ddeikXfOITRQXFg2nTZcuWzfugGDRiLCIiIiIic1hdY+NJFacXP+tZM2YJp3Q8Tryzk0wqdcpjUydO8MQtt9D5i18AEKmr47yrr2bR+ecXdU1/JEK4pgZfMDieLs9JCoxFRETmOTNbDNwOrAUccKlz7jfT2ysRkckzUrA8HZJ9fcS7uws6tvexx3j0hhuItbcDUPuiF3HOxo0EKyoKvp75fISqqghWVo6rv3OZAmMRERH5LPAT59wbzSwElE93h0RE5rLB+cQFpU5nMuz/znd48qtfxaXT+EIhVr/nPZx28cVFLaU0WFyr63//t2Sj5R2trTNm5H2iFBiLiIjMY2a2CHgx8C8AzrkEkJjOPomIwNwKuvJlUilinZ1k4vFTHhvv6eGxm27i6EMPAVB++umcf+21VJx5ZsHX63noIQ5897tEDx8mUFFBvLub4KJFQ9ZwpqWl6Pe2o7WVnZs3Y6HQhNuaCVR8S0REZH47E+gC/tPM/mBmt5vZgunulIjMb4NBV6yra0jQ1dHaOurx25uauL+xke1NTaMeN93SsRjRgwcLCop7HnyQB5qbc0Hxsosv5qIvfKGooPjYrl38+XOfI97TQ3DRIvr37SN57BgulZrwGs57t27FQqEZuR70eCgwFhERmd8CwHOAW51zFwL9wIfyDzCzZjN70Mwe7Orqmo4+isg8U0zQVWwQPV2Sx48TPXwYl8mMeVwmmWTP1q388UMfInnsGL5IhPLTT+fIgw/yp2uuoXvHDrp37OAPGzeyvamJP2zcSPeOHUPa8AWDlC1bxv677sIXDufeR5dOg883ZF7zeNdwHmhrwx+JDNk2U9aDHg8FxiIiIvNbG9DmnPtd9ufv4gXKOc65rc65i5xzF9XW1k55B0Vk/ikm6JrpI5fOOeLd3cSPHDnlsdFDh/j9+9/PgW9/G4DylSsJVFTgMhkClZXEe3p4/Oabefzmm4n39OS27d6yJRccByoqKFu2DH84fNL76AuFAMgknp4xM941nMsbGkjHYkO2zYT1oMdLgbGIiMg85pxrBw6Y2TnZTS8DHp3GLomIFBV0zeSRy0wqRbTA9Yk7fv5zHnj3u+l74gkw4/S3vIXg4sX4y8rwRyKYGf5IhFR/P6n+/iHbLBjkwLe/TaS2lkhtLebzwrzh72O4pgbSaczvn/Aazquam3GJxIxaD3oiFBiLiIjIvwF3mtmfgAuAG6e5PyIyzxUTdM3Ukct0PE700KFTzidOR6M8dvPNPHrjjaQHBghVVfHsm27irEsvJdbRgS8cHnK8S6dPSsf2l5UR6+oiMGzppuHvowUCBKuqKD/9dJK9vURqa1k7zmJZdY2NrG1pIVJbO+G2ZgJVpRYREZnnnHMPAxdNdz9ERAbVNTZCS8tJVakBtjc1Ddm2qrmZnZs3k8IbKU7HYrhEguoXvOCkY6cqaCt0feITe/ey64YbGNi/H4CqdetYs2kTocWLASirryfe0zNkRNz8/iFt+AIB0okEC1asOKn9kd7H86+5pmTvw0xZD7oUFBiLiIiIiMiMMzzoGm15oLUtLawdFvxVv+AFtN1zz5QvJVTo+sTOOQ7+4Afsve02MskkFgiw6p3vpOH1r8+lQQOsWL+e3Vu2kAZ84TCZeJzAAm/hgHQiQWDBAtLx+JgpzOMJXufqUlljUWA8z33+858H4L3vfe8090RE5jv9/0hERMaSX2QLIFBWRiq7/YV33jkkcNve1DTqsZMV4GVSKdr+67946hvfINreTll9PSvWr6dm3bohxyWPH+fxT32K7u3bAShbvpzzrr6aheecc1KbNevWwYYNHNi2Ldfm6iuuILhwIfvuuGNSAte5tj5xoRQYz3N79uyZ7i6IiAD6/5GIiIxtoK2N4KJFQ7aNVmSrmGNLIR2P0/bDH/LnW27BgsEh1aLZsCEXHB975BEevfFG4tml7+pe/nKesWEDgfLyUduuWbcud35gwQLC1dWY38+yV75y3P0da0R4rAcQCoxFRERERESmkX/BAk7s3YtLp/GFQoRrarBAYMQiW+UNDV4xqmxwB5NXkGtwPvH+u+7CgsHcfGB/JEIa2PvlL7P/7rs5sXcv6f7+3L5nbNhA/SteUdA1zOcjXFOTS6MuxvAg+FRp5lP9UGGmUFVqERERERGZ0TpaW0n09OCSSTAjk0wSPXiQZG/viHNrp2Ipodz6xNkiW9H29pMqSGdSKQb27aPviSdyQbEFAqy6/PKCg+LAggWUNzScMijuaG1le1MT9zc2sr2piY7W1lxadKyrKxcE77n1VtLJ5KjrPs/UKt+TTYGxiIiIiIjMaHu3biW4cCFlDQ34gkHILj0UrqkZMb13spcSGml94rL6+pOWZop3dYFzue3BxYsJ19fT+fOfn/Ia5vN56xIvXXpSJerhRgqAd27ezKM33ZRLix4MgjOpFKm+PpJ9fZx48kmOP/EEsfZ2ju/eDcy99YkLpVRqERERERGZ0QbTe82MYGUl4I3YJnt7Rz1nspYSSkWjxDs7T1pLOL+CtAWDxDs7IZXydvp8ROrqCFRU4Jwj2t4+5jUC5eVeqvgpAuJBo80LHti3j4qzzx5yrC8cJh2NEj10CDPDfD4yiQQulaKjtXXUpbLm8vxiUGAsIiIiIiIz3FTOGYbRi1Mljh0jcfToiOcMVpDe9/Wve3Ohs0GxLxwmctpp3kg3kInHKauvH7EN8/kIV1cTqKgoqr+jzQsG733Kf9+CCxcSj0a9H3w+b/QdCC5ZkiuwNZfWJy6UUqlFREREROa4keafziZTmd47UlryI9ddx4Hvf3/UoBig63e/Y8+tt9L3xBNeUGzG0pe/nODixbh0Gucc6VgMl0yyYv36k87PzSUuMiiG0ecFLzjzzJPeN3+2arYFg7h0GgsEiCxbRri6es4X2BqLAmMRERERkTlstPmnsyk4nuw5w/ny05LNDH84DD4f++64Y9RzOn75Sx792MeIHjgAgPn9hJYsoe6lL+UZ73sf4aoqUn19hKuqWJ23fNPgsZGlSwuaSzya0R4crNm0acT3bdF551F22mksPOccKs48k2Bl5bwosDUWpVKLiIiIiMxhc2Vd2qlK781PS3bpNJlUCl8oNOq84OOPP87jn/gEmUQC8JaVitTVkUkmObBtGxd++tNDAuF8wcpKQkuWjDsgHlTX2MixN7yBv3z1q6T7+/EvWMBZl16ae79Get92bt5MiuyyUrHYvCiwNRYFxiIiIiIic9h8XZd2vMobGoh2duIPBnMFtkaaF+wyGQ5897v85StfwaXTYOatNZwtEubz+U4Kprt37ODAtm1EOzpYsGIFZ19+eUmC/Y7WVtruuYdwbS3+FStIx2K03XMPi5/1rFGrds/HAltjUWAsIiIiIjKHzZTCVbPFmZdeyh8/+EGi/f24TAbz+QgsWMDZl1+eOyZx9CiP3XQTPQ8+CHgFtoKLFxNcuDB3zPBgunvHDnZv2YIvEiFUVUW8p4edmzdDCVLCx5MVMB8LbI1Fc4xFREREROaw6S5cNZvmM6cGBkgcOfL0BudOOqbnoYd44LLLckHxaa9+NWuuugoz81KSRymydeDb38ZXVkawogKfz+fNYQ6F2Lt164T7PdDWlqtCPUhZAcXRiLGIiIiIyBw2lWmzs3k+c+LoURLHjrH/7rsJVFQQrqnJ7UvHYuy/+256H3mE/XffDc7hLy/nnH//d+pe8hIAfKGQlybd3k5ZfT0r1q/PzS0OVlYS6+oitHjxkGuWKnid6qyAuUiBsYiIiIjIHDcdhasGzfSRy0wqRbyrK7fcUbS9nUBl5dCDfD6OP/YYvX/6EwCV557L+VdfTdmyZblDatatO6nIlvl8hGtrCZSXs2DFikkLXlc1N6uY1gQplVpEREREREpitPV0Z+rIZWpggOjBg0P6XFZfTyYef/qYvj6i+/fjkkkAVq5fz3NuuWVIUDySQHm5ty5xeTkwuSntU7mc1VylEWMRERERESmJ2TJy6Zwj0dND8vjxk/atWL+e3Vu2kMpkSPT0kIlGvR1+P2e87W2c2dQ0ZttmRqi6muCwUefJTmlXMa2JUWAsIiIiIiIlMRuWAcokEsS6unLrDg9Xs24dsX/+Z/bcemtulNhCIcI1NbTfdx+Vq1ePui6xPxIhXFuLLzBymKXgdeZSYCwiIiIiMs9M5pJKMzn4S/b1Ee/uHnW/c45DP/oRe7/0pVxQHKquJrhkSa7q9IFt20YMjENVVYSGza+W2UOBsYiIiIjIPDK4pJKFQkOWVCrFerozlUuniXd3kxoYGPWYZF8fT3zmM3T96lfeBr+fyGmnDSmW5QuHiba3DznPlx1N9ofDk9J3mRoKjEVERERE5pHZvKTSeKRjMWKdnbh0etRjju3cyaM33ki8sxOApS95CfGuLhK9vUOOy8TjlNXX534OLlxIqKoKM5uczhdgMkf/5xNVpRYRERERmUcG2trwRyJDts30JZXGK3H0KNHDh0cNil06zb5vfpOHr7ySeGcnvkiEczdu5Lyrr2blW96CSya9AmLOed+TSVasX4/5/UTq6wlXV097ULxz82ZiXV1DRv87WlunrU+zlQJjEREREZF5ZLYtqTQemVSK6KFDJI4dG/WYeHc3D3/wgzz5ta/hMhkWnHUWF33hC5z26ldjZtSsW8fqDRsIV1WR6usjXFXF6g0bqH/JS7xlmPJSrKdL/ui/mXnfQyH2bt063V2bdZRKLSIiIiIyj8yWJZXGK9XfT7y7G5fJjHpM929/y+M330wymyq9/B/+gVWXXYY/FBpyXM26dblCW+bzEa6uJlBRMXmdL9JAWxvBYQW/5uro/2RTYCwiIiIiMo/MhiWVxmOstYkHZRIJ9t5+O23f+x4AgcpKzv2P/6D2r/96zLZPtQzTdClvaCDW1TVk9Hqujf5PlZn1mxURERERkUk3k5dUKsTwglNnXnopi9asIROPj3rOQFsbu264gRO7dwOw6JnP5LyrriKydCndO3ZwYNs2ou3tlNXXs2L9+txIcbi6muDChVPyuoo110f/p5LmGIuIiIiIyKwxvOBUtKODndddR+fgMksjaL//fh68/HIvKPb5OOOSS7jgU5/KBcW7t2wh3tNDoLKSeE8Pu7dsoef3v6d8+fIZGxSD94BjbUsLkdpakr29RGprWTuHl92aTBoxFhERERGRWWOw4JQ/EsGlUvgCAVwwyIFt23KjvINSAwPs/tznaP/v/wYgXFPDmquuYsmzn5075sC2bVgwmKvU7Y9EyPj9HLjnHla+4Q1T98LGabaP/s8UCoxFRERERGTWGGhrI1BRQSaRyG3zhcNE29uHHNe3eze7rr+e6MGDAFS/4AWc+4EPEBpWrCra3k6gsvLptoJBfKFQ7ryZTusYl4ZSqUVEREREZFZwzhFZupRUf/+Q7Zl4nLL6+twxB+65h4f+7d+IHjyIBYOsvuIKnvmxj50UFAOU1deTiccxnw9fKIT5fLOmgJXWMS4dBcYiIiIiIjLjZRIJogcP0vDGN+KSSa/QlHPe92SSFevXkzh2jEeuvZY9t96KS6Uoa2jguVu20PC612FmI7a78k1vgkyGTCoFQCoaPamAVUdrK9ubmri/sZHtTU0zJvDUOsalo8BYRERERERmtGRfHwMHD5JJJqlZt47VGzYQrqoi1ddHuKqK1Rs24A+FeOCyyzjyu98BUP/KV3LRrbdSuXr1qO36IxFWvP71PPNjHxu1gNVMHpUdaGvLzY0epHWMx0dzjEVEREREZFKNdx6sy2SIHzlC6sSJIdtr1q3LFdrKpNPsu+MOnvrWt8A5/GVlPOP976f+ZS8bs+38ZZjGKmCVPyoLECgrI5XdPt1zebWOceloxFhERERERCbNeEdc0/E40YMHTwqK88U6Onj4yit56s47wTkqn/EMLvrSl8YMin3hcFHLMM3kUdlVzc24RMJL/3ZuxDRwKYwCYxERERERmTTFzIMdnMv73y96Edvf/GY6t28ftd2uX/2KBy67jN5duwBY8U//xHM++1nKly8f9ZzgokWUnXYavlCo4P6XNzSQjsWGbJspo7Jax7h0lEotIiIiIiKTZqCtjeCwatAjjbh2tLbyyHXXgd+Pv7yceE8Pu7dsgQ0bhqxPnI7H2XPrrRz60Y8ACC5ezJpNm6getoZxPvP7iSxdetLIbyFWNTezc/NmUtl+p2OxGTUqq3WMS0MjxiIiIiIiMmkKHXHdc+ut4PPhD4UwM/yRCBYMcmDbttwx/fv28dAVV+SC4iUXXsjzbrttzKDYX1ZG+fLl4wqKQaOy84VGjEVEREREZNKcasR1sMBW//79BCorh5zrC4eJtrfjnOPwj3/M7i9+Mbfm8JnveAcr16/HfKOP9YWqqkZcu7hYpRqVHW8RMpl8CoxFRERERGRSDAaCqWiUTG8vFgyycPXqXECYjseJd3aSSaUoq68n3tMzZGQ3E48Tqa3l0euvpzNbrCtSV8d511zDovPOG/W6E0mdniyDRcgsFBpShAyNPs8ISqUWEREREZGSy69GHamrI1xbS7C8PBcUJ44dI3roEJlUCoAV69fjkklvRNk50rEY6f5+Bg4ezAXFtS9+MRfddtuYQXGgvHxCqdPFvL7tTU3c39jI9qamU1bZLqYImUw9BcYiIiIiIlJyowWCe267jejhwySOHh1yfM26dazesIFwVRXJ48dxqRSJY8dIHj2KLxTinPe/n/M//GGCFRWjXjNcU0Okrg7z+yf1tY1nCaqZvOyTKDAWEREREZFJMFIg6AuF6H/qqZOKcQ2qWbeO8665hgUNDcQOH4ZMhgVnnMFzv/AFlr3mNZjZiOf5QiFvbeJhc5Qny3hGf2fysk+iOcYiIiIiIjIJyhsaiHV1ESgrwzmHS6dJ9fdTVlc35LjuHTs4sG0b0fZ2AuXlxDs7SfX3A7DsNa/h7Msvxx8Oj3qdYGUloerqUYPmyVDoElT5ZvqyT/OdAmMRERERmRBV2pWRrGpu5uFNmxhoa8Ol05jPR2DBAs6+/PLcMd07dnhrFQcCZOJx+js6APBFIqzZtImlL37xqO2bz0e4tpZAefmE+jmev9/8oH/QqUZ/6xoboaVF/63MUAqMRURERGTcVGlXxuKcg8GvERzYtg1nRrKri0w8Dnhp0RVnnjlmUOwPhwkvXYovMLFwZrx/v+Md/S3Vsk9SeppjLCIiIiLjpkq7MhKXTvPnz3+eQHk5C04/nYqzzmLB6acTqKjgwLZtueNOPPkk8fb2XFAcXLKEyIoVxHt6Rm07uGgRZcuWTTgohvH//dY1NrK2pYVIbS3J3l4itbWs1cOgWU0jxiIiIiIybuOZaylzWyoaJd7VRfTgQQLDimH5wmGi7e2kolF2f/7zpI4fB7x1h8P19QTKy0nHYpTV15/Urvl8hJcuHZK+PFET+fvV6O/cohFjERERERk3VdqVQc454j09xNrbcek0ZfX1uZFggFR/PwP79xM/coT/feMbaf/pTwEvWA7X1eEvKyN+9Cix9nb69+/nDxs30r1jB+ClTpctX17SoBj09ytPU2AsIiIyB5jnrWZ2XfbnlWa2brr7JXPfquZmXCJBKhrFOed9V6XdeSeTSHDg3nvZ8c53sr2piT9s3MjiCy7AJZOkYzGSJ054AXMyCalULmA+7e/+jvOuu45IbS3x7m5Svb0EFi0iVF1NvKeH3Vu2cGzXrqJSpztaW9ne1MT9jY1sb2oac21h/f3KIAXGIiIic8MXgb8C3pz9uQ/4wvR1R+YLzbWUZF8fB77/fZ749KeJ9/QQqKwk3tND+09/Sv0rX0m4qop4V9eQIlwWDBKuqyN68CC1z38+F37601ScfjqR+nrCS5bk5vv6ysrYf9ddBfdlsJhWrKtrSDGt0YLj8fz9FhN4j+d4mR6aYywiIjI3PN859xwz+wOAc+6omYWmu1MyP2iu5fzk0mlvlHdggP13340Fg/gjESBbqRk49vDDnPG2t/HwBz6QC4oDlZWEly4FM6Lt7bn2ou3tuTnJ5vNhgQCBYLCo+er5xbQAAmVlpLLbR/sbLebvt9gq1qraPntoxFhERGRuSJqZH3AAZlYLZKa3SyIyV6UGBhg4eJDUwADgBbW+cHjIMRYK0bdnjxcUZzJgRriujkh9PebzkYnHhxTZGpyT7AsE8AWDmFnR830H2tpywfmg/GJaEx29LbaKtaq2zx4KjEVEROaGLcC9wFIzuwH4NXDj9HZJROYa5xzx7m5iHR24dDq3fXihrUwySaytjXR/P2QyRJYtI1RdjS8UwjnnrfubTLJi/frcOac3NYFzxI8e5cSTT3L88ceJtrVR/YIXFNy/sYppFZtmPZJTBd4TPV6mjwJjERGROcA5dyewCfg4cBj4R+fcd6a3VyIyl6TjcaIHD5Ls6ztp34r1658utNXXx8D+/blAueH1r+f5t9/OOVdeSbiqilRfH+GqKlZv2EDNOq9GYLCykszvzCgAACAASURBVBWvfz0r3vhGkkePko7FvPTjJUtou+eegoPXsYpplWL0ttgq1qp6PXtojrGIiMgsl02h3uWcOxd4fLr7IyJzT+LYMRJHj466v2bdOjKXX87uLVtIHDkCgL+8nPOuvpqa7Ihvzbp1uUB4kPl8hGtrCZSXA3Dkt7+lrKFhyLJMqWh0zDnC+eoaG6Glhb1btzLQ1kZ5QwOrmpupa2zkkeuum/Ca26uam9m5eTOp7LnpWGzMKtbFHi/TR4GxiIjILOecS5vZE2a20jm3v9jzzWwfXhXrNJByzl1U6j6KzEUdra0jBmBzSSaVIt7VddKo53D9+/fz1B135ILixc9+NudddRXhmppRz/GFw0SWLh2yDNNAW9u4g9fhv49nfvSjQ34f5Q0NxLq6hgTdxY7ejhV4l+J4mT4KjEVEROaGJcAus//P3p3H113V+R9/nbtna7Pd3HQvtEBpgZGhdBT9UXEZGGXctTB1cBlt2SzIotDW4VGlRUARilSoC8pYtSM6LjOujFoX1ELFAbpAW7YmbdrkZmnS3P17fn8kjd2SZrn3fu+9eT8fjz7S3N7v93yyEPK+55zPMZuBQ4cftNa+bZjXX2StbctJZSIlaDx0G0739JCIRrHO4H38rLW0/PznPP/FL+LE4+DxcMoVVzDj8ssxXu+g1/knTCBQW4sx5qjHRxteh/P1yNbs7Ui7sKtre3FQMBYRESkNn3K7AJHxZDTHAhUL6zgkolHSPT1DPi/d08Nz993HgV//GoBgOMzc5cupPvvsQa8xxhCsr8dXWXnCfx9teB3O10OztzIUBWMREZESYK3dZIyJAOf3P7TZWntguJcDvzDGWOAha63OERE5ibEs+XXTyZZ/Z+JxEq2tOOn0kPc5uGMHW1evJr5vHwD1r3sdc264Af+ECYNe4wkECIXDeAKDH7E+2vB6sq/HyZZZiygYi4iIlABjzPuAu4HfAAa43xhzs7X20WFc/jprbbMxpgH4pTFmh7X2t0fcewmwBGD69OnZL16kCGVjv2q+DbXcuOHCC0m2t5M6eHDIe1jHYc93v8sLX/saNpPB4/cz++qrmXzppcctiz6Sv6qKQF0dB37725OG3tEsPR7q6zEelr3L2Om4JhERkdKwAjjfWvsBa+0VwAKGubzaWtvc//YAfWchLzjm39dba+dba+eHw+Esly1SnIY6FqhQDXZc0a4HHyS2d+9JQ3Gyo4Only9n95e/jM1kKJ8+nfMeeIAp//zPg4ZiYwyhcJhgfT0HfvvbMZ8jPJhcH9MkpU/BWEREpDR4jlk6HWUY/583xlQYY6oO/x34R+DZ3JQoUjoiCxdy1qpVhMJhUl1dhMJhzirwGcjepia8odDA+9ZajM/HoZdfxkkmh7y2fcsWnliyhPYnnwRg0lvewvx166g89dRBr/EEApRNmTKwnziXAXWor8exHzcUx7J3yS8tpRYRESkNPzPG/Bz4dv/7i4CfDuO6CPBf/bM9PuBb1tqf5aZEkdJSbN2Gj1xubB0Hm06TicUoa2wc9BonnebFr3+dVzZuBGvxVVRwxsc/TsPrXz/kWL7KSoL19UfNJOd6X/ZgX48TLbNORKNkent5bOFCNeESQDPGIiIiJcFaezPwEHBO/5/11tpPDOO6F6y1f9f/Z561dnWuaxURd8xasgQnkSDV3U3m8LLjVIppixad8Pmxfft46uMf55XvfAesZcKcOcx/8MGThuJgXR2hcPiERzEdex5yPvZlH7vMOt7WRrK1FW95edaXdI/W/k2beHzxYh5buJDHFy92tZbxSsFYRESkBBhjTgF+Yq29wVp7A30zyDPdrUpEsiUbwan+H/6B0z/2MQLV1aS7uwnW1nLasmXUL1hw3HMP/OY3PLF0KQe3bwdjmH7ZZZx7772UTZo06P2N10vZ5MmDdqZ2a1/2scusM729BMJhQv0z2ida0p3PoHq4OVgu9l6Ptp7xGNK1lFpERKQ0fBe44Ij3M/2PnX/ip4tIsRhrV2Vr7UDH6drzzqP2vPMGfW4mHmfnunXs+8lPAAjU1HDmLbcMeQ2AJxgk1NCAxzd4vDj2KCZvRQUev59n/v3f2Z3j5cxHLrN+bOHCkx7tlM8u1oV0JvZ47uCtGWMREZHS4LPWDnTP6f/74IeFikjRGEvTqkwiQay5mdTBg7Rt3sxTN97I44sX89SNN9K2efNRz+154QWevPrqgVBcc955nL9+/UlDsa+igrJJk4YMxYdFFi7kgg0bOPvTn8bp7cVJpfI+S3qyJd357mJdSM3BxnMHbwVjERGR0tBqjHnb4XeMMW8H2lysR0SyZDTByVpLor2d2N69OKkUbZs3s3PtWhLt7fiqqki0t7Nz7VraNm/GWkvzj37ElmuuofeVVzBeL7M++lH+7o47CNTUDFlboLqaUEPDkGcYn4ibAexkS7rzHVTd2nt9IoUU0vNNwVhERKQ0XAksN8a8YozZA3wSWOpyTSKSBSMNTgOzxF1dA4/t2bgR4/fjDYUwxvS99ft5ecMGtq5axfNr1+KkUoQmTeLv77uP6YsWYTxDR4VQOHzS4DwYNwPYyY7ayndQLaQzsQsppOeb9hiLiIiUAGvtbuDVxpjK/vd7XC5JRLJk1pIlPHvbbaTpC4+ZePyEwclaS7Kj46hAfFispQVfVdXRz3ecvuZajgNAw0UXccb11+OrqBiyHuP1EopE8AaDo/6YTnSE0pEBbP+mTQN7kXNxnNJQR20N9/OdzVqO3Ht9oo8315+Pw/L9sRcSzRiLiIgUMWPMPxtjZhzx0A3AH4wxP+rvVC0iRe5kM5wATjJJbO/eE4ZigLLGRpxEAugP0NEo8eZmcBw8oRBzbrqJucuXDxmK2zZv5qmbb+bx97+fP3/4w2PaDzzULKnbXZqH8/nOxZgXbNjAmzZt4oING44Lxfn6fLjxsRcKzRiLiIgUt9XAqwGMMZcC7wcuB84FHgQudq80EcmWoWY4k11dJNvbh7x+2qJF7Fy7lnQ6TbKzEycWAyA0aRLnrF5NxfTpQ17ftnkzO7/4RTyhEIHq6jF3Kx5qlvTxxYtd79I81Oc73/LdtbqQPvZ8UjAWEREpbtZa29v/93cBX7XWbgG2GGOudrEuEckxJ5Ui0dpKpn8meCj1CxZw8B//kVc2bMBmMgDUveY1zPvUp/AGTt7Avul738NbVpbVcDZYAOttahryOKXxRp+P/FAwFhERKW6mf19xL/BGYN0R/xY68SUiMpR87ecci1R3N8loFGvtSZ/rJJPs/spXaPr+9wHwVVUx56abCL/2tSe91ng8BMNh4vv35y2cnWz/8Xijz0d+aI+xiIhIcbsX+CvwJLDdWvskgDHmXGCfm4WJFCO397eejM1kiLW0kGhrG1Yo7m1qYst11w2E4onnnMP569cPKxR7AgHKJk/GV16e9W7F+zdt4vHFi3msf+n0kZ/fQurSXAj0+cgPBWMREZEiZq39GrAQ+DfgLUf8UwvwIVeKknFvqNBT6Nw8X/dk0ocO0dvURKZ/f/DJtPziFzx55ZX07NwJHg8zP/ABzr37bkLh8Emv9VVWUjZ5Mh6/H8huODvZiw/juQHUiejzkR9aSi0iIlLkrLXNQPMxj2m2WFxxOPSYQOCo0DPaJk2jrWG0S6ELcT+ndRwS0SjpnuGdwpbu7eX5tWvZ/9hjAATDYebeeivV55wzrOuD9fX4jznaaThHCg3XcJpJjdcGUOIeBWMRERERyZp8d9A91liDeaHt50zHYiRaWwcaZp1M986dbL39dmLNfa+V1V9wAXNuvPG4sH9Y2+bN7Nm4kVhLC2WTJjH7qquoPOXEJ71lK6wW4osPhawQXmwaD7SUWkRERESyprepCW/o6L5v+Qw9Y10KXSj7Oa21JNraiLe0DCsUW2vZ8+ijbPnYx4g1N2P8fk679lrOWrVqyFC8c+1aEu3t+CdOJHnwINtuvz3nS9+zvV+51BXy8v5SomAsIiJSAowxs4wxwf6/v94Ys8wYU+12XTL+uB16xhrMc7WfcyT7rjOJBLHmZlLd3cO6d7Kzk6dXrGDXgw9i02nKp03jvPvvZ+o73oExZtDr9mzciPH78VVU4A0E8JeX5yVwFcqLD4Xq2O+Vgzt3uvpi03ihYCwiIlIavgdkjDGzgfXANOBb7pYk45HboScbwTyycCEXbNjAmzZt4oING7ISiofT6dpaS6K9ndjevTip1LDu3fHUUzyxdCntmzcD0Hjxxcxft46q2bNPem2spQVfRQUe3992V+YjcKmZ1OBO9L2S6ekhEY0e9TzNsGef9hiLiIiUBsdamzbGvBO431p7vzHmKbeLkvEnm02aRmPWkiU8e9ttpOkLeZl43PXZyOHsu84kEiTa2nCSyWHd08lkeOkb3+Dlb38brMVbXs4Z119P5A1vGNb1xuOhYsYMEtHoUcE4X4HLjeZaxXA+9Ym+V/w1NaQ6Ovpm9gvke7oUKRiLiIiUhpQx5nLgA8A/9z/md7EeGcfc7CjsdjA/kaGaTVlrSXZ0kOrqGvb94vv3s3XNGg5u3QpA1emnM2/lSsomTx7W9R6/n1Akwuwrryy4FxFypVgaWJ3oeyVYV4dNpwmFwwXzPV2KFIxFRERKw4eAK4HV1toXjTGnAP/hck0irii0o34G63RdNnkysebmYS+bBjjw29/y3D33DBzdNO197+PUD31o4LzhYx3VdbqxkZn/+q9MffvbMR5PQb6IkCtud0sfrsG+V6pmz+aCDRtcrKz0ubLHOBqNct1119He3u7G8EMq5NqGUqx1i4hIdlhrt1lrl1lrv22MqQGqrLV3ul2XFL+RNI2SEzt233WqtxcnFmPKO94x7FCcSSR47t572frpT5Pu6cFfXc05d9zB7CVLhgzFh7tO+6qqSHZ28ty993Lgd78beE6291MXKre7pQ+X23v0xzNXgvEjjzzCM888wyOPPOLG8EMq5NqGUqx1i4hIdhhjfmOMmWCMqQX+AnzZGHOP23VJcRtu0ygZ2pHNppKdnQQmTGD2tddSv2DBsK4/9NJLbLnmGvb+938DUPP3f8/569dTd/75Q153uOu0NxTC4/Phr6rCEwyOi2N+jn1Bx1tRURRHRKkxmXvyvpQ6Go3ys5/9DGstP/vZz7jiiiuora3NdxknVMi1DaVY6xYRkayaaK09aIz5CPCItfY2Y8zTbhclxa1Ylp8Wg4YLL6TmnHNIdnYO+xprLfv+53/Y+aUv4SQSGK+XUz70Iaa/730Yz8nnt2ItLfiqqvD4fBivFyjMWdJsO9F+4tTBg2AtUPj7qQttK8B4kfdg/Mgjj+A4DgCZTIZHHnmE66+/Pt9lnFAh1zaUsdTd3NxMLBYrio9T3Ldr1y7sEOchSv50G0PPrl0l9d/url27KDtiT5WMmM8YMwl4H7DC7WKkNAzVNKqUZbt7cSYe7+s4PYK9xKmeHp675x5af/tbAEKNjUx5xzuI/vGPNP/4x5Q1NjJt0aIhZ53LJk0i2dWFOWIJcTZmSQu1u/PhujqeegqMIRSJYIwZeGHH4/cTqK4uuLqlMJz0pSZjzBJjzJPGmCdbW1vHPOBjjz1GOp0GIJ1O88tf/nLM98yWQq5tKMVat4iIZNWngZ8Du621TxhjTgV2ulyTFLlsnAlcbLK5fNw6Dom2NmL79o0oFHdt28aTS5cOhOLwhRdy6kc/SvMPfjCwXzjR3s7OtWtp6z+/+FieQIDTrrkG0ums7lfN1/L6ke5tP7IuJ5PBOg6xvXtJdXcDfS/opHt6xsV+ahmdk84YW2vXA+sB5s+fb8c64Jve9CZ+8pOfkE6n8fl8vPnNbx7rLbOmkGsbyljqnjJlCgD33ntvrsqTEnL99dfT8pe/uF2GAFXW0jh7dkn9t1tKs99usNZ+F/juEe+/ALzbvYqkFBTimcC5lq3l4+lDh0hEo9hMZtjXWMfhle98hxe//nWs4+AJBjnt6quZ9Ja38NebbhrYLwz9Xw/69hEfO2vsKy8nGA5TPmUKxuvN6uxuPpbXj+ZopSPr8gaD2P5Jo0RbG/6qqpJ/QUfGLu/Nt6644go8/XsivF4vV1xxRb5LGFQh1zaUYq1bRESyxxhzujHmf40xz/a/f44xZqXbdUlxG4+NgMbavdhJp4nv30/8wIERheJENMr/3XILL3zta1jHoWLmTOavW8fkt74VYwyxlhY8weBR13iCQWItLUc95p84sW8Jcf/vhtnuOp2P7s5HhtzDS6FNIDBk07Aj6wrW14PjYAHncIfnEn9BR8Yu78G4rq6OSy65BGMMl1xySUE1iSrk2oZSrHWLiEhWfRm4FUgBWGufBi5ztSIpCePlOJ/DxrJ8PNXdTaypiXRv74jGjG7ezBNLl9LRvypr8qWXct4DD1AxY8bAc8oaG3ESiaOucxIJyhobB94PhcMEc/x7YD6W148mfB9Zl7+qitDkyRiPB+PxjIsXdGTsXDmu6YorruDss88uyJnNQq5tKMVat4iIZE25tfbYzYZpVyoRKWKjOUfWSaWI7dtHoq0Na4e/89BJpdj14IM8vXw5qc5OfJWVzPv3f+eM66/He8zs8LRFi7CpVN9ydmv73qZSTFu0COP1UjZ5Mr7KylF/3MN1+PMTb2uj58UXObhjB7GmJupe/eqsjTGa8H3s1834fITCYeZ/6Uvj4gUdGTtXgnFdXR333XdfQc5sFnJtQynWukVEJGvajDGzAAtgjHkPsM/dkkSKz0iXjye7uuhtajouyJ1Mb3Mzf7nuOvY8+igAE+fN4/yHHqLhwgtP+Pz6BQs4bdkygrW1pLu7CdbWctqyZUT+3/+jfMqU44J0rkQWLmTqu99NqqODTDzetw+4poam730vaw24RvPiBIC3ooJYUxM9u3ZhfD7NEsuI5P24JhEREcmJa+hrljnHGNMMvAi8392SRIrTcM6RdfpnTY9d3jwcLf/7vzx/771kYjEwhhn/8i/MvOIKPP1nDQ+mfsGCoxpt+SorCdbXY/J8lGL0T3+ibOrUgQZcAOlYLGsNuCILF8KqVcNuGnZks67K2bPJxOM4I1zOLqJgLCIiUgL6u1C/yRhTAXistd1u1yRSiqy1pDo7SXZ2jvjadCzGzvvvp+UXvwAgUFfH3FtvpeZVrxrxvQI1NQSqq0d8XTbk43zr4bw4cVg+OmVL6VMwFhERKQHGmCB9xzPNBHyHZ5CstZ92sSyRkpKOxUhGoyM6k/iw7p072bp6NbH+8Fj36lcz5+abCRwTMIcjFA7nZT/xYMqnTiXe2nrUjLGbxyHlI6hL6VMwFhERKQ0/BLqALcDI13aKjHP7N20adOmuzWRItLeT7ukZ8X2ttTT/4AfsWr8em0ph/H5mffSjTH3nO0e8BNp4vYQikbztJx5MoZ1vXWhBXYqTgrGIiEhpmGqtvcTtIkSK0ZF7VP0TJxJvbeXZ226DVauomz+fRDQ6ojOJD0t2dbHjc58j+sc/AlA2ZQrzVq6k6rTTRnwvTzBIqKEBj+/kv74PFfKzYaR7gHOt0IK6FCcFYxERkdLwuDHmbGvtM24XIlJsTrRHNWUtO7/4RSruumtU9+x8+mm2rVlDoq0NgMY3v5nTPvYxfOXlI77XSJpsDRXysx2OC2X/rltBPdcvQEh+KRiLiIiUhtcBHzTGvEjfUmoDWGvtOe6WJVL4jt2jajMZPF4vvc3NI76Xk8nw8je/yUsbNoDj4A2FOP2662h885tHVVugtnZE+5DHayOqfAf1fL0AIfmjYCwiIlIa/sntAqR0lfrM2OE9qt5QCJtOYx2HTDxOWWPjiO4TP3CAbXfcQdczfQs3KmfPZt7KlcPa69q2eTN7Nm4k1tJCWWMj0y+7jCmXXjriGWY1osqP8foCRCnzuF2AiIiIjJ4xZkL/X7sH+SMyJodnxuKtrUfNjO3ftGng3x9fvJjHFi7k8cWLBx4vJrOWLMGJx0l1d+NkMn17VFMppi1aNOx7tP7hDzyxdOlAKJ76rndx3tq1ww7FO9euJdHejq+qikRHBzsfeIDoE0+M+GMpnzqVTDx+1GNqRJV9vU1NeEOhox7TCxDFTcFYRESkuH2r/+0W4Mn+t1uOeF9kTI6cGTPG9L0NBNi9fv1JQ3MxsI5D9bx5zL7mGoI1NaS7uwnW1nLasmXUL1hw0uszySTP339/X/On7m78Eydy9u23c9rVV+MJBIZVw56NGzF+P95QCI/Xi7+qCk8wyO7160f88cxasgSbTJKOxbDW9r0dx42ocvXCjV6AKD1aSi0iIlLErLWX9r89xe1apDQNtTS32JeTpg8dGug4Xb9gwbCC8JEOvfwyW1ev5tALLwBQ/apXMfeWWwjW14/oPrGWFnxVVRivd6Dr9GhnH93sGF1oS+5zuQ9YnbBLj4KxiIhIiTDGvIu+JlwW+J219gculyQlYKgzYot1P6uTTpNsbyd96NCorrfWsu9nP2PnAw/gxOMYj4eZH/gAMy67DOP1jvh+ZY2NJA8exHPE0tyxzD660TG6EJtR5fKFm0I7skrGTkupRURESoAxZh1wJfAM8CxwpTHmAXerklIw1NLcYlxOmuruJtbUNOpQnO7pYduaNTz3+c/jxOMEGxo49wtfYObixaMKxcbjYfZVV0E6XdTLn4dacu+WXO8DjixcOPDfweEVFMW0jUCOphljERGR0vAG4ExrrQUwxnwD2OpuSVIKTjYzVizLSZ1UikRb23FBfiQO7tjB1ttvJ97SAkD4da/jjBtvxF9VNar7eQIBQg0NVMyYgac/RBbr7GMhrh4YarVDNhTiLLmMnoKxiIhIadgFTAde7n9/Wv9jImM22NLcYllOmuzsJNnRMerrrePwyne/y4tf+1rfGceBALOvvprJb30rxphR3dNXXk4wHMZ4+hZwjmT5c6Ht5YXch9DRyPU+4GLfYy9HUzAWEREpDVXAdmPM5v73zweeNMb8CMBa+zbXKpOS5sZ+1uHKJBIk2tpwkslR3yPR3s72O++kY8sWAMpnzGDeypVUnjL6fneB6moCNTWjurZQZykLsRlVrl+4KcRZchk9BWMREZHS8O9uFyBSKKzjkGxvJ9U9tqO82598km2f/Sypzk4AJr/1rcy+6qrj9q0OlzGGYDiMr6Ji1DUV6ixloa4eyOULN4U4Sy6jp2AsIiJSAqy1m4wxjcAC+rpSP2GtbXG5LJG8O/IIptFy0mlefPhhXtm4EQBfRQVn3HADDWMIWMbrJRSJ4A0GR30PKOxZykJePZALhThLLqOnrtQiIiIlwBjzEWAz8C7gPcCfjDEfdrcqkfzJJBLE9u0jfuDAmEJxbN8+nrr++oFQPOHMM5n/0ENjCsWeQICyyZPHHIqBouwEXqoiCxdy1qpVhMJhUl1dhMJhzlLjraKlGWMREZHScDNwrrU2CmCMqQMeB77malUiOWYzGZIdHWNeNg2w/9e/5rkvfIFMby8Yw/TLLuOUD3wAj2/0vzJ7QyFCkchAk62x0ixlYRlvs+SlTMFYRESkNESBI5NBd/9jIiUr1d1Nsr0d6zhjuk8mFmPnunXs++lPAQjU1nLmJz9J7Xnnjem+/qoqAnV1o+5cfSKFupdXpNgpGIuIiJSGXcCfjTE/pG+P8duBp40xNwBYa+9xsziRbLKZDIm2NtK9vWO+V88LL7D19tvpfeUVAGrPP58zP/GJUXeNPixQW0vgiL3A2TxiSbOUItmnYCwiIlIadvf/OeyH/W+rXKhFJGfSvb0k2trGtI8YwFrL3h/9iF0PPoiTSmG8Xk79yEeY9u53j2nZs/F4CDY0HNWpuFCPWBKRv1EwFhERKQHW2lVu1yCSS9ZaktFoVvYSpw4eZMc999D2+98DUDZ5MnOXL2fCnDljuq8nECDU0IDH7z/q8UI9YklE/kbBWEREpAQYY8LAJ4B5wMAhq9baN7hWlEiWZBIJEq2tOKnUmO/V+cwzbFuzhkRrKwCRN7yB06+7bkxnC0PfkU7B+voTzjYX8hFLI5XNJeEihUTHNYmIiJSGDcAO4BRgFfAS8ISbBYmMlbWWZEcHsb17xxyKbSbDi//xHzx1440kWlvxhELMuflmzrz11jGH4kBNDaGGhkGXYJfKEUuHl4THW1uPWhK+f9Mmt0sTGTMFYxERkdJQZ639KpCy1m6y1n4YGNZssTHGa4x5yhjz37ktUWT4Dp9LnOzsHPO94q2t/PXmm3npG98Ax6Fy1izO/9KXmHTxxWPqGG2MIRSJEKiuHvJ5s5YswSaTpGMxrLV9b4vwiKUjl4QbY/reBgLsXr/e7dJExkxLqUVERErD4em0fcaYtwJ7gdphXnsdsB2YkIvCREbi8CxxqqsrK/dre/xxtt99N+n+vclT3/lOTv3oR/EGAmO6r8fv79tPfIL7nGi58VklcMRSKSwJ11JwGYyCsYiISGm43RgzEbgRuJ++kPvxk11kjJkKvBVYDdyQ0wpFTiIdi5Fsa8NJp8d8LyeZZPeXv0zTf/0XAP4JE5hz883Uv+Y1Y763r7ycYDh8wqXTg3WgPmvVKi7YsGHMY7upfOpU4q2tR3XcLqYl4eoOLkPRUmoREZESYK39b2ttl7X2WWvtRdba86y1PxrGpffS17TLGewJxpglxpgnjTFPtvY3LBLJJiedJt7aSrylJSuhuHfPHrZ87GMDobj6nHOY/9BDWQnFgepqQpHIoPuJC3G58f5Nm3h88WIeW7iQxxcvHvWe4GJfEl6IXxspHJoxFhERKWLGmPsBO9i/W2uXDXHtpcABa+0WY8zrh7jHemA9wPz58wcdS2Q0kl1dpDo6sDY731otv/gFz69d29fsyuNh5r/+KzP/5V8wXu+Y7muMIRgOn7RRV6EtN87mLGlk4UIo4iXhhfa1kcKiYCwiIlLcnjzi76uA20Zw7WuBtxlj3kLfEU8TjDHftNa+P5sFipxIJh4nEY3iJJNZuV+6t5fn165l/2OPARAMh5m7fDnVZ589UELkTQAAIABJREFU5nt7fD5CkcgJ9xMfq9CWG2f7DOXIwoVFE4SPle+vjfYzFxctpRYRESli1tpvHP4DdBz5fv9jQ117q7V2qrV2JnAZ8CuFYsk16zgk2tqI7duXtVB88LnnePLKKwdCcf0FF3D+gw9mJRR7QyHKJk8eViiGwltu3NvUhDcUOuqx8TpLms+vjY62Kj4KxiIiIqVDy5yloKVjMWLNzaT6O0SPlXUcXnn0Uf5y3XXE9u7F4/dz2rXXctaqVcctmR0Nf1UVocbGES3DjixcyFmrVhEKh0l1dREKhznLxeZObp+hnK39zdm4Xz6/NtrPXHy0lFpERESw1v4G+I3LZUiJso5Dsr09a4EYINnRwfa776Z982YAyqdNY97KlVTOmpWV+wdqawmMMlwX0nLjWUuW8Oxtt5Gmb6Y4E4/nbQY7212gs3G/fH1ttJ+5+GjGWEREpIgZY7qNMQeNMQeBcw7//fDjbtcnku7tpbepKauhuP0vf+GJpUsHQvGkSy5h/rp1WQnFxhhCkcioQ3GhcXMGO9uzpsU0C+v2TL2MnGaMRUREipi1tsrtGkROxGYyJNrbSff0ZO2eTjrNi9/4Bq985ztgLd7ycs64/noib3hDVu4/kiZbhWqwhk9uzGBne9a0mGZh3Zypl9HRjLGIiIiIZFX60CF6m5uzGopjLS08dcMNvPLtb4O1VM2Zw/kPPpi1UOwNBkfUZKsQFVrDp2zPmhbTLGyh7TWXk9OMsYiIiIhkhc1kSLS1ke7tzep9D2zaxHP33EP60CEApi9axCkf/CAevz8r9/dXVRGoq8MYc9LnFvIRPNk+mmmssj1rWmyzsIW011xOTjPGIiIiIjJmqe5uepuashqKM/E4z33hC2z9zGdIHzqEv7qac+64g1kf/WjWQnGwro5gff2wQ3Ehzcgeq9COZsr2rKlmYSWXNGMsIiIiIqPmpFIk2tqOW+I6Vodeeomtt9/OoZdeAqDmvPM485OfJFhbm5X7G4+HUCRyXJAcSqHNyB6rfOpU4q2tA/WB+0uNsz1rqllYyRUFYxEREREZMWstqa4ukh0dWb/v3v/5H3atW4eTTGK8Xk758IeZ/t73YjzZWezoCQT6mmz5RvarcKE3fyq2pcbZVsjL3KXwaSm1iIiIiIxIJpEgtndv1kNxqqeHrZ/5DM/fey9OMkmosZFz772XGYsWZS0U+yoq+ppsjTAUQ+E3fxrPS40LfZm7FD7NGIuIiIjIsNhMhmRHR1bPJD6sa9s2tq1eTXz/fgAaXv96zrj+enyVlVkbI1BbO6bziYthRna8LjUu9GXuUvgUjEVERETkpFLd3STb27GOk9X72kyGlzdu5KWvfx3rOHiCQU679lomXXLJsBpiDYfxeAg2NBy193Y0IgsXwqpVWq5bgAp9mbsUPgVjERERERmUk0ySiEaz3lwLINHWxrY776TzqacAqDjlFOatXEnFjBlZG8MTCBBqaMhaF+vxOiNb6Aqx8ZgUF+0xFhEREZHjWGtJdnbS29yck1Ac/fOfeWLp0oFQPOVtb+O8L34xq6HYV15O2aRJWQvFUrhmLVmCTSZJx2JYa/veFtgydylsmjEWERER16iLbGHKJBIk2tpwksms39tJpXjhq19lz6OPAuCrrGTOTTcRft3rsjqOf8IEgnV1Wb3nSOn7O3+0zF3GSsFYREREXHG4i6wJBI7qIss46aJbiKzj9DXXOngwJ/fvbWpi25o1dD//PAAT581j7vLlhCKRrI4TrK/HX1WV1XuOVL6+vxW+/0bL3GUsFIxFRETEFeoiW1jSPT0k2tuxmUxO7t/y2GM8f999ZGIxMIYZixdTdcYZbL/rLmItLZQ1NjJt0SLqFywY9RjGGEKNjXhDoSxWPjr5+P4+Nnz3vPQST151Fb6qKqpmzx7XIVlkpLTHWERERFzR29R0XIBRF9n8c1IpYi0txFtbcxKK07EY2++8k+2f/SyZWIxAXR2vuvtuJsydy64HHiDR3o6vqopEezs7166lbfPmUY1jvF7KJk8uiFAM+fn+PjJ8p3t6SEajWMch3durc3xFRkjBWERERFxRPnXqcU2d1EU2f6y1JDs66G1q6pvFzYHunTt58sorafnlLwGoe/WrOX/9empe9Sr2bNyI8fvxhkIYY/re+v3s2bhxxON4AgHKJk/GEwhk+0MYtXx8fx8ZvhNtbRhjMF4vNpXCV1aGCQTYvX591sYTKWUKxiIiIuIKdZF1TzoWI9bcTLKzMyf3t9ay5/vfZ8uyZcSamzF+P6ddcw1nf+YzBPrPmo21tOAJBo+6zhMMEmtpGdFY3lCor/O0r7B2CObj+/vI8O0kk2AMWDvwAoFWYIgMn4KxiIiIuCKycCFnrVpFKBwm1dVFKBzmLDXeyiknnSZ+4ADxlhacVConYyS7unjmU59i17p12FSKsqlTOW/tWqa+850YYwaeV9bYiJNIHF1fIkFZY+Owx/JVVhJqbMR4Cu9X2nx8fx8Zvj2BANZxwHEI1tcD2Zuh3r9pE48vXsxjCxfy+OLFWp4tJamwXloTERGRcUVdZLPnZN2JU93dfXtQrc1ZDR1//Svb7riDZDQKQOPFF3PatdcONKA60rRFi9i5di0Z+maKnUQCm0oxbdGiYY0VqKkhUF2dzfKzLtff30ceUZTs7MSm0/hravBVVmZthlrd42W8UDAWERERKXJDhZfwBReQaGs7br9rNjmZDC/9x3/w8oYNYC3esjJOv/56Gt/4xkGvqV+wAJYtY8/GjSPqSm2MIRgO46uoyPaHUZSODN+5OLpJ3eNlvFAwFhERESlyg4WXnevWUTljRk5nieMHDrBtzRq6nn0WgKrTT2fuihWUT5ly0mvrFywY0fFMxuslFIngPWZvsvTJxQx1b1MT/v594Ydp77KUIgVjERERkSJ3bHixjoPxeOjdsyenobj1979nx+c/T7q7G4Bp73kPp/7bv+Hx+7M+ljcYJBSJYLzerN9bBlc+dSrx1tajlsOre7yUIgVjERERkSJ3OLx4QyFsJoPNZMjE4yNqZDUSmUSC3Q89RPOPfgSAf+JEzvzEJ6j7h3/IyXj+qioCdXVHNe+S/Ji1ZAnP3nYbafpmijPxuLrHS0kqvBZ+IiIiIjIis5YswYnHSXV346TTfeFlBI2sRuLQyy+z5dprB0Jx9bnncv5DD+UsFAfr6wnW1ysUu0Td42W80IyxiIiISBFz0mkmzpnD7GuuGXEjq5Gw1rLvpz9l5wMP4CQSGI+HmR/8IDMWLcrJ8mbj9RJqaMAbCmX93jIy6h4v44GCsYiIiEiRSnZ1kerowFo74kZWI5Hu6eG5e+/lwG9+A0CwoYF5K1Ywcd68nIznDQYJNjTg8elX1ZHKRWdqkfFAS6lFREREikwmkaB3716S7e05ba4F0LV9O09ceeVAKA5feCHnr1+fs1Dsr6oiNGmSQvEoHD62K97aetSxXfs3bXK7NJGCp584IiIiIkXCWkuyo4NUV1fux3IcXvnP/+TFhx/GZjJ4AgFOu/pqJr31rTnb7xusr8dfVZWTe48HOnNYZPQUjEVERESKQDoWI9nWhpNO53ysRHs72++8k44tWwComDmTuStWUHnKKTkZT+cTZ4fOHBYZPQVjERERkQJmMxkS7e2ke3ryMl70iSfYfuedpDo7AZh86aXMvvLKnDXB8gSDhLSfOCt05rDI6OknkIiIiEiBSvf0kIhGsY6T87GcVIoXHn6YPf/5nwD4Kio448YbabjwwpyN6aus1FFMWaQzh0VGT8FYREREpMA46TTJaJR0b29exovt3cvWNWvo3rEDgAlz5zJ3+XLKGhtzNmagtpbAMct+ZWwiCxfCqlXqSi0yCgrGIiIiIgXkyCOY8mH/r3/Nc1/4ApneXjCGGZdfzswrrsjZ0mZjDMFwGF9FRU7uP97pzGGR0VEwFhERESkAmUSCRDSKk0jkZ7xYjJ0PPMC+n/2s7wGPh4qZM5lw1lm5C8VqsiUiBUrnGIuIiIi4yDoOiWiU2N69eQvFPbt38+Q11wyEYk8oRPn06TipFDvXrqVt8+asj+kJBCibPFmhWEQKkmaMRURERFyS7u0l0daGzWTyMp61luYf/pDdDz2Ek0qBMfgmTCAYDvc1wPL7yQB7Nm6kfsGCrI3rKy8n2NCgJlsiUrAUjEVERETyLN/NtQBSBw+y4/Ofp+0PfwCgbPJk0vE4gdraowKrJxgk1tKStXH9EyYQrKvL2v1ERHJBS6lFRERE8ijV3U2sqSmvobjz6ad5YunSgVAceeMbmf+lL1Exbdpxy7edRCJr3aiD9fUKxSJSFDRjLCIiIpIHTipFoq2NTDyetzFtJsNLGzbw0je/CY6DNxTitGXLaHzzmzHGMG3RInauXUuGvpliJ5HAplJMW7RoTOMaj4dgQwO+srLsfCAiIjmmYCwiIiKSY8muLpLt7XkdM97ayrY77qDr6acBqJw9m3krV1I+derAc+oXLIBly9izcSOxlhbKGhuZtmjRmPYXe3w+QpEInkBgzB+DiEi+KBiLiIiI5IiTTBJva8tbt+nD2h5/nO133026uxuAqe96F7M+8pEThtX6BQuy1mjLGwwSikQwXm9W7iciki8KxiIiIiI5kOzsJNnRkdcxM8kku9evp/kHPwD6Gl/Nuflm6l/zmpyP7ausJFhfr87TIlKUFIxFREREsiiTSJCIRvM+S9y7Zw9bb7+dnt27Aag+5xzmLl9OsL4+52MHamoIVFfnfBwRkVxRMBYRERHJAmstyY4OUl1deR+35Re/YOf99/c19vJ4OOWKK5hx+eV5WdIcCofxVVbmfBwRkVxSMBYREREZo0w8TqKtDSeVyuu46UOHeP6++9j/q18BEAyHmbt8OdVnn53zsY3HQ6ixEW8wmPOxRERyTcF4nJs9e7bbJYiIAPp5JMXJOg7J9nZS/U2u8ungc8+xbfVqYnv3AlD/2tcy58Yb8U+YkPOxPYFAX+dpn36VFJHSoJ9m49y1117rdgkiIoB+HknxSff0kGhvx2YyeR3XOg57vvc9XvjqV7HpNB6/n9lXXsnkt70tL42vvGVlhBoaMB5PzscSEckXBWMRERGREXBSKRLRKJlYLO9jJzs62H7XXbQ/8QQA5dOnM2/lSipPPTUv4/snTiRYW5uXsURE8knBWERERGQYrLWkurryfgTTYe1btrD9zjtJtrcDMOmf/onTrr4ab1lZXsYP1tfjr6rKy1giIvmmYCwiIiJyEm411wJw0mle/PrXeWXjRrAWb3k5Z3z840Quuigv46vJloiMBwrGIiIiIoOwmQyJ9nbSPT2ujB/bt49ta9ZwcPt2AKrmzGHe8uWUTZ6cl/HVZEtExgv9lBMRERE5gXRPD4loFOs4rox/4De/Ycc995Dp7QVg+qJFnPKhD+UtpPoqKgiGw3lp6CUi4jYFYxEREZEjOOk0yWiUdH8gzbdMPM7OdevY95OfABCoqeHMW26h9rzz8lZDoLqaQE1N3sYTEXGbgrGIiIhIv1R3N8loFGutK+P3vPACW1evpvfllwGoOe885t5yS15DaigcxldZmbfxREQKgYKxiIjIOGaMCQG/BYL0/V7wqLX2Nneryj8nlSLR1kYmHndlfGste3/8Y3Y9+CBOMonxejn1wx9m2nvfm7fzgo3XSygSUZMtERmXFIxFRETGtwTwBmttjzHGD/zeGPNTa+2f3C4sX1IHD5Jsb3dtljjV3c1z99xD6+9+B0Bo0iTmrVjBhDlz8laDJxikLBLBeL15G/NY+zdtYvf69fQ2NVE+dSqzliwhsnCha/WIyPiSn5cgRUREpCDZPodbLvv7/7iTEPPMSSaJ7d3b12DLpVDc+eyzPLF06UAobrjoIs7/0pfyGop9lZWUTZrkeih+9rbbiLe24p84kXhrK8/edhv7N21yrSYRGV80YywiIjLOGWO8wBZgNvCAtfbPLpeUU9ZaUl1dJDs63Kshk+Hl73yHl77xDazj4AmFOO2aa5h0ySV57QIdqK0lMHFi3sYbzO716zGBAL6yMgB8ZWWk+x/XrLGI5IOCsYiIyDhnrc0ArzLGVAP/ZYw5y1r77OF/N8YsAZYATJ8+3aUqsyOTSJBoa8NJJl2rIdHWxrbPfpbOv/4VgIpTT2XeihVUzJiRtxqMMQQbGvCVl+dtzKH0NjXhPyage0MhepuaXKpIRMYbBWMREREBwFrbaYz5NXAJ8OwRj68H1gPMnz+/KJdZW2tJdnSQ6upytY62P/2JHXffPVDHlLe/nVlLl+INBPJWg8fnIxSJ4MnjmCdTPnUq8dbWgRlj6Du2qnzqVBerEpHxRHuMRURExjFjTLh/phhjTBnwZmCHu1VlVyYeJ9bc7GoodpJJdq5bxzMrV5Lq6sJXVcVZq1Zx+sc+ltdQ7A2FKJs8uaBCMcCsJUuwySTpWAxrbd/bZJJZS5a4XZqIjBOaMRYRERnfJgHf6N9n7AH+01r73y7XlBXWcUi2t5Pq7na1jt6mJrauXk3Pzp0ATDz7bObeeiuhhoa81uGvqiJQV5eTPcxj7SgdWbgQVq1SV2oRcY2CsYiIyDhmrX0aONftOrIt3dtLMhrFSaddraPll7/k+bVrycRi4PEwc/FiZrz//Xjy3AE6WFeHf8KEnNz7cEdpEwgc1VGaVatGHI4VhEXELQrGIiIiUjJsJkMiGiV96JCrdaR7e3l+7Vr2P/YYAMH6es689VZq/u7v8lqH8Xj6mmwdsXc329RRWkRKgYKxiIiIlIRUdzfJ9nas47haR/fzz7N19Wpizc0A1L3mNZx5003HdV3ONY/f39dky+/P6TjqKC0ipUDBWERERIqak0qRiEb7liu7yFpL0/e+x+6vfAWbTmP8fmYvWcKUd7wjr2cTA3jLygg1NGA8ue+zqo7SIlIKFIxFRESkaCW7ukh1dGBtdk+Ratu8mT0bNxJraaGssZFpixZRv2DB4HV0drL9rrto37wZgLKpU5m3ciVVs2dnta7h8E+YQLCuLm/jzVqyhGdvu400fTPFmXhcHaVFpOjouCYREREpOplEgt69e/uWTucgFO9cu5ZEezu+qioS7e3sXLuWtv7Qe6yOv/6VJ5YuHQjFjRdfzPwvfcmVUBysr89rKIa+pllnrVpFKBwm1dVFKBzmrBE23hIRcZtmjEVERKRoWGtJdnTk9EziPRs3Yvx+vKEQ0D8L2v/4kbPGTibDS488wsvf+hZYi7e8nDOuu47IG9+Ys9oGYzweQpHIQM35po7SIlLsFIxFRESkKGTicRJtbTipVE7HibW04KuqOuoxTzBIrKVl4P34/v1sW7OGrq1bAag6/XTmrVxJ2eTJOa3tRPLVZEtEpJQpGIuIiEhBs45Dsr2dVHd3XsYra2wk0d5+1Oyrk0hQ1tgIQOvvfseOz3+edE8PANPe+15O/fCHXQmm+WyyJSJSyhSMRUREpGClDx0iEY1iM5m8jTlt0SJ2rl1Lhr6ZYieRwKZSTHnXu3juvvvY++MfA+CvrubMT3yCuiGacuWSv6qKYH29K2OLiJQaBWMREREpOE46TTIaJd3bm/ex6xcsgGXLjupKHX7963np4Yc59NJLANScey5n3nJL3htdHRasq8M/YYIrY4uIlCIFYxERESkoqe5uktFo1rtNj0T9ggXUL1iAtZZ9P/kJO9etw0kkMB4Pp3zwg0y/7DJXli8bYwg2NOArL8/72CIipUzBWERERApCJpEgEY3iJBJulwJAqqeH57/wBQ5s2gRAKBJh7ooVTJw715V6PD5fX5OtQMCV8UVESpmCsYiIiLjKOk7fEUwHD7pdyoCubdvYtmYN8f5O1OELL+SMG27AX1npSj3eYJBQJILxel0ZX0Sk1CkYi4iIiGvcaK41FOs4vLJxIy8+/DDWcfAEg5x21VVMeutbMca4UpOvspJgfb1r44uIjAcKxiIiIpJ3bjbXGkyivZ3tn/0sHX/5CwAVM2cy71OfomLGDNdqCtTUEKiudm18EZHxQsFYRERE8sZaS+rgQVIdHa421zpWdPNmtt91F6nOTgAmX3ops6+6Cm8w6FpNoXAYn0tLt0VExhsFYxEREcmLTCJBoq0NJ5l0u5QBTirFC1/7Gnu++12gb9nyGTfcQMOFF7pWk/F6CUUiroZyEZHxRsFYREREcso6Dsn2dlLd3W6XcpTY3r1sXb2a7ueeA2DCvHnMW76cUCTiWk2eQKCv87RPv6KJiOSTfuqKiIhIzqS6u0m2t2Mdx+1SjrL/V7/iuXvvJdPbC8Yw4/LLmfmBD+Bxseuzt6yMUEODK+cji4iMdwrGIiIiknVOMkkiGiUTj7tdylEysRjPf/GLtPz85wAE6uqYe8st1Jx7rqt1+SdMIFhX52oNIiLjmYKxiIiIZI21llRnJ8n+JlaFpHvXLratXk3vnj0A1C5YwJmf+ITrXZ+D9fX4q6pcrUFEZLxTMBYREZGsSMdiJKNRnFTK7VKOYq2l+Qc/YNf69dhUCuPzMesjH2Hqu9/t6tnAxhiCkQi+sjLXahARkT4KxiIiIjImNpMhEY2SPnTI7VKOk+rqYsfnPkfbH/8IQNmUKcxbsYKq0093tS6Pz9fXZCsQcLUOERHpo2AsIiIio5bq7iYZjRbUmcSHdfzf/7H9jjtItLUBEHnTmzh92TJ85eWu1uUJBimLRDAuNvoSEZGjKRiLiIjIiGUSCRLRKE4i4XYpx3EyGV7+5jd5acMGcBy8oRCnL1tG4z/+o9ul4auoIBgOu7qEW0REjqdgLCIiIiOSiEZJHTzodhknFD9wgG133EHXM88AUDl7NvNWrqR86lSXKwP/xIkEa2vdLkNERE5AwVhERESGzUmlCjYUt/7hD+z43OdId3cDMPVd72LWRz5SEPt41XlaRKSwKRiLiIjI8BXgXuJMMsnuhx6i+Yc/BPpmZufcfDP1r361y5Wp87SISLFQMBYREZGideiVV9i2ejU9u3cDUP2qVzH3llsI1te7XBkYr5eyxsaCmLEWEZGhKRiLiIhI0bHW0vLzn/P8F7+IE49jPB5mXnEFMy6/vCC6PXuCQUINDXh8+lVLRKQY6Ke1iIiIFJX0oUM8f9997P/VrwAIhsPMXbGC6rPOcrmyPr7ycoINDeo8LSJSRBSMRUREpGgc3LGDratXE9+3D4D6172OOTfcgH/CBJcr6+OfMIFgXZ3bZYiIyAgpGIuIiEjBs47Dnkcf5YWvfhWbyeDx+5l95ZVMftvbCmZmNlhXVzABXURERkbBWERERApasqOD7XfeSfuTTwJQPn0681aupPLUU12urI8xhmBDA77ycrdLERGRUVIwFhERkYLVvmUL2z/7WZIdHQBM+qd/4rSrr8ZbIMcfGa+XUCSCNxh0uxQRERkDBWMREREpOE46zYsPP8wrGzcC4C0v54yPf5zIRRe5XNnfeAIBQpGIOk+LiJQA/SQXERGRghLbt49tq1dzcMcOACbMmcPcFSsomzTJ5cr+xltWRqihAePxuF2KiIhkgYKxiIiIFIz9v/41z33hC2R6e8EYpi9axCkf/GBBzcr6q6oI1te7XYaIiGRR4fxfRkRERMatTDzOzgceYN9PfwpAoKaGM2+5hdrzznO5sqMFamsJTJzodhkiIpJlCsYiIiLiqp4XXmDr7bfT+8orANTOn8+Zn/wkgZoalys7WqihAV9FhdtliIhIDigYi4iIiCustez98Y/Z9eCDOMkkxuvl1H/7N6a95z0FtXfXeDyEGhvVeVpEpIQpGIuIiEjepQ4eZMc999D2+98DEJo0iXkrVjBhzhyXKzuax+/v6zzt97tdioiI5JCCsYiIiORV5zPPsG3NGhKtrQA0XHQRZ1x/fcEtU/aGQoQikYKavRYRkdxQMBYREZG8sJkML3/727z0yCNYx8ETCnH6tdfSePHFGGPcLu8ovspKgvX1BVeXiIjkhoKxiIiI5FyirY1td9xB5//9HwAVp57KvJUrqZg+3eXKjheoqSFQXe12GSIikkcKxiIiIpJTbX/6EzvuuovUwYMATHn725m1dCneQMDlyo4XCofxVVa6XYaIiOSZgrGIiIjkhJNMsvsrX6Hp+98HwFdVxZybbiL82te6XNnxjMdDKBLBGwq5XYqIiLhAwVhERESyrrepia23307Prl0ATDznHObecguhhgaXKzueOk+LiIiCsYiIiGRVyy9+wfNr15KJx8HjYebixcx8//sxXq/bpR3HGwz2dZ4uwNpERCR/FIxFREQkK9K9vTy/di37H3sMgGB9PXOXL6f6nHNcruzEfBUVBMNhdZ4WEREFYxERERm77uefZ+vq1cSamwGof81rmHPTTfgnTnS5shMLVFcTqKlxuwwRESkQCsYiIiIyatZx2PP97/PCV76CTacxfj+zly5lytvfXrAzscH6evxVVW6XISIiBUTBWEREREYl2dnJ9rvuon3zZgDKp01j7ooVVM2e7XJlJ2aMIRiJ4Csrc7sUEREpMArGIiIiMmIdTz3Fts9+lmQ0CkDjxRdz+rXX4i3Q0Onx+fo6Txfg2ckiIuI+BWMREREZkRe+9jVe/va3wVq85eWccd11RN74RrfLGpQnGKRMnadFRGQICsYiIiIybL2vvMLL3/oWAFWnn868lSspmzzZ5aoG5ysvJ9jQULD7nUVEpDAoGIuIiIxjxphpwCNABLDAemvtfYM9PxOLATDtve/l1A9/GI/fn5c6R8M/YQLBujq3yxARkSKgYCwiIjK+pYEbrbV/McZUAVuMMb+01m470ZONz8c5a9ZQt2BBfqscIXWeFhGRkfC4XYCIiIi4x1q7z1r7l/6/dwPbgSmDPb9i5syCDsXGGEKRiEKxiIiMiGaMRUREBABjzEzgXODPgz6ngBtYGa+XUCSCNxh0uxQRESkymjEWERERjDGVwPeA6621B4/5tyXGmCeNMU+2tbe7U+BJeAIByiZPVigWEZF1d2acAAALQ0lEQVRRUTAWEREZ54wxfvpC8QZr7feP/Xdr7Xpr7Xxr7fz62tr8F3gS3rIyyiZNwuPTQjgRERkd/R9ERERkHDN95xh9Fdhurb3H7XpGyl9VRbC+3u0yRESkyGnGWEREZHx7LfCvwBuMMX/t//MWt4sajmBdnUKxiIhkhWaMRURExjFr7e8B43YdI2GMIdjQgK+83O1SRESkRCgYi4iISNHw+HwEGxrUZEtERLJKwVhERESKgjcYJBSJFPSRUSIiUpwUjEVERKTg+SorCdbX09crTEREJLsUjEVkRNo9Hn5aVuZ2GaPS7unrN1is9R+p3eOh0e0iRPIkUFtLYOJEt8sQEZESpmAsIsM2e/Zst0sYk0xzMwCNU6a4XMnYNVL8Xw+Rk1GTLRERyRcFYxEZtmuvvdbtEkRknFCTLRERyScFYxERESkoarIlIiL5pmAsIiIiBUNNtkRExA0KxiIiIlIQ1GRLRETcomAsIiIirlKTLRERcZuCsYiIiLhGTbZERKQQKBiLiIiIK9RkS0RECoWCsYiIiOSdmmyJiEghUTAWERGRvArU1BCorna7DBERkQEKxiIiIpIXarIlIiKFSsFYREREck5NtkREpJApGIuIiEhOqcmWiIgUOgVjERERyRk12RIRkWKgYCwiIiI5oSZbIiJSLBSMRUREJKvUZEtERIqNgrGIiIhkjZpsiYhIMVIwFhERkaxQky0RESlWCsYiIiIyZmqyJSIixUzBWERERMZETbZERKTYKRiLiIjIqBhjCIbD+Coq3C5FRERkTBSMRUREZMSM10soElGTLRERKQkKxvL/27vXkEsKOo7j39/jtllkF9ou0sWtcNGwMtnEIlJLw3yhREsZmhoRaOQLu1BQVNSbIooQK7t4KakUrWyjwiAto9rSslKLYrPSTUm7+SLTWvffizPCw7LuM/s8c844M98PHDiXYc7/z5znnN9/zsx5JEnaJ0vr17P/U57C0jpjhCRpHPxEkyRJrWVpiUcdeCBZWuq7FEmSOuOnmiRJai3r1jkUS5JGx082SZIkSdKkORhLkiRJkibNwViSJEmSNGkOxpIkSZKkSXMwliRJkiRNmoOxJEmSJGnSHIwlSZIkSZPmYCxJkiRJmjQHY0mSJEnSpDkYS5IkSZImzcFYkiRJkjRpDsaSJEmSpElzMJYkSZIkTZqDsSRJkiRp0hyMJUmSJEmT5mAsSZIkSZo0B2NJkiRJ0qQ5GEuSJEmSJs3BWJIkSZI0aamq9gsndwN/7ui5NwB/62hdD0f2N1xj7g3sb8jG3Bt0399BVfWkDtcnOs8Cy4359W1vw2Rvw2RvwzWv/lrlgX0ajLuU5Iaq2tzLky+A/Q3XmHsD+xuyMfcG4+9Pezfm7W9vw2Rvw2Rvw9V3fx5KLUmSJEmaNAdjSZIkSdKk9TkYf7bH514E+xuuMfcG9jdkY+4Nxt+f9m7M29/ehsnehsnehqvX/no7x1iSJEmSpIcDD6WWJEmSJE3a3AfjJCck+V2S7UnevYfHH5nk8ubxnybZOO+autSiv7cl+U2SXyf5XpKD+qhzNVbqbdlyr0lSSQb1K3lt+kvy2mb73ZLky4uucS1avDafmeTaJDc2r88T+6hzNZJclOSuJDc/xONJcl7T+6+THLHoGteiRX+nNn3dlOTHSV6w6BpXa6Xeli33oiQ7k2xZVG2avzFngjHnARh3JhhzHjALDDMLmAN6ygFVNbcLsB/wB+DZwHrgV8Bzd1vmLcAFzfVTgMvnWVMP/R0LPLq5fvZQ+mvTW7PcAcB1wDZgc991d7ztDgZuBJ7Q3H5y33V33N9ngbOb688F/tR33fvQ38uAI4CbH+LxE4HvAAGOAn7ad80d9/eSZa/LVw2pv5V6a5bZD7gG+Dawpe+avXS27UebCcacB9r21yw3uEww5jxgFhhuFjAH9JMD5v2N8ZHA9qq6tar+C1wGnLzbMicDX2iuXwm8IknmXFdXVuyvqq6tqnubm9uApy+4xtVqs+0APgR8BLhvkcV1oE1/bwY+WVX/BKiquxZc41q06a+AxzbXHwfcscD61qSqrgP+sZdFTga+WDPbgMcnOXAx1a3dSv1V1Y8ffF0yrPeVNtsO4Bzgq8CQ/ua0sjFngjHnARh3JhhzHjALDDQLmAP6yQHzHoyfBty+7PaO5r49LlNVO4F7gCfOua6utOlvuTcx23M1BCv21hyS8oyq+tYiC+tIm223CdiU5EdJtiU5YWHVrV2b/j4AnJZkB7M9cucsprSF2Ne/zSEb0vvKipI8DXg18Om+a1HnxpwJxpwHYNyZYMx5wCwwjSwwtPeTveozB6xb9BNOVZLTgM3A0X3X0oUkS8DHgTN7LmWe1jE7fOoYZnvirkvyvKr6V69Vdef1wCVV9bEkLwYuTXJYVe3quzC1k+RYZh+IL+27lg59AnhXVe0axheF0r4ZWx6ASWSCMecBs8CAmQO6Ne/B+C/AM5bdfnpz356W2ZFkHbPDOP4+57q60qY/khwHvAc4uqruX1Bta7VSbwcAhwHfb160TwW2Jjmpqm5YWJWr12bb7WB2zsb/gD8m+T2zD8brF1PimrTp703ACQBV9ZMk+wMbGMfhq63+NocsyfOBzwOvqqqhvGe2sRm4rHlf2QCcmGRnVV3Vb1nqwJgzwZjzAIw7E4w5D5gFRpwFzAHdm/eh1NcDByd5VpL1zH5IY+tuy2wFzmiubwGuqeas6wFYsb8kLwQ+A5w0oHNSYIXequqeqtpQVRuraiOz8xuG8AH4oDavzauY7R0myQZmh1Ldusgi16BNf7cBrwBIciiwP3D3Qqucn63A6c0vUh4F3FNVd/ZdVFeSPBP4GvCGqvp93/V0qaqetex95UrgLQ7FozHmTDDmPADjzgRjzgNmgZFmAXPAfMz1G+Oq2pnkrcDVzH5d7KKquiXJB4EbqmorcCGzwza2MzsR+5R51tSllv19FHgMcEWz5+O2qjqpt6JbatnbYLXs72rglUl+AzwAvHMoe+Ra9vd24HNJzmX24xtnDiSAkuQrzELKhua8qPcDjwCoqguYnSd1IrAduBd4Yz+Vrk6L/t7H7LzLTzXvKzurahD/GqVFbxqpMWeCMecBGHcmGHMeMAsMNwuYA/qRgbz2JUmSJEmai3kfSi1JkiRJ0sOag7EkSZIkadIcjCVJkiRJk+ZgLEmSJEmaNAdjSZIkSdKkORhLHUjyQJJfLrts7GCdZyU5vbl+SZIta12nJEmaD7OANGxz/T/G0oT8p6oO73KFff8vN0mStE/MAtKA+Y2xNCdJNib5YZJfNJeXNPcfk+QHSb6R5NYkH05yapKfJbkpyXOa5T6Q5B27rfPlSa5advv4JF9fbGeSJKkNs4A0HA7GUjcetezQqQc/nO4Cjq+qI4DXAectW/4FwFnAocAbgE1VdSTweeCcvTzPtcAhSZ7U3H4jcFGHfUiSpNUxC0gD5qHUUjf2dPjUI4DzkxwOPABsWvbY9VV1J0CSPwDfbe6/CTj2oZ6kqirJpcBpSS4GXgyc3lEPkiRp9cwC0oA5GEvzcy7wV2Z7hJeA+5Y9dv+y67uW3d7Fyn+XFwPfbNZ3RVXt7KRaSZLUNbOANBAOxtL8PA7YUVW7kpwB7NfFSqvqjiR3AO8FjutinZIkaS7MAtJAeI6xND+fAs5I8ivgEODfHa77S8DtVfXbDtcpSZK6ZRaQBiJV1XcNkvZRkvOBG6vqwr5rkSRJi2cWkLrlYCwNTJKfM9vjfHxV3b/S8pIkaVzMAlL3HIwlSZIkSZPmOcaSJEmSpElzMJYkSZIkTZqDsSRJkiRp0hyMJUmSJEmT5mAsSZIkSZo0B2NJkiRJ0qT9HyS8or19/pw1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Family', 'firebrick')\n",
    "#Data is well distributed around the straight line agaisnt the Happiness Score variable\n",
    "#correlation is 0.7406"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Correlation of Happiness Score with 'Health (Life Expectancy)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                          Health (Life Expectancy)  Happiness Score\n",
      "Health (Life Expectancy)                    1.0000           0.7242\n",
      "Happiness Score                             0.7242           1.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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07crTk9EYYxERERERkXlA0zBNbDCZoXcwjWNmRbYmoh5jERGRRczMzjKzPQU/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\nNMYAAAAAgFqjMQYAAAAA1Nr/A5tC8Vd5CsrCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Health (Life Expectancy)', 'steelblue')\n",
