regression_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb30b9fa",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "3db59a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a467d95c",
   "metadata": {},
   "source": [
    "### Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "0f18f2b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
       "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
       "       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',\n",
       "       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',\n",
       "       ' thinness  1-19 years', ' thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the data set\n",
    "\n",
    "df_lifeexp = pd.read_csv(\"Life expectancy data.csv\")\n",
    "df_lifeexp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c17c90",
   "metadata": {},
   "source": [
    "### Descriptive statistics before cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "2e23b701",
   "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>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2744.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2385.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2712.00000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.224932</td>\n",
       "      <td>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.321247</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.523867</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>20.044034</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>2.49832</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>4.420195</td>\n",
       "      <td>4.508882</td>\n",
       "      <td>0.210904</td>\n",
       "      <td>3.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.100000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.75500</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.200000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.49250</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.60000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "count  2938.000000       2928.000000      2928.000000    2938.000000   \n",
       "mean   2007.518720         69.224932       164.796448      30.303948   \n",
       "std       4.613841          9.523867       124.292079     117.926501   \n",
       "min    2000.000000         36.300000         1.000000       0.000000   \n",
       "25%    2004.000000         63.100000        74.000000       0.000000   \n",
       "50%    2008.000000         72.100000       144.000000       3.000000   \n",
       "75%    2012.000000         75.700000       228.000000      22.000000   \n",
       "max    2015.000000         89.000000       723.000000    1800.000000   \n",
       "\n",
       "           Alcohol  percentage expenditure  Hepatitis B       Measles   \\\n",
       "count  2744.000000             2938.000000  2385.000000    2938.000000   \n",
       "mean      4.602861              738.251295    80.940461    2419.592240   \n",
       "std       4.052413             1987.914858    25.070016   11467.272489   \n",
       "min       0.010000                0.000000     1.000000       0.000000   \n",
       "25%       0.877500                4.685343    77.000000       0.000000   \n",
       "50%       3.755000               64.912906    92.000000      17.000000   \n",
       "75%       7.702500              441.534144    97.000000     360.250000   \n",
       "max      17.870000            19479.911610    99.000000  212183.000000   \n",
       "\n",
       "              BMI   under-five deaths         Polio  Total expenditure  \\\n",
       "count  2904.000000         2938.000000  2919.000000         2712.00000   \n",
       "mean     38.321247           42.035739    82.550188            5.93819   \n",
       "std      20.044034          160.445548    23.428046            2.49832   \n",
       "min       1.000000            0.000000     3.000000            0.37000   \n",
       "25%      19.300000            0.000000    78.000000            4.26000   \n",
       "50%      43.500000            4.000000    93.000000            5.75500   \n",
       "75%      56.200000           28.000000    97.000000            7.49250   \n",
       "max      87.300000         2500.000000    99.000000           17.60000   \n",
       "\n",
       "       Diphtheria      HIV/AIDS            GDP    Population  \\\n",
       "count  2919.000000  2938.000000    2490.000000  2.286000e+03   \n",
       "mean     82.324084     1.742103    7483.158469  1.275338e+07   \n",
       "std      23.716912     5.077785   14270.169342  6.101210e+07   \n",
       "min       2.000000     0.100000       1.681350  3.400000e+01   \n",
       "25%      78.000000     0.100000     463.935626  1.957932e+05   \n",
       "50%      93.000000     0.100000    1766.947595  1.386542e+06   \n",
       "75%      97.000000     0.800000    5910.806335  7.420359e+06   \n",
       "max      99.000000    50.600000  119172.741800  1.293859e+09   \n",
       "\n",
       "        thinness  1-19 years   thinness 5-9 years  \\\n",
       "count            2904.000000          2904.000000   \n",
       "mean                4.839704             4.870317   \n",
       "std                 4.420195             4.508882   \n",
       "min                 0.100000             0.100000   \n",
       "25%                 1.600000             1.500000   \n",
       "50%                 3.300000             3.300000   \n",
       "75%                 7.200000             7.200000   \n",
       "max                27.700000            28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  \n",
       "count                      2771.000000  2775.000000  \n",
       "mean                          0.627551    11.992793  \n",
       "std                           0.210904     3.358920  \n",
       "min                           0.000000     0.000000  \n",
       "25%                           0.493000    10.100000  \n",
       "50%                           0.677000    12.300000  \n",
       "75%                           0.779000    14.300000  \n",
       "max                           0.948000    20.700000  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df7b984",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis - Outlier detection and handling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bf2978",
   "metadata": {},
   "source": [
    "#### Life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "cacd8a15",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2928.000000\n",
       "mean       69.224932\n",
       "std         9.523867\n",
       "min        36.300000\n",
       "25%        63.100000\n",
       "50%        72.100000\n",
       "75%        75.700000\n",
       "max        89.000000\n",
       "Name: Life expectancy , dtype: float64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Life expectancy '].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "ca7bc26a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Life expectancy '>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k4pxz7je/+Y1buHDhCePLysrcQw89FL1u8KpVq9zMmTOdc87t2rXLjRgxwpWXl7uRI0e6f/zjH9G/5+6773ZHjx51wWDQjR071m3atMkFAgGXk5Pj6urqnHPOffnll2706NFu37597oMPPnA5OTkuEAg455x76qmn3Isvvnjc3pqbm93kyZPdN99845xz7i9/+Uv0tjVr1riMjAx34MAB55xzixYtcg8//LBzzrk5c+a4kpIS55xzjY2NLi8vz1VXV7t7773XvfHGG8659us/T548+czvfJw1ONNFl5SXl6upqUnbt2+XJIVCoeiFUI61efNm7dy5U7fffrskRa/HKklXXnml7rvvPs2aNUvFxcW69NJLo+OmTJmi5ORkJScnKzc3VxUVFfJ6vWpoaNCcOXOix3k8Hu3evVuVlZXKzc1VamqqJOmRRx6R1P5+dYfzzz9fL7/8srZs2aLq6mrt2rVLLS0t0duvuuqq6I/yDhs2TBs3bpTUfsnGX/ziF5Kkvn37asOGDZKkadOm6de//rWmTZum1atXa+rUqV25S5HgiC66JBKJ6NFHH1V2drYk6dChQzpy5Einx3Vce0FqvxpXMBiM3v7FF1+of//+0etQdPD5/v0Udc7J6/UqHA7rsssuU2lpafS2+vp6paen649//ONxl9prbGxUY2PjcWv58ssvNWXKFE2ePFkZGRnKzc3V5s2bo7f36dMn+rXH45H7/5+U9/l8x81dV1enfv36aeTIkTp8+LAqKyv18ccfR399DdAZPr2ALsnKytKbb76po0ePKhKJ6PHHH9czzzzT6XFvvfWWmpubJbV/GuLhhx+WJL3//vv605/+pPXr12vbtm364IMPouPWr1+vSCSiYDCod999VzfffLOuu+461dTUaMeOHZKkv/3tbxo7dqzq6+s1cuRIbdy4Mfr3PP/883r99dfl8/kUDoflnNNf//pXpaena/bs2crKyooGNxwOn3SvmZmZWrNmjaT2T0Pccccd0evI+v1+/fKXv9T48eN1zjnndPFeRSLjTBfHaWlpOeFjY6tWrfqvx8+ePVslJSWaOHGiwuGwvv/970d/ieixfvSjH6m+vl6TJ0+Wx+PRRRddpOLiYh04cEALFy7Uyy+/rPT0dBUXF2vOnDn6wQ9+IElqbW3VpEmTdOjQIfn9/uh1h5977rnoJTGdc1qyZIkuueQSXXLJJdqzZ0/0Jf7ll1+uJ598Uueee66uueYa5eXl6bXXXtN3v/td5ebmyuPxaMSIEUpPT1dNTc1J75sFCxboiSeeUH5+vpxzmjVrVnSdEydOVElJiaZMmXL6dzbOSlxlDD1WUVGRpk2b1isuEfj73/9eZWVlWr58ebyXgh6OM12gi4qKivTNN9/oxRdfjPdS0AtwpgsAhvgfaQBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAof8DaeWCSlAfO9QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Life expectancy '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "aaca2774",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Life expectancy\n",
    "lower_lim_le = df_lifeexp['Life expectancy '].quantile(0.25) - 1.5*(df_lifeexp['Life expectancy '].quantile(0.75) - df_lifeexp['Life expectancy '].quantile(0.25))\n",
    "upper_lim_le = df_lifeexp['Life expectancy '].quantile(0.75) + 1.5*(df_lifeexp['Life expectancy '].quantile(0.75) - df_lifeexp['Life expectancy '].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "3a8e3ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['Life expectancy '] = df_lifeexp['Life expectancy '].mask(df_lifeexp['Life expectancy '] < lower_lim_le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "017d5650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Life expectancy '>"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WkaKTI7poE5/Pp3vvvVfJycmSpIMHD6qxsfF3H3f4mg/SoSt7VVVV+e//9ttv1bt3b/91Mg47/CeWpENXiAsPD5fX69XgwYO1cuVK/33l5eWKjY3Vxx9/3Ooye9XV1aqurm61ln379mnq1KmaMmWK4uPjlZqaqo0bN/rvj4qK8v87LCxM7tdPykdERLTadmlpqU466SSNGjVK9fX12rJli7Zt26acnJy/NjyEJN69gDZJSkrS66+/rqamJvl8Pj3wwAN66qmnfvdxb7/9tmprayUdejfEPffcI0lav369PvnkE73zzjvavHmzNmzY4H/eO++8I5/Pp6qqKq1bt04XX3yxhg8fruLiYm3dulWS9PXXX+vSSy9VeXm5Ro0apby8PP9+lixZoldeeUURERHyer1yzunLL79UbGysZs2apaSkJH9wvV7vn77WhIQErVq1StKhd0Ncf/31/mvNejwe3XfffZo4caJOOOGENk4VnRlHumilrq7uiLeNrVix4g8fP2vWLOXk5OjKK6+U1+vV2WefrQULFhzxuKuvvlrl5eWaMmWKwsLC1L9/f2VnZ6usrExZWVl64YUXFBsbq+zsbGVmZuqcc86RJDU0NGjy5Mk6ePCgPB6PEhISJEmLFy/2X7LTOadFixbplFNO0SmnnKLvvvvO/yv+GWecoUceeUTdunXTeeedpwkTJujll1/WySefrNTUVIWFhWnkyJGKjY1VcXHxn87mwQcf1EMPPaTLLrtMzjnNmDHDv84rr7xSOTk5mjp16l8fNkISVxnDcSsjI0Pp6ekd4jKC77//vnJzc/XSSy8Feyk4znGkC7RRRkaG9u/fr+eeey7YS0EHwJEuABjif6QBgCGiCwCGiC4AGCK6AGCI6AKAIaILAIb+D6FeFUvGWs56AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Life expectancy '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "1ed9a9fa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2918.000000\n",
       "mean       69.318986\n",
       "std         9.402404\n",
       "min        44.300000\n",
       "25%        63.225000\n",
       "50%        72.100000\n",
       "75%        75.700000\n",
       "max        89.000000\n",
       "Name: Life expectancy , dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Life expectancy '].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf84f3a4",
   "metadata": {},
   "source": [
    "#### Hepatitis B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "b8998d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hepatitis B'>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Hepatitis B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "8090983e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2385.000000\n",
       "mean       80.940461\n",
       "std        25.070016\n",
       "min         1.000000\n",
       "25%        77.000000\n",
       "50%        92.000000\n",
       "75%        97.000000\n",
       "max        99.000000\n",
       "Name: Hepatitis B, dtype: float64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Hepatitis B'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "91c460f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Hepatitis B'\n",
    "lower_lim_hb = df_lifeexp['Hepatitis B'].quantile(0.25) - 1.5*(df_lifeexp['Hepatitis B'].quantile(0.75) - df_lifeexp['Hepatitis B'].quantile(0.25))\n",
    "upper_lim_hb = df_lifeexp['Hepatitis B'].quantile(0.75) + 1.5*(df_lifeexp['Hepatitis B'].quantile(0.75) - df_lifeexp['Hepatitis B'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "b6f1c695",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['Hepatitis B'] = df_lifeexp['Hepatitis B'].mask(df_lifeexp['Hepatitis B'] < lower_lim_hb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "8ef922e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hepatitis B'>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Hepatitis B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "4a829301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2131.000000\n",
       "mean       88.558423\n",
       "std        11.628027\n",
       "min        47.000000\n",
       "25%        84.000000\n",
       "50%        93.000000\n",
       "75%        97.000000\n",
       "max        99.000000\n",
       "Name: Hepatitis B, dtype: float64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Hepatitis B'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c221674",
   "metadata": {},
   "source": [
    "#### Polio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "602d16c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Polio'>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Polio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "7cbc9cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2919.000000\n",
       "mean       82.550188\n",
       "std        23.428046\n",
       "min         3.000000\n",
       "25%        78.000000\n",
       "50%        93.000000\n",
       "75%        97.000000\n",
       "max        99.000000\n",
       "Name: Polio, dtype: float64"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Polio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "8be04e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Polio'\n",
    "lower_lim_polio = df_lifeexp['Polio'].quantile(0.25) - 1.5*(df_lifeexp['Polio'].quantile(0.75) - df_lifeexp['Polio'].quantile(0.25))\n",
    "upper_lim_polio = df_lifeexp['Polio'].quantile(0.75) + 1.5*(df_lifeexp['Polio'].quantile(0.75) - df_lifeexp['Polio'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "45375555",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['Polio'] = df_lifeexp['Polio'].mask(df_lifeexp['Polio'] < lower_lim_polio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "75581442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Polio'>"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Polio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "a932c6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2640.000000\n",
       "mean       89.090909\n",
       "std        11.308050\n",
       "min        51.000000\n",
       "25%        84.000000\n",
       "50%        94.000000\n",
       "75%        97.000000\n",
       "max        99.000000\n",
       "Name: Polio, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Polio'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82819fdd",
   "metadata": {},
   "source": [
    "#### Total Expenditure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "284f0a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total expenditure'>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Total expenditure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "d9085679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2712.00000\n",
       "mean        5.93819\n",
       "std         2.49832\n",
       "min         0.37000\n",
       "25%         4.26000\n",
       "50%         5.75500\n",
       "75%         7.49250\n",
       "max        17.60000\n",
       "Name: Total expenditure, dtype: float64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Total expenditure'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "94b44b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total expenditure'\n",
    "lower_lim_te = df_lifeexp['Total expenditure'].quantile(0.25) - 1.5*(df_lifeexp['Total expenditure'].quantile(0.75) - df_lifeexp['Total expenditure'].quantile(0.25))\n",
    "upper_lim_te = df_lifeexp['Total expenditure'].quantile(0.75) + 1.5*(df_lifeexp['Total expenditure'].quantile(0.75) - df_lifeexp['Total expenditure'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "a4577c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['Total expenditure'] = df_lifeexp['Total expenditure'].mask(df_lifeexp['Total expenditure'] > upper_lim_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "18204a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total expenditure'>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Total expenditure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "375412e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2680.000000\n",
       "mean        5.832951\n",
       "std         2.312034\n",
       "min         0.370000\n",
       "25%         4.250000\n",
       "50%         5.710000\n",
       "75%         7.430000\n",
       "max        12.250000\n",
       "Name: Total expenditure, dtype: float64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Total expenditure'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37d74ae",
   "metadata": {},
   "source": [
    "#### GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "e8e0faa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP'>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAEJCAYAAACuby3jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWXUlEQVR4nO3deXDV5b3H8c8vOVmAYAhhMYmABazO0Ft0isONQGIIW4CwiaNliZpqMOIFVxa3uQ7LXNRRK7QVi1Kx0DJUkWXwXiuLJoShVHF624tLKZCEsJNAyEKSk+f+EfMzqyHL4ckJ79eMM8nv9/ye5/t9zuHjye8cgmOMMQIAWBNguwAAuNYRxABgGUEMAJYRxABgGUEMAJYRxABgGUEMAJZ5mhqQn1+kysrmf9Q4MjJM585dalFR7U1H6kXqWP3QS/t0LfcSEOAoIqJLs9ZoMogrK02Lgrj62o6iI/Uidax+6KV9opcrx60JALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAy5r8p5JaY8OGdcrJOSZJunChQJIUHt7tiq7t06efZsxI8VFlANB++DSIc3KO6etv/6nA0G7ylhZIks5crGjyuuqxAHAt8GkQS1JgaDd17peo4mM7JUmd+yU2eU31WAC4FnCPGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKCGAAsI4gBwDKPLybdu/czXXddJ19M3SJ7934mSRo2LM5yJQBQn0+CODPzUwUFBfpi6hbJzPxUEkEMoH3i1gQAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWEYQA4BlBDEAWOaxXcDVUFpaomPHjio1dUaTYwMDA+X1Vkoy333tlSSlp8/T7bf/u3bt+rN+//u1V7RuRESEQkM76cSJPHcuj8ej7t0jdfr0KXdc9+6ROn/+nIKDg/Xssy/q4sULevXVFQoLC1NhYWGt+QoKChQQEODWGBAQoMrKSjmOI2OMO9ZxAmRMZa16HMdRUFCQXnnlFYWF9VB29lEtWfK8vF6vunTpoqKiIklSZGQPhYd30803D9JHH21RcvJkffHF58rLO66RI0dr586P5fF4FBeXqF27/sddOzDQo8jISJ05c1oxMTF68sln9NVX/6fVq1dJkltrZGQPFRYWynGkBx54SL/73Rr17h2l6dPv1a9+9ZoiIrrr/Plziojorvz887r++mg98MBDWrfuHTmO9OijT8gYozffXKnnnntGkkf792dp9epViozsoUuXCr+7Jk0bNryr9PR57viaX8+YcZ/WrXtbkpSSkqp1695RRUWFgoI8tdZIT5+n8PButfayoCC/3rns7KNasWKJFi16QX369GtwTEsUFORr1arX3N7Dw7u5xyRp6tS79etfv65HHnlM27Zt1owZKe5ezZ6dqg0b1tXbg9bU408a27um9qHmmJ49u/q8TsfU/NPbgHPnLqmy8geH1LNixRIFBQWqvNyrf+acVed+iSo+tlOS1LlfYpPXFx/bqYF9emjhwuebtW5jHn74fpWVlbVqjsBAj37723VKTZ0pqXn70RzR0TEqKChQcXGRz9bo27ev/vM//0vPPfe08vKO+2ydhIRR+uyzPfJ6KxodExjocc937tyl0b6jo2PcWhMSRkmS9uzZqaSkJE2fPksPPZRSb53o6BidOJGnO+9MdMfX/DoqKtqds+b8dde4885EzZ6dWmvu9957p9656v2Mjo7R0qUvNzjmh/Ts2VVnzhTWO/7ee+9o9+5P3Lpmz06tdax63zp37qKSkuJ6fTW0B1dST2s01svV1tjeNbUPNcc88cT8ZvUSEOAoMjKsWXV2+FsT2dlHWx3CkuT1Vmjt2rfkyxCWpLy84z4NYUnKzs7W/v37fBrCkrR79yc/GMKSap3/ob5r1pqRsUcZGZ/KGKNPPvlEu3f/ucF18vKOyxijzMxPlZGxR8YYZWR86l5bc866e1FzjczMz3ThQoF7rqAgX5mZtc9lZx9158jLO65//ON/641piYKCfGVk7HG/z8z8VNnZR5WR8al7rHrfiouLGuyr7h60ph5/0tjeNfW41H188/PzfV6rT25NXLhQoIsXL8gYqdIb2OzrKytKlZ19TCtWLGl1LUeOHG71HNUyMva02Vy2rVnzG9sltFhFRYUcx5EkVVZW6r33ftfk+Oqf+6qubf4aW7d+4L562rZts/tTYvW5r78+VOv63/zmjXpjWvIqdNu2zaqo8Naq6623ftXk/+Aa6qd6D1pTjz9pbO+aelzqPr5//OMfNX36LJ/W2uFfEbfFq+GOqLl/kNub6jtqFRUVauqnlKqx1WOMmrgbV28Nr7dC+/btdY/v27fX3b/qc3VfURcXF9Ub0xJV131fb/Ur3ivtoeZ11fO0ph5/0tjeNfW41H18d+/e7fNaffKKODy8m3r0iHTvETdXgCdUfdvoHrGv74P6q5r3Zv1R9RuEHo/nu1c9jQdT1Vh9N8aR4+iKgqzmm5CxscPc47Gxw9x739Xnvv76UK3nWefOXXT58uVaY1oiNnaYdu/e6fbnOI6ioqJ14kRes8K45h60ph5/0tjenTp16gcfl7qPb0JCgs9r7fCviNPS5rbZXCNG3Nlmc9n24IPptktoMY/Ho8DAqtcQAQEBmj37/ibHezyB9a5tzhqTJk1zzyUnT1VAgFPrXN3nWXr6vHpjWiI5eapbe3VdaWlzr6iHuv1Uz9OaevxJY3vX1ONS9/G99957fV5rhw/ivn1vVHBwcKvnCQz06IEH0iRdwQ3GVoiOjlHnzl18ukbfvn01dGisoqNjfLpOQsKoJgOj5vkf6rtmrSNG3KkRI+LlOI5GjRqlhITRDa4THR0jx3E0fHi8Roy4U47jaMSIePfamnPW3YuaawwfHlfrY07dukVo+PDa5/r2vdGdIzo6RoMG/Vu9MS3RrVtErRcAw4fHq2/fGzViRLx7rHrfOnfu0mBfdfegNfX4k8b2rqnHpe7jGxER4fNaO3wQS1JUVPQVjw0MDFR12FZ9XSUt7RFJ0qxZ91/xXBEREe7a1XN5PB716tW71rju3SMlScHBwZoz51Glp/+HHMdR165d683nOE6tGgMCqh5Cp847UI5T/6F1HEfBwcF66qmnvutprltXly7fh2BkZA/17z9QSUmTJUnJyZMVE3ODHMdRYuIYt4+RI8fWWjswsKo3x3F0ww03aNKkaXrwwYfdeatrjYzsoeDgEIWEhOihhx5WaGio+vX7kdLT5yk0NFRRUdEKCQnR9ddHKSQkRP36/UhpaXPVv/9ADRgwUJMmTVNy8lTddNPN7quV6nUiI3vUuOZR3XTTzbXG1/w6Le1R9e8/UP37D3Tn79v3xnprNPaqqe65tLS56tSpk+bMebTRMS2RnDy1Vu81j/XvP1Dp6fPUqVMnpafP+66v7/cqLW1ug3twrWhs75rah6u9V9fE54irP33R0vnay2ci20pH6ode2qdruRc+RwwAfoggBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsMzji0mHD4/Xddd10o4d/+2L6Ztt+PB42yUAQKN8EsTDhsWpZ8+u7SaIhw2Ls10CADSKWxMAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYBlBDACWEcQAYJnH1wt4SwtUfGynvKUFkqTiYzuv6Bqph0/rAoD2wqdB3KdPP/frCxeqlgoP73YFV/aodS0AdGQ+DeIZM1J8OT0AdAjcIwYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCMIAYAywhiALCsyX8qKSDAafHkrbm2velIvUgdqx96aZ+u1V5a0rdjjDHNvgoA0Ga4NQEAlhHEAGAZQQwAlhHEAGAZQQwAlhHEAGAZQQwAlhHEAGAZQQwAlvkkiLdt26bx48drzJgxWr9+vS+WaJFVq1ZpwoQJmjBhgl566SVJUlZWlpKTkzVmzBi99tpr7thDhw5p2rRpGjt2rJ599llVVFRIkvLy8jRz5kyNGzdO6enpKioqkiRdvHhRaWlpSkpK0syZM3XmzJmr0tOKFSu0aNEiv+9l165dmjZtmpKSkrR06VK/7mfLli3u82zFihV+2culS5c0ceJE5ebmXpX6y8rK9PTTTyspKUlTp07V4cOHfdbLxo0bNXHiRCUnJ2vx4sUqKyuz34tpYydPnjQJCQkmPz/fFBUVmeTkZPPtt9+29TLNtnfvXnPPPfeYy5cvm7KyMpOSkmK2bdtm4uPjTXZ2tikvLzepqalmz549xhhjJkyYYA4ePGiMMWbx4sVm/fr1xhhj0tLSzPbt240xxqxatcq89NJLxhhjXnzxRbN69WpjjDGbN2828+fP93lPWVlZZujQoWbhwoWmpKTEb3vJzs42w4cPNydOnDBlZWXm5z//udmzZ49f9lNcXGxuv/12c+7cOVNeXm6mT59udu7c6Ve9fPnll2bixIlm0KBBJicn56o8t9asWWOef/55Y4wxf/nLX8zdd9/tk17+9a9/mdGjR5vCwkJTWVlpFixYYNauXWu9lzYP4g8++MAsXrzY/X7VqlVm5cqVbb1Ms33zzTfuJhtTtYkrV640KSkp7rHNmzebRYsWmdzcXJOYmOgeP3DggJk9e7YpKyszt912mykvLzfGGJOXl2dGjhxpjDEmISHB5OXlGWOMKS8vN7fddpspKyvzWT/5+fnm7rvvNmvXrjULFy40+/fv99te3n77bbN8+XL3+5MnT/ptP4WFheZnP/uZyc3NNSUlJWbKlCl+18szzzxjDhw4YBISEkxOTs5VqX/WrFnmwIED7lyJiYnm+PHjbd5Lbm6uyczMdM+vWbPGLFu2zHovbX5r4vTp0+rZs6f7fa9evXTq1Km2XqbZbrrpJt16662SpKNHj+qjjz6S4zgN1lq3h549e+rUqVPKz89XWFiYPB5PreNS7b49Ho/CwsJ0/vx5n/Xzwgsv6PHHH9d1111Xb31/6+XYsWPyer16+OGHNXnyZG3YsMFv+wkLC9P8+fOVlJSk+Ph4xcTE+F0vy5Yt05AhQ9zvr0b9Dc118uTJNu8lJiZGw4YNkySdP39e69evV2JiovVe2jyIKysr5Tjf/xo4Y0yt72379ttvlZqaqgULFqhPnz4N1tpYDw310lhvxhgFBPjmvdBNmzYpKipKsbGx7rHGam7vvUiS1+vVvn37tHz5cm3cuFF/+9vflJOT45f9fPXVV3r//fe1e/duZWRkKCAgQEePHvXLXqpdjedW3Wt83depU6d033336a677tLQoUOt99LmnV5//fW13kA4c+aMevXq1dbLtMjnn3+u+++/X08++aSmTp3aaK11j589e1a9evVS9+7dVVhYKK/XW2u8VPUq4ezZs5KkiooKFRUVqVu3bj7pY8eOHdq7d68mT56sN954Q7t27dKmTZv8shdJ6tGjh2JjY9W9e3eFhoZq1KhRysrK8st+MjMzFRsbq8jISAUHB2vatGnav3+/X/ZS7Wr8Oendu7dOnz5dby5fOHz4sO69915NnTpVc+fObbDHq91LmwfxHXfcoX379un8+fMqKSnRxx9/rLi4uLZeptlOnDihuXPn6pVXXtGECRMkSYMHD9aRI0fcH423b9+uuLg4xcTEKCQkRJ9//rmkqnfB4+LiFBQUpCFDhmjHjh2SpA8//NDtLT4+Xh9++KGkqqAcMmSIgoKCfNLL2rVrtX37dm3ZskXz5s3TyJEjtWbNGr/sRZISEhKUmZmpixcvyuv1KiMjQ+PGjfPLfm655RZlZWWpuLhYxhjt2rXLb59n1a5G/fHx8dqyZYsk6a9//atCQkIUHR3d5r1cunRJv/jFLzR//nylpqa6x6330pwb31dq69atZsKECWbMmDHmrbfe8sUSzbZkyRJz6623mkmTJrn/bdiwwWRlZZnk5GQzZswYs2zZMlNZWWmMMebQoUPmrrvuMmPHjjVPPPGEuXz5sjHGmNzcXDNr1iyTlJRkUlNTTUFBgTGm6s2zOXPmmPHjx5t77rnH5OTkXJW+3n//fbNw4UJjjPHrXjZt2uQ+Z1588UXj9Xr9tp/Vq1ebsWPHmokTJ5rFixeb0tJSv+yl+g0uY3z/3CotLTULFiww48ePN1OmTDF///vffdLL2rVrzaBBg2rlwOuvv269F/6FDgCwjL9ZBwCWEcQAYBlBDACWEcQAYBlBDACWeWwXANT0pz/9SRs3blRRUZHKysrUp08fPfbYYxo8eLBmz56t48ePq2vXrpKqPkQfHx+vRx55RGFhYcrNzdXo0aP14x//2J3PGKOUlBRNnz7dVktAkwhitBuvvvqqDhw4oNdff10xMTGSpH379mnOnDn64IMPJEkLFizQuHHjJEnl5eVaunSpnnrqKb355puSpNDQUPfD9FLVX2WdOHGifvKTn+iWW265yh0BV4ZbE2gXzp49q3fffVe//OUv3RCWpNjYWC1atEglJSX1rgkKCtLixYt14MCBRn/na+/evdWvXz8dPXrUV6UDrUYQo1348ssvNWDAgAb/Tv6UKVM0YMCABq8LDQ3VjTfeqG+++abB8wcPHlR2drYGDx7cpvUCbYlbE2gX6v4Fz0uXLmnmzJmSpOLiYiUlJTV6reM46tSpkySptLRUkydPllT1W90iIiL08ssvKyoqykeVA61HEKNd+OlPf6ojR44oPz9fERERCgsLc+/1rly5Uvn5+Q1eV1JSosOHD2vgwIGS6t8jBvwBtybQLvTu3VspKSmaP3++8vLy3OPHjx/XF1980eDvcy0tLdXy5csVFxenG2644WqWC7QpfukP2pWtW7fqD3/4g0pKSlRYWKjw8HCNHz9eM2fOVFpamvvxtYCAAFVUVOiOO+7Q448/rtDQUOXm5io5OVkHDx603QbQLAQxAFjGrQkAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDLCGIAsIwgBgDL/h/qB97WcKpI9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['GDP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "16631e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2490.000000\n",