    "#Data is well distributed around the straight line agaisnt the Happiness Score variable\n",
    "#correlation is 0.7242"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Correlation of Happiness Score with 'Freedom'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                  Freedom  Happiness Score\n",
      "Freedom          1.000000         0.568211\n",
      "Happiness Score  0.568211         1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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IiEhNUzAWERERERGRmqZgLCIiIiIiIjVNwVhERERERERqmoKxiIiIiIiI1DQFYxERERER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\nAABfIxgDAAAAAHyNYAwAAAAA8LX/B0OcCA4WakudAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Freedom', 'teal')\n",
    "#There are couple of outliers for Freedom variable \n",
    "#Data is well distributed around the straight line agaisnt the price variable\n",
    "#correlation is 0.5682"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Correlation with 'Trust (Government Corruption)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                               Trust (Government Corruption)  Happiness Score\n",
      "Trust (Government Corruption)                       1.000000         0.395199\n",
      "Happiness Score                                     0.395199         1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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FVanJbjatpPgCJcYiIiIieVZuN9Iis1Eu34NyGXnaWksgEuB04DTBaNDrcIqO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\n9geK9O6T2vUciwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Trust (Government Corruption)', 'forestgreen')\n",
    "#Data is not well distributed around the straight line agaisnt the Happiness Score variable\n",
    "#correlation is 0.395"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Correlation with 'Generosity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                 Generosity  Happiness Score\n",
      "Generosity         1.000000         0.180319\n",
      "Happiness Score    0.180319         1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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Kz2lRa7eRvUexv8c5xWz3c3x107C3KTa+/Hj678d2ugh3/6DiPuNfSI/zL0qNuRQ/E/yE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\nAAC1RmIMAAAAAKi1/w9ncJXOZL1l5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Generosity', 'khaki')\n",
    "#Data is not well distributed around the straight line agaisnt the Happiness Score variable\n",
    "#correlation is LOW - 0.1803"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 7. Correlation of Happiness Score with 'Dystopia Residual'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between variables\n",
      "                   Dystopia Residual  Happiness Score\n",
      "Dystopia Residual           1.000000         0.530474\n",
      "Happiness Score             0.530474         1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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KXIX8cJ6hE8P0Xd6fdbMAqMyVmbl1msp0GYCeS3sYu36S/GBro6qCsYiIiFTd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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze_numeric(Happiness, 'Dystopia Residual', 'orchid')\n",
    "#Data is not well distributed around the straight line agaisnt the price variable\n",
    "#correlation is Low - 0.530"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Numerical Analysis \n",
    "\n",
    "We have analysed the beolow 7 numerical independant variables against the dependant variable : Happiness Score\n",
    "\n",
    "1. Economy (GDP per Capita)\n",
    "2. Family\n",
    "3. Health (Life Expectancy)\n",
    "4. Freedom\n",
    "5. Trust (Government Corruption)\n",
    "6. Generosity\n",
    "7. Dystopia Residual\n",
    "\n",
    "# Results \n",
    "\n",
    "1. Economy (GDP per Capita) : Correlation with Happpiness Score is 0.780\n",
    "2. Family: Correlation with Happpiness Score is 0.7406\n",
    "3. Health (Life Expectancy): Correlation with Happpiness Score is 0.7242\n",
    "4. Freedom: Correlation with Happpiness Score is 0.5682\n",
    "5. Trust (Government Corruption): Correlation with Happpiness Score is 0.395\n",
    "6. Generosity: Correlation with Happpiness Score is 0.180\n",
    "7. Dystopia Residual: Correlation with Happpiness Score is 0.530\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "1. Economy (GDP per Capita) , Family and Health are the biggest predictors of Happiness in a country \n",
    "2. Generosity is the least predictor of the Happiness in a country \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lets  now attempt to understand the categorical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Categorical Values\n",
    "\n",
    "The 2 categorical values in this dataset are:-\n",
    "\n",
    "1. Country \n",
    "2. Region \n",
    "\n",
    "I am going to avoid using 'Country' in this analysis. As country is not a predictor of the Happiness -it can be used as a unique identifier\n",
    "\n",
    "\n",
    "The goal of the analysis\n",
    "\n",
    "1. Does this dataset contain more countries from a single region?\n",
    "2. Which region has the most happiest countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Region Vs Happiness Score')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7euXYev7e8PP71tmjHFY3ZKck+SGSR6U1QHxa0m+P6s37tcv03xv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\n",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Region, plotting this on a \n",
    "plt.subplots(figsize= (18,10))\n",
    "sns.countplot(x='Region', data=Happiness)\n",
    "plt.title('Region' + ' Vs Happiness Score')\n",
    "#In this dataset the number of countries in 'Sub-Saharan Africa' is maximum followed by 'Central and Eastern Europe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b109e903c8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hZAAAAAC6OGLJUFPPqKrnD9cfUVWPn300AAAAYJ7sZE+GX07yJUmePlw/kOSXZpYIAAAA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\nCCGEEEIIIdrC/wFf7Pr3YluTVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We are now attempting to plot the number of countries in each region and the magnitude of the happiness score within each region\n",
    "plt.subplots(figsize= (18,10))\n",
    "sns.boxplot(x='Region', y='Happiness Score', data=Happiness)\n",
    "#Countries from Western Europe seems to be most Happiest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub-Saharan Africa                 40\n",
       "Central and Eastern Europe         29\n",
       "Latin America and Caribbean        22\n",
       "Western Europe                     21\n",
       "Middle East and Northern Africa    20\n",
       "Southeastern Asia                   9\n",
       "Southern Asia                       7\n",
       "Eastern Asia                        6\n",
       "North America                       2\n",
       "Australia and New Zealand           2\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get to the number of countries within each region\n",
    "Happiness.Region.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion of analyzing the categorical values\n",
    "\n",
    "1. The dataset contains a large number of countries from regions 'Sub-Saharan Africa' followed by 'Central and Eastern Europe\n",
    "2. The countries from Western Europe are the happiest\n",
    "\n",
    "# What are we going to do with this data?\n",
    "\n",
    "NOTE : Machine Learning and Deep Learning Models use numerical data only. Categorical values cannot be used. \n",
    "\n",
    "We will now convert the categorical values with more than 2 values to columns using pd.dummies\n",
    "\n",
    "# Using pd.dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating dummy variables for categorical columns with more than 2 levels.\n",
    "dummy_variables = pd.get_dummies(Happiness.loc[:,['Region']],drop_first=True)\n",
    "Happiness = pd.concat([Happiness,dummy_variables],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 9 columns):\n",
      "Region_Central and Eastern Europe         158 non-null uint8\n",
      "Region_Eastern Asia                       158 non-null uint8\n",
      "Region_Latin America and Caribbean        158 non-null uint8\n",
      "Region_Middle East and Northern Africa    158 non-null uint8\n",
      "Region_North America                      158 non-null uint8\n",
      "Region_Southeastern Asia                  158 non-null uint8\n",
      "Region_Southern Asia                      158 non-null uint8\n",
      "Region_Sub-Saharan Africa                 158 non-null uint8\n",
      "Region_Western Europe                     158 non-null uint8\n",
      "dtypes: uint8(9)\n",
      "memory usage: 1.5 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Lets take a look at the dummy variables\n",
    "print(dummy_variables.info())\n",
    "#One Categorical Value : Region has been converted to 9 columns that can have a value of 0/1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Having X and Y ready\n",
    "\n",
    "What have we done so far?\n",
    "\n",
    "1. Analyzed the numberical values \n",
    "2. Analyzed the categorical values\n",
    "3. Determined the highest predictor of Happiness in the numerical columns\n",
    "4. Noted how the dataset is skewed towards countries of a particular region\n",
    "\n",
    "\n",
    "# X ==> Input variables are\n",
    "\n",
    "1. Economy (GDP per Capita) \n",
    "2. Family\n",
    "3. Health (Life Expectancy)\n",
    "4. Freedom\n",
    "5. Trust (Government Corruption)\n",
    "6. Generosity\n",
    "7. Dystopia Residual\n",
    "8. Region_Central and Eastern Europe\n",
    "9. Region_Eastern Asia             \n",
    "10. Region_Latin America and Caribbean\n",
    "11. Region_Middle East and Northern Africa\n",
    "12. Region_North America                  \n",
    "13. Region_Southeastern Asia              \n",
    "14. Region_Southern Asia                  \n",
    "15. Region_Sub-Saharan Africa             \n",
    "16. Region_Western Europe                 \n",
    "\n",
    "# Y ==> Output variable is\n",
    "\n",
    "Happiness score\n",
    "\n",
    "# GOAL\n",
    "\n",
    "1. Create a X dataframe of dimension 158 X 16 \n",
    "2. Create a Y datafram of dimension 158 x 1 \n",
    "3. Combine the above to form the training set to have a dimension of 158 x 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158, 17)\n",
      "(158, 1)\n"
     ]
    }
   ],
   "source": [
    "#seperating the independent variables and dependent variables\n",
    "\n",
    "#selecting all the variables except those for which we have created dummy variables, car_ID, price column\n",
    "X = Happiness[Happiness.columns.difference(['Country','Region','Happiness Rank','Happiness Score'])]\n",
    "print(X.shape)\n",
    "\n",
    "#Creating the dependent variable price as Y\n",
    "Y = Happiness.loc[:,['Happiness Score']]\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158, 18)\n"
     ]
    }
   ],
   "source": [
    "#Creating a new dataframe to keep the required variables for VIF analysis etc.\n",
    "Happiness_vars = pd.concat([X,Y],axis=1)\n",
    "print(Happiness_vars.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next step is to apply different algorithm models to this data! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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