       "mean       7483.158469\n",
       "std       14270.169342\n",
       "min           1.681350\n",
       "25%         463.935626\n",
       "50%        1766.947595\n",
       "75%        5910.806335\n",
       "max      119172.741800\n",
       "Name: GDP, dtype: float64"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['GDP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "4e126e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'GDP'\n",
    "lower_lim_gdp = df_lifeexp['GDP'].quantile(0.25) - 1.5*(df_lifeexp['GDP'].quantile(0.75) - df_lifeexp['GDP'].quantile(0.25))\n",
    "upper_lim_gdp = df_lifeexp['GDP'].quantile(0.75) + 1.5*(df_lifeexp['GDP'].quantile(0.75) - df_lifeexp['GDP'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "759fc1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['GDP'] = df_lifeexp['GDP'].mask(df_lifeexp['GDP'] > upper_lim_gdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "d3b8dc56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP'>"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['GDP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "ed04e691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2125.000000\n",
       "mean      2537.836280\n",
       "std       3035.258436\n",
       "min          1.681350\n",
       "25%        387.753211\n",
       "50%       1253.657431\n",
       "75%       3827.786267\n",
       "max      13893.187680\n",
       "Name: GDP, dtype: float64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['GDP'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bda385b",
   "metadata": {},
   "source": [
    "#### Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "9e3d4a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Population'>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "48fbf18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.286000e+03\n",
       "mean     1.275338e+07\n",
       "std      6.101210e+07\n",
       "min      3.400000e+01\n",
       "25%      1.957932e+05\n",
       "50%      1.386542e+06\n",
       "75%      7.420359e+06\n",
       "max      1.293859e+09\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Population'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "4f7dd19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Population'\n",
    "lower_lim_pop = df_lifeexp['Population'].quantile(0.25) - 1.5*(df_lifeexp['Population'].quantile(0.75) - df_lifeexp['Population'].quantile(0.25))\n",
    "upper_lim_pop = df_lifeexp['Population'].quantile(0.75) + 1.5*(df_lifeexp['Population'].quantile(0.75) - df_lifeexp['Population'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "6d867cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp['Population'] = df_lifeexp['Population'].mask(df_lifeexp['Population'] > upper_lim_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "124e468c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Population'>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "5349939d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.992000e+03\n",
       "mean     2.915384e+06\n",
       "std      4.209360e+06\n",
       "min      3.400000e+01\n",
       "25%      1.384468e+05\n",
       "50%      8.923400e+05\n",
       "75%      3.859758e+06\n",
       "max      1.789956e+07\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Population'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a784d20d",
   "metadata": {},
   "source": [
    "#### Schooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "c9cc8b3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Schooling'>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Schooling'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "4dc56974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2775.000000\n",
       "mean       11.992793\n",
       "std         3.358920\n",
       "min         0.000000\n",
       "25%        10.100000\n",
       "50%        12.300000\n",
       "75%        14.300000\n",
       "max        20.700000\n",
       "Name: Schooling, dtype: float64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Schooling'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "df87def5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Schooling'\n",
    "lower_lim_sch = df_lifeexp['Schooling'].quantile(0.25) - 1.5*(df_lifeexp['Schooling'].quantile(0.75) - df_lifeexp['Schooling'].quantile(0.25))\n",
    "upper_lim_sch = df_lifeexp['Schooling'].quantile(0.75) + 1.5*(df_lifeexp['Schooling'].quantile(0.75) - df_lifeexp['Schooling'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "906f2fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp[\"Schooling\"] = df_lifeexp[\"Schooling\"].mask(df_lifeexp[\"Schooling\"] < lower_lim_sch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "60c27205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Schooling'>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEJCAYAAAA3l/RWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAASfElEQVR4nO3de3CU9b3H8c+GJAImBwTDVJgADtJjS2eklakXIjlIE8BkQdBOGTAUAyW2OW29NJTbkTZAKsEZeiE6KgcyR3CEiqkGqh1lLNTAVKAK7dQiJYZEIxCQ3GBzYfd3/ohuWTbhouH7LPb9mnF2dp998nzzm2ferE+yG59zzgkAYCLO6wEA4N8J0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADMVf6AknT55SKBQ7v8rbv3+STpxo9nqMS8LMNpjZBjOfX1ycT9dcc3WX2y8Y3VDIxVR0JcXcPBeDmW0wsw1m/uy4vAAAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGLrgn+sBnnvu/1RTc9jrMTrV0FAvSerTp68SEnqovT3o6TypqUM0ffpMT2dAbCO6uKCamsM6cPCf6tGzr9ejRAm21EuS6hrPeDuI/jULcD5EFxelR8++6j1knNdjRDl9eJskxcRsn84CnA/XdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMORJdCsqdqiiYocXhwaAMC9aFG96tE+8+eZ2SdLo0WO8ODwASPKmRVxeAABDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADB0WaJbXV2l/PzZeuON1zR79gy98cY25efPVk3N4ctxOAD4XOrrT+qxxwpVXV0VcdvQUN/tx7os0X366RIFAgE9+2ypnHN69tm1CgQCeuqp1ZfjcADwuZSXl+ngwQN6+umSiNuXX36x24/V7dGtrq5Sbe2Hn9xzEbe1tR/yahdATDlzpl1vvrldzjnV1n4Ycfvmmzu6/dWuzznnzveEEyeaFQqd9ykRFi8uOCu60QYOHKRQKKSGhgYNHjzk4if9REJCD7W3By95Py9d6TNXVx9WS7CHkoZleTxVtNOHt0mSeg8Z5/EkUvOhrerZI3hJ5/WVfm5cKbqaubq640VgW1ubgsEzUdt79IjXmDH/pZyc3Is+VlycT/37J3W9/aK/0kU6X3AvZjsAWGppCXQaXEkKBs9o166Kbj1efLd+NXW8kr3QK93k5P9Qnz599dOf/s8lf/2UlGTV1TV9nhHNXekzr1ixVP+sOe7xRLEvLr6nBqdee0nn9ZV+blwpupp5xYqlOnLkIzU3N3f5Sve220Z36yzd/kp37tz8827Py/vv7j4kAHxm1157reLifJ1ui4uL06RJU7v1eN0e3cGDh2rgwEGf3PNF3A4cOEipqZd+HRcALpf4+ASlpaXL5/Np4MBBEbdpaWPUp0/fbj3eZfmVsblz89WrVy/l5MySz+dTTk6uevXqxatcADHJ75+i4cP/U3Pn5kfcdverXOkyXNOVOl7tlpT8ryRp7NiMT269/+kyAHSmb99rNH/+o5IUddvdeBswABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABiK9+KgaWnpXhwWACJ40SJPojt69BgvDgsAEbxoEZcXAMAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwFC81wPgyhBsqdfpw9u8HiNKsKVekmJito5ZrvV6DMQ4oosLSk0d4vUIXWpo6DiF+/Tpq4SEHmpvD3o4zbUxvVaIDUQXFzR9+kyvR7goKSnJqqtr8noM4Ly4pgsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGLrgn+uJi/NZzHFJYnGmC2FmG8xsg5k/+3F8zjlnMgkAgMsLAGCJ6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgKGajm5OTo6ysLE2ePFmTJ0/Wvn37Ira/++67mjp1qsaPH69FixbpzJkzHk3a4be//W141smTJ+vmm29WYWFhxHNWr16tsWPHhp+zYcMGj6aVmpublZ2drQ8++ECStHPnTvn9fmVmZmrVqlWd7lNbW6sZM2ZowoQJ+v73v69Tp05Zjhw188aNG5WdnS2/368FCxaora0tap+ysjKlpaWF17yr781q5gULFigzMzM8z2uvvRa1Tyyt8/bt2yPO61tvvVV5eXlR+3i5zqtXr1ZWVpaysrJUXFwsKcbPZxeDQqGQS0tLc+3t7V0+Jysry7399tvOOecWLFjgNmzYYDTdhb333nsuIyPDnThxIuLxvLw895e//MWjqf7lnXfecdnZ2W7EiBGupqbGBQIBl56e7qqrq117e7vLzc11f/zjH6P2mzt3rtuyZYtzzrnVq1e74uJiz2aurKx0GRkZrqmpyYVCITdv3jy3bt26qP0KCwtdeXm52ZxnO3dm55zLzs52R48ePe9+sbTOZzt27JgbN26ce//996P282qdKyoq3He+8x3X2trq2tra3MyZM115eXlMn88x+Uq3srJSkpSbm6tJkyZp/fr1Eds//PBDtbS0aOTIkZKkqVOn6tVXX7Ues0s/+9nP9NBDD6lfv34Rj//tb3/TU089Jb/fr8LCQrW2tnoy36ZNm7RkyRINGDBAkrR//34NGTJEqampio+Pl9/vj1rP9vZ27d69W+PHj5dkv+bnzpyYmKglS5YoKSlJPp9PX/7yl1VbWxu131//+leVlZXJ7/frJz/5iRoaGjybORAIqLa2VgsXLpTf79evf/1rhUKhiH1ibZ3PVlxcrGnTpmno0KFR27xa55SUFM2fP1+JiYlKSEjQsGHDVFVVFdPnc0xGt7GxUbfddptKSkpUWlqq559/XhUVFeHtx44dU0pKSvh+SkqKjh496sWoUXbu3KmWlhZNnDgx4vFTp07pK1/5igoKClRWVqbGxkY98cQTnsy4fPlyjRo1Knz/3PUcMGBA1HqePHlSSUlJio/v+GA66zU/d+ZBgwZp9OjRkqSPP/5YGzZs0Lhx46L2S0lJ0Q9+8AO9/PLLuu6666Iu+VjOfPz4cd16660qKirSpk2btGfPHr3wwgsR+8TaOn+qqqpKb731lmbOnNnpfl6t8/Dhw8MvvqqqqvTKK6/I5/PF9Pkck9H9+te/ruLiYiUnJ6tfv3669957tX379vD2UCgkn+9fH5/mnIu476Xnn39e999/f9TjV199tZ555hkNGzZM8fHxys3NjfievHQx69nZY7Gw5kePHtV3v/td3XPPPbrllluitpeUlOjmm2+Wz+fTnDlz9Kc//cmDKTukpqaqpKREAwYMUK9evZSTkxN1DsTqOm/cuFHTp09XYmJip9u9XueDBw8qNzdX8+bNU2pqakyfzzEZ3T179mjXrl3h+8658L9IkvSlL31JdXV14fvHjx/v9H+HrLW1tWn37t268847o7bV1tZGvKo593vy0rnrWVdXF7We/fr1U1NTk4LBYJfPsXbo0CFNmzZNU6ZMUX5+ftT2pqYmlZaWhu8759SjRw/DCSMdOHBAf/jDHyLmOfcciMV1lqRt27bprrvu6nSb1+u8d+9ezZo1S4888oimTJkS8+dzTEa3qalJxcXFam1tVXNzs8rKypSRkRHePmjQIF111VXau3evJOmll17SmDFjvBo37MCBAxo6dKh69+4dta1nz55auXKlampq5JzThg0bIr4nL9100016//33dfjwYQWDQW3ZsiVqPRMSEjRq1Cj9/ve/lyT97ne/83TNm5ubNXv2bP34xz9Wbm5up8/p3bu31qxZE/7Nl/Xr13u65s45FRUVqaGhQe3t7dq4cWPUPLG2zlLH5ZuWlhalpqZ2ut3Ldf7oo4+Un5+vxx9/XFlZWZKugPPZ5Md1n8GqVavchAkTXGZmpistLXXOOTdnzhy3f/9+55xz7777rrvnnnvc+PHj3cMPP+xaW1u9HNc559zWrVvdgw8+GPHY2TO/+uqrLisry2VmZrr58+d7PvPYsWPDP6HeuXOn8/v9LjMz0y1fvtyFQiHnnHMLFy50r7/+unPOuQ8++MDdd999buLEiS43N9fV19d7NvO6devciBEj3KRJk8L//fKXv4yaeffu3e7uu+92EyZMcA888IBrbGz0bGbnnFu/fr2bOHGiy8jIcCtXrgw/J1bX2Tnn9u3b57797W9HPScW1nnp0qVu5MiREefBc889F9PnM385AgAMxeTlBQD4oiK6AGCI6AKAIaILAIaILgAYIrq4LN555x3l5OTI7/crOztbc+bM0cGDB7t8/p///GdlZ2d32/FffPHF8KdhLVq0SDt37uy2rw18HrHxlih8obS1tSkvL09r167ViBEjJHW8geV73/uetm3bZv6usOXLl5seDzgfootuFwgE1NTUpNOnT4cfmzRpkpKSkhQMBlVWVqZ169YpLi5O11xzjVasWCFJOn36tB566CFVVlaqtbVVy5Yt06hRo9TU1KSf//zn+sc//iGfz6c77rhDDz/8sOLj47Vnzx4VFxcrEAgoISFBDz74YNQ7i3JycjRjxgx97Wtf06xZs5Senq59+/apsbFRBQUFysjIUCAQ0JIlS7Rv3z4lJyfrhhtukCQ99thjdguHfwtcXkC369OnjwoKCjRnzhyNGzdOBQUF2rx5s26//XZVVlbq8ccf15o1a1ReXq4777xTTz75pCTpyJEjmjVrll566SVNmzZNv/nNbyRJy5YtU9++fVVeXq7NmzfrwIEDWrt2rU6ePKkf/ehHWrRokcrLy7VixQoVFBSopqamy9lqamqUlpamF154QY888oiKiookSU888YSCwaBeeeUVlZaW6u9///vlXyj8WyK6uCzuv/9+VVRUaPHixUpJSdEzzzyju+++W6+//rrS0tJ03XXXSZJmzZoV/hjA1NRU3XTTTZKkG2+8UR9//LEkaceOHbrvvvvk8/mUmJioadOmaceOHdq/f78GDx4c3mf48OH6xje+obfeeqvLuRISEpSeni5J+upXv6r6+npJ0vbt23XvvfcqLi5OSUlJmjJlymVZF4Dootvt3btXa9asUVJSksaOHat58+Zp69at4Y/OO/sj9FpaWnTo0CFJHUH8lM/n06fvUD/3oydDoZDOnDmjYDDY6Uf2ne9PNyUkJCguLi5qjvj4eJ39jvhPnwN0N84sdLt+/frpySef1J49e8KP1dXVqbm5Wd/61re0a9cuHTt2TFLH5w+vXLnyvF8vLS1N69evl3NObW1t2rRpk26//XaNHDlSlZWV2r9/v6SOz1TdvXu3vvnNb17yzOnp6dq8ebNCoZACgYC2bNkSE59jiy8efpCGbnf99derpKREq1at0pEjR3TVVVcpOTlZRUVFuvHGG8PXe6WOT+wvKipSVVVVl19v8eLFWrZsmfx+v9rb23XHHXfogQceUGJion71q19p6dKlamlpkc/n0y9+8Qtdf/31evvtty9p5ry8PBUWFsrv9ys5OVn9+/dXz549P88yAJ3iU8YASVu3blVSUpLS09MVCoX0wx/+UKNHj9b06dO9Hg1fMEQXkPTee+/p0UcfVSAQUHt7u2655RYtXLgw4joz0B2ILgAY4gdpAGCI6AKAIaILAIaILgAYIroAYIjoAoCh/wdCEZj6Pn8khgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Schooling'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "82ee9ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2732.000000\n",
       "mean       12.163726\n",
       "std         3.087951\n",
       "min         3.800000\n",
       "25%        10.200000\n",
       "50%        12.400000\n",
       "75%        14.300000\n",
       "max        20.700000\n",
       "Name: Schooling, dtype: float64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp['Schooling'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9737f3",
   "metadata": {},
   "source": [
    "#### Adult mortality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "f517ae39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Adult Mortality'>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEJCAYAAAA3l/RWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWr0lEQVR4nO3dfVjV9cHH8c+BgwT5gAomoOilPSxbpc17xngSfBYRErfMQpczCzWWtXv5dDvvtWaaXd5527au1a02n7KrGbMHkzQElKuma1nXrNRNIaFEE1FROMDv/oOdXxwEBT18Pdr79Rfnd76/7/dzzrn4+OV4+OGwLMsSAMAIvysdAAC+SyhdADCI0gUAgyhdADCI0gUAgyhdADCI0gUAg5wXG3DixBnV1bX+o7xdu7bX8eOnLymUSeT0rqslp3T1ZCWnd7V1Tj8/hzp3vr7Z+y9aunV11iWVrvvcqwE5vetqySldPVnJ6V1XMidvLwCAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABh00T/Xc7VZt+4VFRcfbvH4gAB/uVy1l73uyZPlkqROnUIue66mtDZnz569NHHipDbJAuDSXXOlW1x8WJ/vPyD/60KMrlt7rlySVFZRY3TdprizAPA911zpSpL/dSEK7jXE6JqVh7dJkvF1m+LOAsD38J4uABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQc62mHTnzjx17Bik22//j7aYHvB5O3fmSZJiYuKvcBL4mjYp3YKCHQoI8Kd08Z1VULBDEqWL8/H2AgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAYROkCgEGULgAY5LzSAYBrVU2NSwsXzlVp6RHV1NQoMjJSgwbF6PXXX9XQoSO1bdu7euKJOTp1qkIvvrhCAQHt9LOfPazVq19SevpErVnzstq1C9TSpc+qfftQSVJ5+Qk999wilZQc0cMPP6qtW99WVdU5HTtWpk6dQlRWdlRPPDFHERGRWrFimWpra+R0OpWRMUXr1r2izMwsdeoUIkkqKjqkxYuf0vTpj2nz5k2aOHGy/vjHF1RSckRPPDFH/fp9317zD3/4X2VmZumzz/6hF19cofDwCE2cOFm/+93/aPbsBerQoaOee+5pTZ06w2P+RYv+W5I0d+5CdejQ0Z6nU6cQlZef0IoVyyRJjz76uH1e42ybNr0mh0OaOfPxZs9zH6upqVFAgFMZGT/TunWrlZmZJcuy7HUtyzovp/sxNpyz4TkNx3mDw7Is60IDjh8/rbq6Cw45z+LFTykgwF+PPz73ssJdisWLn9KB4mMK7jXE6LqVh7dJkvF1m1J5eJtu7BmqJ5/8L+Nrh4V1UFnZKePrXoq2zLp48VP66qtSnTxZfsFxwcHXq6qqSrW1NZIkf3/nv792SKr/vouKitLChc9Ikv70p//T+++/12js+XMOGhRtj5OkiIhIlZaWaPDgIcrImCJJmj//P1VSckTBwdfr7NlKhYdHqKTkiD3HihV/tNfMzd2mwYOHKC8v114zOPh6VVaeUUREpG655VZ7TOP53es3HtPwsSQmDrXPa5ytsvKMx5imzmt4rPHjlWSv2/Drhus1nrO5cS3h5+dQ167tm7+/VbMBaJGaGtdFC1eSKivPeBTnt19/u9EpKipScfFhlZef0I4d25sYe/6cubnbPY6VlByRZVkqKMjTyZPlKio6ZBdiZeUZWZZl33Yf+8c/PlV5+QkVFOyQZVnKy3vfY013GZaUHFF+fm6z87vH5OW9b48pKjqs/Pwd9v35+Tvs56txNreCgh1NnldUdMjjmOfj3WFny8/fofz8HR45pfpdbsPz8/JymxznLW2y05079wlVVJxUz569LivcpSgqOqxztf5q3zfZ6Lq+tNM9ffAtXedfq6go889/QIC/XK5a4+teirbMeuDAF6qt9d7c7p1iw93cpfD3dyo+frA+/3yfRyk2xb1jbri79cb8/v5O3XDDDSotLdG39eNQYmL9rrLhDrkhh8Oh8PCI886LiGh8zPOc+sOWJIccDsmyLDune5ecm7vN4/z68zzHtRQ7XeAK8GbhSvU7t8LCnZc9T21tjQoLd160cKX6XWZh4c4WF25L56+trbF3ot+y7MfX3Lnu3Xjj884/5nnOtz81WPY4d05JKizced75TY3zljb5j7ROnUIUGtr1ir6n+13m57xOUbyne1FtmXXWrOle/bHUmzvd6OiYNt3pXmz+5na60dExkuofq4mdrnu96OiYC+503eO8hZ0u0AZCQ0O9Ot/DD89USso98vNr2besw9H0OD8/P40dO07Tps246BzTp//832s6JEn+/v7NjnU6nRed332+n5+fpk2bKX9/p8f5Y8eOk6RmszmdzibPmzZthsexxuc4nf721+5x7pySlJJyj8f5/v5Nj/MWShdoA05nQIs+ahQcfP153/D1HPaxqKgo9ezZSyEhnZWQkNTE2PPnHDw4yeNYRESkHA6HYmPj1alTiKKieisiItIe73A47NvuY/36fV8hIZ0VG5sgh8Oh+PhEjzWDg6+3546LG9zs/O4x8fGJ9pioqF6Ki0uw74+LS7Cfr8bZ3GJjE5o8Lyqqt8cxz8ebYGeLi0tQXFyCR05JCgnp7HF+fPzgJsd5C6ULtJHQ0FBFRfVWQECAHA6HevToofT0eyVJQ4eOlMPh0PTpP9fUqY9IkgIC2umhhzIVFBSkBx6o/4+bdu0C9Ytf/MKeMyXlHkVG9pDD4dBDD01Xnz43KjKyhwIDA9Wt2w32nCkp96hPnxvVq1dv9e17o6ZNm6GbbrrFY9c2bdoMBQUFKTMzSzfddIumTZtpzz19+s891nSf684aHh6hzMwsBQUF2bvwfv36nTd/YGCgAgMD7TENM7gz9ulz43m7yYbZ+vS5UX373njB89zHoqLcj3emvVbDdZvK2dScjbN6E5/T9RJf+vQCn9Ntmbb+nK4kr7wGV8tzSs56fHoBAHwIpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGAQpQsABlG6AGCQsy0mjY1NUMeOQW0xNXBViI1NuNIR4KPapHRjYuIVFtZBZWWn2mJ6wOfFxMRf6QjwUby9AAAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGUboAYBClCwAGOa90gLZQe65clYe3GV9TkvF1m1KfJfRKxwDQhGuudHv27NWq8QEB/nK5ai973ZMn65/KTp1CLnuuprQuZ2irnwcAZlxzpTtx4qRWjQ8L66CyslNtlMZ7rpacAC6M93QBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMonQBwCBKFwAMuuif6/Hzc1zy5Jdzrknk9K6rJad09WQlp3e1Zc6Lze2wLMtqs9UBAB54ewEADKJ0AcAgShcADKJ0AcAgShcADKJ0AcAgShcADKJ0AcAgShcADPJ66W7evFmjR4/W8OHDtXbtWm9Pf0lOnz6tMWPG6Msvv5Qk7dq1SykpKRo+fLiWLVtmj9u3b5/GjRunESNGaN68eaqpqTGWccWKFUpOTlZycrKWLFniszkl6fnnn9fo0aOVnJyslStX+nRWSVq8eLFmz57tszkzMjKUnJys1NRUpaam6uOPP/bJnNu3b9e4ceM0atQo/eY3v5Hke8/na6+9Zj+Pqamp+sEPfqBf//rXvpXT8qKvvvrKSkxMtE6cOGGdOXPGSklJsfbv3+/NJVrt73//uzVmzBjrtttus4qLi62zZ89aCQkJVlFRkeVyuawpU6ZYubm5lmVZVnJysvXRRx9ZlmVZc+bMsdauXWsk486dO617773Xqqqqsqqrq61JkyZZmzdv9rmclmVZH3zwgTVhwgTL5XJZZ8+etRITE619+/b5ZFbLsqxdu3ZZgwYNsp588kmffO3r6uqs2NhYy+Vy2cd8MWdRUZEVGxtrlZaWWtXV1dZ9991n5ebm+lzOhr744gtr2LBhVklJiU/l9OpOd9euXbr77rsVEhKi4OBgjRgxQlu2bPHmEq22ceNG/epXv1K3bt0kSXv37lWvXr3Us2dPOZ1OpaSkaMuWLTpy5IjOnTun/v37S5LGjRtnLHtYWJhmz56tdu3aKSAgQH379tWhQ4d8Lqck/fCHP9Qrr7wip9Op48ePq7a2VhUVFT6Ztby8XMuWLdMjjzwiyTdf+3/+85+SpClTpmjs2LFas2aNT+bMycnR6NGj1b17dwUEBGjZsmUKCgryuZwNLVy4ULNmzVJxcbFP5fRq6R49elRhYWH27W7duunrr7/25hKt9vTTT2vgwIH27eYyNj4eFhZmLPtNN91kv/CHDh3SO++8I4fD4XM53QICArR8+XIlJycrOjraJ59TSVqwYIFmzZqljh07SvLN176iokLR0dF64YUXtGrVKm3YsEElJSU+l/Pw4cOqra3VI488otTUVK1bt84nn0+3Xbt26dy5cxo1apTP5fRq6dbV1cnh+PayZpZledz2Bc1l9IXs+/fv15QpU/TLX/5SPXv29NmckpSVlaXCwkKVlpbq0KFDPpf1tddeU3h4uKKjo+1jvvjaDxgwQEuWLFGHDh3UpUsXjR8/XsuXL/e5nLW1tSosLNRvf/tbvfrqq9q7d6+Ki4t9Lqfbhg0b9OCDD0ryvdf9otfTbY3u3btr9+7d9u2ysjL7x3pf0b17d5WVldm33RkbHz927JjR7Hv27FFWVpbmzp2r5ORkffjhhz6Z8+DBg6qurtatt96qoKAgDR8+XFu2bJG/v79PZX377bdVVlam1NRUnTx5UpWVlTpy5IjP5dy9e7dcLpf9j4NlWYqMjPS51z40NFTR0dHq0qWLJGno0KE++bpLUnV1tf7617/qmWeekeR73/Ne3en+6Ec/UmFhob755hudPXtWW7duVXx8vDeXuGx33nmn/vWvf9k/Lr355puKj49XZGSkAgMDtWfPHklSdna2seylpaWaMWOGli5dquTkZJ/NKUlffvml5s+fr+rqalVXV2vbtm2aMGGCz2VduXKl3nzzTWVnZysrK0tJSUl66aWXfC7nqVOntGTJElVVVen06dPatGmTHn/8cZ/LmZiYqIKCAlVUVKi2tlb5+fkaOXKkz+WUpM8//1y9e/dWcHCwJN/7XvLqTveGG27QrFmzNGnSJLlcLo0fP1533HGHN5e4bIGBgXrmmWf06KOPqqqqSgkJCRo5cqQkaenSpZo/f75Onz6t2267TZMmTTKS6eWXX1ZVVZX9L7MkTZgwwedySlJCQoL27t2rtLQ0+fv7a/jw4UpOTlaXLl18LmtjvvjaJyYm6uOPP1ZaWprq6uo0ceJEDRgwwOdy3nnnnZo6daomTpwol8ulmJgY3XffferTp49P5ZSk4uJide/e3b7ta687fzkCAAziN9IAwCBKFwAMonQBwCBKFwAMonQBwCBKF63icrkUGxurqVOnNjtmy5YtysjIuOhcSUlJ+uSTTyTVX3vgm2++OW/Mn//8Z91yyy1avny5x3HLsjRkyBCNGTOmlY+g/rfVWnIFPHe+Tz75RFlZWZLqr9+wYMGCVq8JuFG6aJWcnBx973vf06effqqDBw96bd6dO3c2e19ERIT+8pe/eBzbvXu3zp07d0lr7dmzp1Xn3n777XbpHzhw4IpfTwRXN0oXrbJ+/XoNGTJEo0eP1urVq+3jzz//vIYOHarx48crJyfHPj579my9/PLLzd6WpDlz5kiSJk+erNLS0vPWvPnmmxUcHKy//e1v9rFNmzZp7Nix9m2Xy6WnnnpKo0ePVkpKiubNm6fTp09Lqt+xPvbYYxo1apRycnK0fft2rVq1SmvXrtWxY8c0ffp03XvvvUpKSlJGRoaOHz/usf4HH3ygMWPGqLS0VMuXL9fu3bs1Z84czZ8/3+ParNnZ2ZoxY0arnk9891C6aLEDBw7oo48+0siRI5WWlqbs7GydOHFC7733nrZu3ao33nhDGzZssMuupRYtWiRJWr16tcLDw5sc415Pks6ePas9e/YoLi7Ovv/3v/+9jh49quzsbGVnZ6uurs6+GLxUfyW3d955R8OGDVNSUpJ++tOf6v7779dbb72l/v3769VXX9W2bdt03XXX2es0Fh4erqysLA0cOFCLFi3S/fffr9dff92+8PXGjRs1YcKEVj12fPdQumix9evXKzExUZ07d9Ydd9yhHj16aOPGjSosLNSwYcPUvn17OZ1Opaene33tlJQU5eTkqLq6Wjk5OUpKSvK42EpeXp4mTJiggIAA+fn5KSMjQ/n5+fb9DS/v2dDkyZN11113aeXKlVq4cKH279+vysrKFmW69dZb1aNHD+Xm5urgwYM6evSoYmNjL++B4prn1Wsv4NpVWVmp7OxstWvXTklJSZLq/wzSmjVrlJSUpIa/Td6wDB0Oh8d9LpfrktYPCwtTv379lJeXpzfeeEOzZ8/WiRMn7PsbX6avrq7OYy33xU8ae/bZZ7V3716lp6dr0KBBqqmpUWt+M9692+3du7d+8pOf+NylTOF72OmiRTZv3qyQkBDl5+dr+/bt2r59u9577z1VVlZq4MCB2rJliyoqKlRXV+fx43nnzp316aefSpK+/vprffjhh03O7+/vf9G/T5WWlqaVK1fq1KlTuvnmmz3ui4uL0/r16+VyuVRXV6e1a9cqJibmomsVFBRo8uTJSktLU9euXbVr1y7V1tY2m6FxzhEjRmjfvn16991322SHj2sPpYsWWb9+vR588EGPXWzHjh2VkZGhVatWKT09Xenp6frxj3+sDh062GMyMjJUVlamESNGaO7cubr77rubnH/kyJHKyMjQF1980WyGoUOH6rPPPlNqaup592VmZio0NFRpaWkaNWqUampqNG/evCbniY+P14YNG/Tiiy9qxowZWrJkiVJSUpSZmam77rpLRUVFzWbo37+/iouLNXPmTElSu3btNGLECA0YMMC+1ixwIVxlDLgMlZWVeuCBB7RgwQL7Ty4BF8JOF7hE+fn5Gjx4sOLi4ihctBg7XQAwiJ0uABhE6QKAQZQuABhE6QKAQZQuABhE6QKAQf8PMxvplx1sfxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Adult Mortality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "15b8f0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Adult Mortality'\n",
    "lower_lim_sch = df_lifeexp['Adult Mortality'].quantile(0.25) - 1.5*(df_lifeexp['Adult Mortality'].quantile(0.75) - df_lifeexp['Adult Mortality'].quantile(0.25))\n",
    "upper_lim_sch = df_lifeexp['Adult Mortality'].quantile(0.75) + 1.5*(df_lifeexp['Adult Mortality'].quantile(0.75) - df_lifeexp['Adult Mortality'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "1d590830",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp[\"Adult Mortality\"] = df_lifeexp[\"Adult Mortality\"].mask(df_lifeexp[\"Adult Mortality\"] > upper_lim_sch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "f9995dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Adult Mortality'>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Adult Mortality'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e162bf",
   "metadata": {},
   "source": [
    "#### Alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "ed63454e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Alcohol'>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Alcohol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "74303c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Adult Mortality'\n",
    "lower_lim_sch = df_lifeexp['Alcohol'].quantile(0.25) - 1.5*(df_lifeexp['Alcohol'].quantile(0.75) - df_lifeexp['Alcohol'].quantile(0.25))\n",
    "upper_lim_sch = df_lifeexp['Alcohol'].quantile(0.75) + 1.5*(df_lifeexp['Alcohol'].quantile(0.75) - df_lifeexp['Alcohol'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "cd727ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp[\"Alcohol\"] = df_lifeexp[\"Alcohol\"].mask(df_lifeexp[\"Alcohol\"] > upper_lim_sch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "ccbcad2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Alcohol'>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Alcohol'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d24431f",
   "metadata": {},
   "source": [
    "#### Measles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "15c5e741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Measles '>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEJCAYAAAA3l/RWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUQUlEQVR4nO3de3BU9d3H8c9mlwEUK4IEDCBWUNraZwRLp00JCeESCblwk4HBJFBEKKAtgxVB8enUW1vAoZQwFXqjWni0w3B1aLVWuYZpwdqiMwwiAwYIEiCAEBDYze/5I81hs2Q3AbPfhOT9mnGSc87v+vXwyWaz2ficc04AABMJDb0AAGhOCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgK1Nbg1KlyVVRc+0t527dvo5Mnz13XopoD6hMb9YmN+kTX0LVJSPDptttujnq91tCtqHDXFbpVfREd9YmN+sRGfaJrzLXh6QUAMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAoVr/XM/1WLDgZzpzpkxf//r/aNy4gnhMAQA3pLiE7oED+3Xhwnm1afOVeAwPADcsnl4AAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDcQndYDAoSTpz5rS2b98SjykA4IYUl9ANhapC94y2bdscjykA4IbE0wsAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoChQDwHv3DhvPbu3aOJE8fFcxpJUmJiR40YMVpLlxYqEAgoGAzK5/OpY8dOCoVCOn68VAUFE7Vt2xb5fNJjj83UmTOn9fOfP6evfOVWlZYe0+23d9CJE8fVpUtXZWcP09KlS5SXN1GrVq3UtGkztGHDGo0bV6BXX/29fD4pP3+iXn3195KkwYOHaOnSQnXp0lWTJk31zj/++Ew55/TKK4s1btx4rVz5R40bV6CXX16hSZOmyzmnl1/+mUpKjigv7/tater/NH78JC1f/ht17NhJM2bMknNOhYULvXVfGa9yLaFQUH5/QI8/PlO33to2ao2Kiw/qF794XgUFj+rVV3+j2bP/V127dotZ16o+dWkrSadPn1Jh4UJJUkHBI1q58o+aOvWHMdcV2f+VVxZr7tyndS23Z1W/qVN/6NWnLvOG96upbW3X4z1/Q6rvtTXmvUaK51p9zjkXq8HJk+dUURGzyVUmTcpTRUXFl1rY9fD7AwqFgjFa+CRV7iU9fZD27t2jkpIjtYxV2eemm27WhQvndccdSV6fpKTO3ufhc4efT08fJEnatOnvuuOOJB09WuJ97N9/oCTpvffeqba+8LGq+le1iRwvfP3p6YOUnz8x6u7nzn1SJSVHvPGTkjrrhRfmx6jXlT51aStJr732e2+tSUmdvX3GWldk/02b/q7MzEw99FBenfqE96uqadXntc0b3q+mtrVdj/f80XTocIuOHz9b5/bX43rXZjVeNPVRmy+z1oQEn9q3bxP9+pdaWSMTO3ClqsCVpC1b3osauNXHquxz/ny5nHPV+oR/Hj53+PktWzZp69bNXt/wj9u2bdbmze9etb7wsbZseU9btrznHW/duklbt266ai2V1zbrzJnTNe6nuPig175q/JKSIzp06NOoNQjvU1tbqfLRwdatm73jK/vcEnVdkf23baus1TvvvFOnPpH9tm7d7NW7tnnD+9XUtrbr8Z6/IdX32hrzXiPFe61NKnSvRSgUMponGPWLQTAYrPU7glAoVG2twWBQwWDNaw8Gg1q/fnWN15YtW1Lj+aVLC6POHdknVltJ2rBhTY17raioiLquyP5V31XVtU9kv2DwSr1rG6O2+eq6nnjN35Dqe22Nea+R4r3WZhu6lqI9g1PLMzuxRox6fseO7TVeifaoPtaj/chrsdpK0o4d22vcUygUjLquyP5VgRUM1q1PZD/JeWuobd7wfjW1re16vOdvSPW9tsa810jxXiuha8Dn813T+TqMGPV8cnLfGq8kJXW+pvM1XYvVVpKSk/vWuCe/PxB1XZH9/f7KH54FAnXrE9lP8nlrqG3e8H41ta3terznb0j1vbbGvNdI8V5rsw1dv99vNE8g7B9kdYFAQAkJsf8X+P3+amsNBAIKBGpeeyAQUG7uyBqvTZ48vcbzU6Y8FnXuyD6x2kpSTs6IGveakJAQdV2R/RMSfNfUJ7JfIHCl3rWNUdt8dV1PvOZvSPW9tsa810jxXmuTCt1o4XbFlUdhqanpMR+5hT9ykaSbbrpZPp+vWp/wz8PnDj+fmtpf/fqleX3DP6akpCktbcBV6wsfKzU1Xamp6d5xv3791a9f/6vWUnktLerLW+688y6vfdX4SUmdY74MLLxPbW0lqW3b29SvX5p3fGWfqXV62U3btrcpJaWyVoMGDarzS3XC+/Xrl+bVu7Z5w/vV1La26/GevyHV99oa814jxXutTSZ0ExM7atKkH0iqfLQhVX773qnTHerQIVGSVFDwfd19dw91795DubkjNXnydLVq1UqJiR0lSbff3kGS1KVLVz366A/k8/mUnz9RrVu31tSpP9Q99/TU5MnTvTGqPr/77h7e3F26dK12Pjd3pHJyRvy372PeGN/4xje8a507d/nvXN9X69at9eijU9WqVSt163aX1yZ83VfGq5ynW7e7vLlimTx5ulq3bq1Jk6apdevWtT5yDe9Tl7aSvLXefXcPb7/X8kiham9jx46tc5/wfuH1qeuj61ht6zpWvOZvSPW9tsa810jxXGtcX6fbuvVNuvPObnrqqWe/1CKbIovXWd7IqE9s1Ce6hq5Ns3qdLgA0doQuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADAUl9D1+wOSpFtvvVUpKWnxmAIAbkhxCd1AoCp026pv39R4TAEANySeXgAAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYCsRj0K9+tbvOnClT167d4jE8ANyw4hK6P/7xHHXocIuOHz8bj+EB4IbF0wsAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ7X+uZ6EBN91D/5l+jYH1Cc26hMb9YmuIWtT29w+55wzWgsANHs8vQAAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCG4hK6GzZs0NChQ5WRkaEVK1bEY4pGIz8/X1lZWRo2bJiGDRum//znPyoqKlJOTo4yMjK0cOFCr+2ePXs0cuRIPfjgg3rmmWcUDAYlSSUlJXr44Yc1ZMgQTZ06VeXl5ZKkzz//XJMnT1ZmZqYefvhhHT9+vEH2eD3OnTun7OxsHT58WJLiXpNLly7pySefVGZmpkaMGKH9+/cb77juImszZ84cZWRkePfQ3/72N0nNszaFhYXKyspSVlaW5s2bJ6kJ3juunn322WcuPT3dnTp1ypWXl7ucnBy3b9+++p6mUaioqHApKSnu8uXL3rkLFy64tLQ0V1xc7C5fvuwmTpzoNm3a5JxzLisry33wwQfOOefmzJnjVqxY4ZxzbvLkye7NN990zjlXWFjo5s2b55xz7qc//albunSpc865NWvWuB/96EdGO/ty/v3vf7vs7Gx33333uUOHDpnU5Le//a179tlnnXPO/fOf/3SjR4+22u41iayNc85lZ2e7Y8eOXdW2udVm+/btbsyYMe7ixYvu0qVLrqCgwG3YsKHJ3Tv1HrqrV692c+bM8Y4LCwvd4sWL63uaRuGTTz5xKSkpLj8/3+Xk5LjXXnvN/eMf/3AFBQVemzVr1rjZs2e7w4cPu4EDB3rnd+7c6fLz892lS5dc7969veAuKSlxAwYMcM45l56e7kpKSpxzzl2+fNn17t3bXbp0yXCH1+fpp592O3fudOnp6e7QoUMmNcnLy3M7d+70xho4cKA7cuSIxXavSWRtzp8/7x544AH3yCOPuOzsbLdo0SIXCoWaZW0+/vhjL0SdqwzJxYsXN7l7p96fXigtLVWHDh2848TERB07dqy+p2kUPv/8cyUnJ2vJkiVavny5Xn/9dZWUlNS4/8i6dOjQQceOHdOpU6fUpk0bBQKBauel6rUMBAJq06aNysrKDHd4fV588UX16dPHO452T9RnTWoa67PPPovrPq9HZG1OnDih7373u3rppZf05z//Wbt27dKqVauaZW3uuece9erVS5J08OBB/eUvf5HP52ty906tb+14rSoqKuTzXXlrM+dcteOmpHfv3urdu7d3/NBDD+lXv/qVvvWtb3nnqvYfrS411SdavZxzSki48X72GW3v9VmTyD43Sq26du2qJUuWeMf5+flau3atunfv3mxrs2/fPk2ZMkWzZs2S3+/XwYMHvWtN4d6p95E7depU7Qc+x48fV2JiYn1P0yjs2rVLO3bs8I6dc+rcuXON+4+sy4kTJ5SYmKh27drp7NmzCoVC1dpLlV/VT5w4IUkKBoMqLy9X27ZtDXZWv6LdE/VZk44dO6q0tPSqsRq7vXv36q233vKOnXMKBALNtjbvv/++JkyYoCeeeEIjRoxokvdOvYfu9773Pe3YsUNlZWW6cOGC3n77baWmptb3NI3C2bNnNW/ePF28eFHnzp3TmjVrNHPmTB04cECffvqpQqGQ3nzzTaWmpqpz585q2bKl3n//fUnSunXrlJqaqhYtWqhPnz7auHGjJGnt2rVevdLS0rR27VpJ0saNG9WnTx+1aNGiQfb6Zdx///1xr0laWprWrVsnqfKLYcuWLZWUlGS/2WvknNNLL72kM2fO6PLly3rjjTc0ePDgZlmbo0ePavr06VqwYIGysrIkNdF7Jx5PFK9fv95lZWW5jIwMt2zZsnhM0WgsXLjQDRkyxGVkZLjly5c755wrKipyOTk5LiMjw7344ouuoqLCOefcnj173KhRo9yDDz7oZs6c6S5evOicc+7w4cMuLy/PZWZmuokTJ7rTp08755w7deqUmzJlihs6dKgbM2aM99PuG0XVD4uci39NvvjiCzdr1iw3dOhQN3z4cPfRRx81wI7rLrw2f/rTn1xmZqYbPHiwmz9/vtemudXm+eefd7169XK5ubnefytXrmxy9w5/OQIADDXeZ9MBoAkidAHAEKELAIYIXQAwROgCgCFCF3Fz+PBh9ezZU3l5eVddmz17tnr27BmXX2seMGCAPvzww3ofF6gPhC7iqmXLljpw4ICOHDninTt//rz+9a9/NeCqgIZD6CKu/H6/MjMztWHDBu/c22+/rYEDB1Zr9+6772r06NEaPny4xo4dqw8++EBS5a9kTps2TWPGjNGAAQOUn5+vkydPSpJWrlyp3NxcjRo1SuPGjdMnn3xy1fzRxt2/f7/Gjh2rkSNHasSIEU3+fZ/RiMT1Vy/QrB06dMj16tXLffjhh27IkCHe+fHjx7u9e/e6e++91508edIdOHDAZWdnu7KyMudc5Vv89e3b15WXl7vly5d774FaUVHhJk2a5H73u9+5YDDo7rvvPu99aNesWeNef/1151zlb3vt3r075rhz5szxxi0tLXUzZsxwoVDIrDZovur9XcaASN/85jfl9/v10UcfqX379iovL9e9997rXd++fbtKS0s1YcIE75zP51NxcbHGjx+vXbt26Q9/+IMOHjyoffv26f7775ff79eQIUM0duxY9e/fXykpKUpLS6s2b6xxBw8erKeeekq7d+9WcnKy5s6d26jfeQtNB6ELE7m5uVq/fr3atWunYcOGVbtWUVGh5ORk/fKXv/TOHT16VImJiZo/f752796tUaNG6Tvf+Y6CwaDcf39zfcGCBfr4449VVFSkZcuWad26dVq0aFGdxv3a176mt956S0VFRdqxY4eWLFmi1atXq1OnTnGtA8CXdpgYNmyY/vrXv2rjxo3Kzs6udi05OVnbt2/3/jbV5s2blZubqy+++ELbtm3T+PHjNXz4cLVv315FRUUKhUIqKytTWlqa2rZtqwkTJmjGjBlXvWIh1rhPPPGENm7cqKysLP3kJz9RmzZtVFxcbFMMNGs80oWJjh07qnv37rrllluuek/gHj166LnnntPMmTO995P99a9/rZtvvlnTp0/XvHnztGjRIrVo0UIPPPCAiouL1a5dO02dOlUTJkxQq1at5Pf79cILL9R53GnTpumZZ57RG2+8Ib/fr0GDBunb3/62YUXQXPEuYwBgiKcXAMAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAY+n9rvoZESckgBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Before\")\n",
    "sns.boxplot(x=df_lifeexp['Measles '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "84dfecb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_lim_sch = df_lifeexp['Measles '].quantile(0.25) - 1.5*(df_lifeexp['Measles '].quantile(0.75) - df_lifeexp['Measles '].quantile(0.25))\n",
    "upper_lim_sch = df_lifeexp['Measles '].quantile(0.75) + 1.5*(df_lifeexp['Measles '].quantile(0.75) - df_lifeexp['Measles '].quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "433d4c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp[\"Measles \"] = df_lifeexp[\"Measles \"].mask(df_lifeexp[\"Measles \"] > upper_lim_sch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "f0cce175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Measles '>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"After\")\n",
    "sns.boxplot(x=df_lifeexp['Measles '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "f3fe280f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have masked the outliers only for the features that are being considered in the model\n",
      "Variables in our analysis are: 1. Life expectancy, 2. Alcohol, 3. Income composition of resources, 4. Schooling, 5.Development Status\n"
     ]
    }
   ],
   "source": [
    "print(f\"We have masked the outliers only for the features that are being considered in the model\")\n",
    "print(f\"Variables in our analysis are: 1. Life expectancy, 2. Alcohol, 3. Income composition of \\\n",
    "resources, 4. Schooling, 5.Development Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75fdc31",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis - Missing value handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "40bdebb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of missing values in each of the columns before handling\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     20\n",
       "Adult Mortality                     92\n",
       "infant deaths                        0\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Hepatitis B                        807\n",
       "Measles                            542\n",
       " BMI                                34\n",
       "under-five deaths                    0\n",
       "Polio                              298\n",
       "Total expenditure                  258\n",
       "Diphtheria                          19\n",
       " HIV/AIDS                            0\n",
       "GDP                                813\n",
       "Population                         946\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Income composition of resources    167\n",
       "Schooling                          206\n",
       "dtype: int64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The number of missing values in each of the columns before handling\")\n",
    "df_lifeexp.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "48046a57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle missing values\n",
    "\n",
    "# Replacing the NaN values with median of that particular column\n",
    "\n",
    "df_lifeexp_new = df_lifeexp\n",
    "df_lifeexp[\"Life expectancy \"] = df_lifeexp[\"Life expectancy \"].fillna(df_lifeexp[\"Life expectancy \"].median())\n",
    "df_lifeexp[\"Schooling\"] = df_lifeexp[\"Schooling\"].fillna(df_lifeexp[\"Schooling\"].median())\n",
    "df_lifeexp[\"Population\"] = df_lifeexp[\"Population\"].fillna(df_lifeexp[\"Population\"].median())\n",
    "df_lifeexp[\"Adult Mortality\"] = df_lifeexp[\"Adult Mortality\"].fillna(df_lifeexp[\"Adult Mortality\"].median())\n",
    "df_lifeexp[\"Alcohol\"] = df_lifeexp[\"Alcohol\"].fillna(df_lifeexp[\"Alcohol\"].median())\n",
    "df_lifeexp[\" HIV/AIDS\"] = df_lifeexp[\" HIV/AIDS\"].fillna(df_lifeexp[\" HIV/AIDS\"].median())\n",
    "df_lifeexp[\"Measles \"] = df_lifeexp[\"Measles \"].fillna(df_lifeexp[\"Measles \"].median())\n",
    "df_lifeexp[\"Hepatitis B\"] = df_lifeexp[\"Hepatitis B\"].fillna(df_lifeexp[\"Hepatitis B\"].median())\n",
    "df_lifeexp[\" BMI \"] = df_lifeexp[\" BMI \"].fillna(df_lifeexp[\" BMI \"].median())\n",
    "df_lifeexp[\"Polio\"] = df_lifeexp[\"Polio\"].fillna(df_lifeexp[\"Polio\"].median())\n",
    "df_lifeexp[\"Total expenditure\"] = df_lifeexp[\"Total expenditure\"].fillna(df_lifeexp[\"Total expenditure\"].median())\n",
    "df_lifeexp[\"Diphtheria \"] = df_lifeexp[\"Diphtheria \"].fillna(df_lifeexp[\"Diphtheria \"].median())\n",
    "df_lifeexp[\"GDP\"] = df_lifeexp[\"GDP\"].fillna(df_lifeexp[\"GDP\"].median())\n",
    "df_lifeexp[\" thinness  1-19 years\"] = df_lifeexp[\" thinness  1-19 years\"].fillna(df_lifeexp[\" thinness  1-19 years\"].median())\n",
    "df_lifeexp[\" thinness 5-9 years\"] = df_lifeexp[\" thinness 5-9 years\"].fillna(df_lifeexp[\" thinness 5-9 years\"].median())\n",
    "df_lifeexp[\"Income composition of resources\"] = df_lifeexp[\"Income composition of resources\"].fillna(df_lifeexp[\"Income composition of resources\"].median())\n",
    "df_lifeexp_new = df_lifeexp_new[df_lifeexp_new[\"Income composition of resources\"]!=0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "e02eb990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                            0\n",
       "Year                               0\n",
       "Status                             0\n",
       "Life expectancy                    0\n",
       "Adult Mortality                    0\n",
       "infant deaths                      0\n",
       "Alcohol                            0\n",
       "percentage expenditure             0\n",
       "Hepatitis B                        0\n",
       "Measles                            0\n",
       " BMI                               0\n",
       "under-five deaths                  0\n",
       "Polio                              0\n",
       "Total expenditure                  0\n",
       "Diphtheria                         0\n",
       " HIV/AIDS                          0\n",
       "GDP                                0\n",
       "Population                         0\n",
       " thinness  1-19 years              0\n",
       " thinness 5-9 years                0\n",
       "Income composition of resources    0\n",
       "Schooling                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "b0610b8f",
   "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>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2.938000e+03</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.337917</td>\n",
       "      <td>153.077604</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>89.778421</td>\n",
       "      <td>68.152485</td>\n",
       "      <td>38.381178</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>89.588836</td>\n",
       "      <td>5.822155</td>\n",
       "      <td>82.393125</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>2182.479777</td>\n",
       "      <td>2.263989e+06</td>\n",
       "      <td>4.821886</td>\n",
       "      <td>4.852144</td>\n",
       "      <td>0.630362</td>\n",
       "      <td>12.180293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.373126</td>\n",
       "      <td>104.180408</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>3.921946</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>10.099046</td>\n",
       "      <td>159.267133</td>\n",
       "      <td>19.935375</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>10.821033</td>\n",
       "      <td>2.208424</td>\n",
       "      <td>23.655562</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>2644.380351</td>\n",
       "      <td>3.592402e+06</td>\n",
       "      <td>4.397621</td>\n",
       "      <td>4.485854</td>\n",
       "      <td>0.205140</td>\n",
       "      <td>2.978300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>44.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.300000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.092500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>580.486996</td>\n",
       "      <td>4.189172e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.504250</td>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1253.657431</td>\n",
       "      <td>8.923400e+05</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.210000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2578.751476</td>\n",
       "      <td>1.882107e+06</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.772000</td>\n",
       "      <td>14.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>13893.187680</td>\n",
       "      <td>1.789956e+07</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy   Adult Mortality  infant deaths  \\\n",
       "count  2938.000000       2938.000000      2938.000000    2938.000000   \n",
       "mean   2007.518720         69.337917       153.077604      30.303948   \n",
       "std       4.613841          9.373126       104.180408     117.926501   \n",
       "min    2000.000000         44.300000         1.000000       0.000000   \n",
       "25%    2004.000000         63.300000        74.000000       0.000000   \n",
       "50%    2008.000000         72.100000       139.000000       3.000000   \n",
       "75%    2012.000000         75.600000       218.000000      22.000000   \n",
       "max    2015.000000         89.000000       459.000000    1800.000000   \n",
       "\n",
       "           Alcohol  percentage expenditure  Hepatitis B     Measles   \\\n",
       "count  2938.000000             2938.000000  2938.000000  2938.000000   \n",
       "mean      4.546875              738.251295    89.778421    68.152485   \n",
       "std       3.921946             1987.914858    10.099046   159.267133   \n",
       "min       0.010000                0.000000    47.000000     0.000000   \n",
       "25%       1.092500                4.685343    88.000000     0.000000   \n",
       "50%       3.755000               64.912906    93.000000     3.000000   \n",
       "75%       7.390000              441.534144    96.000000    36.000000   \n",
       "max      17.870000            19479.911610    99.000000   899.000000   \n",
       "\n",
       "              BMI   under-five deaths         Polio  Total expenditure  \\\n",
       "count  2938.000000         2938.000000  2938.000000        2938.000000   \n",
       "mean     38.381178           42.035739    89.588836           5.822155   \n",
       "std      19.935375          160.445548    10.821033           2.208424   \n",
       "min       1.000000            0.000000    51.000000           0.370000   \n",
       "25%      19.400000            0.000000    86.000000           4.370000   \n",
       "50%      43.500000            4.000000    94.000000           5.710000   \n",
       "75%      56.100000           28.000000    97.000000           7.210000   \n",
       "max      87.300000         2500.000000    99.000000          12.250000   \n",
       "\n",
       "       Diphtheria      HIV/AIDS           GDP    Population  \\\n",
       "count  2938.000000  2938.000000   2938.000000  2.938000e+03   \n",
       "mean     82.393125     1.742103   2182.479777  2.263989e+06   \n",
       "std      23.655562     5.077785   2644.380351  3.592402e+06   \n",
       "min       2.000000     0.100000      1.681350  3.400000e+01   \n",
       "25%      78.000000     0.100000    580.486996  4.189172e+05   \n",
       "50%      93.000000     0.100000   1253.657431  8.923400e+05   \n",
       "75%      97.000000     0.800000   2578.751476  1.882107e+06   \n",
       "max      99.000000    50.600000  13893.187680  1.789956e+07   \n",
       "\n",
       "        thinness  1-19 years   thinness 5-9 years  \\\n",
       "count            2938.000000          2938.000000   \n",
       "mean                4.821886             4.852144   \n",
       "std                 4.397621             4.485854   \n",
       "min                 0.100000             0.100000   \n",
       "25%                 1.600000             1.600000   \n",
       "50%                 3.300000             3.300000   \n",
       "75%                 7.100000             7.200000   \n",
       "max                27.700000            28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  \n",
       "count                      2938.000000  2938.000000  \n",
       "mean                          0.630362    12.180293  \n",
       "std                           0.205140     2.978300  \n",
       "min                           0.000000     3.800000  \n",
       "25%                           0.504250    10.400000  \n",
       "50%                           0.677000    12.400000  \n",
       "75%                           0.772000    14.100000  \n",
       "max                           0.948000    20.700000  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lifeexp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "0f87c3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp.to_csv(\"Cleaned_WHO_Data.csv\")\n",
    "df_new = df_lifeexp[[\"Life expectancy \",\"Schooling\",\"Alcohol\",\n",
    "                       \"Income composition of resources\",\"Year\",\"Hepatitis B\",\"Population\"]] # For our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c979f2d0",
   "metadata": {},
   "source": [
    "### Checking for correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "49d212c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(df_new.corr(),annot=True,cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4aedc07",
   "metadata": {},
   "source": [
    "### Distribution of our vairables after data-preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "ff6aae2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "\n",
    "sns.histplot(data=df_lifeexp, x=\"Schooling\", kde=True, color=\"skyblue\", ax=axs[0, 0])\n",
    "sns.histplot(data=df_lifeexp, x=\"Life expectancy \", kde=True, color=\"olive\", ax=axs[0, 1])\n",
    "sns.histplot(data=df_lifeexp, x=\"Alcohol\", kde=True, color=\"gold\", ax=axs[1, 0])\n",
    "sns.histplot(data=df_lifeexp_new, x=\"Income composition of resources\", kde=True, color=\"teal\", ax=axs[1, 1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416fbf01",
   "metadata": {},
   "source": [
    "#### Relationship between schooling and life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "8c9d7619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assessing the relationship between schooling and life expectancy\n",
    "\n",
    "# Dependent variable\n",
    "y = df_lifeexp.loc[:,\"Life expectancy \"].values\n",
    "\n",
    "# Independent variable\n",
    "len_x = df_lifeexp.shape[0]\n",
    "x = df_lifeexp.loc[:,\"Schooling\"].values.reshape(len_x, 1)\n",
    "\n",
    "# Building regression model\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "6077cb28",
   "metadata": {},
   "outputs": [],
   "source": [
    "life_pred_sc = lm.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "9b5234a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###########################\n",
    "# FOR PPT - 1\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x,life_pred_sc, color=\"black\")\n",
    "plt.xlabel(\"Number of years of schooling\")\n",
    "plt.ylabel(\"Life expectancy in years\")\n",
    "plt.title(\"Schooling vs Life Expectancy\")\n",
    "plt.grid(color = \"grey\",linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "c6052588",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_income = df_lifeexp.loc[:,[\"Income composition of resources\"]].values.reshape(len(x),1)\n",
    "x_new_income = df_lifeexp_new.loc[:,[\"Income composition of resources\"]].values.reshape(len(df_lifeexp_new),1)\n",
    "y_inc = df_lifeexp_new.loc[:,\"Life expectancy \"].values\n",
    "x_alcohol = df_lifeexp.loc[:,[\"Alcohol\"]].values.reshape(len(x),1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f3fa8f",
   "metadata": {},
   "source": [
    "#### Adding income composition, alcohol to the linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "545cffdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For income vs Life expectancy\n",
    "lr_income = LinearRegression()\n",
    "lr_income.fit(x_new_income, y_inc)\n",
    "life_pred_income = lr_income.predict(x_new_income)\n",
    "\n",
    "# For alcohol vs life expectancy\n",
    "lr_alcohol = LinearRegression()\n",
    "lr_alcohol.fit(x_alcohol, y)\n",
    "life_pred_alcohol = lr_alcohol.predict(x_alcohol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "0fbd247a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d5+ijj87mq3nxzDPP8M53vjPb5rp166irq8vm5u2///7Z6IE99tiDwcHBgjb88dquM17znbtp5h//+Af/+te/OOecczj88MM5/PDDufjiiznuuON48sknUVU1z/FKJpO0tbUBruOVQQiBlBJwH5DcYxzHwbIsbNsucOKklFiWlddWLrquZx/i3GM1TcueDyj6oC9cuJA99tiDv/71r9x1113ZZebDDz+cP//5zzz11FM8/fTT/OAHP+Cuu+5i3rx5pW/aODiOk7cEbhiGpxErh7a2NoLBIFu3bmVwcJCamhqAvDxIx3Gynx3H4frrr8/mGAwNDSGEoK2treC+v/766wUP78UXX4xt2xxzzDEcdthhtLe3593jYDAIjP4dpJQFv4+hoSGGhoZYtGgRp59+OnfccQddXV2cccYZk7oHPj7lMpHfZzEblSEQCOS17WWfij1vY3nHO96BZVm88MIL3Hfffdx2220AnHHGGRx++OE8+eSTPP7443z/+9/n/vvvz17HZHEch89//vPZweDIyAipVGpSbW3atImamhr+85//YBhG9r6MtUGKouA4DlJKbr31VsLhMAB9fX0Eg0GeeeaZvHsjpeS1115j7dq1eef70Ic+xG677cYhhxzCMcccw/PPP5+1QeO9C3Lp6enBcRyampo4+uij+f3vf8+9997LFVdcMal74DM3iMfj3HPPPQQCAY444ggAhoeH+fWvf50N8wP395L7+2lvbycUCpW0CZnfWa59KXWMF2PP39LSQk1NTd67dmxbxX77ueMucCdyMlx00UWcfPLJPPnkk9x1113cdNNNWUeiHPuWe82Z6x3PoRp7LzLHZK4hM/ni1e8M/nht1xmv+WqZ00xdXR033ngjf//737Pburu7GR4eZs2aNRxwwAE8/fTTdHV1AXDrrbdy7bXXjtvmwQcfzK9//WuklBiGwe23386BBx7Ivvvuy8aNG3nhhRcAeOONN3juuefYf//9J9zvQw89lDvvvDM703LfffcVXf077bTT+MlPfkIikeDtb387AJ/+9Kf54x//yLHHHssVV1xBNBpl69atE+7HWA4++GDuuOMOhoeHAVfR7tJLL51wO5m4+muuuYbjjjuOL3zhC9nvnnnmGTo7OwH375GZSTv44IP5xS9+kb3v//u//8uvf/1rVqxYgRCCJ598EoCXX36ZD3/4w1lDkzG+TzzxBOeffz7ve9/7AHj++eexbXvcfh544IE88MAD2ev93ve+xy9+8QvAXbF48MEHefnll7MzhT4+M0mx3+chhxzCnXfemZ3pvPnmm9lvv/0KBj3jUex58+LUU0/lq1/9Krvttls2CuKMM87gP//5DyeddBJf/epXGRoaoru7ezuv2O3Xb37zGwzDwHEcvvjFL/Ktb31rwu20trZy1VVXcdNNN7FixQquu+667HcPPfQQsVgMx3G4/fbbOfzww4lGo+y77778/Oc/B1wb9oEPfICHHnqIffbZhw0bNmRVmB966KHs6kXGBg0NDfHiiy9yySWXcOSRR9LR0cHWrVuzOYnFWLduHffdd1/2er/85S/zhz/8AYAzzzyTX/3qV0gp2XvvvSd8D3zmDvfeey81NTU8/vjjPPzwwzz88MM8+OCDxOPxPAXFdevW8bvf/Q7HcTAMg09+8pM899xzRcct4zEZO7Ju3bpslFMsFuPDH/4wW7ZsYZ999uE3v/lNdvvdd99d8vy1tbW8/PLLSCkZHh7mkUceAcCyLI444ggSiQQf+MAHuOKKK3jttdcmrOz4+9//HsdxGBwc5E9/+lPWac4dN+Re1xNPPEFLSwsATz/9NO3t7dlIrXLxx2u7xnjNX7mbZpYvX84PfvADvv3tb9PR0UEwGKSyspKrr746O1Pwmc98Jisr3NjYyNVXX83mzZuLtnn55Zfzta99jfXr12OaJocccgj/8z//QyAQ4Prrr+erX/0qyWQSIQRf//rXWb58Of/6178m1O9zzz2XK6+8kvXr11NZWUl9fX3ezFAuRxxxBF/5ylfywhLOO+88vvCFL3Dbbbdlk6r3228/z+Mzy9sZxhsonXrqqXR2dnLaaachhGD+/Pl5Ii+5JJNJT3W7a665hhtvvJHDDjuMgw8+mP33359TTjmF3/zmN4TDYZqbm/nMZz5Dd3d3NqkWXMGAq666KnvfDzzwQM455xx0Xed73/seV199Nd/85jeznzMznN/61rcwTZOLLrqI888/n0gkQjQaZb/99itpQA899FDefPPNrBrVqlWr+OpXvwpAfX09e+21FytXriw6M+jjM50U+31GIhHa29s59dRTcRyHpUuX5jkv5VDsefPi/e9/P9/61rfybMcll1zC1VdfzXe+8x2EEFxwwQUsWrSo4Nh4PF5QDuHWW28t2q/zzjuPb3zjG5x44onYts3uu+/OZz/7Wc99t27d6mmDbrrpJj796U/z0Y9+lDVr1vClL32J9evXZwebDQ0NfOxjH6O/v5/99tsvK5py3XXX8dWvfpX169djGAbHHXdcNpzzuuuu47LLLsO2baLRKN/+9rcBN0TsM5/5DF/+8pf5+Mc/zoknnkgkEqG5uZm3ve1tbNmyJRvq5cUZZ5xBW1sbJ510ElJK9t9/f8466yzATReorq72Iwd2AW655Rb+67/+K2+lpKqqirPOOis7gAW44IILuOqqqzjhhBOwbZv3ve99HHnkkey3336e45bxOOWUUyZsR770pS/x5S9/mfXr1yOl5Nxzz2Wvvfbiuuuu48orr+Suu+7CMAzWr1/PSSedlI2U8uL444/n8ccf58gjj6S5uZn9998fKSWapvH5z3+eSy65JLtSePXVV09o8grcMcopp5zCyMgIH/zgB1m3bh2Q/8xmWLVqFVdccQUXXHABtm0TCoX44Q9/SGVl5YTO6Y/Xdo3xmpBea7c+uzx/+MMfiEajHHrooTiOwyc+8QkOOuig7PL6zspdd93Fn//8Z370ox/t6K6UpK+vL2vkMqsVPj4+c5uxCn2zma1bt2brjWVCRX18fEqTUS0/+uijd3RX5iz+eK04flimjyerV6/mxhtv5IQTTuC4446jqamJU089dUd3yyfN7bffzvve9z4++tGP+o6dj4/PjHP99dfzgQ98gC9+8Yu+Y+fj4+NThB0xXvNX7nx8fHx8fHx8fHx8fHYC/JU7Hx8fHx8fHx8fHx+fnQDfufPx8fHx8fHx8fHx8dkJ8J07Hx8fHx8fHx8fHx+fnYA5Vwqhv38Ex5n+NMGbb/4pZ53lLbu9K+Hfh1H8e+EylfdBUQS1tRVT0taOZqZs045mV38OdvXrh13nHuyK9mlX+dt6sate+6563TB3r72UbZpzzp3jyBkZQA0ODu4SA7VS+PdhFP9euPj3wZuZsk07ml3977+rXz/492AuUq592pX/trvqte+q1w0777X7YZk+Pj4+Pj4+Pj4+Pj47AdPq3P34xz/mqKOOYv369dx4440APPXUU6xfv54jjzySb3/729N5eh8fHx9PfNvk4+MzW/Htk4+Pz/Ywbc7dU089xb333sudd97J3XffzfPPP8/vf/97Pv/5z3PDDTfwxz/+kZdeeolHH310urrg4+PjU4Bvm3x8fGYrvn3y8fHZXqbNuXvllVc4+OCDiUajqKrKIYccwm9/+1uWLl3K4sWL0TSN9evXc//9909XF3x8fHwK8G2Tj4/PbMW3Tz4+PtuLkFJOSybh008/zdVXX83NN99MOBzmvPPO45lnnuGYY47huuuuA9wZqp/+9KfcdNNNZbf7ne98h8HBwenoso+PzwxTXV3NhRdeOKPn9G2Tj49POfj2ycfHZzZSyjZNm1rmunXrOOmkkzjrrLOoqalh3bp1PPHEEwghsvtIKfM+l8NZZ50zI8o2N9zwLc477+JpP89sx78Po/j3wmUq74OiTOz5nwrmum3a0ezqz8Gufv2w69yDXdE+7Sp/Wy921WvfVa8b5u61l7JN0xaWOTw8zJFHHsm9997LzTffTCAQYP/996e7uzu7T3d3N01NTdPVBR+fmcG2EakEOA7CNBBGCqREpJIow4MoI7H05wRYlsfxlvudlCgjMXf/XKzR72cVmWuyPa5pFuPbJh+fGSLXRjgOIjGCSIyAY3vvb1mIRByRjI/u4zgosQGIjyCS6e9sj+Mzdlg6o9scB5FMFD/fLMS3TzsJmd+ebee946cFy0Skku4zkHTHIp7kjFW25/0tkgmU4cGyrkcYKXdMBNlxUd44KLvNnHA/fIozbSt3ra2tXHbZZdx5550kEgnuuOMOrrrqKi6++GK2bNnCokWLuO+++zj55JOnqws+PtOO1teF3tOBcBwkkJlLyf137meJwKqtx2xaCEKgd21D6+9GSJl/vKKSWLIKfbAXrb8XgcTRdIwFS3Ei0Zm7wCIoIzEC7VtRLBMpBFZtg3tNcwDfNvn4TD/K8BCBjq0oloXMWj8XKQRm00Ks2ob0Bone2Yo20Ju3j1VRhTY8mD46376aDfOwGuYBoPV0oPd2unZU1TDmLQbbItC1DeHYSEXBbJyPVds4U5c/aXz7NPdRhwYIdLYgbJuM+yMAR9Mw5i3BiVZNzYkch0D7VtTYQN4zIlUVo3kxdlVNdletNz1WkQ5SUQDhPhtCcOiqVeWdz7YJbX4NxTQgfT5j3mLsmvrCfS2TYNtm1MSIe2gwhLBtd8wAWDX12FV1BNq3oJgGErCr69xnd4Kr0j6FTJtzt3btWo488kiOP/54bNvmIx/5CG9/+9u55ppr+MQnPkEqleLQQw/l6KOPnq4u+PhMK0pihEDXtuznXHM01jSJ7H8len8PTjAMqore1+V9vGMT2vomSs6Ms5I2lomVe4Cy40pUaopCsG0zIt03ISV6XzdOKIJdVbvD+lUuvm3y8ZlmbJvgts2I9AqCIH+GX6SdOTtcgQyF0QZ60Qd6C/cZHs0RG2tfAz0dOOEISPff2e9si0DbprxjhOMQ6GxLny8yZZc5Hfj2aY5jmQTatyDSq1q5v1vFsghu20xi5Z6gqtt9Kr23Ey02kP2c/b3bNoH2LSQiFaDpKPFhAt05Y5WclT0hJfstWUJqqL/k+zuwbXPWscucL9DRQqKypuB6Ap1tWccOQE0l847TB3pRh/pRsjYCtME+nGAYq272T8LMdqbNuQM4//zzOf/88/O2rVu3jt///vfTeVofnxlBHR7avmNLOGjCI5RI2BZKYgSnonLS595eFtXUePZNjQ1iV9Xy3WcfojES5QNvOWAH9K48fNvk4zN9qPFY3gDSCwGow4NYoTBqjhM3ofPEvI8rNu+vxQYxZ7lzB759msuow0NZx84L4Tio8Rh2Zc0UnKv4cyOkRB0ewq6pL2usknl/j7tPfKRgW+Y5tqvryu5bBsXDRqjDg75zNwVMq3Pn47MzI7dn5k1Vkcrkjpfajn1sE6Z3bLxUNU6+7Ub+vm0zwKx27nx8fKYPqZZpo9L7lb1/kePLZUfbTp9dgDJ+k5P+vU+wnczvvZyxSjl9koqCsAsdMqnpnu2JSeTRTdW92dXZcbFdPj5zHKuqblKGSAqBWduAVVuPFMUfQTscRY6JPbcrKpHB8ITPOZV0xmLY4Yq8bVII3nX/rVnH7t//86Ud0DMfH5/ZgBOuwC6xQuaoGlZ6pcCqbcjJyBtlPLkGqShYNfVYNfXpHKKctgMhHD2Qv3/O+Xx8pgs7WoUTCBX/PhTBGfP+nCxmXWPRZ8QJhHAq3Nw+q7puXAfPtO3R/NfxzldfKOLjqJqnDkCp1TeJwIqMGUfAnMiLnQv4LrLPLoEyEkMb6neT9GvqpybvQtNILl2N3tOBMhJD5KlOCWRmuCIEUg8gEchgEKuuKXv+5NJV6L1drsqmZSEsN57dCYRAgNQCyHQCtFNZ42lccxFGCq2/B2Fb2NHqvITqqSS1aAV6XxfKSAypB3jLvb+iJx1f/9J5X6EyWPzl5uPjs5MjBKnFK9B7u1Diw+4my0RYFiCRmo5Z14Q6PIg6EkPqOkbTAvT+LoRlkclWkoqCMlbNTyg4oRBS0dD6urCq61073NuFMFI4kQrM+maE46D1dqIkE8hgCLO+ecIrfT5zg9z3u11d7+Zi7gCEaaIN9OAEgjia5uanSQcQoCjY0Wr3HZ4zaSuMFNpAD8KysKNVbrhmGYIiwjRQ4yM4kSjCtlzhSiFAuJMrZn0zamwQdXgQqWqk5i9Fiw0gUkmcUAiEgpKII/UAtz78ACfv+faS57TrmjBwxVmE4+CEI6QWLAMhUOLDaIP9SOEKo1jV9SiJOErcVf+Wqg6KgjANEAI7WoVZPw9nsBd1eAipaVh1TTiRqXF8d3V8S+ez06P19xDobB39PNBLatGKKVGskoEgIpUoHIBk9OEUhcSqvYrm18lQBGPhMtTBPgLtW7Nz16qRBCNnP8AKRWCcUE6RTBDa+kY210Ub6sdMNmE2LZj09RVFVTEb5yMb5rH8O59Dpodjr17wVcJjZsx9fHx2QVQNs2kBWl83ga62vK+EZRIcsy1XDVNktniFgEkHNRF3P4yk7fnilRgLlubvp4I5b/GUXIrP7GU63+8TQZgmoc2vjZnkHUWC63TmTDCIVJLQltfz39k1I5jzFo1/LiNFaPPrebnvVnUdxvwl2c96Vxt632j5DG2wl+SS1chQYeRPZyxWsK0YVl0TVl3+JPPY8Ys20IvUdJTcsMwx5UuUgV7UxAjJpWuyqrc+U4cflumzcyMleo6SGqSVmsZsmzTJBEqOCtRYhOOg9XcX/T6D3tNRVAQAyuuz3tdVIGKg9XdPWx06KSXLvvPZrGP3xiev8h07Hx+fUaRE7y3P1pYrfl6gRCwlem/nhLrls5Mw3e/3CaAN9BR17MC7X57v7IGekjXftL7uAlEzdbBvtJ6cbaH19+Sf33Hy1LmnkrHjFwH5jl0RlFQSNUft02fq8J07n50b6Xga3Fw53+1BTSZKDkpEKlGyHVFGf4SZGv97o/B7ISWiiADK9mA7Dsu+89ns542f+joBP+TJx8cnF8dGeBUcn2KEMTX23GeOUfT9Pv67cjoo7x1ujPsZ0o5Riba8vhc57QnT9FTs9BojTAXlXPt0HOtTHN+589m5UVTPxH57igqB29Gq8ZP+Abvao8DnGMopTG5Hxi9/4FQUtiFVDTnF+W+2lKy4/nPZz5su/DrqDqy75+PjM0tRNZwZyL+1PWyfzy5A0ff7zJcKKmdMMXYfr2Okorp1cCd4Lqko2VxDGQx5ir1NVwmlcsYv03GsT3H8EZnP7MVIQrL0qlfJZuYtwsmRwHYCwfLy0CyzIE68gHQSsJeDJ3HVLTMGVZgGeNSHAzCaF+HkyAmPbc/RA26fkwn3vnhg1jXnqVhKRSE1f0lZydnlkrRMrjNGQzs2X3gNyjiKnz4+PhPESEEyvqN7MXlsKy+szGhe6KnUJ0V+afNc5eDxJswcIfLUMZ1gCNPP2Zl9OM6MrMpM+v0+xdjVdViV1dnPEoGT85t2NB2zMb9fVl1jnqMmhYIxf3HxGriWCbaFVduAneOoSSEw0vmlGcGS1LzFeWrbdrgCs258QTZ3R9ttI32uchg7fnH0AGZVXfY5znvOc/5r1jX6zt004cdR+cw+LNNNFrbMtCiJSmLJSpikwqXUdGQgBNawm9QcDI8rCyyMFIH2LaiJOBKBXV2bNZxemE0LMKvqCLVtdFUvAVvTMJoXIStrUBJxAu1bUYwkUihYdY2YjfPH9FFDBkLI9KDIDle4RUGlRAaCOIpKOJ1ALdPXlFy2BnLry6gqqaWrURIjrvJWJArbU4tvDMNGij1/4JY4qA6GeeG8L09Z2z4+uzyW5YorpG2IVBRXGGKuDH4ch0BHC+pQv2sDwxU4oTDaQB9COjiahlXXjBMMIRzHXW2zLILbNqMkEyAd7HAFVk09TjCEGhtE7+3Mhr1nBoVCSuxIJVZ1LWi6Kys/hRNYPtuP1tPhqpdKBycQxJi/ZMrk/8ciQxGSK/ZwVVmFcJ+XHfF7EAJj4XLswX707jYUywIEdiiCYiRRLJNgywbMpoWjKtaKSmrJqpLvbGEaBLZtQU2MuJPGVbWkFi5HGEkU08CORNH7ugm0t6SfNR0hHYSUbsmQ2oYCx7IAKdG72tAGerMhnRKwK6sx5i0ZdywhgyGSK/dIK2MKlGQ8++w6qobZ0AxawB3nqBpKKuGOwwLBSdxon3Lwp9x9Zh3Blg0oaccOQDg24ZaNk2+vvQU1I8cNaLEB9O72ovu7RjSe3l+iDfahlUjY14f6UNKDMgDVsgj094KUBNo2oaRX24R00Hs7UYf688/Z2Yoaj7lx84CWGEFNjGDXNuBUVBJu3ZhNoM4kKwdbNnj2xQlXYFdWT6lj158YyTp2tUL1HTsfnykm2LYpz4YIxyHYOnm7N9PovR1oaccOQE2MoPf3IKQrGKFYFnpvJ04kbZ8UlUBPRzZvWaSPUePDyGDYFZvIaV/k/E8bGUIbGd5xA3mfoqhDAwR6Okb/7kaKYOsmcAqVT6cMRcGJVrlRMjvy9yCdHMfOfd+ryXhWNEWxTALbNhesaJZ6Zwfat6KmSw0JXFVNvbcTGYpgV9agjsRcZ0qOnieT6+qWBOlClIiC0vp70s/r6DqbO14aRO/eVvrahcjW1Qt0t49es20R6G7HjkRxwhXIQBC7ssZ37KYZ37nzmXV4qk/aFliTUH10HJSRoYLNamzQc3dhGqgeIVFaCUUnL8UnJR5DGRnyVI1Sh/L39+pPtk3L8gyPGE+lcyrpHB5i3x9eCcDb5i/h44HSxU59fHwmhuJhd4TjTElo+kygDnnb1FyEbaHER0aP8bCbamwAJZnwFIQYu5/P7MPr7yJsCyUxUrjzToYSH8k6dsUQFB9/eGJb2cnpXHLv89jxhNc5JzOGyaBNoL+ef3/HQR0pv9yCz/bjO3c+s5AiM2+TEe0QArxyworVnVMU7/y5cerLue15fC9E8ePGztB59Cd7bNHrnv4Zytahfvb/yVUAvHv57vzujPOn/Zw+PrskHisOEsatbTmbkGp59jkvJN7j2qSi5uXUFW3HF3GalRRNedgF/l7jpXvk7TeReyFEXu6cZxtlnLdk38axMxPqb5F2yrUPPlODf7d9Zh12TlJyBicUnrRzZ9UWqlVatY3e+6sadlVt+funMWsLV7Os6npkJJoncgLpguQ1+X0yPdq3Mm0qinv9Y/C6T1PJhr5uDvrZNQCctPvbuOn9H5nW8/n47MpYVXUF26QehMDcqB3pZSPHTpTZ4QgyJ3fa027WNiCDIZwSNTNL2WSfHYNVU48cM/FohyLTlnM3m5ChiOf7PhdH07Ara8pvVFGxqgttQ+7v373n4/RLUbE8xjW5mLUNRduYyLNm1dTniSOBK3Lj7AAF010ZX1DFZ9ZhLFwGm19DTYcjOZpOasnqiTdkmWiDfWDbWFW1KKmEK2hSU49dU7w8gTFvMVIPoA4PukaxtnE0AboIdk09KSHQ0jkmVlUtdkUVetc2nEAQpHQFVVQNo3F+/ovOtlHiI656nJSgKMhMsrGUrvLVktUEt76ZDd2yo1XufcpBiQ+7IRHpl0E5Me3CSLn3yLGxK2uy4g0vd23jfb+5HoD/3m1frnjPSSXbAhDJONpgPwh3sCo9nFIfH59CzHmLwEiipUOwHFUjtXy3Hdyr8rGr60g5DnpfJ8J2sCNRrJo6N4/HNLArKvNULUUqibBt7HAUYRmueJQecMWvAKNxAYHO1mwdM6mo6VUBBylUtN4utN5O7HAE0iINSNycnqoa7IqqdIhnHBkMuwPkXWD1aEcjQxFSS1a6OWAef/fpQEmMuKGJQrjvvhkov1GM1KLl6D0dqMNDgEBqWroen40TrnDvRWYVzbZcwSEzhROJuk6fxyqd2bwIqelosQFXIKWmwRVcS+OEK0gtWOrmutkWdsAVLVIsA6kHsKJV6D0dyGAIq7oOYdtoA72QzvF995o1WUEYrbfTfTaRSEXFjlYjjKT7rIXCaMMxEAI7FHa1CaRNJhvWjlbhRKvcv39PJyIVz7aBbY0KwFmmK9xiWTgVldM+Ub0r4jt3PrMOvb0lrzi4YpkEWjZgLC3fwROmQXDL63nx72ZNgzuAKoWiYDbOL1C0LIVdXZc1uMpIzFW+G5M3IhyDQFcbqXAFMj0zHX7z5WwitAA38Twxgto2glVdhzF/SXaQkhUrGImhjMSyZRa0/m4CnW3Z82h93aSWrBx3tlRJjBDcuiF7br2/B6N5Ec8mRjjpthsAuHjlnnxuxe7ITa+RXLp6XIdRjQ0QaNuc7aPW101q0XKcqG+4fXxKoXW3o8WHR59x2yK4+XVSK9bu0H6VjWOjD/SgmG6OsTY8iJAOqUUrCgasykiMYOvGPFU+AWCZqG2b3IFjTq6hVFSMBcsItm9JO3tWXlt5GEm04UGkquUVuFYHe91JQt/Bm3acSJTUDKm8qoN9BNq3jr53+rtdldlpqulWukMaZuMC1+FMJiBdUN0OV2DkliayLUKbXx8tSD7Qi1U5WDBpC7hOa8M8rCJOsjDdcUVmvKPl5O+KVJJATn6+lp5syR2bvHXRImjZgFHfjJASJTMesS2Uwd7sftnnFNd5GOuG6gM9mHVNmE0LcIJB9JEhwELt60If7CO5dDUI4aqhZ57NgR7M2kbM5oXF7qjPJPCtnM+sQxvsLVBJUxMjpWvO5bbR312Q2KwN9MxI3R0AvaejqCCAYllo/d0AqH1dWefKC3WwD5FKone3598TKdF7OtwP0kHv7sg7TkgHvaeEwmdPZ8G5n/rPP7OO3Zd325fPrdnbbc8e7XPR9sb2EVc1y8fHpzR6b1fBYEkxkjBDNmt7UYf6C0Se1JGYK1E/hrH2seC6x4jICMcm0NWa56yVYuy+ajLhi7DsbEg5/rtxB6HGBvImJ8Adw+SKu2kDvaOOXWZbbAAxiRqXXuOdYihGqujYRO/tdFfAizD2/e7Zl75uSCXc/+bub1tofd1ofV0Fz6bW3z1jY7NdBd+585kTuLO65T/8wkh5tuG1fTpQSpwn0w8vFay8/XBn3rwUNzPnEJadLZOQf47x1TTHfv+nzlZOffYhAP5vz/04f8Xunucr3p7HPZ+h++3jM/cpHHAJmNRgb0dQzD54bVdK2SavbZNRSy6jLz5zGOkUeTfOjJJ0Mcp5FibyvJRiqt6zUyHRJpCo8RHPthQj6Xl9AnznborxnTufWUgR1bhg+flbXuGIUghPYZLpYGxS9Vgy/TOriycxQ7rPkSiORw5B5hxS0zzFB0olsOd+f0fbZs7+5+MAfPeo0zhr2Zqi5yunvXL74OPjk8ZD1VcCco4IERR71r3sRilb4mUTvWzgRCl1Xp85hqLieIwL7PDMhIQWo9jvLPcZ8dpHMrl35lS9Z6dCgTaTY+fVlu0hMAcgheL5d/SZPL5z5zPrMOYtznu5S7zVJMfDqm3IMyISMJoXgTomzVTK0uGeUnrWmfM+sQWW6cac55wr93rscEVWCVNWRHE03bv8AmA2LQRNc+9JjsSwo+mYTQvcD0Kkvx99nB094OYM2nbR4rFm43wcPcAvtr7B/77wNAA3HXUaJ+zxdozmRXmKZ3YoUlIxy2heiMy9ZlXD8OPofXzKIrlgaYHds6rrypI5nw3Y0WqsMcJTZl3TqKhSjq01G+bjZMQVwPO6c7c5eoBU/TzsQHkOnqRw8GxV1+24PCyfacOYt6j4u3FHICVOKFL4LNQ25jt31XXY6aLfkH7fN87P5uJn2sqOPex0vVuP97nXeKcYVmWNK/I2ttvpccRYVc2xz+Z455BCYDQtAF0vOoawahtdEaScdozmhXPGzs0VfEEVn1mHXVNHojJKoL0NpOMai4nO6igqqSWrUOLDCMvEiUTzjSag9XSg93UhHCeb7DxWMESNDaJ3taGYBuesW4caG/RWdnIsgpvfyAs5ELgzUnY4grAsVCPpfk4b9MC2LahD/aP74s5sWQ3zEKaR12cnEMQOV6COuApcTkUlMmdw5FRUkli5B+pIzK3VFwgT6GhxxQbSCqFm04I8YQOpB/hOXxdff/nvANy1/+EcFIpgjMSwq+tIVFS67Wm6q6LpoeKViwxF3D4MD4HAvU5fvMDHpyxkZTWJ1W9B72hB2DZG43zIGQTNeoTAWLAMqzaOMJI4oUhWtVDvbnfzahwHJ0foJDNAtKNVroqfaaDFBl0FXyGwVQ3FMlFMg3DrhqydtKI1CMdGSY6gOI4b4RAMYYcq3DIKwTCB3s50+wK7shpj3uKSNsxn7uGo6hhHY5z6stOM1t+N3t2RTZOQiopVWYNV14AcO4YRgtTiFSjxEVctM1yRN/5QB3qz6pdSCISU2eu0ahpcAZLM73nseEdVCXZtQzFS7kpaWtlSSAdhGqTmL0XYFsIykULhoQf/yOEnnA6aWwrKrGtESbnPMIqC1tvl5tQ6No6qYdXUIRzp6hhIia0HsqGVgc42rFQSs2lB0TFEasnqbF/tispRFU2fKcN37nxmJ2oAY9Hy7WtDiKIztepgH4GcpGs1MUKwdRPJHGU6YaTS6o+uSa0Jh5Ftm0muWFvgBAZbNqF6xZJLJyttnvkc6GlHTQyjjlF5E7g5eEYgBGOUxgIdLWjZZGyJNtiHVLX8GcqcGn3BLW+4IjTgCq70dyM1Hau+Kbv7N5+8nx/87REA/vTO9/KO2gYwUgRbN5FYuTtoep7cclkoSsmyET4+PkVQVUwvtbw5hBOO5Dml6kAveu+ouJPiEQWhDQ+BUBC2lWO3JGpOPpXI+a82PIDUgyjpVQwhJWoygV1dj13bQLBlQ459lWixAZzeUFG1QZ+5S2jrBpScnHNhGQRbN5LySC2YTpSRWJ5iNbhCQNpgr2et3QxOpALIX2VW4iMEO1pG25GZaQoXfaAHqWn5v+fMeMdxCG94JTuBIhw7bwyiJuME2zaRXLlH1tn6T2cnh2s5UTehCHa6HqVIJV0nLtM320Lv7c6OiwDU3Hy57HhDw6pv9h5DjDM285ka/Gl1n10SLb1ilotiJBE5CldqbDDPgEE6WTg2WHhsZkBSJgXy3dn2XaniPBzH85yqxzUACNMcHSDlkHvNlz98d9ax++tBR7uOXeZ46aAOF57Px8fHZ6JoZSpUqrGBkgJTGQSgmIWTaepQP9iWp331svk+cxzLXX0ai7IDRIiK/b4EoA0NTKgtNVb6t1rsfEp8uKSqrGKZniq23n0ZKFBBGDsumkj/fGYGf+XOZ5dEFgvPUYT3v4vtk0EINz6+XMbbf2wooyiyf5GQRylEXj2a7Pb0/p/80y3c8+q/AXjs/f/F7ikPZTEPcQcfHx+fiVLU1hYgkHlZOpNAUSiq+efbtJ2PWRT2P+7vfKLhwOX8VovtU+65yr13k3xupP+87VD8u++zS2LVNBRssyNRZE7CvlVZWxC778bQ1449FDtaVbAte4zHZ7uypsi+Iiu2kkUoWNWFYR1WTZFQD03zbN+qqefDv7sp69g99dHPsmzRigJVK6lq3nmFPj4+PhPEy9Z67ldbX9QujsVLMMU9Vz2oajY8PRdznNA4nzmKouB4iOzkCpXMFFZNfVFhNGuC6Q1WdV3JSZFiv2cnEi24J2P75QTDbj5dGdhVtYVjhDIcw4JxjM+M4q/c+eySONEqUguWofV1IiwLO1rlqkvmomkkl6wi0N2OkhxhMBajsr4RvbcDJxhGMVNIVcOqqsOORFFHhiGTSK2qgEDqAaya+nQB01g6760ZO1qFEwyh9XYi0nkjruNYjdbbidR1FMNwhVQqa3AqotjJOMJMgaJi1TaMq15pzF+CxM1lkYqCWdfEcX/8f/w7Hcf/3Mcvp6miEkwTq6oWdSSGcGzscNS9DzsoId3Hx2fnwrW1S93ixZaF1ANuTSvLTK+xCZxA0K3nmUriqCoCgVQUHEVBHRNZIBWF1KKVCOlA1zbXJmoBzPom7EglWn+PKykfCIJlgqpj1TVil+lk+swMSiKOOtiHMFPIQAirpq5QdKQMUsvWEGzZkE6NENjRSowFy6a8v6WQoQhG82L0nvZRwSA94KpGeqhTjttWMERq8Ur07nZXFAWJsDNjCzfXrujvOS3UEmjfipKIu7lvVTWo8WG3LVXHqqxxVTfLUKiUuk5q8Sr0zlaUVAJHD2DMW4RiWdln2q6oBCFcMTVVxaqoREklEX3dWNEqtOFBNwxUKNiV1e4kji9uNK34zp3PLotdVVNS/EOGwtgVlagjQ9REIpAYKchn07va82LQJWAsXO6qQ2XO5bHKZtXUow32ZQuQCscmMNhX2IeutqwDCOBorsM4nnHUBvvQ0rHywnY46I4fsSUdY//8/15BTSiCSCYIbX1j1LnEXYGUU1BPysfHxyeDXVU7uprmOIQ2vJITNiRRjSTkajIAil0YWuToAZJLV6PFBgl0to7ub5s4epDQ5tdQxhRDlpgzVt/Upzy0/u588ZGRGFp/N8aCpZ6rruOiKKSWrp7aDk4CNTZIoLMlLyjYVY9sJRlaPWFFSCcSJbV0NYH2ra56bBrXWRv/Hmm9Xdn81YwCrR2ucBUzbZtATzvaYB/JZasLy0N5oCTjqOk8RtVIEWrdTHLpalLLdsvbz8RVxg3kCCjpXW1590SLDWBHekktXuk7eNOIH5bp4zMejoPe0z7uLoWiK6B3tHrvnIM20JtXOqFo+2Pq2iipBNrgOMnKjoPe3Z41qCseuCPr2L18/pXUpMMx9J72vLYFEOhun1juoI+Pj88EUPt7PBUzcyk25FNMg9DWN9E78+2rsG0CnS0Fjh249lPvHt+G+8wgRf4eAtC7tk38/SMlWk8HoY3/IbTpVbT+7qnp5wTRu7Z5/m4V00AfK5JWJiKVyHPsAJRUEm2wt/gxRgptIP98ipEqbMdMFYq3eeHx9xKOjd7bUbivZaL1duXv69GkGh/2RdumGX/lzsdnHIRpFDhX5aCYKZTECFLTC+rrZffxEjIpt1+pRPHvLBPh2Egpabr/1uz2LcedBTnhIV7nd2vfWEjdrzvj4+Mz9Xgp+U6EYhNiXo5d9rtkAqTkLxtf4d8dLXx63ZGos0iMYzagDvWjDfZxyr77og72Y1dPcAWtTMZ7pyqW6aY2lLGalEHv2oae49AFOttAglVXPG1hypGOp3prhnHf16aB1tuFkorjBCNY9U3ZMUOxMcJ4YwcllfB0qDwdzzLGIJnxRDnHKkaqLCVNcEss4FdDmDZ8587HZxxkIIDMKbpb/oGS0JY33FDHqlqM+UsKQhCcUAQmKRfseIgJZE+tB7BUlfn3/Tq7bdtRpyOiVbmRTzjhSMGAyNF0pOabBR8fn+nBrqhEm4ZZeycQyoaOjUU4Ntfd92u+9+ZLAFz4zveg+oFLWXLDJJfV1UH7FgzbxKprKnHkxBnvneoEghNy7JDSc/VJ6++eWedOKG4efhEnzgl5vK9tC62vG72vK1vHTk3EUYcH3Xq7iooTingqX48nhlLsGM92wqVFVaTu/ffy6oMTDCGF4ubDlmC8MYzP9uNbN5/ZzWRDBMc7rlSbud8LBaN54bhzUQ75alS5RtStcdOP1tfttpv5H27OnZfiW0H7Y2L17Uh03FxBSzp5jl3H0aejBoKYYwr4mg3z89qWQmDMW7R9cfB+SKePz/YzF5+jTJ+9+p6zza6ucwfx4zVFocLfeGREHuwiURJHPfWXrGP321PPJTARB2IXYGwoHeC+s6aCjBPT2YYyPFj0nSoRGM2LJta29P6leNW+m26M5oWeKpJOMOwqR+Y+H7ZNaMubBHo7s45dBsUyUYcGQEp3ora+Oe97O1wxrvqm1PSCd70djmDnaAAA2HrQzd3PnL9YhJJw/y65RUocPVBwDqQERcVsWpA/HvIYT1hVNXmaBD5Tj2/hfGYfUqJ3b0Mb6AUpsStrXKNfhrKTSCYIdLaiJkZcA9Q4P5ugrfV3o/V2ISwTp6KyQMVK6+1E7+sGO62e2bwYYSTRezoRgJQS4WGoBGA0L0Lv7UTJKsDlE+jeht69LfvZrq7DaF6IVV3nqkplVDYVBauyBqu6DjWZwAkEcSoqEakEanwEJxhyjWIRByxlWaz53heyn1s/fAmWpmFHqwvq2shAkOSK3d1i7Y6NVVk94aTv7D1IJQh05Nz3hnnYE5R/9vHZpZESvafDXYlwHOzKaneyZbY7Ipbl2tx0sXKBO6A1mhfiBMMEutrc4uK4A09hGiAEjqK6dk8I7HAUOxhCi8cQRqpgwFuMTEkEY/FKAu0tqDmRCBKwHYf5f74tu+35w06gdszg0wfPVbQpcZAsk9CWN7IRInp/N2Z1Peb8xSTDFW6BbCOFEwy5CoqTeP/MFkkOJxIlsXIPtN4utKF+FMvE0XTscEVW6EcKBaRTss/aQG92JdWqriWxZDVqMp4dD3i9/wvewdV1qCMxd0xiGqhW/t9YMVOEX38RgE8ffjji9ReQQsGYtxB7TOklu6qGZDiCOjyEVNX88YTjoHe1pXP6BFZ1Hcnlu6HGh13F7tgQSjzmKmWGK7Aamn3HbgaY5W8Nn10RvafDdbLSaOmBgbFg6fgHOg6hlg3ZF5ViGgS2bSGlBxCWmafOpY7ECLZuIrl8N1fCd7DPFRPJnHN4CMXciDCS2YGGl2MH6dW5vm43X2Ac8hSjBvvc5OORWP4+joM22IddXZcXViJDEaxQBHVogOCW1xGW5TqojQsgHUaZMA3Wfv+LAOiKyhufvAq7WJ9TCZRkEiccnnxuhZRovZ1ofd0Ix85en2IaBNq3ktIDvhH38SkTrb8bPUdlTosNIByH1OIVO7BXpQm2b0EdY8eUVIJg60a3NEFOCKbIOF9SIqSd/bcWj2HXNiBGhlAmsGopcHP4tJ4OtFh+iHtvKsnuD/8u+7nlyNMIqiqJ2e4s7wCyTnfutiKroBNB7+8pCP3XBnuxq2txItHtD/sUApmZJMhB6hMrPTB1CPSB3mx/FMtEDPSMRvKUEa4oIS+8WB/oBQTmvHFWNaVDsGVjdgyimAZisG/0nWx5OO95H0S2f4H2FpKhigLVbKkHPGvX6V1t6T66vdcHekAIzOaFhDa9OpqbJx33Obf9epMzgR+W6TPrUD3y0NShgeJhA5l9RmIFM5ACUAf7UT3UJRUjmU10HqskBenE5DIHGuMlU4/XXy8EFKphSone2Upg22bUZALFMtEG+wi2bQJgKJXIOnZNFZW8+amr851Ry3LrzFgWekcL4U2vEWzfQmjjq65C2STQ21sI9HSg5Dh2416Dj49PUbyeF2VkCCaa7zuTWGZxO+Y4E1LEUwd6ylIPLjgPoPflhxU+P9iXdeyWRaJ0HX0GIVV1HRbfuStAeA3+ze1fuRNeol2A1uOhtDipEwikxztaluFETQfq8GCBozmRlUWJ8FyV04YKxye5KCPDBZPLk13RFDAhxVEvu6UN9iGScU/RFdVjrOUz9fhWzmduUIalksX2EeUcv33BHVMdGpJ3LY5NsHVTtm5NLmpihP6BHvb9+bUA7FY/j7+cfVHePlpvJ3pPB0JKpFseOK/fel+XWyi9jORqgIiuow70lHzhFP17+Pj47CQIT6GGSWGZJdsq9n3uttvaNnHBC88A8JHFq7h2r/1Gj/fjMYvgcWOm4I9aTHV5exVTsziO52rYeMqp08p2122TRZJNS7Q75fXiJtCeoLDPYrxBlz8wmAn8lTufWYflESbolTM2FqeiskB8RAJWdR1WdWEogBMMI9OKT1ZNYX7Y9I8DvEWDJYzmq0lJsGWjp2MH0JGMZx27dy5aUeDYiWScQHf7aGhpkatSyplNs20CrRv534MPJtjRWnIQ5ufc+fiUj5fdcyoqZ/dKk6ZhR6s8v5KKil1Zfsi3mkqWHPaV+v7Sl5/LOnbfe8sBeY4dgLKDVnRmPZ7OwfYPwq3Kau/TSYkydsVXStTYIHpHq7uy57GaWIBpzCpXwY5WIcc8rxMZRxRziazq2nFnJpxIFKdEGG25/ZBMrIyE19jKilYhQ2HsULjwO4+xls/UM4vfGj67Kl4hIkoyjjrY5yZdF3PyhEJqyUoCndtQ4sPItEKkDEXANLAqa1DiwwjHxo5WYzYtBCndbYk4diiSn2M3zdO8AokEnLR0cGYCLKNgpfV0gnSKznJujg+z36P3AnDUyj358fFn5+8gJYEyQy7VVBLPV6llubksUqIk4mjDQ+POEkpABkOYDfN8qWMfnwkg7MJaUnhtm2VYVXWIdKh4Bidcgdm00JVG11S0oX4kAqnpKGbKFZYQIi3a4Vq+7R2k7//ovWxKT4I9eOBR7OMxuTRWMdAnjecs4+SVqpX4MEoijtS0oqut6kjMnbxIo3e2uflamc89HZjNC7Fqx3E0xCxbn1BUkotXEujahpIYwQkG3bzToX63XtwkmpS4uYvaYB9m/Tyseo88RSFILV5JoLMt7TQXPk9jo3aKnk8I9K5tGE3zIZCTd+fYqAN9qPGYm+eYDhe3QxFsPYhiprLn1Ab7sCurSS1YRnDbFpRUAqlq7rgg6u3we2KZaEMDgCusNxV5oLsKvnPnM+tQhwYKtimmQbB9K05vJ8mlq4vOZstAqECAQBkeJNi2OeusSUXBrG9C6jqBbVuygi3ZNpi5wAFBfpK1wE2gHk1Q9ua12CAHP/FHAE5f+1au32s/lNdfxAmGMBvnI1UVvber6IrfWKSmuQ5lXzfaQA9IBycSdZU0M/etVBuAMW8Jtj8z5+MzYbxyUdRkHCxz0iq2042X/QSQqooTCqeFFRZh5kjci2SC0JY3cuze9k2ipWybRX+5Pfv5lSNOpHGMGIR7FoFVXY862IcTrshTSt7VccKRgndFuWH6Ywm0b/X8TYwlt56qMA33vZODwHX47FAEWWyisEQe/nQhTMOdQNYD7iRmzoSnDIVJLVnpfrBtQlvfKCm2Nu65Mv91HALd25CKwPZweGUgSGrxCkQyQXjzawXfK2U+Z4qUKMODbr6sEMhACLO2Hr27A8Uj/3esKFymz4Guba6jnw6RFZbpPYFVrB+JEYItG7IF72V3O6lFK/ImBHyK4zt3PrOOsQnJuShGCr2vG7NxftntBTra8lbhXCPZjlnf7PkSmk1hHl48P9jHe576MwAf3/MdfG3Zboi0gVUTIyhb35zwNTiqhtbXTSCnXIMyxskePwxTYCxYOm79PR8fn+J4ydHL7P/NPpTESNFBvDY8hDUSwxkbsuk4BNs2laUaWA7tyTh7P3LP6OejTkfziOxwSyaECbVuyH42G+Zhja3VtaviNege5z1cDCU+XJ5jB3m12oSR8ny/CCDYtjlb1LsAfeaHsFp/D3rnaFqCHQqTWrLaM6JIG+z1FBXZHgKdbRhCYNc0pCNqRpCKkk0xkcEQjqZvl0MJ6fe9lNkSCxMdU3j9TfWedjcss4xQc71rW9axAzeSKtDVRnL52gn2ZNfEd+585hxKMlH+zraNYhUmVyvxEZTg0BT2anrJrCY+09fN+mcfBOCyVW/h00tW5xlAKO2cmpU1rsx6zrbAQM+kxpCOHsCuqMSqa0QGCmfLfXx8ysQjDE4AwjKKClPsSErZYSUZL3DuAm2bpkzsItcW7ltdxwMHHuW5nwSkqqElRuXlBW7Yn11V66/g4SpDF26buFNS9rtZUfIG+E4wVDRiRrFMtP6egmLeLjM882FZ6F1tef1UkwnCb7yIVVmLVd+UV0JgQmOVMhFAoKMVwzDQ0/X0wA2PTM1fgj7U74bDTjIMtNg5p+IYISVKKokTDCEsy332iqR5FP1NSjkNAjKzg2K1lCeD79z5zDkcjyTdoiiK90tDOgTGyGfPZgTwcHc7p//9rwB8bfe3ce6y3SbcjltEdB5auuDw2HOUwlE12nt7mDdvAXZ1LVZVrTujupMaWx+fmaNQdk4CUpudeSal7LATGg3rE0aKYOsmFGNqVjFu2vIGl73ydwA+sWJ3vrTbvuPu7xVOJnBXmmzfucNb8nDilPtuzv1tgOtoj/cGUeMjWJ7l0Yq836cJJRn3zMUXUqIP9aHF+kkuX5udMHCCYWDqSwIJIDhm/KIm44S3vDFu5NOORgqBOtBHcKgfgcTRAxgLlnrm5zvBcIHegBMI7ZRjjdtffo7P/OUOAK55z0l84C0HbHebvnPnM6dwAkHMCSg5FUsKn6h5mKkXiBQKjq6jjqn3dG9HC//9rycA+PZe+/OhxSsn1b6wTDcsqtz+MHrdUgiM+Yu55YG/cP5/H0Cgo4VAZ5ubKF3fPCGFLR8fn3ykonjXyJqlYxknXIFVVesZhufoQYSZAttG2BbBrW9ud5hYhnP//RR3tW8B4Ka3Hsz6eYuL7luytIJHbp7P5HEi0aK/iQwSMBsXjG6wbc86s3ntBmeHAy4DwXF/UyJdj1aGIjh6AKu6Br1728zl8M9ixw5cNVE9p4SSYhoE2jaTXLlHgdNmNi1Ayc25EwKjeQE7C4PJBOf94dc8sfXNvO1HLN99Str3nTufWYeXqpMEjPlLxlfL9MCriOrE+uLOIClGctqKJEnc8EbFNBDSKXDsbmndyCdffBaAn+57ECfMX5J37EReHBMJiZKA0TgPoajgSOwqV61KEYJgy4bsbLiwLQJdbTiBYGGOjY+PT3koAsakokmYtWIq4Npkq6oWra/LXdVwXNVfxUwR7GxDdrYBU+OfSilZ89BdDKRt2OMHv4+1RaT2y8EORXxF3zR2VU2BQ2ZX1kyqLWP+Ehw9iN7rvRpnNC/CCQQItG9FjQ26kxrjyfxrWnHFTLn9KqsTQQaC2JW1roJ0EbSRGKRz4CVuSZCJOF0zuRI5k0hAGS5MhVEsk9Dm19zfRY6arROuILFi951OLfOxLa9z1l0/y9u2uKqO/3fKx1gyheWjfOfOZ9ZRTK7XjlROyLHDcQi2TFxcJIMUComVexBs34JITV9svwDUIk7XT7e8zude+QcA/+/th/LepvyZq+l8CQgg0NNJYs3eebNqi2pqvFWz+roxfOfOx2dSeJWAEQDJBEwkFH0mEQInFEZNO3YFX0/RaUYsi2UP/Db7+c33nEx1GQO9Yud3VI3UklVT1Lu5j9G8EGFbqGmnxK6oxMhROJ0o2lCf572XgF1ZTXDbluy5ijk+UgjMuias2obiExyqOuPOkLFgCbTaaCOlc/YFE19Nm6lrsUMRd0Jmhs7nKoMXqbObShJs2Uhyxe75+cWavlNEBBm2xeUP381tLz2Xt/38/Q/nkgOPRJmGkh6+c+czJ3CVmwyg/FlsdWQIZZJSyRJcWWHpZF9CU8FEXkTf3vAyV7/+AgD3HPBuDqzzqG8zRecqhpDSlWLPGUjVRrwlshUz5bndx8dn8ohUAjlbnTtAGxrwdOymitx6ngCdR5+Bsh15NxJcxb2JTBTu7KgaqcUrwTL55S9+xIfPuWDybTlO0QgRu6oWIWXJd6pUFBKr9vRWyMwllZj5VS4hMBYtxx7oIdDZNmdX2ZxQGLNpgVtuYJpr+paDkA5qrB9rguOc2cwr3ds48dYbSOaEpAsE93zgfPYZJ5x8KvCdO585gQRQJhh3vx32yq6sxolEEVOk7CYBs64RJ1RBcNvmki+Er7z6L76/6VUA/rLuSN5a45lNXhRHCND07e6/e9/zB0F9I95F1X3VOR+fqUUCMjh7HTuX6RsY5opIHdYwj9/ud/h2t2k2zAfNH/p4oumMGNv5zlNVnEAQZUx6gaMHMeYv9lyhHosTCJV27AC0wI4JYxRurTlDKAQ6Wrbr/OX2fyqvUwJ2dT1OOOLWs53gBPZ4fZloGGpBw3McKSXfffYhvvX0A3nb37/2rXzjvScTmqEwe9/C+cw6nEAIdYyqmlTVCb+Q7WglUlVLFs7M2JOscIiquQMAQOoB7HBFgWrTRHAQJJftBiE3ed8eiKJ5FBfPGMyLX/obN7e49ZgeO/gYdp9g7oMUCslVe6IN9RPobPX4XpBctBy9vxdteHD8vocjBTVpWgcHPevomDvRjJuPz0zjhKOoiTF2QVFmb0hmGruyBtndPuWz/9dveIWvvf48AF9YszcXrtxzu9u0IlGset9OTTdG08J0PUP3NyEVldTCZSAU950aiRYUTc/FLjf3SNOQQhT89uQMOe92TT2JikrCm1/3rFMJpZ0yJxhG6oGS72KpB4pO1pqVtWgjg2WvoDvBcLZIvdG4AAZeIjyBcitOKIKajBdsl4pCavEKtP7udK5c+Ugh5nSd3G2xAT5010/Z0Nedt/1nx3+Y96zcY8b7M61PwD333MOPf/xjAN71rndx2WWX8dRTT/H1r3+dVCrFMcccw0UXXTSdXfCZg6SWrSG45fVsnR2p6SSXri77eJFMoCaGcfQgyUUrCHS0oKaSOEJxV+OMJKppIBUFq7IGs2khwrFRhwYQZgqpB/NWq1ILlxHoaEWNDWRzz+xQBBmuQOvvwZWAyZ90yny2quqwK6vREjFsHGQogrFkFdZAL4Hu9uwLwaqqxWyYx/n3/IL7WjcC8Oy7jmNZtAopFHBspKq5+wuBVVWLcBzU4UF3FjEUAQROpAKrrsmtESMlZnWdW/YgLXTgBIIYzYuQFZUYkUpkT4d7DY6DXVmNVBQ3sV5K7Gg1Ro54SwYpJanFKwl0tqLGh3E0HbNhHk5FZdl/ox2Nb5t8ZhupxStcVcn0oElqGsnZnhdmWSgjMczaRrThQRQjhaMHwLIR0s5T2rWiVQjLyk6U5Q56M/bSDlegJuOc8bdHeLinHYBb33EoRzQuyO6f+98MTiiMFY4S6O/O/04oCOngqBpWbcOcKVo+1+2TE60iuWIP1NgAUhGuOEvOJGFqwVL3nTo8CIrqOmi2BULBrG/CmkCkSnLF7oRynCtHD5CayULXeoDk0lUEtm11n13hlmdQpIMTDGFkxhcDfa5DK0cdMPcdu9g9pqstqxrqhCJIRPpZSb+L5y1C7+9Jv69tnHAFdiSKE63CCVdg2haBbVtRx+QCSkVxhWfSDrATrsjLN5WhMD97+mk+fvJp6P29iLwQwkw5Ft1djZOusInRvAhtsBe9p9Mdm2g6VnUdTiCEkoxj1jVj1tQTaG/JCdEVSD0dTaRqWJXVqImRbN07o3HBnBRMueuVf3LRn2/L27bfgmX8+PizqduBgk3T5twlEgmuuuoq7r//fqqqqvjABz7Aww8/zJVXXsnNN9/M/PnzOffcc3n00Uc59NBDp6sbPnMRRSG1bDdXWUlKV4GxzPwIvbsdvbcz+zl3hUmRDlI6pFasBUSeSIiUDtpgbzaURHZvw5i/xJ0tSyWxwxUkejqpTK++qck4pBIgQCoaViiCNjKUN0MnAHV4ME/616xrwqqoRO/vRthWdn9tqJ/THr6bJ7q3AfD8YSewIBzBaJiHVd+MMA23KLAedOW7hXBzG0ZioLhOa+Z6Au1b86Slrcoa10kTAnWo35Vm7rDdVU3LwgkEseoasatq3T5mYsHH5rVIidbfw4f3359g2yas6jpSi1agJEbQezvReztxIlGMxvmzWuHPt00+sxJFIbV0NUpiBCwTJ1o9q/PClJEYwdZNiPRgVSoqZrQaNZUERSAZjZrI5Fll1DQhJ1Ii57OID9N0/63Zc/ztXcexPD1p5AC2oqE6Vp5j6KgaZv08gu1bR+2vEKQWr3Tt4hwreryz2CepjyOGoekYi5YjkglCOcrLEtf5mNDfSw+QXL0XZFatZvqZcWy03q5RcZK0U5eqqkWNjxDYtgUhJVIIpKKg2Dmra4qSrRNrzlsMioLe152dALEjUVILl4PqhqiajfMxG+d7/6ZVDSccKRB6EY6TP/GcShB+zV0Rl4qKXV3D2xYuJNDTObrSKhRXvddOT9BYFqmFy9yxmBAgJUoing2/FJaJ3tuZN/4x65tJZVasHGf075Luu5KI41RUYYcjCMdBSSUQRmpOpHcMpRJc8Mf/x6ObX8/b/rUj3s9Z+6zbQb3KZ9qcO9u2cRyHRCJBJBLBsiyi0ShLly5l8WJ38Lh+/Xruv//+WW2gfGYeYRqu1H7G0VI1jLpGN5RRSqzqOs+wDWGk0HIcO6AgdFCND6MO9mNX1yGSCXcQouoEOlvzcgQErpOUa6yCoVDe95nSCMK2UIooZyljYs/1vi50j+LpRzzxJ15KFxb/zxEn0pCuv6R3d4CUeUVeraparOp6gm0bs2EYjqaTWroGYaYKagZpsQGsanelL9i+dfSLzK2xTNRtW0gqijugLPJi1fq6CXRvozEaBSPlrjymUmix/uxLQRnsQ0nG3TDUWTqg8m2Tz6zEMgm1bHRX3XEHWKnFK/Lkwacdx3Fn0nV93Ama+kjEtY85qxDCsdHHCS0rJm6VsRJDpsHKB+/Mbt/03lOI5vRBARSnMPRNsa2C2p1CSoKtm0is3mvW2qFizHX7pMYG0AZ63Xd1VS32OKtwgfateeGMQjoEOltJrph4rS9hpECIGa1dqPV1oXcV1rFTU0nU7vb8/nkdP9SPEh/GmLcEpI02JqRPjQ+jxgYK76HHb1qkkug9HZ79zHs2nPxnVunv5aCVK/NKPQnpgJ17vCTYtonUgmU4VTUoIzE3Imic69N6O1Fjg1i19Vg1DaNfmCbBts2oqXSEQs7xErBqGzGbF3pex47mia1vcuadP8nbNj9azW2nnsvSCeoiTDfT5txFo1E+9alPccwxxxAOh9lvv/3o6uqisXF0JqepqYnOzs5xWvHZFdHHOlq2RTDHUKrxYQzLxKpvzjtOSYyUlXCsDvSi93WNOo94G96ZGhK89ZF7aE2HYo2V+BbkO3bgvhDU2GDewEqxTALbNmNHves+qYk4yjg5DgB6fy+pIscDaAM9hds8JK+VVBIlMTKzg9IJ4Nsmn9mI3t2edewgPdDdtsWzwO+UISUilUSNDaDGh1GSCYR0Z/qtmnrM5kX557ZMgq0b+a93vtNV0Z0iXosNcvATfwSgStN58z0nIyZwzZ7227FRB3qxaxs8vp29zGX7pA725U0guu9qC6uhuWBfrbcTNef3nkExUmBbBbneuYhkHK2vGyEldiSKPtCTTeOwwxWkFi0f9/ipQBke8nTsJtyOZRJq3VB0HKImRsZ1kIH0BHD7tJdGCm7bTEpZURD6WWx/YSQJdLahDvSSWrgcvbcDbbC/IMIp9996f3dW0M6z3VQCrb8X4djY0eppz9MzbZsrHrmH36RrDWf4n3ccyqUHHY06S6Mrpu3X/+qrr3LnnXfyyCOPUFlZySWXXMLmzZvzDLaUckIGHODmm3/K4OD4iadTxQ03fGtGzjPbmen7cOGhh2bDEIphtW3hhtt+k7dtz3nzOGaP0omramIk73e3I+d1l/z5dhLp1b3N7z2VijGJ4MWekVzHLstIjHuffpKT99234Kv7H32I/ZcupbmyeF7cttYt3Hbv3UW/P+/gg4kExsTEFwl5+uO9d7Kxt7doWxmqq6u58MILS+43lewMtmlHs6vbxum4/k+8610FolGKZfKrm25gODX1ZUZ2a2ri8NWriQYLw6AEoA/08sBTj/NKx+hqwLF77snuzYUD9e3hDx0tfORfTwDw/vlL+Mm+B01Z2xuf/zt/eOWVSR+/q9qnyf6+P7L//gSj+YNyu30rN9z+m4J9zzv4YAJj3ydA0jT58U++j1Vkpfct8+dz5Nq12esfu4KkJkbY8Ohf+POrr07qGsq59hX19Ry/116IEuOUiVDsr/nYc8/wz9Y7ih63qqGBI9euRfO4l1ONALStb6JN0KFRU0nCG14p+zf7z0cf4KlNmwq2r21u5pjdd886VNpQP888tZknNm6cUH/G4vU373Ysfm32YYyR8PyQXstCJQAvvsqPXpzcb2wqKGWbhJTTU+Dipz/9Kb29vVx22WUA/PWvf+VnP/sZqqryi1/8AoC7776bZ599lq9//etlt9vbO4zjTL9e6g03fIvzzrt42s8z29kR9yH86r9LOlwSMOYtduO8TQMnEsWKRAkWCUuYbUgp83JLWo88jeB2vigkYDYtQOvrzgtHtQNBUsvXovV1ERgTKpKL0bzILRhbBL2jFd1j9a6gH6pKYuWeZeU+KIqgvn5mV/jmum3a0ezqtnG6rr+Y3Uss222769yJVNINPY8PI/UATiDo5sCVOM6qrMFYuGy0j6+/MKU17b722vNcv9F1vr6+x9s5Z+maKWsb3Lwfs3H+pI/fFe3T9vy+w6+9UDDxKIHEmr3z3wdSEn7t+eJF5gNBN7R/zDtEGClCG/9T1vgAIUAoWLUNmA3zylr9LuvaLdN1VGagNpwjBKklq5BFhDmEaRDa8MqcrbVXjNT8JQWpN+pgP4H2LZ7X6uhBjOYFblrJBMn9m0spueG5v/LNJ+/P22f9mr259shTCc8iwZdStmnaVu7Wrl3LtddeSzweJxwO8/DDD7PPPvtw7733smXLFhYtWsR9993HySefPF1d8JlihJFC796GkhhBBkKYDfNxIuWrAYlkPJ2nlcAJRTAb5xfWcCoiJ+xFbn0ZNT5cMuxwtuBISXOOY9d+1OkTngkrRqBrW8E2xTQQpoFV14QwTbTBXjfBW1HcZOv0C7CUQpnZNB/h2IiBXpR0f8caWgmkFq2Y1UIQvm3yKQvbRu9udxX9VA2zvikrOuSFEh92Q6iNlKte1zi/qDiAMhJz901PTJn1xVfDgptfQ4YrMBuacSqqyu6+SCVRUgkkgkBHSzb/V5hG0SLTY8mVlBfxYaQjp2wgefRTf+Efg+7q/j0HvJsDp7iUiqNp+bk+c4QdbZ8aKioItG5EScaRwTBG43xkKFL6QNvOU4LMIDWtwLHTO9vGbUoxUqhDfdi5fz8p3YLbZVxDNide2ui9nWh93UhFQQaCmA3z3LFMT0dWfVoikIEAuzWV/g2qg/3jOnZe4ZWZbU4giJQStcznT5GS0JY3QFGxK6sxmhbkhZuqfd07nWPnBEOuumouUqJ3tha9VsVMEWzd5CqtNi6Y8Dk7h4c4+3c/49UxiwM/Xn82R63a/hIsO4Jpc+4OPvhgXnnlFU466SR0Xectb3kLn/jEJzjooIP4xCc+QSqV4tBDD+Xoo4+eri74TCWO48p0Z1aErGGUlg0kl+9WnrqRZRLa+mZ21lcZHkJNjJBYsXuesQq2birfeJexbbaQMe6W4zA/Rza38+gzUMqYURzvheH+WyCKVAAVUqIO9bsKVak4TjiKWVOPU1UDlpVW7CrDGVNUjAVL+em9d3POOecT9JBddiKVrtrZLMa3TT7lENy2ebS4r2US3LaFlPB+TkQq6Q48M8JCsQFXWGjFWhhzjEjG8wapylA/6vBg8YELQGIEpWUjyWVryhpol7vKPh4S3AGybbkCCFM0eTbWBmaUgacSJxAiuWTlrFbtLcYOtU+2xWlvfSvacNquWzFCiTiJFWtL3kthGt6/4TG5b3pHK/pg6ZB9JZnI1fRASSbKnpQo6Jt0ELYDCQtlrIMopfvuTCVZv9deJIcH3RUg20bv60QZcVe7zfomlGSCQHfhBGreuTLN5ryTM9vM+nnY0SrCb77snVpRrD3HRhvsQxgpUumyUCIxghrrL/cWzHqsSBSnosqdZB4zHlFigwXidGMRQKC3yy2zUOZE0d2v/otvpDr5xk+uym576/wl/Oz4D1M/SzUDymVaM04//vGP8/GPfzxv27p16/j9738/naf1mQbU4aEC5UkhHbTBvrLCXrSh/oJwHmHbqLHBbLKwSCW2q1j4bCdp2yz+y+3Zz11Hn1F2DLrXXlIPkFy0HGyb8NY3xz1eHR5ETY4mryvxGCl1xYRWAjIYtg2qhtG8kGBLMvvCdfQAxrzZqXI1Ft82+YyHMFKjjl0O2oD3oFQb7CuYzVdMA3U4hl2ZHyqkDRSKEJUT6ijS57fqNbd21ZhBszI86IoV2NaUOGKZvDtlZBjVnJqcv55Ukt0f/l32c8uRpxGawrwlSIfsNy+ck45dhh1ln7ShgYI8OOHYaEMDxcsapJGBoFtex84fhNs5g2SRSqKV4di5O+cP8OUUzd6WaiYjLBZs3Tg6HknG3Tq3RY73mnz1mmzVBnqwo1VQZCK2FGpiBDEyRLCzLU90bmdAjQ8jbMu9P2YKvb8XbAOpamiDA2W3o/X3juvcDRspPvnHW3ho03/ytn/5sOP5yL4HTjiXdbYyvXJCPjsPxWaZyo07LxLrLxwnWwNF7MT5SnHLYtkDvwUgoqpsOfK07W9USjes1XGyIZaeuwmBksxXJROA3tdNahLOXbbdQJDkit2z4bC5tfZ8fOY0xexaUTtY/vZyZ+y90IYG0Ad63YLfVbVuAWTpOn2BrvFD3SbLVDl2zw/28Z6n/gzAsnCUvx163LQMpATkFWL2mQDFJhnK+c0qCkbzIgLbtmYdGycQzAs5DowTWld4zvxnUIYieXVrpw3poCRGCiaai+YHqhpS1/MmT8dtO5XYrny9QG/XTufYQboucCpJaNOr2c+Toshv9emWDZxxx4/ztjVGKnm/GeDyCy6d7NlmLb5z51MWdrSqwIGQgFWmDK1dVYMcI9UrEW7ttK42nGAIo2khjh5EmaLBxGxh0DRYla7ftDhcwT8PO35K2rWrat08hLZN48/8yyJ5MhPIbyyKEDgVxRU4fXzmIjIYwgmG80oTgFtj0gu7qhatvyffvikqtsfkiVVVU1CLcryw6lyyRYNJ18gaHkJx7EmuA7g23I5WoQ4PTWtY++1tmzj/hWcAOHvxSv5vr/2n8WyU54z4jOI46B0taEP9BUqcEgpzoIpgV9WSDFegjMRAVd3SPJm2HGdCK8rSQ8U1tXgFoU2vjRlHTG1KhlVV6+YPloldXYcTCKB2tJbet6oWqQfG7XNGDMbLAXT0IEoiXnbfJsN4558JtvdvmZsXbdo2Vz56L796/um8fc552yF8/pD3oSrKTqv87Dt3PuWhaqQWLs8W+3Y03Q3HFArqYB9OMDSaC2JbqCMxpKK6A38hkIEgxsJl6B0tKLaNFAKkRLHckD4llSTYuhGzrslVwJxg96bawHvhOE5WSKQUmf50p5LskQ5D2ruqlocOmpo8CUk6xKWz1TN8LJdi96VYTTwfHx9ILVhKoHUjqumKYVt1TQUKbhmccAXGvMWuYJRtuZNVzYu8S7ooKlakEjUxjJASJxDEqqhEH+McloOS4+yVw9iCwXZFlTtgHGflf3u59OW/8/OtbwDw3bccwAcWrZiW82RwHVbftk2EwLYtaJkC9DmOnaPpmM0L8/PqpUSJDyMsEycSRUnG0Xo7UUwDu6IKs3EBTrgCJRlHpBLZcUExwTMJyDGTuk4giFVV+KzJYJjUklXuOCSVnPCkhhQKZuN8hJHKq9sqcCNcntm4kX122wekg1TU7GRKMRxNw6ptQKoq1tAA2jjOq1Q1rNpGEAKrur5o3qEAzOo6ZDCMOtiXXemz9QB2ZTV6X9cEr3riGE0LJ7bKuoPJOKR2VS1mwzze6O3k5NtuZHDM5Nwdp/0v++Wo/+7M+M6dT9k4FZUkV+zurvgoKnp3e17BUquqFquyhuC2LdnQIycQJLlkFWg6elc7SnpGzGtWSEhJoLdzUrPQ02WEcp3Gch070sdsS8TZ56/3AHBY/Tx+u//hU9YvAWhlFBIthlQUrCIDVR+fXR7LJNjyJorlrm4LQI0NYNYXz+Wwa+pJVNeBY3sXUJaSQNvm0UE0aftipAhMY5iVFAIpFBTHLigYrI0MwTSmOR/w6H1sjLuTTw8ceCT7VpcoxrydSCEw5i+d0/l2OwI15zeZi2KZ6bzRGneDYxNsGc1Fy50sAHc1WY0N5oUeW1W1GPOXoBcpU2RXVGEsXIY61I+aGMEJhLBq6orWunUiUZLL1xLc/Fp5oZA5JJatAVVFSSUx6xsBBVTFvRBF8ORDD7GPECBUUguXEdi2BaVIhIsViWLVNiBSCYLtLUX3y/Y7EMg6zua8RTiRCreQu8ezr5gGZmUNupHMjpVU00CdAcdOgCsaI5RZuwKeGZdJIbBqGzEbmpESfvyvJ7j61h/k7XvMqr341tGnE5lFZQxmAt+585k4qoZIxgtmkDJhQrmGXUlLDks9UHa45WyaLZpIX3IdwY0jMQ547D4ATpi3hJ++deoK85aLIxSUIsZZOA56Xxdm86IZ7pWPz+xH727POnYZFNNA7+0c/0AhvB07XOdQGzOInglbJ6REyPLDzKaClG2zKEc86pUjTqQxGJp0e+VEZkhVI7F8N9+xm2K0wV6sqhqcikq0/p68XDRP1eox7xxtqB+rohIl6R1OqCZGQLiTI3aJcjzZcyQTE3bsAEJbNyAcyy0FBFjj1EF0KipJrtyD0IZXPB03NTFSdKXO8/eq5DirQmBX12FHqz2VM+1QxC31NE2r6aUQjoMdiqAW+ZvtaDL3Nrl4JZ1S8pFbvs/LY8pA3XDsmRy7Zu+Z79wswXfufCZFMVVLL7laNT6MLDLg2ZnIGJz/xAZ41xN/AuDDi1dx3V77bVe7jqqVnBX07I+igF385aDGR/BlB3x8ClFHvAdtSpHt5aDEp2aJbCZC0LeH9mScvR+5J/t521Gno29H3ctS1yuFgl1Z7Raq9h27aUFJjOBUVKJMUs1aixUv9SEcG5FKIUPhInt4HDPJfDDFHn3jCUDv7cSOVhUt36MO9Rd9947XB69rVeIjKJkyC9kTqBjNC/Nq9tqhCFZVLYFSE0nTiCvCNjsduwx3t2/lY3+6JW/bW5oW8vMTPkJjuKLoyu+uQlkj7uHhYaLRKK+88gqvv/46xx57LLruG9FdGadIbTvpkYjrBCY/YzuXkMC/Bno56um/APCJ5bvzpbX7bne7im1NakDnhCLY0Sq0vi7PGkTF/oZzDd8++Uw1TjCUzQfORW7P6tMUPW+zxbHzsknP9HWz/tkHAdi3uo4HDjxqys8x9vvk0tUTcgxmkp3FNmV+u5P9DTuhCLJILUcpBHKC98QJhXECwSlRjVSHh4o6d9p21orMRUiHUOsmzLomzKbRQtt2TT3JikqUkRhS13EilYD0LCsxE7g5qzVo21FDz4pEcQIhtMQIwkyB40yJ3Rq2TM5/4Rn+2JkvXnP5u47lnLcdghYbRG/fimJbOKqK0bwIp4gI1s5OSefu+uuvZ+vWrXz605/mnHPOYdWqVTz33HNcddVVpQ712YlxIpXYkWie+pUTDGFV1RDoHo2tl4qC2dCMOjSwA3o5cbyc03J5sq+LE599CIAvrNmbC1fuOWWz7BkBhLKFE4R7351wBVZtA1pvJ4Hu9tHvFSVPonqu4tsnn+nAbJyHOpKvICmF2K5nxqquQxvomXEZ87F5UVOFWdeI3tedbfemLW9w2St/B6ZuYmu8Pjuq5op9zFLHbmexTXYogl1Z7ebExYYm/E5zgiGcYPF8J7O+uWgoc1GEILVoBYHWTahGcmLHjkEd6seuqHRL+eQiJUrK+1ndnve61teFWdsIOQ6t1ANjQlIFZsN8Ap2lFTiL9U+qGsK2QChZhd5iNfqQ0lW+DoYx5i9GWNZ2OXdafBhLOiSXrUEd6ieQo80wGXInjTLUBUL89gPnsypd006kEgS2bR7VSLBtgtu2kFJUnOjkSz7NVUo+UY8++ii/+c1vuO222zj22GP5whe+wMknnzwTffOZaSwTvbvDzQuRDo6ms/eC0RkmYRroPR1u8rWUOKqOWV3nqmEGQ65Ah6LihKNpZ04iLJPgti3IKahhN53hSNkBUNqxKyVVPPa7v3Rt48x/PArANXu8nY8uXZNpMaf14u3Z0SrsyhpXeayYap5QMKtqXJUwTXeLFXuEyZg1DVh1jXmzrFZ9M04oghobBFXFqqlH7gQJxr598pkoIpVE7+lASSXc1e1AkEB/D9g2TiBAasEyZChCcsXu6N3tiFQSJxTGapiHOjzEh97xDoKbX8OurseqbSj7nIH2rQgjlSdwMl1I3AkeIadmxjwXOxjm4ef/xRFvHy2B8j//foo727cAcNNbD2b9vMVTfNZ8Us2L3MHwLK6rOddtU0Z1VOo6oQ3/mVB9OSsSdYuap8cFoU2vF/wOJekVuLFOVbn9CwRJLV5JZMPLkzo+g2IaBFs2klyxlvqKCpSclTyvmpR5jpNXvwAnGEZNeecECtx6f8ai5eP2y6ptwAmG3OLpUqIMx1A9ogky2KEITjjiOnM1dchACBy3Zl+gfWvxsFjIPkeKkUKLDWLWNRXNuSt3HKYl4ogNr6BOoi6hBCzgy6/8gx9veT3vu48tXcMVbz0Ye9mavOdfGxrwKCQPgY4Wkqv2nHAf5jplTZeEw2GeeuopTj/9dAAMo/gPzGeOIiWhlg0oqdFZMNVIceTatRh9XVi1DQS3volijv7tVSeFOpgiNW9x3qyTE4niBEOEN75a1ABOhul8jXsZhXL3vbt9Kx/795MAfH/vd3L6wuU5+5ZRuyr9X7u6Dru6DrNhPqGtb+T9LQDsiijm/CXZz0p8GGXrm3n9saLVmPO8RVKcisqdsiadb598ysa2CG19M2uXFCOFyugzqBopwlteJ7H6LdnyLRm0ng4CPR3Mq6qCZAI12QqOgzWOgiYAlkVo8+ujA8UJCJxsz4RWMTGl7UVJJThizRq33I2UrHnoLvrT74XHD34fayunrwxBpnj7bHfsMsxl2ySgQACoXBTTwFFUlNQAYmTYU0xNAGoygbJ1A8llq0dLKU0ETZuSSV8hHYItG/ivAw6A1o1IoWDMX+JZ6xIYNwdeAMa8xa6dKfIMqvHxyxdlcMdSYUKbX/MME8/FbJxf8H4XRpJgy4ay74+QjluKykiSWryS0IaXUcaIukzkXpfj2I39+705PMRxf3uY3jH3/Z4D3s2BdU1IwPSor1ykmi/CMhHJ+OR+X3OYklnOtbW1fPnLX+all17iwAMP5LrrrqOpqcTLzGfOocSHC5yJDFp/D+rwUJ5jl4ve312wTY0NTKljN1v5dcuGrGP3i7cenOfYTYS8e6+qpOYvQeYkBDuanhenD67hTy1eiV1RhR0KYzTMw1iwdFLnn6v49slnImhDhXapYGJHSrT+wlwbLzuneWwrPGd/0UFeKSY7aJ3uiTBVURixLJruvzXr2L35npOnzbGTqkZy8UqSK3Z3bdwccOx2ZdukmAba8CBqYgR9eHDcKU6BRO/3rvlWEtuast96bri0kA6BbZtxEMgxYkClzufoQaQecFfPiu5Vfq/HTqp7YVbXFTp2pkFwnBW78VBjgwjHLrqqOlXlzR1VI7V4JY4Q/HDTqzT+6RbWPf6HrGN3TNNCNr33FLqP+QAHZsIvgUB3O3pnW15bdpH7LQBtYJK/rzlMyZW7b3zjG9x+++386Ec/IhwOI4TgG9/4xkz0zWcGGa9Yp7BtGC+x10OuV4yj0riz8MNNr/LFV/8FwG3vOIwjikgql8PYhHIZipBYuSfqyBAgsKOVbt2ZMTgVlaR2wtW4cvHtk8+EKDMUUthjZpyl9FSeLVXkeCLnLMVsUsncHB9mv0fvzX7uOPp0VA/7NBVIwGheOOeiDnzbNErJ3+1kn5EpGmd4PVsC0FJuWKKV1hcoWY5DCIym+YRa3iw6WQ5u7b9yEKbhuXKY11a0Oi+iJ3NcaPPrk55gFwCOgx2tRh0e8rw3sP02qSc+zAfv+QXPj8ktvOHAozmlpm5c/QNtoBezcV42X1Mqipt64hFKuiOEaXY0JZ27T3/60/zyl7/M++yz82FHKpGK4llXxaqqcWPvRavnw2Z7zNbaldXI7m2zZjAyGcYzXN9840WuffMlAO494D28s65xu85l1Yw53nHQO1qzSc1WTT1m08I5MWM9k/j2yWci2NFqZHd7SQVGc+zzLAR2ZTVabCC/vUxx5/HOWVmN7Okoek47ECpbFGI2OHiPdLdz2t//CsBh9fP47f6HT9u57EgUY97iKVManUl2Vts0Hb9BrzFEWQhRXg1E3MH/2BDDzHd2tApteKjo8WU5doBVWYOwrHEdOwCplRh6S+lGSyVGSp7XywZpfd0lHTsJmLWN6AO9BZEFTjCE1teJPlhCVEVRPCf3S3FfRwv/9a8n8rbt0TifX7z/v1mYSrpF1EsI2wkkwrbd/EcjRWjLG0WvuRw7vbNRcqotFosRj8/uehc+U4Cqklq4HCenTpAEXu/qwmxcAJpGauEynJxCnBljZjaMrlgJ03ATgAFjwdJsfbupWsafSYoZ1cv/88+sY/fggUd5OnblXq8EjPpm7DEx5IFtm9GH+tIFiCV6fw96R0vZfd9V8O2Tz0SQwRDG/CWjdknVsAOh7PMqwbV3ioYyPIgSH3YHGVK6k1zBMDJTADlajdG0sIxzhjGaFxW1CWbjfMyahtI2Q1Ex5i9Fpm3wjrCp393wStax+9zqvafUsbPDFdhpIQuJu7qRWrRiTjp2MLdskz2BfKSpcOxGnzdXgdb2WsmSEmVkCGUkljfQVxIjrjCYbYMQZfXHqqwpugok9QBWbVOJ0NHSCNwQ7FxV6mJosXFyGW2L0ObXCLZtQu/rKvmcB9q3oI4JIxceOY5Anp1zwhVosQGkprO5txeZnjh2AkGsiiq0Uo4drnCMU6Yw24hl8d//eoLGP92S59h97uBj2Pypr/PnEz/KfAHqUF9Z7TmBYFYUTu/t9HTsRn9fNWW1uTNRcuUuHA5z+OGHs9tuuxGJjBqAH/7wh9PaMZ+Zx6moJLlyD4RlIqUEReX3D3+X8951JABaf29eGJIAlGTCnT1RFPTudrTezqw+pFXbSGLVnm57QiG88T9Fw5hmw4x0OXzihWe4tW0TAE8e8j7W5BYkzaHsBGZwFa5ysSxUj1lEbbCvIPxiV8e3Tz4Txa6uI1FV69olTXdXwy0LxUjihCIoyTjhN1/O2ionEALpZPNehg0DddWeyIoyVP4sE72nY1wZcsU0MOctQhnsRR2vMLJju7mA6ZnymbaXZ/z9rzyUHrje+o5DeXfjghJHlEbirs5ZtQ04mdl1y3T/JhOVx59lzCXb5BXKNp2I7H8lIpUA6eSlHYiUKwSSUeh09ACphcsJdLVlyy9JRcFoLj254igKdnUd+phV9wyKaRBo34LUNIRVfhhjsVDOckJMx+bx5aL3deet/JUqg5RR30xU10J64seJRGHMGEIKQWLZbii2hdbbiTYyKuqyrL4es7IGYRpoiRECfV2lrwG3jIUMBAltfs0z6gvguf4e3vfMA3nbqjWd+975XtY0NGM0LyKwcVSNVXpEJ0lAavro70HTMeaP5t6KIiulyaWrkEVqGO7slLSep5xyykz0w2e2IISnRL4yEiuo+wSgmCn07m1uzaPeztFmcAUI7GiVmyth224NuWKnnbILmD7O/sdj/KnLTeL9+6HrWTpJCeexiJzxnDBSBLe8MSfux2zAt08+k2KsndM0HC0KUrqy4TkDNGVMyGQ0GMTuaSdVsXr8czgOoS1veioFZrsB6F1tiGQcpYz6mmqysPTJdONISfP9t2Y/P/uu41gxRflvTjCMsWRV/kZt7hX59sK3TeWhDQ/hDPRi1Y5GwAQ6WvJKLyimQWDbZtRc0RPHIdC5rWT7iuOgDg8WTTsBJlTmIXv+CR8xynglVJSRQiXNUucSgDrYj51u16ppQB2JoabbkkJgzFsMwRDSNLLbc9GHJlbXzmhelK0fl1y6mmDblqyttKXDla89zw2bXs075r+WrOaq3d+GnnZupWkSHKP47bXCKnBVLx1Nx1iw1C1VkeMEeuXaSUVFBsepgSndUl0761irpHN34okn5n2WUrJly5Zp65DP7EQtMvMMrtKmWuQhUuPDOBWVaEP948oHz3bWP/Mgz6SV8V48/ATmhSJTstooEW79wK42nECIQGfbpJX1dkV8++QzlQjLLKlMB7j1JR3HzTkpts/w4LiOXfacTHxgNVMMmQYrH7wz+3nTe08hOoXOV37h5p2LXck2OUIgA0E3dz8URu9qQ5mAiIUyMgwZ505Kz/qtuWqWGcoSNCItvlHXiN7XPaWD+YmMAaQQSD2IWd/kHYaK69gpk11FzV3xEoLU4pVuzp5pYkeikMnzk8WKBpSPFCIv1FFYFnZFlDeTcU5+9Pd0JPNFYH63/xEcXN9c2OVxIrmg8N4qlokyEitQ8bTqm1FHRhXdMyJMxeyzOthHoGsbwrb42IEHosYGdrq8vJLO3a233so3v/lNEonRP1ZdXR1PPvnktHbMZ3Yhg6Hi3+kBZMA77jobjz2HHbuDH/8Dr6VDHF5990nUp3NApuYlIQl0tZXeDXaKouNTjW+ffKYSqWrjzvBncDLhnB6IVIJARyuKxwB1LvH68CAHPf5HAKo0nTffczJiCgSdJICiYtY3lV0Efi6yK9kmRUpIJd1wQiHGVTn0Im/8IAROTgje6EnUgpDHcp0rgRvuaDbMQ9i2Z1mTyWBW1aKNxMaduJZCYDTMx65rLCmINl6x8fGQuKudzkAPKApKfASEglVVjROqcMMmLROnotLN/1XUsh1jL9xyMb1YDc1oXW386l9P8rlX/pG3z3sbF/CjfQ6kUi+cDJK445liE2lSD4Bte/ZRG+xDhsLY0ers/ZS6TnL5WtThQdfRjFYVzdUVqWTefa4KhZDbtpBcEdmpxlglnbsf//jH/PznP+fGG2/kwgsv5JFHHqGjo2Mm+uYzi7BqG9F7uwoeNgmYDfNwIlHsMUvjTiCUnaGyo1XQM/d+N3s8dBfd6RnDje85xdNQbQ9lz/pBQZ07H98++UwxioJZ10Qgx1bJdNZL7rNqNszzHqg5DsGWjZMK8/JiR+Ui/6GjhY+kRQ9OmLeEn771oClp11FUkit3n/P5dOWwK9omASVVDsfiCrPlr2SZjfMJtm/N28dsaEbv6cibeJnI85Fx8BKr9kQdiRWEW08GfXgQq7oeZRxnUUhJoLeDRHXd6OqZF+kwwYmSew/U3BUz6bhqlznCKOpIjGDLBlLzFhHctmW7bMvQts2c+Zfb+efYMgZ7v5NTFi4v2rbEFdtTR2Iog4XiKRJAKCiOt+OnWCbBts3YoQipJSuzeYYoStEV0VzU2IBnbVM1Noi1narns4mSFrampoZ99tmH3Xffnd7eXv73f/+X973vfTPRN58djW1x2KpVBDe/jgwESS5cjj7Q7SrIIVwJb8sg2LoJqWkYTQuwTQMlGccJhrFq6rPL4jIUwaisQU8/WLNdQEVKybz7b8NJBwhsOfJUIjtgQCJxVaHM+UvcOHOfPHz75DNlSIne1Y420JN+6gUyEMBoWgCq5oamS8mdjzzI+g/um3eoMJLovV0o8ZEpc+wyWOlJs5myl1e9/jzf2fAKAFfv/nY+tmzNlLTrKArJFWt3CccO5r5tkoBZU0/AowB0sbC5ySCAYNsmZCCIVVOPHalESSawA0GElDihCFZdI064Ajtajdbf7T5nqURpufex53JsglvfnLLak8JxXFGmMvbTYgPuSrWUaP3druKnomLWNiADQfQSJVqKtj3B/RXTAE0nuWw3N7dxgrblT52tnP3Px/O2rY1Wc/t+hzG/iOqqFAI7VAGBAFZ1HU4kihMIoQ71563yOpqOEwqPW5Yig5qMow30YqWLm5dNkVDN8URu5iIlraymaQwODrJ06VJeeOEFDjroIOxdsCDgLoeUhLa+yTuWLIFkHJJx1OEhkst3c5euLctVlMsMg0yDYNtmEsvXwpiHTR3odWPwc2bcZrtj15QjINB21GkEckpATOu5ySkQKgRG4wI3nMPHE98++UwVgY4WtLyZZOkKHLW3kFixFmfeYgBaBgbyD7TMdI2lqf/dCUCbQSXDY57+C39PD+bvOeDdHDjRgVMRJGDVNe80YinlsFPYpiLvval+fyuWCZbp5uirWl6Yo7AsV2zFcdxQOylRSxT2Ho+pVgZ1whHM2ga0/p4S9TPdsZLe2YY+MFq6QPEQqptuAulV0XLyiwEStsWnXnyW3+WspgJ8fs3eXLhij6Lh2hJILlmNDEcKIh1kKExy6Wo3D9Jy8wLtiipCW98o+zqURBxhpFwHu0znzKqqRe/uyNM2yEzm7UyUdO5OO+00zj33XH74wx/y/ve/nwceeICVK1fORN98diDKSKygEKdwbDcxuXG+W1dkTAUWAQS6t2EsWuHWuxvoRRvonVNCKrZ0mHf/bdnP7Uedjlam0cidzSy1MukIgVNZgzCSeaEUZsN87GgVimlgRyp2mVnuyeLbJ58pQUpUjxAhAGFbaIP9RUN2tL6u7XLspKJgRyrdfJFJt7J9WI7D/D+P2r1/H3Y8C6cwUkAAek+HG81RqoDzTsJct02ZMMaZZux4QUiH0NY3kIqCFa1BK7MO2limI1rI0QMoIzGEZZds2wmEwLbRBsbUpCtx3HT0u1yn7h8DPRz9dH4ZgwpV44/r3sseZQqQyGCwaK6hDEUwFixF6+tG72kn0NMxofqdysgQ4dgAUlExmuZj15SRw6vpmA3NefUI3XISLSQqq3aaMVdZpRDe9773EYlEuO2223jxxRc5+OCDZ6JvPjsQr4KQgFs4FIrGhgvLQhkeIti6qcD5m+2YjsOCnAFO59FnoExAQEAU+bf3zgrGgqXpQq2xtDMXzQrX2KFxJHx9svj2yWdKkM64z2yBPXQc1NgASmIEzSNsrezTKgqpRctBuuqaO4JeI8Xah+7Kfm458jRC6tRHKggk4TdfckUd5i3eqcQLvNgZbNN47/CZTq0QjoM+SccOpr6vEjdiKZBTAqr4vgIZDCNMY0L92BHpK7Z0uPr1F/juxv/kbT978Uq+vsfbs1FMsgzhHCcSLeksKfHhPFG5iegQZKLBhGMT6GglGYogi4SG5p3Toy6ekBI1PrzTqGaWXJJwHIdbbrmFyy67jIqKCjZs2IA+xaISPrMPu6LKs5ikXenWNbFqGz3NvqMoBFs3zjnHLmFbeY5d1wQdu4mSDQkQAidahVXbMK4iqY83vn3ymRIUFaeIuhqAVVkNuIO5fRcuJLThFYLtW9EHerdr8CUch9DWDQRbNuyQVbvnB/uyjt3ScAVdR58xLY5dBkFa1KF107SdY7aws9sms7YJs3rnLWUBYFZWYxQZ6wjKd0SsukbQNJQJhJPOtGO3OT7MWx+5h3n335bn2N2x3+F0H/MB/m+v/bOOnVPm6laqaSHCSBFo20T4jZcIbnmjoI6fWqS4fCm8CshrsfImyIrlSZaTPzkhLBN1sM+95gkKDW0vJf9C3/zmN+nr6+PFF19ESsnjjz9Od3c3l19++Uz0z2cKEaaBMFI4oQiUeoFrGsaCpVibXqciEEAKgVlTDwhwHJxIBVZNPVp6cJOJWdbjw9N+HVPNsGWy/IE7AKjRA7zxnpOn/ZxSVd2HfRodyF0B3z75TBWpRSsIbnk9rz6XFAKzYT7CtlG7tqH3dfGe3Xab8tIuO8IK3N62ifNfeAZwZ+X/b6/9Z+zcSiqBSCXGLzI8x9nZbZMMhbAqqtAGt2+CYzajxQax6ptJrtidQOsm1EkobNrBUFbpWhkpLRSSYabu6S+2vsFnXv573rbDG+bx030PoqrI6no5qTZSUVBsi0BbSzYMVE1YKC0bSC5ZiYxUphvzHouOzb0sB1mmNoJV2+COXXOEdexIdEpF69ShAQLtW7Krm3YoTGrxyhkL+yy5cvf0009zzTXXEAwGqays5Kabbtop67Ts1EiJ3tFCaMMrhFo2EH7z5aL5JbnYlTX86MknSSxdg11RSaC/Z/T4/m6UMYUq59pqHUC/kco6disilTPi2AEoloVWRjiHz/j49slnqpCBIMnVbyGxZBXJhctILF6J0bQAva+TUMsGAn1dO80g9tKX/5517L77lgNm1LEbZWe5m97s7LZJ6+0CVcVoXrSjuzJtCEDv7QQpSS3fzTOaqRRKKunWbDNSaEMDU97HydBnpFj/zIM0/umWPMfuu285gO5jPsDt+x1e1LErF+E4BFs2FOT3CSDUsjG7kmVV1RbcV0cPkly2W1EFS6uypmD1UCoqVnXpUgjg1tFLLluDWVPP5r4+jIb5bnj8VOE4BDpa8sJW1WRiRnNYy1LLVHJucCAQQNtFEqJ3FtTYIHpOXoiQ7g8vUVFZUr3MkRI1MZInTSscm0Bn28RyzGYhnakEez18NwDvqKnnT+uOnNHz633dWPXN/urdduDbJ5+pRgZD6J1trkz3ju7MNPDOx+5jQzo06oEDj2TfHRBaZ4crdvow9J3dNqlGkvBrz7sqh5Eo6hyM2ikXbaAXs3khVrQafYJhhAIIv/ESZk3dDrcnf+lq48x/PJa3bVVFJXfsd/iUCihlKHa9QkqUeAynogq9ryvPCZKA0bwQdLdcg97T4ZbXCoSwKypx0toEwjTc7xIjOMEwZsPE1HhlIIg5bzF33PVbzjvwiO270LHXZyQ9C7ArM/iMlLQ0a9as4Te/+Q22bbNx40Z+8YtfsHbt2pnom88UoXqEAmSTR8ss+lhwfJnnnq317FoSI7ztr78H4D2NC7jlHYfOfCccGyURx4n49esmi2+ffKYKJT5CoLPFM9l+ZyBl2yz6y+3Zz68ccSKNO8DBkgisqroZP+9MsyvYJgFo8eEpidlxhIIMBLerzMF0oQ30YDbMm3QZBYHMm2CfDooJnCRtm4tf+hu/3bY5b/tlq97Cp1ftWbSMwZT1C+8xoLBtRCpREEUmAG2oHyNahQwEXeE5cBWNY4OosQEcqwInEsWYv2Ra+z5ZpBbw/HvIcfK6p5qSYZlf+MIXePnll+nt7eWDH/wg8Xicz3/+8zPRN58pwimyvF5O8uiK+nqUxMikzz0bHbs3h4eyjt0pC5ZOyLGbyEus1L4CCLZugJz6fz4Tw7dPPtuLMjxIaNOrBLe+sdM6dh3JeJ5jt+2o06fNsZOAFErBtgwCSaCzBbGT3usMu5Jtmor3vCIdnGCI5PwlZQt2zBRCSvSubWWXEPBsYwr7MxaJG97o6AH3+cMVS5p3/60s/svtWccupKg8fNDRdB/zAS5ZvdeUOHYSsAPFbYnXOFMCjhYgtOVNb8fPNMCyCGzbQvj1F92Uoo2vEty2mUBPhxsmP6bm3mTZa/58Qhv/Q/j1Fwls2wLWFORTa1pBcXWpqJj1U1M3tKwulNqhra2Nq6++eib64jNNWDX16P09eXLedtid+fBEyqxDd9jq1bPSQZssLw31c/iT9wPw0aWruWaPd0zo+IneC7O6DmE7KIlYnlBDtj3HQR0eLGsF1acQ3z75bA8iGU+XbSmP2RqJMB7P9ndz3DMPArBPVR0PHnTUtJ1LAmbzIqyaekRihEBHK6qR9Fa2G+rHbJw/bX3Z0fi2aeLoQ/3oQ/1Y0SqU4eLiI7k1ZWcKbXD7V96my34IQB/sw5GSa954kW9teDnv+w8uWsE393gHwQko4Zbb19SCpTjRarTBPpSRIZREHMW2kEJgVde5eg0drVlxFCkERv08Aj3bPEMXAexoFcFtm7Ohvl77aUP9WLUN2yWCosYGOHr33cFIZdsUpkFq6epJt5nBbJyPHa5wS9yoGlZN/YyWfynp3H3yk5+ktraWD37wgxx99NEEAjt3bZqdEk0nuWwNWl83ipHCjlRgV1Sid7SgGG59NauuARQVkUoSbN2YnaEKRUrXDJkr/L2/h2OecQtyXrxyTz63Zu+yj52MURaAOtiHUDWsyhoYHkTxmBUSqdQEW/bJ4Nsnn5LYNlp/N2p8GCcQwqprRAaCaL2d6D2dZT3Xmed/rjl2P9/yBpe+4gomXLB8LVesfeu0nUsCTiiCOtiH1tPhDsjGkf8uJpaws+DbpsmjDQ+N+87dEc/hVJxzuvq9NT7MyX97hM2J/Jyu295xGEekJ1Ak4KgqCIGwrJJ9Kaevjh7AiVSComDVNkBtAzgOSjyGRCHY1ZoNR3U03Q1tjQ0S7Gkv2qYdieIEQ3lFxouhJBPFnTspUQd70WKDSFUrcASFkULvaC04TE2MIFLJieUEOzZaXzdqfAQnEMCqa0IGgjjRKpxoVenjpUQb6EUd9u7rZCjp3P35z3/m6aef5vbbb+faa69l/fr1nHHGGSxZMjtjXX28kXoAs3kh4C55hza9lp0NUeMx1HiM1OKVBDpativ0YLbyWE8HJz/3CABX7LYvF6zYfULHT9YoKwC2NW68vZoYwUmMTKkM766Cb598xkVKgq0bUdORCGp8GC3Wj1VZizbQU/K53h6nbkev8v3v809xx7YtAPxs34M4fprzUwSUnZMkhbLTRyv4tmn7mKlnRwJWpAI9Pvn0kx3FzS0buPilv+VtO6S+mZveejA1esB1vhQVR9dRh4fGlHlRkIqCcOySxciLoZgGoa1vkFy2GyiKm7e8bTOKZRbYP8Uy0fu6UIzik9kSQEpCZdbBdMYpWK53taH392Q/q0P9pJaswolER8fA0jslRuvvRjiOKxZUXVdS9C7YkvuOAW1ogOTy3cpeqdM7W/PGiOrQAKklK4tH15VBWYHN69atY926dTz99NNcfvnl/OpXv+LAAw/ki1/8IosXL570yX12DFp/T8EytxofJvzGi4idMP/rT52tnP3PxwG4ds938JEl27/kPpWo8RjKlhjGgqU7/YBnOvDtk08xlMRI9qWbQdh2tj5nKSY7wJSkV6YcZ8YdPCklax+6i770JN1jBx/D7pU1M9yL4jiajrFw2YyGKO0ofNs0+zEb5mHVNaFsfQM1OfvEXMYyYBr81z8f54m+rrzt39prf85avDJvmzF/CU4kSqB1Y2FotHQQ9vaP9xQj5aaWVNYQ2LYpG53kmUs3jmOXOWasvS6GVV2HEy7i3NkW2pgJdbesRRepSNQdAxdx7CRkHS1tqB9reAhjnDIJSny48B3juO+YssLOLa++SrS+bozpdO6Gh4e59957+e1vf0sikeDDH/4wJ554Io899hjnnXce995776RP7rNjEJbpvX0ndOzuaNvM/77wNAA/3GcdJy9YtmM7VAQB6F3bfOdugvj2yWc8itm6iUkjTeK87Bh7OmJZLHvgt9nPb7znZGpmiRMlcYsHm00Ld4nyL75tmv1IQB0ZRh0amFSB8pnkoe5tnPH3R/O2LYtEuWO/w1kSrcKKVmMbKdRkPBsCmVn5KW4HpwZhmW6uXQkxkql66lONC7DHEScRtvdqZOY+jHc/xvZRGx7ETMaRRVYJRZFrLveeC9s7RHZ7/2YlnbtDDz2UdevWcckll3DggQdmtx977LHceuut23Vynx2DHa1CG+rf0d2Ydn6x9Y1sgc6b33YIR8/yYquKZaL1dGI1NO/orswZfPvkMx52JOotSa3p0z7gmWm2xId5x6OjDkPH0aejitmT12bMW4xdM/M19XYUvm2a/bgrRbO3Pl/KtvnMy89xS1t+mOKnV+7JpavfgiIEEtyC3xmZfSkLJk+ciqppW5WUgF1RNaMx6KVSWKQewNGDKGb+SqGdzn+b6BhYMVLYRZw7u8L7HWNXlJFrh1sewdEDBelQdjm5euNQ0rm77777mD/fe2nx5ptv3q6T71LYFnpvV7rgYgirug4tNjj6ua4JqQfcpMrYAFIIEArCtpCqlg7vkdiVNVg19d4zn47jCgeMxJCKwK6sw64uXAmyK2swa0ayOSc7OjdkOvj+xv/wldf+DcBd+x/OIfXzdmh/yr3Hek87dnXtLhGyNBXs7PZJiQ+78f+2jR2uQDENhGngRKKYdU2gqm4ydn9POhlbxQmGURNxQGZfGlIIrJp6nGh1eSd2HPTOFtTYIMKRSF3HbJhfaE8yNmd4yM3rrWssOsNZDGEaaL1dKKkETijiykVPoBjtuGg6xrwlrvS+46RXjxoxa+sJb3o9G5oz123gX3vaOfW5vwJwaH0zd+w/tUV5twcJ2NV1bu7KLsTObJvm+vMyW7H1AKpp8OJQP8c8/RdSOav/mhDcv+5I9hnzHAncfDK3MPiwW5y7rilPEMSsb0YZHipZQ1CCO+4sErLotb8TCIIQyEAQu6LKs67yVKOYBjY5Dp5jI4yUO25SNRACY8FSAm2bUNKTeHZFFWa9O3FuV9bgqG1ZBc/stYQiBXnDkhLOpKphzF9CoCPnHVPTgF3p8a6VEpFKgKqNjvGyfd2c7asVrSoopTBRSjp3xYyTzwSQktDWN7M1lNTESF7Oh5oYQYsNYkWr0UvI7arxYUQqiTmvcBUq0L4FLTaY/awNx3B6/j977xkg2VWeCT/n3FS5c06TZzRKICEJSYBASEISCISQkMDGLCw2AhYvrLHXmGCz9i58a3axDcasjY2NjBE5CRAiCiEJkEAgUJrYPZ1zd3WlG845349TdbvCvVW3Osx0T9fzZ6aqbji3uu573/g8kzAH9paKJxICu7sfTlsnjJNPg55l7Zj/68jj+EieCvjbz70Wz2lpP6Pr4Zoub+oAlQICgOYyYI3gLhDOZvtE00kYo6uzEgVaaEDaDJpegTm0H/rUWClNd5ENKCa+VlNJmD2DgZxs49SxkoccsS0YkyOwyn7D+sQw1AJleTYNZWUJuaEDEKFwsItkDMbIUfehpmTTUFLLyO0+BGwQmyJrakE2npDMaroBqBpoNg2hKCAO3/aO6kdPPIn/8cyvAQDv3n8B/tu+c8/wilbBNR3m4L4dmaw6m20TAcAVtcQ5bmB9YELg/zz1S/z10d+UvP/qvl34P+deilAVGQN1YRa0wKOQTUNZXoDd1gWnrUvaUUph9e9G6PiTnraOcQ5nYC+UxTmomZWKzzlVVo9fBAJAsUyETjyF3OA+mL1D0CdPgWZSIPmCxGZAXZwFSzQBhEJZmoc+My4DK0LkDGVbF3g4gtzew6C5DISilvjAxMxW/HYJZBXNUTWoKfkMFQDszj5v+yUElJUlmdhUVeQG94NwBqEZEFplcpJm024AJwCwRLMUYScUPBz1Xeuav6N1H6GBmqDpZIU4bsVwK3MC66ioy/OwO7plhqKwv2WWBHbuuW0L+tQozMF9FZ8JTfecCxGQbUt0G7Yt/emTj+KfR44CAH545fU47wzNsMnsTRtYohXq0lxdLQC8iiBoAzsH2vxs1aBDyWVAV5ah1KG/pC3M1AzuSH5uw29/dzsztxrYFd4TAtrirHxoBYCaXKywM9S2oKwse3YdrBlUWWUeE6IkS7qdA7vXPHo/vjc7AQD47HOuwjUdvWd4RWUgZEcGdjsBQlGARnC3JhQnlMayadz6yA9xPF0aVH3m4hfgus6+QMcrD7wIAH1+GkomJX2//H1ot3dDn5uq3J8Q6JMjvnPChDOIKhU9AsCYGJHtjqlKP3SjoeQyUBdmJYnL1Kj7XRIhoM9Oghd0nAmRVbd8IEazaXA95G+TOIfVvxt2LgNqmeDhqO+22vQ4tKVVNk51aV4mNj0COwgBfWKk5JmjJpfAjbAMwIHVtW4QGsHdaYDfwGXFdkGPJwQIY7Jds/BeFSOrZFIAZwAtzfxoRTdFOez2bhhTowFXVIozlQm/81cP4UuTkvr7py94KfYG7HkGNmfNLN4EoapQ6gjs7Oa2+vRVGjhrQVjt5Aq1rbp+t0FsUTVbgqLhb7/tgtq7wvG817B5iSViZjc1cXU6BJa5EOi6d3Vu62cveBn2ROObeMa1oRpVeQPbG0oN5sPtgDPlqwhFwd0jR/GHv/lZyfuXt3TgXy96Plo3oHID5Ds8MinwvG1w2rsBQqHNTpRcNyEEqNLBRQCIGq2a1LFBqkg+BUE9fw8llZTazD6fFcsI6BPDJcUPZoTAPQoYLNEs1xGK+M7YAQCxbahFgR0gCbS0hRnPxCYxc54SY0oquRrcbTACBXc///nPsby8DFE0MHjddddtyoLORvBoPNCP1muo0nM7PVRRtuWhCISiejpcglKgbLCeWLmq2mt0ZXnNhu9MGMs7Hv0Rvp8XvfzlC1+OgTozIE40ATWd3LC1EwCh0RNwQuHaWlqEwkk0yz7wLeigbXWcrfaJxZoqKv7FEITASbRAXZqvGBz3P2bthAcPhT0HxAE5mC/c7bxtTj2D4DyaAMoyye6A/mZBUTfNqeO6ARaOQlte2ISjSyRtC3u/9yX39clrb0Vsg2YUN/J7EYTKudAdjLPVNp0NEJCES4QxKGYWghD88tQpXHjOeVDy1SceiUMIASWb2pD7Imlb+M+PPYgfzZfavA+fewle79FdtRGg6ZVVv0IIOcO9huNspnRMAXZbN4RhgGYz4IYBJbXi/i3Kjy0UFUL1DmGK36eZVEVXm2LmYLV1QU0tg5o5CErhtHQEZisnjndSlfj57z7PHL/1bwRqHvm9730vfvzjH2NoaMh9jxDSMFB1QGi6HLicHnP7glkkLg1GoU+4rQss3gRjbNh11Ao/huIfBdd0mL1DlSchBGbfLuhjJytK9HZrZwUBSzWnEQC00zAUu1G4/qH78It8a9pvr74ZXUbAeZ88WCQGnmgG2YRrVqswVDE9BB5LwGlpb7QurRFns32y27pAzKzb+iioIgfH8/+3ugcATYPZNwRjfFgSpwAym5m3AcVBGovEYHUGaNsjFGbfLhhjJ0seRlw3YBXP+lKan7EYAXUclzjDqYMRkYcjsDp6oc1NgggBQSnsjt5NrV5rs5ObloAilgl1EysaR1LLuPKBbwEAYoqKE9feKrPuG4R6jlQtEGRGSGrZ7eAW87PZNhVju86sEgBqJgVmhJHZdy5AKX74/e/jnGtvAhxbklSlV6DkMuu+vmLCowL6Ywl86dKrsccIV51M8wwKPN7zRfHssuBbatxGAOCRGISqgcWbXRKSQpDFmtsB5kCfHC1p9xQAnNYO2X6pGyXC6EJR4SRWRw+Ij8QF4Qy53Yfwb5/8GF7/xrfWNePNjTBE0XO2AOajSyc0DSzRUjKeUyD32izUDO4efvhhfOtb30IstnYxvQak05ONN4GaJrieZ/ThrPQ1gNyeQ5JNhyoQiir7fnVDZsc5gzDCvhpBPBJDbv95UBZnXYfQSbR40k/zUMTTQHBCQQMyJW0FXHr/N3AyTzRx5MW3oKXOdgY7EgNRVGhz02tew1ofbtSx4ITCjcBuHTir7ROlsPr3wLbM1XufMVDbAjdC7sNIhCLI7TmnhIWLmDkAAsIIg+SyACV1Odo81oTswQtBU8uA40BEovL85dtF48jtPbeSAawOOG2dcJpbQS1LEp5UIQ5YFxiDNjuxITIwfvf8Zjq535oew+t/+QAA4OXdA/jnZz9vE89WG1ZnL6jjQEklQayce+0CRAboOziwA85y27SBOB1tzNWgmFkQ2wK1TLzs3HOhTY1JBt91JnstzvCnT/4Cd40eL3n/v+45jD87cAGEEYY5tB/ZfDLfmDzlJt2FosDq6gc3wqC5DIzJUyXHEKoGJxKHkk7WJLVhBYZkIaBPjm6pYJyHI558EMVQk5K0BCiwc4Zgd/e7bZe5wf3QFqbz1T7JPI+iihgPeXdxFebb0pZVP3kXpbJgMzHiziGySAxOq3+wZvUMgofCUFaW5d+vpQM8snEzduUIxJbZME4bBKqAhyP+rwE5+FrU61v4XAR1eAgBa+0Eq9EOIzQddkcP9HwrIyDbN6sxZ55pI1yOfd/9IpbzWai1tiZpmfVr3Kz1+yCcQ588hWwkXmKMGgiOnWCfSlqwVRXc67dSZjeKK1+BmSs9jsnjzYG2q1f+oAKKCh7exHtACBijx32JYuo7lNjQalkQ/M8jv8bfHH9S/v+ci/AHuw6e1vOX49djY9h/6FlgAEAItPnV7DiBgD49hlz0nB0hVu6HnWCbgGDPv2oBxVb4hWjzM1BTyzjU1QUsza2L4/GJ5CJu/On3kCkLur5z+XW4qDjRbuVgjByBueccAFKrjmbTAOcycMkHHMwIwQSgLs6BmlkpeeDYoMmFmuu0Wzpc+68sL0BdWVrHla0NgtK8lFfZ+4TCrkEYQywT2vTY6pw3AMXKwS62K6pa9TgiFIbd0gFtcdZ9j0UTYEGebVXA4k3I7jsXSiYFoWqVvnw5CIHT2rluiYOgqPk0veiii/DOd74TL3rRixAKrToMZ1trwU6E09YFFmuSP07OYeRZ1/xgN7dDLxsiPRMQQqCziEzg1HW3Iaxs7eCI50WTK1hShYCSWQnc691AKRr2qYEgoOnkhgR2gKQNVzeruuiBGx/+Lh7J292vXvpiXNl2ZufYuKrju0eOYP818jX1qHBIPUZzR1fvdqJt8gvivPTDguxXvg0CbFcvOCHuXFcB9Z5DCIGPHH8SHzz6eMn7t/QM4W/Ov9TXP6GWCeQyQCgik2k+bX2sqRVCNxDKM4FXW6cA3DGf4qRbvfpzLByVfoljQV1elFJOHoyZfpIYsuUyDqurD4Q50GYmQM2srFrFm+G0dZYwvntBSXnzICipZF3MknZXH5ymFih5tswN4zZQFG89uy2Amh7xY489BgD4whe+4L53NvaN71QIIwTHCAVjdFRVCEp96XJPB8pZ4iZecju0spK6HxnEWmE3tULJpAKR3XhBUAXm4D6oywvQ5ivbP8VGCTbvQDTsUwNBsNZ71wuL2Sw6TkNFxuEcPd/5nPv6Vy98OXo3kCq7GPW0apVLVEj7VTpbLEBK2Jx3Is4W21TPb8Npaq0gExJUgdXeg/DYcZ+94EsGV7yG3O6DCJ98JuBKvI/hqfEWTUBbI33/RDaDVz/6QzxTJglz10XPx/VdlVrE5SAAlFyuKjOju60dbFaOID/HVnZMPz9DEIqcZSJcRuHvNLe5sjlOXmKF5LLQp05ByWUhqAK7tRNOWwfCx56s+PtZ3f1yZg7yuzd3HQi0/kBrXkOnkwhF4Owg9t6a39Bdd911OtbRwBkGi8QgQHxFJwWk4QbgqZMSBOvt9WaCo/veVWdn6vrboRSxgApC4DS3we7qhzo35btOFolBKGpFi0L5+goClk5rBzA9DlpU1g8CboTAYk1wmtsgNB1OcxvUxbmSIVxW0GNpYE1o2KcGgoBFNiZTKwjBd556Cr9z6aUlCSSuagCl7mA/C4XhxJqgz035Zter2cJ5y8Sh73/ZfT163auhh0IwO/sQGh9e93VwQkDz63eicdBc1ptpGauapwIAizfDLqPudlo7KzLsTnNbzaz82Y5tZZsIBcoqMvKZ3wbCWaB2Pkly0QlQCnVxHgQCXNVg9QxCRONg0YRv9chu7YReRs9f8nlbF4QeqiDPKJw3iF/htHaW6HQWwFraoeTSoGUSLoVEduF3zyJx6LNSLPvz4yfxtsd/WrL9pS3t+PRFL0BbHXP/Usw6GDMwi0QDJa4FIZ4tgk5LO9Sl+Yr97fYufOO+b+HWSy7N3+cETkubZzeRCIVh7jooJWyKWNjNvl2ujpur8dsUnFjLDyyeqPibc1WD0+h0qglf6/s//+f/xHve8x7ceeednp9/4hOf2LRFNbAOCAFiW3JGr56Hq6rB6u6XjJ5lN7/L6qPpcNq7IVQV+uwkCGPu56Ro2wLDp7wBCahtgocj7mslm4aSWoYgFCwWB9cMGDPjJcPEQpQKc5qcof87n3dfT19/B2i+71ooKszuATmcmr9mp60TNJetYFhiTW2S8U8IEM6g5IVDBaWw2ruhZNMgtg0eicEuahuw27uwMHIcXXlDLKgCQYjbjlDyHSgKrI4eN2vlnl/Tkdt1AOrCDKhlygHcTWRLOpvRsE8N1ANhhCQr5+wkCITL7ElzGU/mYBnU6KDOasVPUAXmwF4s/OhHMHuHoCaXQBgDyzPeghAQywSEcGceTSMsnVbLXLWLTW1wmlpgjB53Ayz3HITgN0vzePFD3wEADISj+MVVN4FFYsj17QbhDHZLh0tnXt6qxqkCHo66TMzw2MaJNcHq7gfNZSF0A0I3QLNpGCPHSpJ7AoDZvwc8GpeMc3mSL+LYru0FZJXAHNovE1fMcRNaOxXb0TaZ/bthjB4veY5b7d1gLfIZRhwLSla2VQqqgOshKLm0u38hESqMEOyuftht3SCOLe+D/G/F7NsFdXFWPnPzv3uhqHCa28BjCdiESP01IUr9iHhzXp+NwOoZlCy++ecuC0VgtXcjNHaiJMBj4Siclg4oSVlFZE2tYPFmCEqh5RMuhWCUR+OweiTjcCHxyiIxmH27ZCsiVSE0DStmDv/lt4/gRyNHSr67v3rejXj93nNhzE4UkWtEpbZZ3kfiebKpwsyc+521dwM0oJ+marC6Sn00J9ECksu4moOCEFi9QxWaxgAg9BByuw5Cnx6T7ZGKCru1C6y5FacWF5HbexjEzMlqWaEyxrn8O2paqZxWmW/JI7Gi/VVgo7qRCM0TpsyA5jJS9Lu1w9+3dderV877ur6xunmkXVsIvr+qyy+/HADwkpe8ZE0H/sIXvoB///d/d1+PjY3hFa94Ba655hp88IMfhGmauOGGG/DOd75zTcdvoBIkl4ExMQJqmbKK1dQGHomC5gc+neY2QNVAclmoyQVAAE5Ti1u+Z81tktEzm4HQJePeoz/+Pi669iZAX2XBY83tyEWboM5Pgdg2nKYWebNRCqHqIGYWwghBKKo0rjkCoRly/oJSOKGwdIYAgHOoMxOAZbkGvTxoXAHBnnxgpxKCiZfcDqEoMFu7ICJR2XtdfiMTCqt/N2wzC5LLSYcrHHGdLprNgOsGuKKCh6NgiWZos5NQVpblGgSX340A1PlpaMsLiCSkxhfXNFDHAeV5JzEah9UzKJ0lVa1KLiF0A3b3wLr/1jsd67FPDdu0A8AY1OV5EMvM398tcFrbIQigpFfAQxE580GoJDEQQrITZ1MQiir19xQFNJsByaYhNB08Foc2O4W3PO950MaHwfMSN8WVd6EoUJcWQOenQTNpUEfaNaEb0l5FZdcAmAOntTM/yyI1k7ii4e70Mv5rPrB77YEL8b8vvxa5SBTq0jzCx36bl8Eo6lbQQzB7BkGYA6HpELoOZXlRkmPlHRkWS4BF4qC2CaFqUJfnET7+JIgQUt6iowfawhwKYSAnBE5LB5yOHhDHhjY3BTAGQEBNLoJwjj+44gooK0suKQEPR2FtUsvodsN29J14NA6zexD67CRsMwtdVWHMTUHMTcGJN8PqGZKM0tyB09wOKArE4izAGFg0Lqs8xQ696qFBRimcti5f0WantQNOolk+RymFNj8DJZ2EtrIE5XhK0t4bYWSH9oNaJmg2DcIcKGZWBo7zMzL50doJ3tQqyeXyotTuOdq7wZpaZRBihFx2Xx4Kg0XjoMlFUEJAGANxHAgjjIdGj+M1X/zHkuN0xxL43G1vxq7mdhk0pJbhxOKgpkzu0Ew6H0ASsFgcVvegGzDR5QVQ23L9sXrAmtuQjSWkj2YY7kwrMbMyKR2OVg1chBHyZ6ckpIR8S12ck8kwziAUFVZXX3VugLL9NwyqCjuAhI+6OJtfL5etwF39bgs5zaSlZI9tSd+4pSPQMbczfIO7q6++GgDwyle+ck0Hvu2223DbbbcBAI4ePYq3ve1t+P3f/3285jWvwV133YWenh68+c1vxv3334+rrrpqTedooAhCwBg76WqYECGgLc0BRQQo2uIcrI4e6FOjbpZLXZyF1bvLNYLUzIHmMhCcgcWa8NOREVykl9KbE8tEaOSIG4Sp6STs1k73ZhGaJtczcgRKkc6bsjQPc9cBECsHZSUJmlmBkvEWBy28VyzY26GH8OSL5e+RcA59bhJ2R0/VtkbCOfSFGVAzC65qsDt65NqmRle/upUlOGapqDs1cwidPAJpnkvXpRT1vhMAanoFPLl42liQGliffWrYprMcjCE0cmS1lWdpHmx5EYCAUmDHTa9ATS0jN3QAPBIDzeS7CTQdPNbkMtXxcATItzgpyUXZ1pV3nqhtwRg7gey+8+T2jo3QyafdbH0BBNJmGtOj4KoOxTY9W8ne/fjD+JdTkjDhb8+/DK8+9Cw4ulFi1wGUzDxTKwd9Yhjm3sPSrg0/A6WsEqmmkzIJFUuARWLQFueK9jdlxaJoeyoEtMVZsFgCobGTFVpOABAzDIiJEWT3RjcuS3+WYDv6TtrMJNSFaRAARlFQRgBoK0tQU8tutUgtmy9TMimYoYhsTV4DaDYNml6B0HSweDN4LAF1cbZEioA6DujKMrCyDG1xDswIQ82suJ8X30/K1Cj4zATsrj53ZqwYQtPdoI7YFvTJUdDMitw/nyRm2TTe/+3P4l9PPl2y71sOX4z/fsVLQGIJd1tteqzEdygGgYCaSoKeOorc0AFAUcCbWrEm1gLOoU+PufwILN4su5CoAmGEPaVq1gqSy0CfHlt9zRzoEyPIhaNbUrqJZjPQp8fd14Qz6JMjYCuLsLoGYIyvVnuJENAWZuTYjMfv42zBaWmK/4u/+Au8853vxOjoKIaGhjAwICsXN910E+69996GA7VecA51cbamOCVhTkVfO4EU9WWJZmmkih78LBSGVsgCCSENoG3LgKzMgVEXZsDCEZc6XUkulQR2gNST0UeOBhYFLZ47ORRrwgPPv7H0egDos5PgRhg85tG3zhiM0ROuc0IdG/rkqYqWBSIEVA8WUL/5Qy8oK8uN4G4bomGbzh6QXFa2WVq5irkcpcgRLICaORinjpW0SgGAmDyV1yFql/d03olTkkuV5+Qc6tI8eD4Iq2YziBBQ8ppW5fbv8h/fg2P5FvH7Lr8Oz25uA1JJX7a4kmuzLRgjR2Ulw+/ckE65kqqcefJMrgkBY/KUZ2BXcj2ppKeOagMbg9NlnwqBnR+qzXkRIaDNTflXhJgDfWZCJk8UFXZrhzuyoM1MlMzB8Zlx5HYfgrLiT3BCmFMS2AGVv2HKGfTJUzB1w5tVkXMoqSS0mfESv+nplWW89KffRbLMl/rmC1+OSwvHGT8JJ9ECq3cINJWE6hPYlazHMqHNTsLu6oOyNA99dgLgHKAKrM7eQPeQNjMBtYisRk0uuq2qGw3V4/snAJSlOSi5HGg2Da4bMrm+UcyT64CSWqp4r2DzCBv2nCdWUsuN4G49eOihh5DL5XDDDTfgnnvuQUfH6oxRZ2cnpqfrE4++665PYnl5bcxG9eLjH/+/p+U860EiFMLtz342IuGAWRvHqWhhpLaJu//1E3jjc59b8r6Sy+K8nh788//7W7z24ovRlmeIE0JUHIMAUE8dx7/87GdI5nJ4xXnnYX9nZbCjBqQjn8plcP4PvwYAuLylA19/7jW+25549CF868knK94/2NmJm847r2Kd8HBY1kuvfPLUML76zW+s8yjbBxt1bzQ1NeEd73jHhhyrXmxn23SmsdVs44v278fFA/W3O3vRsxMAxLGhz07i4YcfwCOnTuG8nh68cN8+qFpldWL0iV9hV1sbSL1CuABMxtB/3+os8RNX34zOogx8ULukZNO1N6rjeEAwhtFv3/dNHJs78/I4m4WdYp/+6EUvWpcmobO8iNQjD4AQgl+NjeGZmRlcvns3BltaENV1qPnuH8IYjKkx3PP972B0cRFvvvLKkvNSxrD0i4eQMk0c8PAf6gEB8NSDP8T3j6zOyEV1HXvb23Hl7t0wDEl8IoTAR088hb888uuS/V/ePYDzVhie09e/GtjloSYXMT58HEOtwYMDbWkOi6Mn0RGLrepk5oPQf/3aFzGfrn4P/5fnPx9amf0RC7P4+Fe+GHgN1VBs0y/q78fVByrZLfnUOPT8GpRcBuTUMdz1yCOYTa1fL3g9uGRwEFft804ukEzK87f95DNP4b6vfQXA1nueBUEt21QzuHvmmWdw8ODaBVPvvvtuvOENbwAAcM5LxF/XIgb7ute9CZxvHM29Hz7+8f+Lt771v236edYLfWIYqkdG2Q9e37cA8IYrrpCZpDIMtrTg6oMHSzJ3fn8zVVHwn15+C5yWDoSP/dYdmq4XI5kUnnO/DJRu7OrHv130/KrbHzh4GLteeH3F+8rKErABzHK1IAD0X/RcvPX51276ubYCNvLeoHR9YfV67NN2tU1nGlvGNnIGdWEOSmp5wzTsyvH8cw7j8suugD5fybJXwJ6OjjUlh4oTWIC3rMtWBtcMXHfb63DdWSxUvh77tK18p6d/tdZlAgAMTUNn3um/9tAhvPjwuSWEaOW44ZLLYLd0gI6frPisp6kJZu8uiInhdSddzz/nPBy85mUAAHVuGtrcpHvMqVwGdzx6P54oYwL91LOfh5d1D4BrBoSmrbZyl6GewK6AznhllYsQgtddcx2svt1V9zWO/layVBZBVVW84xWvBISUKFmrMHeFTXcciJNPlXRocaogVJbbooTgdVc+D7m9h9d03g2DY0OceNqz04BQCmaES54RghDsv+KF2Hf1jVvneVYnatmmmsHdH/7hH6KlpQWvec1rcMMNN0DXg/fbWpaFRx55BB/60IcAAN3d3ZidXaWTn52dRec6szObDZpclNpkApIedhPYDYllyuF/RzKN1SOKSDPBMrZVzw94BnYAsLe9vS7NOMIZtIXpNevMHUkt48oHvgUAeE3fbvzdBc+tur1k5fQ2slwP1S2/UO/2XFElqcIWaE3YiVirfTobbFMQ0ExKzmgQCqepdXMG3r3AOdTlhTzDWUiSB3gwuBHLlPTceQpuQqTgsdPU6s69+cEYPRG4YrVWENuqGtitVd7lZ4uzeNlPvwcAOD/Rgh9cWZmcOhMIej1PTExgz/OvXVe152zHTvadqgV2AADOoc16SxUR5FvmXIkOSeazll8azTv0JJeFPjcJAPjyxDDe/OuHS7a7uKkNd138AnQYq2Lz1DYBu7S9e9NQReOvAKe5rUInlwjhtlCqqWVYXX2S8IYzaW/Xen+qqmSpnJ+WfAWhCLgRhj4zXrEptS0Qy4SoQwKiAozlpRXWul4NuaH90CdGoJil40BOUyvsjl5o89NQ0klwTYfT2lmV+O5sQM3g7jvf+Q4efvhhfP7zn8eHP/xh3HTTTbjjjjswOFi7z/eZZ57Brl27EInIL/HCCy/EyZMnMTIygv7+ftxzzz141atetf6r2CSos1PQ5ld1iuj0OJxcFvYG9jgTMyfJSfLBlbq8UEJOUgtCDwE1Zu3WCgGUUF4HgmV5zrcEwa+XF3BNninuzl0H8ZfnXFRzH8mu6X39NOBsX/nxgkAAsDt6fJm/Gjg9WKt92u62KQiUpfkS4iB1cQ7mwG7JBLmZEALG6DGXOh0A1OVF5Ib2lwRsJJdF6NTREoIQAMDyApSVJZgDe30f9jS5uOmBHVDbHqzFFfnUyFH8yZOPAgDetvsQ/uLQs9dwlI2FIBRE8EDXwyIxfPvpp/HWq2+svfEOxk72nWqCMXf+1AtanjRkvaC2BWJbyC7M4A9+8WPcWxac/NU5F+EPhg5IGZMNOWNteCVQlGxGzrH5Mc4yyVjJ85qTftBmp6AuzILaliSQ6+yrYAwNvE4jJGUVitagzYx7f08+xYFaIJYJffIUlGwaglLJYtnRs+b1mrsOQF2YlRwKQoAlWvJyExR2Zy9snN0MmcUINHN3+eWX4/LLL8fDDz+M9773vfj0pz+NK664Au973/vcAV8vjI6Ooru7231tGAY+9KEP4e1vfztM08RVV12F66/fGhlLL2gLMxXkI+ryAuyu/ppZ5cDnmJ+ucG7UxdkSjbViENsCTa8AigIWa4Ld0Q06kqqL/INrGkie8dHrRi1oMWnLtQeFy6EV6crVg58uzOKmn8lM9p/sOw9/vP/8wPuqi3Pegps12LtEkVJUUMPOqQKntRMs0by+TFUDG4a12KftbptqIk9yUAwCAW1uGuYmB3dKarkksAMAamYldX7RALuX7XOPkUmBplcqiJKIbUOfGJbyBZuEcl24jcRbf/0wvjAxDAD45LOuxCs2gQyhFgrXxxItsrtBVaFkUpKgocZ+LJaA1ZByCYyd6jvVAj1NFbGHFmfxio+9r+S9Dj2Ebzz3xdhbZAdPZzM9i8QqWMKJEFDnp2H176ncwbERGjkaaAYWnLlVU+pIW5kzDrkSUOuCosBp7YC2MFvyNteMtR2/wPBuSXZfwjm0+Wlw3Vg70QkhcNo6pczNDkfN4C6VSuEb3/gGvvCFLyCbzeL1r389XvnKV+LHP/4x3vrWt+Ib3/Ankbjxxhtx442l2b3LL78cX//619e/8tMBUel4uC2MGxTcEY8blhSLLRZBSS5CnzjlBnJcN5Ab3AfW1FLColQNBf0PyexWZbvTKPL4g9lJ3P7ojwAAf3no2bhz96G69ieWt9HjoUhFlqxYHLWegBgAsrYNcfj8HSGAuV2wVvu07W1TLQjumeEtZ5HcDPjdj9QyUdysRWo4eNQ2XcpwJbkkGYFzmTW3fAfFZgR1Qggc+v6XsZC39/c/7wYcXuN8zFphRxOAEYITb5btuYQAnEGbm/Jk0SxAoEC7PtCwfXVgR/tONbCR91j5M97hHO97+jF8skxs/M5dB/HnB58F1cN3K1SuS49LAFLKFCoolTpqIHASzVL3MeC63PU1t0H1mOPzs81avhIXBOXnI5DcA47R7bV53bA7egEhoC7OSW1O3YDZu2tN7ZTEzLmBXTHU5OJZzWJ5ulAzuLvqqqtw+eWX413veheuuOIK9/2XvvSluPvuuzd1cWcaQlErKFQFoa4Y5UaAh6MV7UVCUV1xytUNpcZJcUBCLRPa/DRYNBE4uLPbuqDOVac9BuDbj7zWGRM/3DM1ijc89hMAwEfOuxS/O7C3/oN4LYhzGKeOeRo7v12qQQD40q9+hVvOv6Tu5TWwedjJ9qkqqAJuhEHL5g9YZPOFprnPOViZHiUPxyrkUkq2D8cAx4E+dapCW2szUKjh+yV9BACualDqbIPPMAdD933BfX30mleh+QxoRTldfRXdBvrEiO93yzUD5sBeKZq+gc+8nYKdbJs22k+ohsJ5jqaSeNlPv+smUAr4ykt/F1dyXvW+puWBnaIg1y/bwrW5KSjppEy6cy7vi75dUkLFo9pduHav62chKRfFtcmKgK3cPhZQbsP94NuySVaDWWV5AWpyUQp5N7d7S0jVAGHOqh9lmTJ41I3qBQ8hQKwcQOiqDfIhBBFk+5BKbWXUtNj33HMPHMfBwMAA0uk0RkZGcPiwZMa56667Nn2BZxJW7xCM0ePuD1kAMLv7N/QcdlunJD3ID/4KQmSGtPhGYQ6IaVZoywEATadAU/4zbsVGlkViEITUlCPgRhgsnqgwFgKA1dEHfW7SzXJxTYcQom6HBwA+N34S/+XxnwIA/ulZV+LmNbYoFYwFyWWhLi/IgFyIisHa9UBoOqZW1jZL2MDmYSfbp1qwugdgjB137QbXdJl5XQscWw7oB+hY4OEo7NZOqEVt7XZzmyQdcmwUaud2e5dsvyy7TwUAp60LwgghdOLp09LCtWonvR1AJxIFS7RCnxrz/NwPxcy/ADB1/e1QNth5CdJGKhQVooxGndhW1aCZOLbMyDcCuzVhp9omQSnMnkHoMxMgtrWpQZ4QAh8ffhp/Ucb2+dKufvz9BZcjqqowewbAlhd8f+te67O6+iHCMsFNHLukekdtE9qCTKoHXichcp6sTepmWt15Ye18Wzo3QnI2zAM8FIGS9vY9WDgCq6MXoBRC1RE++XRJQUJQ6o6sqHNT0Ita9dVUEmbvkOdIix+U5GIJOzsBoM9PQ1uag9U96EkGSMwcjPGTbmWSRRMwu3qlaH2+RbUYTnObZFpnjhxNapA2rQk1rfb3vvc9fP7zn8c3vvENLCws4O1vfzvuvPNO3HbbbadjfWcUPBpH9sAFUOdnQASH3dq58Q86RYU5tF8K0DoOWDTmztqVDJsS4j2I61HWLgGlAOcQhIDrhifbUTEEALu7HyAU5sBezP/yYfS1tkEQAsIZjFm5v8iz7/FIFFoRaUNQfHLkCN795C8AAJ+5+AW4rrOv7mMUoGRSCD/9a6yVUSsYGgZmK2In26da4OEIsnvPhZJOQhAqg6s6H5Q0m4Y+NQpq5iCoAjsggRAPRWTGOJ8EUlJJKCeeArVM145xIwSrawAQHIQxcEUFZbZsp9YNqLOTp202p9q3wkIRWIP7oU2N+Wb/vfCjuUnc9siPAAAvaOvCly69en2L9EHNLgwAVldfSQYfgKfeZ8lxBYc2Peo9B9RATexU20Q4h5JeCdxKuBZMm1m89tH78XhZ5ax8jlUoKlg0AWWuPk1AUmiR5MxTZkVJp2B1DUBQpYR+39c65Ak9CuDReN42r0AoCngk5mub7dYOKCvLJS2MsvLWJvkfipAb3AdtdgI0l4UwQrDbe2RSR4iKWTkA0OZn6gru1CLB+WIQxqBPjCC773AFV4Q+OVLScqqkk4icSEIoCuzWTnDdgJJKQqgqnNYuECEQOv4kqGPnSWF661pjAxI1I5XPfe5zbgvBwMAAvvrVr+K1r33tWW+gXFAKp2Nj+pV9QYi8ucugT4y4hmUtcyYCcDNDRAhoS/4EKS77Y1ObG8Bq89Pob2mRzlfZ6YngUJfmQJbqF7D9yPEn8L+OPA4A+OqlL8aVdQ6/ct0AscwyspvNncOpRc7SwJnBjrdPtUDpmrWPwLmczc1nggln0GcnsKetrepuxLagl2lUFXcAuOzD+Yxudu9htyLouknMgebjSNSLWi1iTNWhOJWOaIFApOBA+bHyeuGjJ57E/3hGiiL/6f7z8Uf7zqtjxRsLHo5WOEfqwizU+ema341SpSukgerYybZJreJrrAdfnzyF//yrB0veuyDRgv94zlXoMkplXgSAXFcf9LETUOvs4qFmPpAi1HM8h2s6oCgwB/ZAmx5f9dP8DsgYjOEjsDv7VtvWFaU6kyVzoORlDnL9QwifPOp2TBEhoC3OgcWaSmSYhBGqTMYIAZpa9taAC9hxRdMroLnM6vfiASI4lEyq9Hnj2L6t94Qx6LOTyA3shZ0naSK2hdCJp1x/V5LCjCBnhDeGFGYHoWZwxxhDLLYaeMTj8brFMxuoH8QyPTNGospMSDFkFry2doo8JmAO7C0xEiSXqcmetpZfwQeefgwfO/k0AOC+y6/Ds5urO4rlYEYY5u6D0MeHoZaJj24maDaFQ10N2YOthoZ92jzQTKrCqQGAgzXuA2VlObBtIMyBsjQPNb0CktdTYrGEZCpea0Kr/BxVtueqBhZvgrJYmtUWlMqgs5CFFsGniF776P347uwEAOA/Lr4K1waUtdkMCACsjF5dSS7W7OBw92+0ZK4ZO9k2beRVph0Hb3v8YXxzurQl+gOHnoW37Drk+50SAKGJkTWtxSW6IwR2ezf0onMLyERP6MRTcBItMAf3IXzk8arnIQCUXAZ07Diyew7X7ACj2bQcCcon5/2sj7KyJP02zkEzKwChpVVAIWCMnfBv66w1cyeE9LUCsqCXkwCCUtn1VcWWu9eA/LOjbNuNJoXZKahpuffs2YMPf/jDuP322wEAX/7yl7Fr167NXteOh6DU+4amBODVnR6uqDD7diF86ligc/FQpEKEu1qGZq34o9/+HJ8ePQ4A+PHzbsA5a6goUDMrnc4AGaeNHOomAK7ev39DmVIbWD8a9mkT4fM7t53qSSNR5/2hF2kn0dQylFTw4LDi3KoGu7kdNJeGkkpWD+wUVTov+cDOZdJVFJg9g25gR1NJ6DPjNZlGuRDovvduN/X2sxe8DHvK7OrpBgGgLc3DyWs9AQhMvgUATmvHJq3s7EfDNq0PP1ucxct++r2S91o1Hd947jU4EKuc7fLCWu1IcSeV09IOrutQlxfdpHtBSkqfmwJxHHf8peZ6OIe6sgSnpb3qdtrMRIlMjO91ECoDwbETq7PVep4ISdOhrCxVmdeLwqqReFJSyboCu4oONKqARePVCbGKnhe+z46Gz1U3an5jH/jABzA8PIybb74Zt956K4aHh/EXf/EXp2FpOxyq5tlO5QQIiAhnEKFwRcbWC1xRYBV61DmHujgLffwkqAdV73rwpscedAO7n73gZVUDu2qhK4Fs+QjkQOYD5I1CRNc9pSsaOHNo2KfNAw9HwStanQgen5iouh+LN9cV4NVTaasFp6UDTnsXnOb26oFdfh6wuLuhQO2d3XsueKzJbaUKjZ2oGdit2Da6igK7k9feesYDuwIIZ6szRAHBNR1m7xCc1oZe1FqxU23Tep65Dud431O/RMe3P1sS2L1p6AAmXnI7nrnmVTgQa9rUIQyuqBWzxTyagNU75NnaqC4vyHGWDQTNVie9A1bn7vSp0RKyPWqZ0GYnqx7HauuCObQfhAvAg6jPPVYN8r2S9VAPmRTOoWT8NUkFISXfHYs3V1T/BFXgNGbu6kbNyl17ezs+9rGPnY61bDnQVFIOvKoanOZWT1FxF0JASS2DZlIQmgGnqXXdmkBWz6CbfRGKCqe1A7TKjVK8FggBs383wseeqChzC0gnhiVa4LR1geSNgZJcLBmC3qjK160//wHun5cDzb964cvR5xN0OpEYnNZOGGMnqh+Q2eCROOCTkSrATyB5rTAdB+IMUJg34I+dbJ82HYQgN7AH+uxk3q7psNu6QB99BNrMOASVRABKegVgDILK9hseiSM3uD9PxCLnLZxYE0CprKZxJqmxN2CJXFHzLJ7SSXAz4sypab+oh32QQZy0l9rcpGdrfDmOppK44oFvAgCiioqT1966pdrvBKUldstpavXN5rv7aHqDxGCd2Im2SQCyJbDOlurj6SRu+un3MVtGEPe1y16MKzwSDMUM5ht9p5lD+/19Ny+fQnDY7d0Qqiqr4oz5jsQI5BP0nEt/q9CKnmguITwSRgjEZ05QAOCROOz2LghV9eyyKiTnuc+cmjAMGMPPQMllIQjBiw8ckH+zMrvlt78XeChc8R7NZT0D4gJYOFo6S6cokhRmbgo0l8mziPYADc6DulEzuDtx4gT+6Z/+CUtLSxBFN+wnPvGJTV3YmcZVe/ciVBRkqIuzMIf2+zr3+uSpkhk1dWkOuaH91QPCWsgzLNmdUjiSZlagBiAw4dG4e16nuQ3aYuk+LN4Mq2+XPEVq2VfQfCOM5tUP3ovf5L+XJ69+JTr8jA0gB4EDZfxlxkpdXjgtoswF/OT4cVx+3nNO2/kaqI2dap9OG1RttbIPQF2cw+8+5znAQmkrYwkW5+A0tcLcdaDicLZjwxg9DmWD2r4pc+BEYq49AwBtdhLavDc7XuEXUq5rVQBXFBDHgZJagFJj5hgAvj09ht/75QMAgJd1DeBTFz2vrvWfDtjt3SXOKku0gM9MeGti5cHL9PAaqB9nq22qFlARIHBgJ4TAP44cwXuf+mXJ+9d39uEfLrwcsQAO/Xp8FK/rGF9eRkuV3z5LNIOWsU6yWBOgKHDauuC0dQFCyBk8r++BUEBRYJw6VqJvzJYXYA7sdYMrq7NXtlp6HIM1ta7aZCEgFKVCJqsgD8USLWBL8yVJKifeBG1+xg0KiRB4dn8/rMXZiko9y1dJazLyUsVTykFoetX9lUyqYtRFGKESe97A2lAz8vjTP/1TXHDBBbjkkku2VDZyM0FsCxcPDJS8Rx0b6uIsbA/KfpLLVpCPUMuEujQvb/b1Qgjo4ycDCfly3ZDzInnYHb0gjLmOCjfCbssBsS0Yaxw4DoJn//BrGMsblWPXvApNVapeBEDo+JMudXpVEAIoKnK7DkBJLkFdmgfNZdZ0HYEMF6Rm2GM/+AEuX8M5Gtg87ET7dMbAudvuU4DfN64uL8Bu7SzJytJMniQgyD1eB9SVJbDFOck+p4d96bqrrbcAFmtC6OTTgchc/teRx/GR408AAP7qnIvw5l0H61m2C67p66aN99IkZfFmOK0d4B6dEsII5zUHKyGo0pi12wCcrbZpvVcyZ+bwu7/4MX6xXMqo+f8uvBy39O5a59GDo7z6xyIx3PPgg3jdZVdVbiwEaCYFboTgxJvzLJYCLJaQusQlB5Y6dvrkqYrvisWb5BxctrQDS8mkoM5NQRhhsGgcPBpHbvchKMsLJTIILN4Mq9gHdQlfVgmSBMhqWymlMAf3QVlZAjVzstVe1xE++UzFJSory5Vt2JTKQoFHJZKrmjxeKAKnqcWzuiY0DU5zOzSfogSBJNUStNERtdGoGdxls1m8973vPR1r2TIglgnqUUGipneViPpozdVDSkJsC+r8NGguA5EPwArZFznUWj2wKzzMrd6h0tI6pbB6BqEzBjWdhGJmERp+Rla+kosb3rpYwOB3Po9svhw/fO1tiAZgXQvK7skj+VkWqoA1t4E1t4FmUtCmxkCtXF0Pn6pzOVQBa2qF09zWoOHdotiJ9ulMgTCnaotNOaiVAzNCMiicn4a6MOsb2AUR4q4GI89mF5RN2AssHJVtowECuxsf/i4eyTssX730alxZZxKPaQZ4NCaraKEI1PkpaPMzVa+/WiJKaDpyvUNQlxfzszitEKGI98aODXi0fHHNAIs3SXvXqNytG2ezbVrL/frNqVH8p8d+UvLeufFm3P2cq9Dt91s9DSCQCQ1zYA9WvHw85iB06rjbYi4Ihdk7CB5N+LZvsqZWmIRCnx5z/RoWicHq7PPtvtLz3QaCUpj9e8AjMTgdPXA6elbn4jzO57R0gOshqMlFCErhNLVBFLdIUgrW1OrKzBDb9iHr8yHQauuEPlM6Z2129oG1tFfXTWUM1MzmE0wRORtYZlu5pjdGXTYJNT3uoaEhzMzMoLNz5wxW81AYNmPQym4kFvY2QDwc9bxZWKQoY8qYzFBoeuUNwTmMU8dWs7e5LJRUEtndB6GkV3xbjApwwjFYfUO+fcmSanw1OCyQkmxGLlEIgc5773Zfj133ahg1Zg/r6ZtnRgiOh3wCj8Rg7jkEOA5oJlViVGuu2ef8BIDd2VMp/tvAlsFOtE9nCkLVKipEvtsiL2QOwBg7CSVTfcbLbu+GkknJNp11gECsaQ5HABXZdC84nKPnO59zX1ebIfYDC0dhDu4reQ44Hb1QlxY8pSeKYUcTUNOVLKA8HAGPxGB56KUWg5g5hEaOVgTpghDkdh1Y95x4A6s4m20TgXwWU8usmgyxOcdfPvMr/MNwaaXofQcuxNv3nLNpFc16g0/CmcuAWQ5tbtoN7ACp52ZMjiK751DV+4UnmpFLNINYOQDETZhwHz9ydS0c+tQocrsPrdqIGvclj8ZhBSRwEpok6yuXkrJ9GDyd1k4IVcsnjgDW1AYWr85WqiwvQJ8eA+Fczhm2tCO7+xBCp465zw9BFVmMaGBTUDO445zjZS97Gc4991wYxmo2b7v3jVeFouKHR4/i2kOHXOPAQhE4Ld6tKkLTYbd3Q5ubWt0+EgNLtAJCQJuZkILfQoDrBqyeoZIbXFlZqmjLIcyBMTESyNmhzK4M7IQAIABCoZZpOAFVMsAg4JoOxa5/lo0Lga6iwG7yJbdDrTJDV3DC6jHvTltX9bk8VZVGNd4EurwAY2q05vF95wc4AzHN0ixYA1sKO9I+nSnk242UkaNu4ssrkBIA7I4eCE2HOjdZNbAThMDq6geLNUFdrD1PDOQrbFUCMeKzLs/zI7gNmrdMHPr+l93Xo9e9GqE6gyGu6ZXdFQXUSCIRAFo6CRaKSKKCvAvL9RDs1mCVQ31q1FvMWAgQ24RQzlwF5WzD2W6bqs3NjmczuGv0GP597ASm84FRQg/hKzfcjgus08M4bfUMerZGekEoCoRHcpyYOc9KGxEcoZGjyO0+WJNXQeilXT88HKupQ0wtU7ZCbhKRiNUzCKEbUFaWIRQF3/jZw7ju0LN8t2eJluAES44NfXLUtU8EgLY4Bx6JIbfvXNBsGuBctos3JA42DTWDu2uvvRbXXnvt6VjLlsLjExN4/o2vlCxxqiZJSqpkmZz2brB4M2hWsmUWhCSVpXloRcEVtUzo4yeR23vYPZ7f4H5QOQJqmbLUrmn5YHIc6tICIDiEooBWobotB49EwY0QlMX6grvyjPb09XeA1sjKrSW7TgoPBseGmlwCICR9bnlpnxAQQtY3cE2I/E4b2LLYqfbpTIHHEvjnhx7Cm257rZxxC8fyQ/EsP9jvgEfiELoBkstAm/PvOhCQQYU+Mw5nZSlQpb0QONoAiJUDTaeglWWgBQCzqx/69DhqiaEEtQ+PLy/gxQ99BwAwEI7iF1fdtKaqQ4EBGZCt+MrKEgACJ9EsW7+d2o6vkssgO7hPOoCKKoWIg6yFMd+gWNq6RnvURmKn2SYuBH48P4V/OXUc982MgwuOF/Xtxl/vOoBr481QqAKmhYDTFNyxplbkIjHQ5BK0hZmqDJZWR69noGGMn/StTFLHhrq8WPd8Ks2ma9o6kSde2TRQKu1oRw8A4Ng3v4HrNujQSnrFszWeplfA4s2eM8ANbDxqBnevfOUrS14LITAyMrJpC9pKELoBVsfsgTBCcsakCOpKpQAkdWzQbFrSiC/NQ/WhpQ7qOghCIfKGQJubKmHHLGdRqgUlk6qbxjjHGAbu+7z7eub6OwBCXFrkIDMsQUAA6HOS0EGbn3bnd8TspOxRL2tL8MrEAYCdaIG6slRzXU5r5/rYThvYdOxk+3SmkLVtsKLWaJZo9txOXakuRl74jHDuawO99tGmxyUTZyQmq11lwR0BEMrP4FVD0OreF8eH8ZbHHwYA/G7/Xnzk/EsDrdULSioJHomBpldK2PC0uUlYHb2gmZVA9pLmMmD1atBRCkEVz8qd09rRsHUbjJ1imxYtE3ePn8SnTh3FyUwKraEI/uDiF+C1F1yGfankavufECXjIZsNfewElExadkt194MwJnXbhFidKRUCLN4kCYbKQMxcTTZuEqBFvWKfIORJgoPmstsyEPLzu/zeb2BzUNOa33333fjf//t/I5td7TlubW3Fgw8+uKkLO1sgfLIvBaFGdaGyZbLmMVHmlFAKY+wk7LaOQPTdvscpHC7A7EkBacfBru9+AQAQURSMXPfqspPUF9hxRQUPhcHDEdBcDmqqMjjWFmZKiBlk9n9CtkgUHysSAwtHoBQJeQpFhd3ZC5rLQPEw3FzVwKJxsHiTFDJuYEujYZ+2LvxsnxfqqYEpZhahk0+DhSJQAjAIeyFoYPenTz6Kfx45CgD42/Mvw2v796zpfO5589+JPjNeEsQRzqHPjEMoGuBYNdemz04i59WxUA2ESHKEIsZTAUmY4HT01nEVDQTB2W6bHluax7+MHsNXJ0aQ4wyXtHTgnZe8CNdf8FwYqiq13CZPbeoanEgcqkfbNwFcEjollwEdH0Zu18GSpFRNUKWmnWAB59zcddkWtLmp2ttBdnRtx+BO+l2lrfNCUT25EhrYPNQM7v7xH/8Rn/rUp/AP//APeMc73oEf/vCHmJqq/eNsQMJpaYeSXCwxECwSd9kXKas/88PyOnY0vQLKHBDmQMmsgGZWpHMQEDwcAc2uTUIAAJZtC/u+9yUAslXply98+RqPtAoWS8DuGZTXlvZpS/WaGfES/CQEZv9eqItzULIpcN2Q1ThVg93ZBzp2ouLaqWPDjsal08SZFEhuYMuiYZ+2LpxEK7TZyQ2r3BeDWuaaNS6DBnZX/PibOJqvNHzn8utw0QY4J9yISFp1j3klIgRIgLbMwrba7GTdhAROWxeEZkBdmpPMei0dFR0PDWwMzkbblGEOvjIxgk+dOoZfJxcQUVS86vDFeN3hi3FO72Dp3CjBmgTNg4LnK0FB7mcCwBg7AZZogd3aEWiWzYt4pHAuAVnt9rt3lJUlqPMzILYFoeuw27rBYwlos5OBCKnkyQSImZNVxu0kpUEIzIE90u/K5P2ulmDfeQMbh5rBXXNzMy688EKcc845mJ+fx1ve8hbceOONp2NtZwV4nhlNm58BcSywaAJ2++rwu1C1ulonnVgTrL5dILaFcFmVTgqIBj9WcUWrHLUM5qyZw+EffAUAcH6iBT+48vrA560GYttQkkvQJ4arnL9gXlfh1VYBQIqLtnfBQSnhAI8lfPVX9MlTIEJAUAq7oxeOD4tUA2ceDfu0haGqMPt2y/bDM72WItRai8kY+ovazJ+4+mZ0+tmXOiFUBSAE3AiXMPCtBRUz2QVZmyokBcS2oC7MuKLGRABmKNJgydwEnE226VgqiX8dPYa7x05g2bFxKNaEDx2+GK/adz70ved47kPTKxCaDrLGJEw5BGRinDIbXFGhZlLBAyXkx2EWZqCsLMkunwCJW6tnENwIQV1Zll0/Ta2AqkLohm/VXF2YhT6zqjuHrANl7ASsjp68Pl5tCEhGc21p3iVhOu1VvAD2xBe0SNS9gTOCmsGdqqpYXl7G0NAQHn/8cVx55ZVgdc5x7UgIAZpZcXu+zb5dnjeJ3dIBY2o00CHtplbYBYFyn7/BRunWVXOAJrIZXPijrwEAXtDWhS9devWGnBOQsyR0prpWnaAUgHCvVRACq9OjrYgzKEnJRMqicUlyUwQWjXkGd4VKA+Ec+vQYWDjirxnVwBlFwz6dWdBsBkpqGULV4CRaKoIEHkvI5NbsJGguCwheM7har+bdejCVy+D8H37NfT3xktuhbRCjG1dUKOkVIJWE3dIumSvXc0BFgTY7CWaEoCaXoKSWARCwRLMUVi5bN83Tk9OiZ4SSTkKbnYTd3b+elTTgge1um2zOce/MOP711FH8eH4aGqF4aXc/3jC4H5e3dIAQArOtE15XpM1Olkg4rUWepBwEAI/GYLV1IXTsiarbVjsftS0oyaVgLZqUyjZDIcA1HTzeVDPY8ZOu0mYng0szlK1XHx8uIeHbaFBCoCQXQc0cWCgstZWTi3ImMdYEq2cg0EwuMbMygKWKfB4E0DduYHNQ85t/9atfjTe/+c34xCc+gZtvvhnf/e53sWfP+uYOdgL0qVGoywvua7Y4K7WNyrJFrLkNzspyzUFjpuugZg7hZx4HD4UlTe4Go0BcW00E+ER6BZf9+B4AwMu7B/DPz37ehq6BcgZRQyiZcAarvQckH+RVsGVynmcMXdXy0+anYbd1uexQxLagT49XHNsLSioJpxHcbUk07NOZgzo/XTK/pc5PwxzaX3EvEseB09QKamSgLc3XPO7pDuoKjuDPF2fx0p9+DwBwXrwZP3zeDZ7b1XNcFO1DmeN+XwLw1Jqq59jUzIGaOWhln6jJRRDHhtUzCIBAmxmHsrLku3YltQwbjeBuo7FdbdNkLoO7Ro/jrtHjmDKz6AtF8O79F+B3Bvagq6yCrSYXS4MkzqAkF6GWBTgbdU8XtOjWQmRScpyA+2sz49CKeBH4wgxyQ/v9Ax0hfH2z9XwH1LFBc5nNqd4Jjtue9SwYE5Lsp7x5Uk0tA5OA1TskE0icS5274u9AcOhjJ/NMmRLa3CRyQwfcEaQGTi9qBnevetWrcOONNyISieBzn/scfvOb3+CCCy44HWs7I1CW5qEuzeP3LrkE6vy0nNGqM1tCctmSwA4AlFxW0uZ6tPixRHPN4I7aNoiQ8xhBxHbrxarj4h/YPbWyhBf85NsAgN8b2Iv/c97aWeOqIVD//NwkrPZuOO3dAHNAU8sQmgGhKFIo06MVRJ2fBjPC4PEmqAszwVs6Gi1LWxY7zT5tGTCnghiAOja06XFY/bsByASKceqYq+G5OZM36wcB8K+njuKPn3gUAPDW3YfwgUPPLtlGQJIC1BIZ9zq23/s0k4Ld0uFqoNYTPNbaTsmkEDr+JARVQGskyxr2bXOwHW3TJ04+jb945ldgQuDq9h789bnPwbWdvVB8NBiVTArq/Ayctk4QM4fQqWO+94jf77t4jq3W75qHZKDAowkoPj5ToOMEmDNVlhcqCO+oZUJdmAMohZpcgCAETnN7SYArNANkDTrBtSCqVc4EB82kZct3OFqXz6oklzDQUl3DTkktI3T8SVfCQUyPw+zfLb9HxqBPjlQwHhPOoc1NwerbFXgtDWwcagZ3t9xyC77yFTlb1dXVha6uLrzsZS/DPffcs+mLO91QF+eg5ym0O+NxYHYSxHFgd/XVdRy/WQqSy4DkMjKzXXSj1qKILehB1Yt6hXyr4bGleVz38H0AgLfvPgfvryJ4ebqgzU1BCAF9Ycb9fgShJUyaxSAAQhPD4HoIggYzfkJRZXtBA1sSO8k+bSVQy/S0SUpqWcq8hKPQ5qbcwA44M22WQfC2Xz+Mz08MAwA++awr8YpC63sRCADCHF8pAS/Uul7KHOTaOmG3d8tjE4Lw8afczgkBQFAFQtOqCkZXO3+Qtdr1Sio0EAjb0TYNRWL4wz2H8Zq+3dgdkGhHm50AD4Wgzc9WTX6wWJMn+zWQH7XgAjVTQFT6TVZ3P4yxE97ERFV2F4RIcpMaFTCSy/gKoKvLC6BFxEfK1CgszuG0duRtXmVgt962VBaNr8o3eKzVGDsB6sjvnhsh5Ab2BiYw8foOPbcr+tsSIUdW7PZuKVju43Otd664gbXDN7h7/etfj9/85jfI5XK46KKL3Pc55zj//PNPy+JON7xkCdTFOdidPaUsUDXAfVr41OQitOWFvIHpklUnwDcDVYCgdE2zdATSmK2Xre7B+Rnc/PPvAwD+7MAFeOfec0vXhzPjuBEA+vx0ybn9jEwxqJUDD6BfyFVNttI29J+2HHaifdpK4HrI07YQSJtphaOVhB9bDEIIHP7BVzCXr/Lf/7wbcDjeXH0nzvDZX/wCr7r2emgLs+uye1zVpG0hBEJRQMvEfwvBmTDZpthYQQisniFfncIG1obtbJtu6OrHDV31tegSANrcdFUJJW6EYfUMgI6YoFZpMEGAVfKOKhCAHEkBIDQdud2HQFJJhMdOeG5bfr9YHT1wmtpqzoHR9Ar0qTH/mT0PRlt1aQ5OSzvUxUofcr33LtdDMPt2+35uTI66gR0ggzV9djLflh3g+EFGTggFynwrapku+dy6jt3ApsD3V/73f//3WFpawp/92Z/hgx/84OoOqoqOjo7TsrjTDe8+bCH7qOu4O4URgt3SAa3oRi+uvhEhoM9NgYejsqxte7cHFowCXQdJSpDArtql/b/hZ/Dep34JAPjg4YvxpqEDnkcwu/qhz4x5ni+IcVurAVyr0STMAVfUkmxUAZxSSZnc0dtoWdqi2In2aUtBUcB1w7uixBxoMxPBxHrPEDLMwdB9X3BfH73mVWgOoBlHACxkMmCJVmiLc3XRvBfbOAEiO0KK2qcUD70uYHMSZwKA2b+nIYOwCdiJtolm094tl4oKs3dIkpkRAqujB8b4yTX9pp22zorqldBDvr6D09QGmk5CqCqc1q5ASYwL+/oQGj1e99pcxnMPX83vWkvsAaVg0YTnDK6TaPYncWGOZ3WMpr1tiech4k04MTeHPe2rI0MsFJFVfyHAEs2SOKssWScUpSrTuyAEdr6A0cDph285KhaLob+/Hx//+Mdxzz33oK9PtiZ+8pOfRC5Xf4vItoDHg1rKC9R/KLurD05ktfTvdYMrqSSIZVb0Kped/Yzh/zv6Gzewe8+BC3wCOwAEYC1tMAf2rnmuhkCyyW0GvNYkVM2Xppc1t8PuHmgEdlsYO9I+bTEQv6QUVaAtzGzZNsxTmVRJYDd1/e1oCih1wCIxZG0b+lT1jLUXCACrtRNWZx9ye88BK6sSCrUOQXKsbYaRqxrs5lZk957bCOw2CTvRNvnN0lntPVCX5hE+8jhCx34r75s1HN/s6JHJVvfgAurcFMLDz/gez+oZQG7fuTB3HQxWnRYCV+z2r5BVg5NolhX4gK2QLBRGdu9hWF39sLr6kd1zGFbfLjCPSpe2MONf2aQKhIekg59MgycIwZcffxy5/j2wOnqRG9wHc9cB5Pacg9zew7A7emF39EAUda8JyHZuT98KeTu557BvK2kDm4+avYbvfve7sbS0BABIJBIghOB973vfZq/rjEB4OPMCKM2aCAGaToKmkhVl6hJwDiVTm/hEXZjxnIvgqlqVtXKz8SdPPIoPH/stAODd+y/AO8paMYvhiolqhqehDTr351VF89yWEDgRb8dEULq6HkJgtfd4zkwSMwcwx/MbZrFEoHU0cOaxk+zTVgKxTE+7JVkct+6cxf1zU7j4/m8AAJ7f1oXZG14D1tVfEWh5gas6iGPjD664Akqu+jV6Oj2KCqejG05rh6fzJUjwgE1QxZNgQZT9WwwWikhnrXsQ0BqCwpuNHW+bCIGaXIC6sgQiBKjjgK5BCkJAygEUyz+pi7PQ56Z8Z0q9fLma4BxR3TsoqnZfCsiZOCCY7yAIgd3ZD2g6nJZ2SbKXbxX1mlkknIMwBzSThjF6HKFjT0CfGJGdEYSU6CYX1lP+XhDwWAJOW2eFZBQg9Zpzuw/Cau+G3dqJ3K4DcNq6wJpaS89NFeR2H5LjLA0bc0ZRs1QyPDyMj370owCAeDyOP/uzP8PLX/7yTV/YmYBvECIEQAiIZcIYPe6SBHBVgzmwx1NAmzi2bLmplt0VDDTlXbWzOnqgz0zWDHg2Yxbj93/1IL46eQoA8P8dfg7eOLS/6vbUtqCsLIGFo2sicalGguIFFm8GD0UgMisl5+KqJqnYVQ3EsaXzkw/MaSZd0vJAIDNiVmcf9LlJEM7dWUgv49bA1sROsk9bCerclD+1/gaJFm80PnbiKXzgmV8BAP50//n4o33nSTtkWXBicajL/jINdlMbtPzniVAwam+u6SC2JWefqSK1Tv1mt20b+sxEYFueG9gDauYqNFLt9m7pcDk21KV50FwGIBQs3gynpW3TdLIaqMROt01EiA1h9iYAtKV5UNuCObAXACrYyL3OXTcUBdMrK+iKVyaOq901BIA+M4lcrAlOa6eUIimqtLFwFE5Lh9QDpRROcztEyLtTgEdioGXXxnUD4BzG6DH3umhyETSbRm7POXBaO8F1A2pySbZ3NrWBhzd+1k3ohssTUYDVPQAWjkJJr8huqJb2RrVui6BmcOc4DlKpFGIx6fCm02mIdRJ0bFUIiKo3sT45WsL+Rh0b+uQozF1F7YpCQJ8ahbK8UPNBrS4t+lbnQpOj+TXVCJZqBZCFZfkcp/z9Dx553A3sPnHh5XhV766axyaQun65gX3BAru8oyM0HeAMxugJEBYsuCtQkuszpRp1LBKTQ8f5jJ2bGc9nvbwyfEQIiFAY2b3nrpKsNMhTthV2kn3aKtjV2ioFbj2wVUOH33n0ftw3OwEA+I+Lr8K1nbLFSzqOc1Uz/Swah5L2ZvmrBlo2cyg0b6dHWZqHPu0/r1xYp7ueUAQiHAULR2FSRQalQsBpal3NpGs67M3QxGogMBq2aWOhpFdALDNQ8MDXqK1239NP43cvvayuZDMgSdrAGYRuwGrrzs/jcrBYE+xOObsfpDXU6ugBzWVcBktBFVjdA1AXZivsA7UtKKkkWLwJPNYEK9ZU15o3BISANbcFE4Rv4LSipid7880347bbbsP1118PQgi++93v4pZbbjkdazsD8GkqzGc7abaS/U3JZdzKHpDXRinLvHg9oGVQtT6yE7u5PR9EVj8OD6J1BOB9T/0Snxh+BgDw2edchWuKe9xrrZMxEObP6sbCUdgdPSCMgUVjJWLuIhQGAgwACwBmzyD0Mn0tACBWrmJGTpudzBtF7t2XDpIP6JTNEQdtYNOxs+zT1sBzBgcDB3Fnikm3AC4Eeu79HHjeRv70BS/DXo9ZMz9iAK7p4JoOtU7mz/JrJpxBWZ6vyHwTy4Q+NVpVD68AQQicplbYHT3ueyzR3GC73KI4G21TPfcz1w0IRd1YXd58RcxpaoU+M+G7Gc1m1nT46ZUV5Ab2InTqaJm/Rqr6WVxRAUKhzU5CKxJwJ8kFsObW4P6FqiG36yBoJgXCuWz3pBT6+LD39nUGoQ3sHNQM7t785jdj3759ePjhh6GqKt71rnfhqquuOh1rO/3waFkhEK5Io1/lqxhKqlLWgACwm1qhrCyDcCYprte5VKaHYHf1QV2aq7qdQHWto8I6/vDxn+Kz4ycBAD953o04GK8vC1RQqPH7jlg0DmJbYPGmksAOAOz2Hsm2lTfcfschkDS/xPHoSy97T0kulhpZXhl4Om2dgbVgGtia2FH2KSBILgslI9tkWLypLhmXIIjUM0tBFSCgLtx64JVAW7Ft7PneF93XJ6+9FTG/+11wOGU6XIJQUNsCXfJv1yyAUwpCCMAFeCjs6dB62S0lnQwuXC4EWFNbo7tgm+BstE0FsfHC//0gKIWdaAWLNyF88umKbdeS9BGQTOQA4LR0AJxLOZINsi/EtvGK889H6NRRgCrghEhW7XAUTrwJRpVgksUSAOdQF2ZKjykE1PlpWP17apzbgrIi2zZZvLmU7MhxPMdzpF/V4AdowBuBnhLnnnsuVFXFlVdeiZmZmdo7bFOwWMLzQR468aS/HAFV5DyebUGbmfDVrNNq9IgXEEQkVwByYHWDxHR/7xc/xrfzbY6PXHUTdhXNnAkhpNNSC1SBMTHi+3Gh2sZn83NxRYQCPBxBbvchKPledSeakLONnlkpARarpAwuH2RWkqWfA/kgu7kdoBQslmjM1p0l2Cn2KQjU+Wnos5Pua26EkNtArUZi5qAGJCvg+Zbx01G5Kz/H0VQSVzzwTQBARFFx8tpbQavYsYL+qNXWJdufLAtaaqliO9/OhKZW2AV9MMdG+PiTFW1UQqGg6RWXFh4AiOk9n+gn00JsC9iEeZoGNgdno20iAOxECzSf1myZUOYw5iYh5ibrIlkLNIaS/9dp74bT1IbI8SeCLt0fzIF+6ij2F6Qq8kl4s2dQtjozB2J20rd12mnvluMfHp+Xt2eXQ0kuQZ8Ydq+bz03CHNgH4tiy0ykS9eQl4HqopFuJ5DKglin5CBpzbzseNVO6P/rRj3DHHXfgAx/4ABYWFvDSl74U3/ve907H2k47/Ghsq+nM2e3dgBAwTh13WaHWCq5qMLsHajZrOi3tIJwhdKIyI1aOWp+//KffcwO737zoFSWBHYCqgV3xOglnoMybGr34CNSxoXm0VQpNh9PWJSl3I1Ff1ikeiYF7VA6EqoHkMiC5fDuGjwPqNLfC7uxtBHZnCXaSfaoJj3uLmjmoi9Wr+4HBGEKnjqE1Uhpc+NFhUyF8EjSbi29Pj7mB3cu6BjB83W1VA7sCiBBu0kj1COyAKk5pcfCsarB6Bt1WcNnVQKDPzyA0ehyh4SOA40BZnPPsvOCq5sl2JwgBizTax7cLzmbb5DdzC5TeI/Umdgjk7zwwFMXXXwqq9aasLCF87AkoHkFYYcRGqxLYWT2DEJoOkW/hLgfzYfaWBxDQZsbLfCQHoZEjCI0ehzExjPCJp8Dipf6QAOAU2rOFgD4xgvDwERgTIwideArq3DQa2NmoGdz9/d//PT7/+c8jkUigs7MT//Ef/4G/+7u/Ox1rO+2oZrC84CSa4bR2gGZWQO31M8RRx4aaTsLyoO4vgCsqiJlD6MRTgaUD/PD8B76Fh/NC60+/+BZ0e2isVF3HGs9La9CIA/506komBW258u+kLs0jPHwE4eEjCJ18Bk4sUWH0WTgKUcc1NrD1sZPsUy1QM+edOc6tbf6kHMrKkidVtxfO1JzdB488jt/75QMAgL865yJ86qLn1bU/tcy6NfoEUEEJzhItyO47F9mh/RBKqawNNbPQ5qelw+hxPKt7AKy5HU6iZfUchMDq6m+0kW8jnM22aTPvb78EOfPSZfRJHhGg5sgKAIBzOfPql5QnBMrSPDSf1my7tXP13iekJKkDACwcqSpLQBwb1KlMihezbRLOQbMZmF39YNE4nFgTzIG9suUegLKyXOK7EgDa3CTIFmUtbuD0oGavDmMMnZ2d7utzzjknWJvedkSVCl05mB6C1T0AACB84xiwaCblmf1xP2cOaJ3D/V449wdfwUyeken4Na9CIoDopQBkO0RTK7geglHGWBkUPEDLgB/BAUzT06CXZL7MLNTlRZgDe6HNT4M4Nlg0LqusDZxV2FH2qQaEHvJsa/KSalkL/BygVUkTsq7OhfXipT/9Ln6er1J+9dKrcWWbdKrqDdTq/fXYPrp1oBSgimcSTsms+JNcKQogBKzeIdhtXSC2KQkZGrN22woN27RxEADsvL9V8YEPgvhlxMr5+xqQxC1alSpY+Rwtj8SQ3Xeu9NECELUJVZPJnxpJM2pb4OEozJb2ys88/EGSf5812jN3LGo+LcLhMCYmJlyj9Oijj8Iwzs4fDEu0gC7U7ot3IjFYA3vd/m8WjUMoSlUjUQxOqW+rp9ANOVexSRBCoOc7nwPLO2Ej192GSECnoTBMzeLNoJmVtTPhWSbU+WkIRZXiweUtlFUcRB6Lw9E030xaAUo6Cat/N0yvbF8DZw12kn2qBaFpcNq6SoiEuKbDbunYkOM78SZo02P+zI5nKLBzOEfPdz7nvn7shS9H/xrZb+udBeKaXsGAWbKfqnnrePo8KwQAfXwY1LHBjRCsrn7wM0Fx3sC60bBN9SdLCgnkclvCYgnvBIrq77s4AVhkhab739uKApZogZ6XhvKCmlyA3dlTWlGnFDyAmDkAWe3r7IU+eWo1SeazHm1+Glbfror3qU+FrjF3t7NR06t/17vehTe+8Y2YnZ3F7bffXiLMebbBbusCTa+Amllf1ke7pR1OYXC+AEph9u+BPjXq6pOU7weqyCF52/QP7CCzLXSTnCQhBDrvvdt9Pf6SV0P3kAioBiIEjPHhdTFUKWYW6qxsu+RzUxUEK9rclG/LqQhFYCdaQLPpku+6olrRaF/aEdhJ9ikI7I6evC7bCrimS5r8Ou9xPxAB70DlDGLeMnHo+192X49e92rohgFHM6D6ULBzSiFUDYplQhAis+u25TlzAwBc00Bt73liq2ew+verKLDbOkvkWwQhnq1YBatf+IyaORijJ5Dde7iqE9vA1kTDNkEy9dZhL4Smw2luKyGFEoTCaatMoKgLs1DnvZPxXq3SnlBUXxI7oee18qr4YwRS/9gaqM6G6QWaSkpfxzbBIzEwIwRoBrhmwBg/UeHTKEVMvi6Y4ynRxVWtwSuww1HzifHsZz8bn//85/HYY4+Bc44LL7wQra0BbpptCHVpHorPrBcgb2QlnYLj2G6mRlmac4du7ZYOKKllqGVyCAQAOINSIyAiQCBB8rWACY7ue1ez25MvuR0qLR25DJplWy/1cDnBijo/XdJyoVRhFiVmDloqCcUjiC6G3ebf597A2YOdZJ+Cgkding92ZWUZ6uIsCGNw4k1w2ro85V/8oE2dOiMEKX74TXIRVz94LwCgPxTBL1/4cpkJb++GPu3fMk45h7AtZIcOQIRCUBfnofm0mDvhKHisCfpsJQ06V9VADpTT3g0eikjnjCoQnEP3mAcSqgpa1uZFBIeSWgJrrmzHamBrY6fbJq4ZEJoGpY4xEru9B6ypRcqJrMj7xWluq6hCqYtz0Nc4FlIMmkl5tkcLoERPshrUdBJi4hRYohnCCHlXGMtAclkYY6sBnJJJgdgWcnvOAQjx7gTzSCLRVNKb6KUeuZoGzkoESgc+9thjeOCBB6AoCiKRCC677LLNXtcZgbpcW89IsXIInTqO3O6DUBdmSjJMSjYDpm1uKXxNMyGco7eobWn6+js82ePO1DRABcFKlcomD0dLvnP3M0LlwDWRffKNVqadg51in9YDZWUZRl7HEgB0MwtqW7LyFAA0m4YSkH3udOCL48N4y+MPAwB+p38P/ub8y+DEmuDkZ1JqtYgSIWRSbllAXZr3tX1KNu1ZARQAzL7dwRYrhKQ1t0wo2bT/2nzX3JjT2q7YybaJOBacWKxqcCeoApaXBpHPbdnOyKMJ8CoabmoA7ckg8CObchLNbuImyEycllyAllyQLJYtHbCrkOIB0tcsv6upbUmplFgCTmtnhZ9jtxa11zMGY+yEr0A8YUzyNzSqdzsWNdkyP/rRj+JDH/oQ4vE4QqEQ3v/+9+PTn/706Vjb6UfAqhm1cqCZFWgLs5WfOZs3LxcUxVeRZU5JYDfjE9gFPZ7fN8T1EHKD+0spwQOCh0oJH/wqgwQAsU2fv5OA1b8bVt/uRmC3g7Cj7NMaQWwL2vRYxfvK8gIQkP1SXZzbMiHGu5/8hRvYfeS8S/E3518m59P6d4NH4+BGCEHU9dTlBWhVAjugMqziiorjc3MwB/dBBJzr02bGYUyNQs2kqgadfqMAniyBDWx57HTbRIQA9dFxdMEZrL5dsPp2BZ9TAzasw4n7kE3xWLP7f6tnsKY8VQEEgLY4C5ry1jt24UdOlX/faeuC1d4j2ysVFXZzq+y0yEObm/IN7AA5h2ecOiarnw3sSNT0xL/+9a/jy1/+MuJx+YB54xvfiDvuuAO/93u/t+mLO91giRbQ+WD6IOryoreI+CaSCgRloysQn6QdG7u/+0UAQJOq4di1t67pvK72jEAJpbe7LgC5vt1QMytg4SiU1HJgR5CrWonRqrkWxuAkWqCVtTWxItrwBnYOdpJ9WguImUNo5KhnwoRA0myLACN5QSUQirFmwqUquOLH38TRtHScvnP5dbiouU3KBHSuZsqJY4NFolCLKgblaxHAmmYHhW7gK48/jrc+75pgOzAnsM6goJWtWASoq3W2ga2Dhm2SlW+uGdWlooSo+zfuNLV4dvDUCx6Nw4k3u/qWgEymFGQGAIDHEsjuPRf6+EnQXEbazRrHVfIVOD+wREtFx4BQVDeRQywT2uKMaw/o0gJAKOw830OQLgoCQJ2fLrmWBnYOagZ3zc3NiEZXM5SJRAKRyNmpFWa3dwOcyYexEFVpi9XkogxkyrInPBSBUkNXSoZHMgSrx6QFZaMTABYdGwfzgd2uSAyPXHWT77aB5uyqnNtJtCA0esyTJMB3n2gcLN5cwZZJq2SaBCRrFos3y1mUvLYLizdX1QZs4OzFTrJPa4G2MONbCWehcKD5EEBW5r0cCpbf34uMZCNDEpMz9H/n8+7rJ66+GZ35rHtu6ABEvvqvLsyWzOIISmG1dUMYIegzE6BWbl1rq4uoSQipDVhrM0WRrV8erHcsHGlo221T7CTbZLW2Q1uorO4TIWS3jR8IWVPywmntBGEO1IXZddsZq3cITroVNJfBN39wH15y2+sq16RpsHYdkP9nDKGTT1f1d3xn3hiTs7eEwOrqgzY/A+rYYKEIrO5+KZ8CacfKEz3q4hyc1i4ITZPHt6rzDgAy0dXAzkTN4O7iiy/GW9/6Vtx+++1QFAVf//rX0dvbi/vuuw8AcN111236Ik8bCIHd1Q8Wa0Jo9HjNzQuZloLTw0IR13moehoAucG9oJlUCYta1XMhuEMyY2Zx3g++CgC4uKkN917h/zeyOnuhLczWFZgVr4nFmwAf9reK7akCJyEDOl7eaiQE9PFhqF6MUAVQRbJvUSpnhboH5CpIze7iBs5S7Cj7tAb4CdlyzYDVuyvwcXyDQEWtmcxaL6ZyGZz/w6+5rydecju0vBMkCF0lW2AM2lxpNp9wDmqbsNs6YREgNHoi0DkFIeBGuOTaBCGw2zqr7FUE5iB06jhoFYIuAHDCMSi5DChb/TsVbD0LhWH1DAU7XwNbDjvJNjntvVDS6aqEdF4gQsgZ+3I5pJo7EtidfaArSSjVgseAx+KxBHgsgeNzczWDTW12oqq/w1UNjgdTJ81mYIwed5NtQlGR698DYYTcoM7d1sNuF8ZShKa5zO61fMJqc4sNnN2oGdw98cQTAIB/+Zd/KXn/rrvuAiHkrDJQBfBQBJbjQK9FP60oMHt253XpCPTxkyABhdAFVeC0d4PmslUDGkEoeDgSmHFqLJvGs3/0dQDANR29+OxzrvI+LmRrAGvpAI/GETr5TN0ZsNzAXohoHMbwkarbFWiJrc5eXyFeZWW5emAHOYunpJKS3h3IG+FGy9JOxk60T/WAR2IV3QWCKsjtPljhUFSD39xX1XarDcAji3O48affBQCcG2/Gj553Q8nnTnObex3ENj3tb4GwqYK4qQpYvBlWzyCU5QUoqSSEqsJpbncrhLWgLczWDOyYEQZLNEMtozInAKyWDjiNboRtjZ1im3g+OLE7ekDHKin8q+8brj+wK4JvHLZZrcwFIqYq8PNztOmxki4KwhzosxMwB/dVbMsiUSiZ0k4JQanLT8AjMZhDB6BNj4LmvKW7WDgCKyDjZwNnH2oGd5/85CcrhDenp6fR1XUWU80rCr535AhuOHy4qqit09wm/6/psgUnYAabhSKuk2D1DICMWlB8HA8WbwI3QoGCu+PpJJ77428CAG7tHcI/XHiF57qtrj7waMLNeAsjDBZvgbqyGGj9gDQcIu/w1WpFZYmWmqx8tMpwcCnOjFByA1sTO9I+1QG7tRM0veLen4IQWN0DdQV2ACCMEOy2Lqjz065NdBItoJkUCNYnjeKHfzt1DO964hEAwFt2HcT/OOci9zNmhMCa2lx2TEDOwwlKKwI8HoqU/BsI+XYx1twGlrfz9aCaPROEwOrqB2tug+LH+rcOh7eBrYGdYJsECMz8s53HEjD790BbnAWxTFAf3cjVfSFbEdcBv84EsUHanh5Hru2CeCWwffxDmvVh62ztgJJecRNz0mYMlMgh8HAE5q6DCD/zuKeWoDm4vzGvu4NR8wl/2223YWRkxH39/e9/H7fccsumLmor4MmpKWT3nQuzexBWew/M7gE48WbZihmKSFbGYrY0QsADyCBwRYXZXyR4qagwhw4gN7AXdkt7id3gqgq7vRss3iwJTYogCAUviGwC+G1y0Q3s3ji43zOwAwCzdwispaNCN8Zu9dZR4l4aKgDMvtVrcNq6wIvatryuoRa4Ufu7E4oCVg+jVgNnPXaqfQoMRYE5tB+5gb0we4eQ3XvuauW7TtgdPcjtOQfffOIJZHcdgNU7tKFERsV2422PP+wGdv/0rCtLAjtBCMzB/XBaO0qdF6rA6uwrtT+aDiffSlkgTyiGE42De8y0Oeu8Lm6EKt4TAMyufmT3necGjCzeBFGhN0rkLHID2xrbyzatLQiwuvshipImPJaAObAXuT3ngEWqs7w6rR2lPtQaIDzuMwBrSsgEAqFV7aeveDgh4Hqlj+Pr91AF5uA+5Ab35e32YbAmb5vkZWu4bjQCux2OmpW7173udfid3/kd/Mmf/Al+/etf4yc/+Qk+9rGPnY61nXmoGljzau90LYNhd/ZAHx92zWTByXBfEwKrbxdQ3u5JiKTwjsbh5IXQBVVKyEbMvt3QZydAzZyk/e7sg9AN6FNj+OXYCbd16b8euBDv2XvYPXRhDSLPSsl9nBYRjsKJJqCmVyl8uaLi8ZFhXNjfX3JNVmdfyTUITUNu9yFJHuA44EYI1MpVXEM1sEQr+OJ8SSuTAIC8mCcLRSRT1KZl5BrYjtjR9iko8vZlIyB0A09NT+NFeYfO7ujJtyrNA0JAKGrJPApXNanxVnwMeEgMqDpyQ/tAU0kM/Otfu+/f/7wbcLgs0LHbun1tCmtuQy4cdVspWby5pEpp9e2Ck14BzWXAjTB4NA5imdCnRiWzn6rBbu9e9/fltHZCTS6VsIw6bZ1gLWVJtHyyT5sZh5LLgusG7I5eX6e1ge2D7WSbzN4BGBOnKu7LcoZuoahSPkVRYbd2+vtEhMAc2AOaToLmslBSyZLKFVc12K0B51erwOrogTE27LJ4F0jXgiSU13zOrn6AEJfQDYSAcA4WjsquCJ+gyu4o8w8JqS6UTkggnTq3HVasfgdBBdgbOHtRM7i77bbb0NnZiTvvvBPt7e34+te/jpaWYFnNH/zgB/jYxz6GbDaLK6+8Eu9973vx0EMP4YMf/CBM08QNN9yAd77zneu+iK0CFm9GbtdBqMlFgHMQ2wI1s3I4PxyF3d5dUTErh9ANOHmjRywTNLUMcA5hhJDbdVBuVGQ8vi84ficf2P3Z5dfirec+BzmqSBpwIeAkmiGMsDRAZhbKyhJYOFrBwEbTKyCcgSsqoCjgqgZqW9jf0QEWjspWJ0pLHB9imdBmJ0FzGQg9BKujG6Ip3wKFBIiZgz51CjSXBTfCsDu65VoqvjgGJZOC1dkLapkgVg48FJFVAULWRJXcwM7AWu3TTrNNGwLHhpJNoy1a2rFgd/XB7ux131KSi1AXZ0FsG4IAwqVCJ+DhiEwOCQ51ZRlgDkQ4ChaOIr00h8P//rfucZ6+5lVojiaQa2kH5QzEccBiCc/Ai2bT8vNIFMIIwakSHBUSaQUIIwRzaD8gBGg6CW1+Btr8NFg0Abuj23dOuBqEpiO7+xDU5QUpzeCzbiA/P7PrYMPOnWXYTraJx1vAYktQUkmZpCFEJkVUHU4oDFAFQlUh9BBYNAYoKohtQVlZAjfCELoh/Z1cRupM6qE8UUkTeKxJ8guklqGkVyA0XRKOrOG+qlh3rAm5XQdcP0NQCh6Orek+Uhdn8XuXXILQiafhNLVIP8zrOIpSROiGwD4Kizcjt/uglNEiBE6iZUOSODwaX/U7hZDHDTgb3MDZi5p312c/+1n83d/9Hd7znvfgyJEjeM1rXoO//uu/xvnnn191v9HRUfz5n/85vvCFL6CtrQ2vf/3rcf/99+PP//zPcdddd6GnpwdvfvObcf/99+Oqq7xJPzYLxLagzk/LKlgoIhnQqtBNEzMnDV7xDePYoLbtDhMXIEJhOBAwxk6WZLCJY9eVTdKmxqAulVILs1AE5sAeaVgtE/cdfwJvulcKlP/vC56LNzS3A+PDEJAOldA0UN0AM8LQJ0bkzQ/Z9mN39cJJtEJbmAFNJUHNoqFc5rhsTVHDALJpcE2XJAwFcA7jVJH8gW0hlE0ht/uQnEFkDkKnjq7qtNgWlGwK2T3nlBh1ZWUZ+sSwm3Vi4ShYOAJ9ehxiahRClW1Vm9Zm0cC2xlrs01a2TWcUQkBdmoOyIjsHnJZ2NyBRF2ahzYyDAHjDZZfBGT4Cp71LMlQuzgMQcJrbwJpaoWRSqzPEJeN4AjSbhj41BsVcnQFkzMHTR3+DFz94r7vl5Etuh0opYOVAZyeR23XAOzHGOYyxE+5MsiAUVu/gmtoaaSYFY+ykawfp0hyolfMkPKgFZXnB1ULlRljWFWo5gISAplfkXCNnYIlWOVPYCPi2JbaTbdJHT0AtEHgQIu8BzuX9Z+VKqu2CyvEIJbnoauqCKgBn7munuR12Vx+ImQUUFULT3UBv3bBMGBMjcl2KCkYolILECefQZycA24LT3Q8wBmrlZJtilWBSnZ+BPjuBzngcsHLQZydBGCtJWlWg+L4s+j8xswDyEg+cl/iNwgjD7qwz8BJi9XskFNQ2JRFNsd9phCqqdSSXgbYwC2JbYNG4DFbrnLVuYPuiZnD3mc98Bv/2b/+GAwekxsd9992HO++8Ew8++GDV/b773e/ixhtvRHe3DGg+8pGPYGRkBENDQxgYkBmPm266Cffee+/pdaCYA2PkqBuUKNk0lHRSVsXKf/iOA2P8pDvUyvOkAtrcNIiVk4ZMUWB1D0jtNduSQZ0HSxoRAurSvKexIGZOBnKMgcUSEFSpEOkGACWXkZUy28JXj/4Gd/76YQDAJy68HK8qojUvUObCNqFkUnBSy1BTyaLPBbTpcSiL865RrAVqWyBLixCxGKBqUBbnKuiACZfac05bV74lqUyQlzGoyaVVEgTOoU+OlLR9KNm0+30TQF7D1CgsMwuna33D1w2cfViLfdqytukMQ5seL7E7SmoZZp6quxDYFaDmMlDHTpbsr0xlwOen8+zB3iCAG9gB0i5+9unH8N9++4j73uwNryndhzPo0+MysVUGdXG2hGyKCA59chTZaKJuR6ZcVBgAlEwKxMzVlWFX56dLBJaVXAba8jw4IbAG9/nOGSnLi9IeuvuNg+QysHsbcgjbEdvJNpUzM5aj+L4gnLmJYvezYhZIANrSHNQV2ZYsIMdCWLwZTmtHYG1NT3CO8MmnXZ+BcMtzWlBbmgMMA9rMJIjgEITC7uh2u6LKoXr4W+rSnAyYAiZX/Pw/QSlYrAl2W6d351IV0EwK+sQIqGOXjPkIqsDq7vedeSZmFqGRo+73VPCrzIG9dZ2/ge0LIkR1ZWzTNCsYn6amplzj44c///M/h6ZpGBsbw+TkJF74whdi//79+NGPfoQPf/jDAICHHnoIn/zkJyuogqvhb/7mb7C8XJ0yvxqe3d+PF+eNbTF+NT4OhzFMLC/j6OwsBIDrzzkH5/WUZkOEh7i5zRg+8eCDeOnhw9jT7k1MAgC/HB3FD44eLXmvPRrFay++uER2YSGdRmvU2wHIWBa+MHXKJRv49EXPxw01gh6vNa8FhePMp9NoDoeheDhPv52cxL1PPYXnDAzghfv3V3x+/7FjeOTUKQBATzyO37nkkkDnZpzjb+6/HzV+rg1sMzQ1NeEd73jHmvdfi33aqrZpPYjpOg739MBQVRydmcHUSnVnrRy6ouCtz3++rJYVYXhhAU9OTeHGw4d99lwf/tMvH8A3p8cAAC9o68KXLr3aczubMfzt/fe7rzVFwbnd3bhkcBBN4UqH6Vfj43j45EmkreqMfcW45YILPO33vz/ySF3f51uf9zxEdG8HNm2a+MSDD3oS7v3BFVcgESoNIoUQ+Lsf/xg22xxG0gaqYz32aSvZJqC6ffqjF71oQ3yEWshYFu565BGsmJUsl2FNw7nd3YjoOo7PzWHcY62XDA7iqn21K+kFP6H8mj79859jJlXJPO51z9brc7zqwguxu82/w8hmDJ9/7DFMJpO+2xSDEoI/uOIKxHxIVxzO8Y8PPoiMXam5d82BA3hWf6Vf+K8/+xnm0kGZyRvYyqhlm2pW7pLJJN7znvdgZGQEn/nMZ/Df//t/x4c+9KGaJ2aM4dFHH8Vdd92FSCSCt7zlLQiFQiU321qCjte97k3gfO0OvjY7CcxPV7z/rL5VTSEnlsDffP2rOLdflvWL4bVeTVHwB3f8HvSJ4arnPnzli3Do2pugLsxCXZgBcWwIVauogLX4BHYA8M9jx/E/nvkVAOBLl7wILwjQ6rlRRrtwnLYq6zt46FzsedENIJYJceKpCiKFS6+7CZfk26tIKgmMBRMVVijFW970Nii5LIhtgkViJSxdpwMf//j/xVvf+t9O6zm3Ijbye6B0fb/Ntdin7YRG7wAAocpJREFUrWqb1gqSy8oW6LwEwGVDQ7C6+ktkAmoew7agHn+y4v3Bnl50P+tS4NSxDVtvAR3f/qz7/7889GzcufuQ77aKbuAPX3W7nAc2QtBnJz2Ffgt4Vl8fLhwYRG5of0XVjWZSoNkMeCgsCQvyf2dleQGYPFWyLdd03PK7byrJ3lf9/QuB8DO/9l1X1DDw1jfeKW2X40BdWYIgch4nfPS3FdsTQvAHr//9027ramGn2ML12KetZJuAGvbp6V/Vfby1IKLreONNr6zoYCKWidDwEVcH7tKhIVjtPbL1uwja2Emghh6uPCDxrOjdcePL4Xj4TPr4SWCl9LiiqRVveYv/bKOyvABtbgrEtsAjMdAaclWaouD2F14Nq8CWLgSUVNLXn6HZNEIjRz2OJKFSiv/86t8B8xBM132+pztufa3n7O8/feJv8LZX3CJJYgiB09wGu6N3R7SEb1dbVss21exb+cAHPoBrrrkGhmGgqakJhw4dwnve856aJ25vb8fll1+O1tZWhEIhXHPNNXjooYcwOzvrbjM7O4vOzvUzJgUCY9Cmx6D66QoVQU0l8eYrr6xPUs02fZkcBaXgmg59YgTGqWPQZ8ZB8wxy5YEdgNU+9jL8ryOPu4Hdt557baDA7nRD5PvahW7A6t3l0oxzVYPVu6t0bkZVA3/FAoAxPgxj/CT0mQmEh49Am5nY2MU3sO2wFvu05WzTOqHNTVVou2mzk3JmJiCEpntSarNYE0gd4t9BwIUoCey+/dxrqwZ2AgBlDvTFWehzUwiND1cN7AognEErS+TpEyMInToGfXYCodHj0MeH5SwcANbUCruty5UmYKEwzP7d9Tk4AZhJtdkp0PQKwieehD49BmNqDOHjTwKk8nEsgJokXA1sTTRskzeUlaWKpLk2N10i8C3fm5Tz/9mMnO8zc5Iobh0QqgYILsldViRZnTY7KcmdCttASqRU0+Cj6RUYk6fkuAoQSIcYwKr2X56zoJo/I5Ta/pHw4YrwkowSVPFtCX/xwYOS/EkIEM6hLcxCm5uqeT0NbF3UDO7Gx8fx6le/GpRSaJqGP/7jP8bk5GSt3fCiF70IP/nJT5BMJsEYwwMPPIDrr78eJ0+exMjICBhjuOeee/CCF7xgQy6kFoyJEWiLcyXU1IB//BY3jApjU/X4s5OeR7Ob2wDOQW0L1LEDGQEvqvA/ffJRfOT4EwCAH155PS6pIytffNzNRvF3xhLNyO05B1ZbF4SqQV1eAE2vtjcRUZ+6TrkIqLowAxJwZrCBsxNrsU9bzTatF9TjHiCcVdi6WjB7hkq0mJxYAiSXhTEzvu41FjBn5tB1793u62defAueU8WWednCekCzGejjwzCGj0AfHy6ZFQIANbUMWiT/Ynf0ILvvPGT2nQtz18G6Z2QAwOoekKzDPlDTSWhToyUBOeHcU4gYgBt8NrC90LBN3qC2BX1ypPS9bKVfRAAYo8cRGjkCY2IEoZNPB/bJBFVKtHeBvAZdKITQ8acQGjsJY/wkwsefhFqWACKQDJRVCVjK7Ehhv1pgeWkDdXnB5RZwj7kwUyLKLnSjKjEUK3QeeH2WaCnRXRYAnFiT9xyyEDjU1VXxtrK8UOVKGtjqqNmWSQgBL3oIpVKpktd+uPDCC/GmN70Jr33ta2HbNq688kq85jWvwZ49e/D2t78dpmniqquuwvXXX7++K6gBYluSaj9d2efMjDAgOJQqWeB6nIvy7DkgnYt6nBMB4mq2FPDWXz+ML+RbPh9+/kuxb41C3qejwE7Kvkt9egxqkZGg6STM/j3gsQS4YazLeSOQ3y/T108n3MD2xFrs01axTRsFHopUVLK4qvlmdf0gQmHkdh+S7MCKAppczCetNgY/W5zFy376Pff19PV3gNaoiq3XZhHbhGrnv5uy5FABSjZTyuJH6bpY5YSmI7fvMMJHn/B1RpUqpDMNnB1o2CZ/qKkkbNtaJVfxsQNKUddAPbaAcIbsrv3QFuelPEMoDKe1E/rUWCmLuU8CjG5wtwIg2c4LjOnUg8BG+jNpsKIEm9U7CL4QgZJO5jsKCAhzwCMx2K0d/t9bOpmXn1k9tpZcgNPW6UkO1eAyOPtQM7i77rrr8K53vQsrKyu4++678YUvfAE33HBDoIPfeuutuPXWW0veu/zyy/H1r399bautA8QyoU8MQ8llfStWxLZAA2SCWDhakWUJiiDtQyKvkyJv09LVvvbR+/HdWVmu/+ULX44Bn7L6VkEJE5bjVGR/CABtcRZmLFHBprm2EzaM0k7GWu3TmbRNGw27owc0m3ZbfgQhsPNCu77Iz3vQzIqUG2nO604RAhDAGDsBam5cVfxjJ57CB/It5UAlI+ZGoZSynXom3MrB11CdqwUllaxaZeAes9aeDfmEbIgeWAOnHw3bVANFz24n3gzdgwthrSAAwieegdPSLqVM8raw1lxcAV4t6sVg0QSU5YWaAaeArOQLI1TSEunr+5S7M4TCaeuE01ZfG67fddJsCqz82gjBU1NTuKCIdwIAnIb81LZGzafGnXfeia9+9avgnOOhhx7C7bffjttuu+10rG1dMMaHXUpavxswUGAXa4LVM4jw8SfratNcRWXwIRQVgsgMDBGiRAqgeK03PHwfHs3PCP726pvRtQlOSC0wziGiMVDb9pwPLIaAbDegjiP71d2AtQyFgJc5wauifh80dFt2NLarfdpICE1HbvchKUDMpZxKNd1OANCmx6AVzR+ri7PI7ToAKCqMseGSrO968dKffhc/X5RU4zf3DOKfnnVlXfv7Vfe93rd6d0ladqpASS1BTS5VPTaLxMDiG6C9VXFg/2cFi8ZhN7fDKNL3FIQAIBXJKiKEFIPX6qvCNnDm0bBN/uCKWjJL6rR25H2H6j5GARUxkMc2BALa4iy4boDlW7+FrsvOhGpr00M1AxuaC9aR5bS0e2r0+nZVbJA/4zenK7TS92kmBX16HBf09cnKoJBrcJrb4LRVtmo2sH0QKCV488034+abb97kpWwciGV6as0FaQEU+eypEAI83gSrZ0DeyAECO6/js3gzuB6CtjADwhm4psNu7wFLNCN87AnApy3g0vu/gZP57MuRF9+CljM0VP/k1BT2Xn0jwBjU5XnoVUhMXH2mdBL6+DDMXQdkEFvmsFDbgrK8IPvCFRXUYw4yiOEUhILVIC9o4OzHdrNPGwFi5aDNTEDJpsH1EOz2brBEc8B9zQpiKerY0BbnwIzwhgZ2xcQpf3v+ZXhtgSkuIKrZAqe5DUomBWqZknhE0yEUBTz/PRCfZ4DV1Q/q2OBGWAZ2ZRVOkstCn52Q7VxGGHZHjy8RgR94NO7ZYm+1dUsGQEJkQJ5cAoickQl5sJUCgBAMQCO4247YzrZpvfOu1WB3lVaJCiLoQUEAZPadB6gqwjWYPvW5KWSb2yQLZKIV2uyEd7KIEPzk2DFceuULQWzLJYfzPH+VtmonloAwwmCRmC+5kpNorpjbk/6M9wxdvXASrVAX50o6x1gkVjqjxxwYYyfc7gbCOQSA3OCBunQ9G9iaOCv7PQRV1myYnLZO2G1d+Kd/+ih+/83/Vb7pw4JZsl80Aae5HdrCtNvCySIxWF19gKJK4c+FGVDbgjE5Aie56Nvvvf97X8JS3nicvPZWxOqcndlIHOzqAjNzIMyBsrIc+HtVchnQ9EpFYIf8/vr0GPjyQvABacjWDXVlSYbfhMDq6m+0LDWwrUHMHLT5KVAzBxaOwmnrrl2l4RzGqeNullvJpkHHTiC3+yBE8fypEFAX56AuzYE4jhTTbWoFC0U872FlaR5anSQsfnA4R893Pue+vv95N+BwFXIAL1Sr2LFoHCzWJBnv8ttJEeETyA3uh5pahrpYKUxcYLaz+nZ5n5Q5CI0ec9umlEwKdPQ4crsPBRZfVpbmoS4vyO0dC0QIKaLc3l3SXiV0o4Lq3etaQWo/fxpoYKOxWYGdoBQs0QIluQhtfloGUlyA1kH5xo0woAZ79hPmgKaSUJMLJayYlRsSPH/vXmBqFADgNLXC6h7wbG9n0TjUlaXS6wIAqoA4DiAy0FNJcE2D09pZQXxCuABXNZA8a7qgCszewY3zZxQFuaH9UJcWQC35bGGJlpJrUVaWK9rWCSRZjN3Rgwa2N85Oz1hVwRItFZmRasaK6yHYbZ2uZogrGisElOX5mkGN09oOHk3AjCckqQghrjNAzBy0hZnSJaaTFVUtIQQ6i5jkTl13G8JnOHjRFQVschg0l6vf2HtQe7sfcR6YPhjIG52VZXcNRAhoizNgTS2exreBBrY8HBuhkaNugoOaOSjpFeT2HKp67yipZEX7EhEC6nLpQ1mbm4Q2v2p3CGeg89Mg0bh3Rb2OwK6aPVywTBz8/pfd12tNUPkdX2g61PQK1LQHKYEQMCaGVynHPaB47Od+trJcMQ9DOIeSXAzUpqTOT0MvI6GxWztgt3UDSv1BGgFAHKvRltnAWQOu6VCW5mHkgyigvkCSqxrMngH5Ih8c1ULNwA6VhHjq8gKceDO4B4Eda2qFk1lx275de8hZCas3NbPSpg/td3XslOVFGGVsoU6ipZTUaSOgqNVn9Xz8JtHwp84KnLUDS1b3AKz2bnAjJFkxa4CFIp5ikMryIrSl+Qox7mJwTQePrJbfhW7InuqC0+ZHxlLkXPGywG7iJbeftsDOiTXBLG+TKEK1wM4v1ybbpDSw6NqYPb1Q3uJEzVzgAekGGthqUD0q19S2oKxUMvuWwI9EqJhKP1+184KSXoHd0rEuaRQ/e/Cb5KIb2PWGIpi5/o4N7TwQQNXADQE+rwa/dqtyFmA/aAuzFe8pySUZ2AlRl/YgkLejawgKG2hgq0Ixc2vWUHPiTcjtPbwq+C0CSjw53h1C8v5SpUyA11o9JBoAAITA6t2F7O5DyPXvrjorJxNOp6BNjYFmUlAXK22Eujxft21YMzgDhACLNVW0ngpCZIWvgW2PmsFdOp3GBz7wAbz+9a/H0tIS3v/+9yOdXhtz5GkFpXDau5HbfQjm4L6a2Qihl7bctEYiMEaOwpg6VbEtAcA1DYJSGRgN7C3JgqgLMwgf+y0iR34DY+SI7zkLezDBS7Sfpq6/HdoGEoVUMxkCUo+ONbVBBGg/LYfft0oAgDlgIQ/a3brPUuX8G8G42cC2xba1T6jy263RqsxiCVdouwABmf1dfcM/kCCQVN8bnZ/90sQwrn7wXgDAa/v34NcvegWIh93dTH7bIMd2Ei0Ac6AuzkKdmy4lWPB7TgRl5fX42xHmQJuZQPjobxA+8jiM0RNAQOIIAoBYDdmE7YjtbJs2GyTg778YAoDd1lV6jwZMfCimtwyKObAP2f3n+RKo8KI2d5pJQZ2bkgzgedsqjBB4NFEzMKNWDtrSHEKnjnkmn4gQ/jqXGwSaXkHoxFOIHPkNQieekhXFgb1g0Thytg0WjsIc2FtJxsIYlKU5qHNTID5yMg1sPdSMIP7qr/4KiUQC8/PzMAwDqVQK73//+0/H2jYOiiKNgg/kDEcC+sQwwvkf/u3PfnZV+QOrZwjZAxfA6t9dcjMoK8vQZyZWZzayGWhzUxXVQ64bYKEILM7Qfe/qbMrM9XdAqdKSVS8KfeB+IAC02SmAUnlje220xkBTnx6HNl+ZparlVPrN2Xi9p0+PQZuZaEgi7FBsZ/vkxdIoCJFsl9WgKDD797p03VzVYPUMrWazAYBS32F+rmpQPHSW1oM/e/IXuPPXDwMAPnLepfjb8y/z3daD8D8wvEgOSo5FSFUBcW6E4DS1InziKejT49DnJhE6+bQr2SJ8NDPV1HKg6h3zqQBIQi0uZ/7SSRgTI57blUMAED5CxQ1sbWxn27TpqJVsL/yb307auMFSGxfgOO5mebKQchTE03k07gqMFyAJl5oBANrMBEKnjkGfm4IxeQqh4SOrZHhBbHbJgSsTQMwIby5/gOPAGDvpEqxQ24I+MQwQAnNgLz72wAMwh/ZXzgbaFkInn4YxNQZ9bgrh4SNQPboTGth6qOm1P/XUU3jnO98JVVURDofx4Q9/GE899dTpWNuGgZg5UDMHrmrgmgEWjsJVlVNUWL1DUmw7uSTnUiwTUcOfnZKFoxU3QQFK2ZwfIJnolDxzmwDghKOwuvqw3NGDvu98Xu5HCGauvwOElDcf1g+BvGBmohks3lyTtITYJiAEeDji7TwpGuymtrrXpWTTFa2UnusNaKCt1k6IosCXIJ8VX5iRAV4DOw7b2T7xcBRWZ59bhROKCqtnqLqMgRBQF2ahzYxBEAqrsxe5vYfl7GkZrO4BMA+WR0HphlbtnvfAN/FP+Q6Fey+/Fr87sLfmPtXO72cxOFVgd/RUpUEnQkBoOrjPd8h1QwZaRVVTAkCfmQBdWQbXDXAPZmLCObQAOlxWVz9YuMxB8kg8KZlUoOqd7IBodCdsR2xn2+QFrmqwm9tgtXev30dRqrdqC1VDZv95yB68EJkD5+dtXNnYjGNDnxxdl5ySmlwEOAfNrMBq78G9Tz0lr7GrD7mhfQClILYFtYwzgVo5qIurjMN+ttZzHUJU2jDmrCaoOQdNr/iP86wBSmoZRHiTp1SDOj9TMd+tzU02bNI2QM1UAS2r2jDGKt7b0mAOQqeOlTBTcsGQ3XsYRDAITQcxzZIhWD9wRYXT2gmnZbWEr6wsuVlf1lRb9JEAULNpZE88g4Pf+yIAoEMP4ckXv7LOC6t+FqtnAPrEKTeorAahG9DHTwLwJlUgjgUejoAsz1d8VvPYHsQNxbBbO6GkkzW1Zwgk9bnT3oXwsUrNQXV5vpJeuYGzHtvdPjmtHXCa2/KkGUbNTLQ6Pw29aF5FyWUAITzJPvzYHZWA82O1YHHmJqcA4Imrb0bnOrU4q0oftHSANbXCIgTazCQo8w6OqG0hu+sgwsefqDgWD8e8WTSZg1DeBjqRWAmFuHvcXG1bClUFD4f9Z3XykBaxfHXeNU3CnAahyjbEdrdN5aCODZHLwuzvBjVzFWyR9UCoKuD4txtTxwbNZWX3gVfnkRCyxdHHlgVmSxcc4WNPuP7Eoa4u2J29JeckpncLOzWzAOdQF+egpFcgdAPcNkGd6sRUgiqVs9aODZpeyeuMnnD9VRaKwBzYs/6qnh95So3fo5ekGOEcxDYhlIjHHg1sFdT8xVxyySX467/+a+RyOTzwwAP4zGc+g8su82+52WpQPSQHqONASSeLxCUDVJcAWL1DJa1O6uIc9Omx1depZEVp3wvzlolDedKBg/Fm/OR5N5R8vt6sulBVhE8+E2xbANQyfY0kIDNNehGzVfn+VbPwNYI7Fo0DzAGtEdwJAKim9dfoytyR2O72CQBAqW87YDk0j8BEXZzzDO42OvtbjKlcFuf/8Kvu6/GXvBp6kUMUTFO0dJtq+wgA+vwU1OQCCHMqmO2KwUNhQNNgd/VDmx5bnW2OxOA0t4Fm01W1/NRMylOjjodqB67q4pwnqUo5WLypgspdUALCS88pgIbm1DbFWWGbyqDkMggffxJ2ew8s3ZBSBms4Ds1latuIMr+BmFmoSwsgnINrenWfxetwHu8TxynxT3a1tsJamIXT3r26nxH23JeHwjDGTpa0uHt1IQlKXXslqAIWiUJNVZJmESGgTZ4q8VeVXAba7BTs7n6/Sw2EAnlK8bEFoTXJU3goXDGeJCj1FUkvB00noSaXIKgCp7mtYctOI2qmkd71rnchEokgHo/jIx/5CA4ePIg/+ZM/OR1r2xgwH1KBosyJCEXAPbLcLByFUFSwUBhW766KGRbVo02nljM1lcu4gd1lLR0Vgd1aIAiVjJ1UAVeUijJ6NaynpaH8/fL4ioWjoFWcMGaEwUPhmoGdex7bgrI079k25Xi0pTVw9mPb26d64UXY4dN2rSzPrztR5IVHFufcwO7ceDNmb3hNSWAHBLMr5bajWut3YVtqW1UDO0EprI4eENsGOIfT0gGrowe5wX0wB2Wbld3SUTNjXRHY1ZjbBudQlhd828MFZFsbVzXYLe2wegYrz8k9qnYIztTZwNbC2WqbiBDQZydkYnatx0Dt1mxeJOhNMymEho9AW5yFujwPfW7Sf+d61uGReFaXF0rsrND0iuQZ1w0QM1sxu0yEkGM7uiHv9dZOZPeei1z/bpi9u5DdexhOa1eFnROKKpNOVqUvVE26JTAUBbnBvWDRhPRpIzGYg3tranc6rV0lvrEAYHf0eldTy6DOzyA0egLq8gK0xVmEhp9psJufRvhW7t70pjfhk5/8JP7jP/4Db3vb2/C2t73tdK5rw8DiTRBzkxWORMnguxCehBxOc5unPEIBxKv8XqVKNZJJ4Tn3fwMAcH1nH+66+AW1ll8VArI3Pde3G8b0GJR1UIBvBAgAq6UdUDVwPVSh7VcOxcwifOLpwEW30OjxkmydFPiV1L12Z6MlcyfhbLFP9YLFm1xtpdX3mj23DVoNrAefPnUMf/TEIwCAt+w6iP9xzkV1H4OjMqtIAGjL83DizdCK2r2CtldxQNqBrj5Qy4Jx8qnVbDkAq3eXPI+VQ2j8ZMlnQY4vQhH/bDXnME4d823tL3R91KYY95O4aLQlbCfsFNukZFLguuHb5l18bwVuk8yDh6MlWp/a3FRFIFbvMYNW86htITR8VM7c5dsh7Y4eOPEm2X6p6SCODd0vkaOqMIf2l15Pkb/JI1FYPYPQZidBHRvMCIFalq+/tFEt2cIIyxZPACSXgZKVLf1+/BGFc+d2H4KysgTiOJKpOUj1jXNo86VyF0QIaPPTMBsEUacFvsHd8ePH8Y1vfAN33XUXent7IcpurOuuu27TF7cREEYIVs9gnsHSkaQFXX0lD2piZj2rXerCrBvcETMHaplg4YhLeMBi8YryutB0wLYqDMaR1DKufOBbAIA7+nbjoxc8d93XZje3w+nshZJcDDQzeDpACIXd1gVlZdmTbbTcmBLmyL9FAAHl8jYMAiBbJA7awM7B2WKf6oXV1Q/CmJzPgJRFsDp7K7aj6RUoqWRdrY+18F8e/yk+l59L+8dnXYFX9gyt6Th+NTMCOf+b69/tOlHUR6y8HHZnH1hrBwBAmxspqe4RANrMOFi8CdrcdElrUmHKrSaDb5Xuglr2l8WagmlHEeIdyDV07rYVdoptIpyXBGDlcJpaAEWTDLaqCmN6PPixbQvEttzKkpf+ZNB7twDPVms9BMWjWkatHNSl+ZKKnQhF4OR9DWPkqO95gmj7sqZW6VsKAX38pK99ESDVOwbWAG1qDNrSans/C0UQVqtMaFG66gfbFvSJEdBsGkIPwW7vkoF4Gfxa5xtdCKcPvn/Rt7/97fjiF7+I+fl5fPrTny75jBCy9Q2UEFCX5qCsLENQBWbvIIRmSGNR1hftK1zLbHnzTZ5yWYUECOyuXjhNrRCaDkGoy0IkFNVTw+Tx5QW8+KHvAADu3HUQf7mGbLcX9KU5aCuLYJG1t0cAAFfVmkPAQVGYOfQzVp7ZM0rBwlE3GPQy2H5G3Jg8BbutqyG8ucOwXe0TzaahLsyCODZYLAGntaOqg+Tul0pCXZoD4RxOogWsZxAAWZ3bEgLq4qwb0CmZlP/8Wo052IrthcB5P/wqZvL39I+uvB7nbtb9RhXwWJOb6aahCJT0Su0WriIqcuJBAkAdG+DMmyAAgNXWBWrlJLmWl7Pn2DBGjsLs31P5WZXAj2tGYKIn+SwpbbEtiCw3sH2wXW1TvRBCeN5PBagry8juOUcmwwUHX5yrOidXDOrYUOenYXcPAAB4JAaaJ64rRl2JqrLkCVdUmIN7oc1Pe84yVx0X8WnrduJNVbu9SpZj5qAtzPi2XTrxFthtHRChiExyLc6BcAYWb5a6fAFZxkuWnc2UBHaAnOt785VXgi0vVF+7kB0Kro9rW6CZFHK7D1Z0NYg8M335bDNfp6/aQHD4PjVuueUW3HLLLfjgBz+Id7/73adzTRsCbXq85EespJa9BRoByVLnAUFVKCtLJXSxBALa9DiU5dJsrSDUcx7mp4uzuOmn3wMA/Mm+8/DH+89f8zUxIwTiOCWMloQxV6tlLeCajtzewyDZNIzx4brm9crhRBPuXCIPe1fTioNhdw2hKOzufhAzB8K5p/C7nxmjZg7GxAgsx4bT2rnmtTewvbAd7RPNZmCcOuYGVko2DZrLwurbVXU/JbkEY2J49XUmBbutC3ZHj/tecQKqGgjyQ/1GSNLx10CGORi67wvu6yMvvgUtAYfp64WglQP+PBKD1TUAfboa5TlBsQAwD0VAyzoquG7IwDEUqXDahKLCae8ChED46G99z6Jk056tU7xK50AhQRgE5XYRWJ25EwHIXBrYGtiOtmkt0BZnqwZXhHOo8zNwuvoAQpEb3CeZKQMeX8mmUfBG7Pae/Eza2is/5fcXZQ7U5QU4rZ0ycCrbvhqBktPcXhGUsUgMVt/uYGuxLYRGjvrOS3NVg9U7KMnmVpZgjA+761MyKRDLXBM7OM15c0KoigJlahTZWMKXmVNJJSuKF0RwqMsLJc8i+YFkbDfGTrrXyPUQrI5uNHB64Bvcfe1rX8MrXvEKdHV14VOf+lTF5294wxs2dWHrAnOgLpXS9hNIIVnTawjYb7heUTyzKgSoaMMholIk84ezk3j1oz8CAPzloWfjzt2Hgq0fPr3gZs5TH4Y6jnTWAhCTVJ5IrlqEozCH9ss+8EwKoLQuQyoAWD0DbjaJR2Jw4s0ldMkCAAtHKqoK1MzIFgwj5Dt/JyBFRf2kHdSF2UZwt4OwHe2TujhbUTFTVpak814lYFIXKomb1MVZ2O1dAJE6TF76mn4gzIHI62lWc7RGs2lc9KOvu6+nrr8dSlGVsXAl6yFtEYRAKKpsn+/oWZ0vEUI6E9kUlORidScSAurinJvltzt6oWQzbvulIARWZx9AZIuTklxa7bYAYLd1yuopd2pWNGm6kuWOJZrBkgvez4o8VXowR8xbCkE02jK3FbajbaoGPzsR5L7XFmdBmQOre0DO4hd16NQCL5o1K8x+0UwKxuiJQPq5QaDNz8Bp6wJrapVEKoVzq5qsjvmAxZtg9g5JHTjbAtd0OIkW6U8FqKipS/O+gZ0gBHZrJ0LDR0DNrOf3ry7NwW7vrrtlm1eZwyZCgGYzJV0QpTv7EFn5vM8jMWT3HZYt9lSRs31rqDY2sDb4BncjIyMAgKNH/XuLtyoIY543vycBClAhleCCUk9mRj8IVQPJV76+OTWK//TYTwAA//e8S/G6vLBvYIIATZfMcMXrdI9Qdl5KYQ7sQ+jk0546dVXPU5SdEpoOq3cIcGyEAkopFK+N2vaqeDAhsPp2gZ54yg0SCSTNuBOOQM2uBsdKNgN98pRks6tyfLN/N6iZhTE2XPH3JeuoODaw/bAd7ZPXb5QgH2zB38542S3CuXyoKhRgTt0BVmGOrfguKj7Gj+em8KpHfggAuLK1E1+97MWea18PBCEw+3Z7OhNBK5HuWoq+I2GEkN1zjgychYATb3LnpJWVUjFfAlkZdVo7AUUFi8QrGPBKzmN5tPATArN/D9S5KegeDMq+z5cycMOAUqalJwgBajDaNbC1sB1tUzUIRQFZo2h1QShbKCrsrj5XiiSI7VDSKdD0yipLOSHg0ThYorku21AVnAH/f3tvHiZXVa3/v/uMNfc8pofMCTPIVyQQ4IarEIUwySQQlEFQBAVFAYGLigwCVwWcr/pDRVEvggNeURABgSAiMwIJGTqdnueq7q46096/P3b1SQ3nVFV3Oul09/48Dw/p6lPn7Kqus2qttdd6l+NAHh3JeliyLUjjY/6BDngvrTLYB0IdyEYScnc7nMQIjKZFRYMYP3/FKquEXVUPvX1n+aNnYM0YCHUml/ixLSjD/YVHzhSwNU4k5ll55cTK/a8pyb6CX4Ldi29w9+lPfxoAcNttt+X9bq82WoyBWKZnH5njc6PSQAiUSJByP7TBMOyyKihD/Vnlik4w7GmgqKqB6gE8uPE1XPHa8wDyhQcIuLG0yqsLzojxVb507Lyb06qqAxReP653bi9YB58JAy8V0zrbQBUF6vAAdxg9hmyWci7kGCziMz8v14EBeKkBbDtv9lPW+WUFNBACU9W8Pkm/v61gbjIb7ZMTKcsrhaSKWrCsD0jbm5yBwU4wzMtnKC+LKdSnWujLfOJxmjGL6Ttb38JNb78CYNdLyb3gKrcSnHA0v/TJtqB1tkGZpGR23v0vyxlzTDMe9hi8LKfG3d1To7EFemcb5PFR7+oJ6qAitPPvRVJJaH1dIEYSVA94Oz8l2ibiMbaHMMbtqiKGmM8WZqNtyp2BlonX53KywkzKUB/fwSHEV7WSKWqWn0UYg9rXlVdtZdYt4KNHJgKyDNvlnq/EvmInFIWUHPUMXpXEMMwC966XkJI8Foc0Ppo3Ngu2Da2vC9JYHExVPechM0Jg1TZCMk1P7Ya89Q32Tao0U+/YVnDX1I6WF1bClBUYTYugdbdDskzux1Y3eAqqCGae4p38Hpx11lnTvY7pwbGht23kkvm2nZWVtiNl+apDjPEgLZX07HeQjBSgKEgtXA6rohp2MAKzthFWZa2ngVKSY/hx2yY3sPv5oUd7KsoRxwENhosP+fUQWpiYD8MIgR0tQ6ppkavoxPQgUotWwCrVmUC67jw+BHWwD4RSfv5JBnYT59K72rKDrpy/gYuH0WUgbraLKX7ZI4ZA+5Y8w0c1HVbdrg35FMwd9lb7ZFdUw45VuPcE76toLZjlVbt3ZI0GAACqB2DWN4OkxqHt2Ao1p18kt1yS5DzuxcQ8yvX/etoN7H5+6NHTHthNrIcwCmV0BIH2zTvtAWMItL076cDODkV2CgE4Ni8rL1CNkQsDoHW2QR4e4A5My1KML9vft4TpoMa0OqltI7D9Xe7Q2RbfDSU7yygZAKuiuuTMtVcWntvFKX1NC/ZC9lbbxArOLfOwHITADkdLLo4k4AqUfm0VNBDy7PeXzBRPEifHdtoJWYHZtAjJZfsjuWx/T8Ehqgc9B4rnrctKQe3P320HADhOwX5ZxUOEBfAQYmEMge3vQhkZgGRbkJPjvBw0Ws7vb/B732xsBWSl6AzOCdShvoIqvpkQ0/AM7Kiiwo6V49G33uLXLwINR5FavA/Gl+6H5NL9YVdUl3R9wZ5nSjJcudK+ewvqQE/WrtDELlmqdXleT4s0Pgqtc1tBlUgpXZ6jDvS6DbfMGIdV5l2L/c3Nb+KWja8BAH572LE4soCELZNlt/TSD6uqNh10eQwuZgxOOJY1P2Xn73xP6ct0VEITSiHHh2BX1EDr3JY3JqLQtZyyCrd+3A5HoOUoY9l6EFIq5bkrSYPhosM4BfOHvdU+gRCYja0gNQ18B14PFgzspLFEnrIZAFAiQd+xNU+JzL2M3+XhLQcOAJQxNP3517DSSa4NR5+ApSVIemcylTELkpGClBwDDUWgDPb6viY/qKLCbFoMEAJlsBdqXxcIYzwLXt0Au6o253gNuW7sRA+13D0OyzK5OAAhngk/AKiJ8Ky7Eh/Ms82EUpiVtXBi5WCKOqkdNzscy9sJcPSAGIUwh9hbbdNk7zswBmUsMS2db4xIMJoXQ2/fnF/VQyQEt7zFj5MVGAsW7pzLJitcAdfDh5JS46WVfloWYHmXSCqjI5C2vJUvxMcY5MSwb4UUVTVe7inJIKlx6O1b8tplJlpskouWQ6IMVA+4iSemB+CEIiUJXkmpcW4jir1O3zJWBquqDm90deHoUvvhCBGVBLOAKaUEyV7aFCl53AzEcbKU1ADw8QadbcXl/4kMOTGSpQpFKIU61JeXXfnKO6+4gd3vT/lYwcCO6gGwYBhmXZNvdsmOlsOuqkNy6b6wfGTHveaI7Cn8jDqhFOpAT/78vyLnk0bjbsmU6iF5rBjJ/L/jBJPsMxTMbfZW+zQBUzU+m7HIOv2+3JXU+OSdsTRU0/LuxYRloe7RX7qB3dYPnI4lkwjs7GAYqabFsCtqprQmMAp5qA9qX1fpTwGBHavgfbqSxMsjezvdUizCGLS+TpCMYIkYKajxfNuSiTLQA2k0DrW/xzfx1j82xlWU+7s9f68O9QGYvAOkDvflPea30yGYnezttsmLQmIq0/FqzLoFgKzAqm3K8qsYIdkzKR0bWmdb9m4akaZJWsUbyTKhdbfv/Hl8DIHNb0LvbPMtLw10bEVw05tQ+7qgd7b56iDIiRGEtr4DtbcjryTWWLAIVkU1aMEdVe5LFoOYBlQfWyXZNgLbNqI+KkYUzDXmVL2H10gDRgjPoGbgN7Q8F6uy2lMdjZcuUreU4eo3/ol709mlJ9aejfdUFh466QR4jTKNxDwHVGaVa0ky7Or6PAPGCIETzd+14wvcfV8gLL0+P8NmR8t937NCSGnD7TtzEACbpll8AsFeD6V5Payl4lXOPYGc7i2bsCfvjsax+PEHAQBBSUbv2rMRUdSCMuBZ1wJg1TeDhiKQ44OTdrQYIWCSDK2nY1KOolXTALOx1c2oyx42B+Dy3V7/9oMA0Du25gksTEBlBf/u6uLJQZ/kGmHMc2RC0Wt79P0QACRRfN0CwWyEyQqv2gFAQ2Ekl+wLo74ZRkOLZy+yZFvZcywdZ1oCzEJI46M8sUwp9I6tBTcF3KCX8SR3IcVxd7RBcgxa1/bsX8oyrLomGItW+G4AOHoQKBL8Adw2FlQbZgzvbWkpeh7B7MK3LPOQQw7xzDIxxpBKTUFyfw9gVdXxD3LGl+6ECloWsupZQjQhqsIIwT+2bMGBKw+GVCCb7IQiuPT5x/D79s0AgOc+eDZa6pp5rXYBlJEBHrCpqqd8tmRbIGaKl20BYJoOs3Eh1N4OSLYFqmqwaheASRK0ru18cLGiwKqs5XOiduOOHg2kDYqH42lV1YMFgulgOkf1DcUDPJKWP/diKiVfgrnLbLRPJWNbCLRtKqmpPhNGCJxQBE4oynetfI6TTANUkvGXru0476WnAQAn1DXh/3vPUbyXJlYJs7oe+o4tkIykv7IawAMsPQCtfTOkAnbP7/51ghEoo/7OBwNgVddDHh/lIieEwC6v5sPfM/FxgDITe7lJPj8IY6AeK2IAUq3LUP/mG0XFGvySVJOFAWD67pkrKNg9zGnbNEW4iBIB1YO8j45SUD0Io6E5u6dUVlwhJHksnutG8J2x9i1wIjGYtY2AJHnbFkJg1DVxsSMPEbpJIckASFp4ZXIJ5lKvK0/0Cef4qlzBPO37WSbf2WSM+0pGEoEtvFcudz5o1jlKsHuxQPEdQMHswje4e+SRR/bkOqYFFggitXAFnyHi2HAiZZ67W0xV8+aaAAABQ6ppEWg4imf++lccCMAu52qZXn1vZz39Rzzdw7fs/3nSR9FqmSA9O/g1PBScdl4HIJYBpqo+TfTIy8g4sXL+WtK13CAE+vZ33dIt4tjQu7bDjg9BKSDlvavY5dUgtp1XMsZkxe1vsStrIec4bHZ5NSBJRTPahRq7J+bgiSBPMBvtU6mo6dlJk8HRg5CMJJSxREm9MHe88yr++93XAeycwWnWNPIGeUmCMtBbtCSQabrrVHglqbKOTffH5AZFTNchx4e9nwOC1MJlYIEQbABkfIw7heH8eUlewgIMXKF0AidaBjqglzS/0ymrhJQTINsVNYCmwyhBFt7xmqdaBF/FQtFPPKuYjbbJS+XV91hM/jvYqqwFDUV41cBET5qHEAoxUiCODRoIwa6oTY8u2WkzCMDnGI8MgpgGjNZl3krBElfLTZZVQm/blNfLOqm1V9elq6j8KyKo5m1XmKaDlGBvGPEXTnKiZVxxlzrQundkzQ4mALSeDiSj5b4JLidSBqoFIJn+iYW2oSEcWHSVgtmEb3C3YEHpEqt7E0zTYdU2Fj3OrG8GMZLZAiyMQevthNG8BP+xdCkCW94GA4MTjkAaG4WUEeC9/9k/49V0/8a/jz0VNTnOGKEUdiTGBQNyfseIBKpoUPq7Qez8rJITKfMWCCEEkBVuLJz8AAvYOb9qd0BlGUxR+XyX9HBSYKcq3ITxo6EIjNZlPChOB9l2eRVvwh7q9/0SoYrKA1iPzVICgJTYJC2Y+8xW+1QKko8jQmUFNBSBNJbIskUMXJgkfyamNyc9/zg2DPH+rv89Zh1W1zbCKKvMUnVUhgeKrtMJx3gPTJFKAQZk2YqJtTmhsCtU5fWcVPMS3pvoOLxUMm3vqKLAXLAoS4LbS9zAnSGYMV6FKSpQxNmiegB2ZQ1oMOQm9uxouavI+W5fX0EH1wlHecXIJPEK7Ah4z0xBiXLBXsWstE2EFG+Mnzh0kqemisp1CwZ7ecIlVgGzIacMkFIu1Z8ur2YgcKIxGAsWQokP82RxToJdTo6BJMc9e4sm7iVpPOFrT3NxAiH89vnncMqqI0BME0xVYU9UQ4ELt1FJzrK9QLo30CuwA4HR0AIplYQ62AuSM7c4E7usqmDwOOH7edo5xwaxzDzRwMznplqXQh3sg5QcA5NkyOOj7vvphCL4R1ubCO7mGFNSy5wTEOI5HFgyDejbNuL/tbQAE5kO0+Dqa7FyyKkUDn3sf7Ej7Wi8+/4Po8wvsyrJSC1ckVaB4gaGATBrGxHo2Jp1ozJCwFQNTrQMVlW995KNJPSONkhmakpNxKVm3PyOkxwH+o4tcKIVWU3CBIA61M9HMqSzRzQYhpk7/4QQmA3N0Lq2u9k4JklgsgIaDMOqri9o4Jgke5fTllB3LhDMFqge8JStlhwbzDJzAjsCo7YBgd7Ooud1GEX9o79yf37xuDNQs9//Q+4eITEN3yG7O6/LZ1cVsicMXO1RydhVIwAcVYPZtJgLoPg8d0LB0g5HuApyRiJLsnl/bmrxPjvHp+hBIHcAuCRlJcmUwd6iCnR2KAyzcSEvIQtFYHrMo8qcc5f7elPNS8CmsGtXEMsERHAn2I1MZfxRqWTNrgMfau6EIllzKJXB3qy+WQIGJTECaXwMZkOL53xKAAVHFcgjg9Bze9kKYFXWYuvgIIzW5d4H2Jb3+8RYnh1z9CCsugVgwTDkwb6ClRiMkJLn1VE1f4eQAZBHBuCUV/urhssKVwF2T0R5oCfLYIEQrCkOqRfsvczf4A4A0wJ5vWOMSJ7qRoQxQJKx8I/3I5l+zpYPnI5ogXpmqnEZa6N1GaSxBIhtgYajkFLJvAwMYQyWVy+JuzAGvWObe2NPZQdrMs+hsuzZQ0MAT7EB4thQ+ntAgyFIqXEwPchLCXJKBZxYBZKhiNsnSMP5x3jBABDb8nwNcmocQmpFMNshqXFo3e2QU0nfBEtueREBg2ykiiZuBkwDK//6kPvz9uPOgFpenRfYSclx6NvfLV6i5VHGleVmSRJSTUugd27Lfw2WCTk+lC2M4IE8FoddVevp2EmWmZWttqPlkOJDkCaSRgCsmsasZJGfSIq7fkmCuWCRZ7lYJkcsXOirIChZJqbbTfKa4yUQzGbksXhWcOdX1i05NvTObZ49rlx1PAQmyXlBF9MCUAd85td5wMADXDkzuWxbUOLDAGPp9p78IA7wtrs0FAENRUBMI6uM0gu7rKpkETym6UBO3o8A0AZ6wYYGeBm7z3zOLCQpf9C6YE4xr781rOo6SO2jWYaDBoKeWXPGGBof+Jb789ZTL0KkwHY/VTXYFWnjRQhoxmBx4jf/LTdb7jjQejsgj47woNMjm76ru3Gexyoqr//2zeZ4Z8vUge7s8tJwFEZ6BlUWippl2EuBwF8WfLL9SQLBXgelCLRvccuJJpOI8Ut6TPBGfAhrnn0UANCgB/HqmpMBWUYqYwYcSc+bk0cGigZ2DqUgmgJiZx9HAIwv3S9rBABTtfwEGgCtBMeLKSovZfK4vxng9ivL8WFonduy3gOqBWCXV3EZ8N5OyOOJgmVnVA/ArGsqGtgBQH2Zj0oxdjG4I8RzJ4Ioym6VexcI9jRM0XJ+9k+Se2kXMFmGsWAR961kBXJOcEcVxdOP870GAL27HeceeijgOJDMFPT2ze61WV8nzPrmor1r7vpU/nqK9iKD7xiWTIGdSkIdqAO9+SWvgnnJ/Anu0vPX5NERMFmBVVkDGinjAiwjgwClcCIxSKnxPKNAGUPdo790f+46/iwoPoGdEwzDicR4j5mPo+CEo57BVm4Tvt7Z5pYqEB+Xwc+po7IMkpYJprIMp7y65EwWk1XIhn/g6oQikMdG84Yh565FHktAHh3J6uWZKgx+ebOSWwUEgr0WeSxRVImNpgU38uxGtBySkfSU6H6ocxsufXUDAODslmX4+pHHw5YV2GWV7q6X2tdVsm1wQhH87G9/xbnHnwApZ8A61QOAonJRBNvkpdZVtZB2bC3YD+gpiEAkWJVpQQWPddBAmNtX24LatT3f9pgpyIlhqH1dRZM/VmVtSX3a7rULlDA5kdLnA+adNxCEnMy2uxPl+gLBboVI/nNkpxkmSVy4KQNH0yfljDrhGLdflHrO/JSTY3DCMSgFduupquX1wdVGo6BtG0EsKyvJRQBo3e1cNG7YgeT4l61TRYUdq4DW2QbFd3j4zvNKjg2KEu5xxrL6hz3PV4J4ixcVoRCksXjaroo2l7nAvAnutK7tWdvj0vgojKbFfNZcbSOk0Tjf/p/I1DAGQghsStHw5519Kj1rz4ZUYAvdicR471kBuOjLAqh9fOguA2DHKiCPj0Id6AVVFNjRCt/ZTcVgksRFT9JqoMShPDgixLO8gQHpmTIMkpEqGNhRPQizoQXK8AA0n8GYmUip5LQEdwTpMQwetrqkMgSBYC+GSYX36rxESRi4qqNTVgmmatx+ZQQe1//7X/hB20YAwNf3PwzntSxFsr4567zESJUc2FFFhV1ejf6xMVg19ZCMpJsIo4oKo74J2o6trkPFJAlmQyuMlqVcoIVSX2cruWglH3cwGgdTVFiV1WB6ENKAt41xomWQhweg9ezwHUsgDw+UtKs/WWXLjX19eG9ra97jE73DU4VYHg4jY54S6QLBtLIbAzsGrjouWXyMk11Zs1NFkhDIoyO+NsiKVUKyLb7z7p6PcOXa9Lo9LSdjINTJUi2nqgYnEIJEKZxIFCQ5DtXDPvip6RIAykAvv8eT+feqE4yABoOwK2ohpZJFAzuA6wiUNIg8NQ59x1a3esuvEot69AgXXgCD1tWGiw4/HGjfAkYkmA3NBUcrCGYH8+Ibg1hWXt8GFwHpgxGJcaWmrras7X9CCEbDMSx68PvuYz1rP4IiPhiUob6iwR0A2JU1cIIh6J3bIFkWlPjQzqGWQEmGwQsm8Yx3ZuBFwKAO9PAZMx5zqwgmFPpI3m6ce17wbJlV0wAoKuyKGqhpNcxClDoMuRgMAI2Wgw50Z+1QMGBSWXeBYG+DpJI8qCkyPiXz31TVYTQvdnffaDiK5JL9II8MQh4fw5r/+zneTgdSj676AA4tr86/s20L+o4tBdeWmRCSbAt65zYc2NgIyAqM1mVcwZZS0GAYylBfVvBGKIXW1Ybk0v1hNkYAxiC/86p3L+HoCOyquqyMvjwyCDmRHwwyQmCHowi2bSw4b66YeAoA2GWVk+492bBtGw5dtDhPNc+q9hbCKhVfERvHWzZeIJguvPrW3N+hcJm4X0VBJnZlLe8dJYSPbdq2MUtkzldFsqoWTFXTVVcJnvipqgUNpkWNfETWCHbe/xP9t3ZlTVaLSGDgzQIr9oYr8Hq/T2ZDs2uPlZyqBi8YITDrmwqrZKbRu7bnCdM4EzMD0zbQCQRh+Wk2+CCPDPK+wonzMgqtazuS4aiwObOc+fHXo4638Ug7UpKRzLthx2zbDewCioqtp14IyadXLhPilJ4B03p2QEpna72M02ShiorUopVQPQavT/SsGTUNUBIjHsIMQKECRwJAGYtDHkvAaF4EGo4h1bIEWm8XpDHvIcROIJw1Z2qXoRRG63KoPTv4QGNFhVXTKBqDBbMWaSwBvX1LVlKllP5YyTLyZmRKyTGga3tWpcEbx56COn0iwZJ9Vq17h+/OFiMEZkML1N7OvKDjfRm7ViwQclcuj+UHU4RSSGMJUI95o+4xALS+Li41ns48y8MD0Lrbve1KpAzq8EDxQeLwrlYw6psxMVCZTSH5ZDoOUkv2hda1ndshWYZdWZNXajYdEACwbcBP5lwgmAYKqWVOVAn42aRMATq/QEtv38JtFpHAFCXL7viOFAkE3REgVk0jLK+4pQSVxwkROBoKu5UBdqzcN5FWCF5lVZ5XteQEQlmjCKjP/cokGU44Aicc5b5RCQPGYVuQPOZ4EsuEVVkLpihgWoDbzhKFWSbw6gkkjEFKjoFOp+8m2OPMi+CO6QHPRtiJAedMUbOM0ohlYunjvwEALAiE8NIHPuwZ2Hn2zRVyYlK8jIlqOqgezJqx57t2SeJBaIGs/gSSzXcoJ5p5vZCTY6DhaMlDPR1KsxSkCBjU/m4Y4RiYHoTR2IrAtnfyRA+4uqUJZbDPHW6+KxAAcGywYAhm0+JdPp9AsDeg9nd77pYXC/CYJAOSDNgWnwHFKAY72nDAY//rHtNx/JnQMsaE0EAQcnyIOxWSVFA9kuoB0GDYczcprOt5KpsAwHwWLKXGeXBHCBdJ8dmh4g4Y3+HT+vzHJEjJMU9xKS+YqnHBheQYqKLCqq6ftJiT92JlmE2Ldv08JUAcR/QVC2aUUkMG33s23RdHGPUUSMqFgnDl2mlam2SmEGjb5P6sDPTACUeLKll62WF5dARWWSWUxAhAHdBwjO/AZeBEy+EM9Wf5WU4oAqN5yaQDMEiyZ1WHRB1oAz1gkoRUy9LJnxfw7efNFbwRzD7mZHBHjBTU/m7uVASCsKobYJdV8h639DFMkmBHywHGoPbscB/vN1LY54mHAQD7R8vxt9Uf5JlTD5gsgwbCkNK9cTQcg+kzr0Tt7YA62Of+7IQiJWXoaSDEDQJjCGx9u2gPiRIfgtG4EMpgn+dIB8gK7PIqdzhvMWSPkoHMmnStu91zTQS8zEjr40p1xDJ5hruiFk6svOh1PSmhfEEgmE149Xdk2gQG3scrj2bvjltVdXzXr2MrCGN4cagfH3z+MQDAvtEyPLX6Q1nnZOCJnYkgx2hdxstuPGwEA2BV1ad3FPPZOjAALytHAyHAIwmmxIfgxMq5CFOhoMy23WoKv9InBpQc2AG8D85oWcJ/ILPTfjBNOFqC+YUEBik+BCejxYUYKUimAScYBiaERdJKskXDmhx7ItkWWBHxESZJ6SHkKaiDPSDpeXZyKgnJSCG1cAXfrfMKqiQJRstSyPEhSKkkaDDE/Z4pBGCQJFiVddD68yuygHT5e3c7zMaF/oPMfbArqqGMDGTZWztSNqWKBsHexdwL7hwbge3vur1gkmWmt55Jdt8KpVAHe0GDEShph6QzOY6DnvwdAODoqjr85rBjC16KpfteXKnv3C12SkHMFECdrMAO4PXgxbKxDOCNw4QAhMBsbIXWsa2gc8MY44Zl4Qro2zdlBV6MEFgVfNClWbdgUgM+M3EmmnYd23+4aAaZW/9ychsMsnBqIivyVIpVBYK9FycYLqjoRsDLHY3mxbw3glI4sQo4kRgCW94CYQw/a38Xn33jnwCASxeuwFf3eY/neSaQbAtqXxesWCW0od7s9QRCfKAuo56S30yS8cTGjVh/1AfyX0usAqy/O8/RkiwTgW2F++MAQI0PQRkdgRPwFyWZrAWQk2PQujtgNjQXP3gvhAFTcwgFghnAU/nW47FSUAd6wUJRUE2D1tPh6hAwQmDWNRXcgc+8pqMHPcco5c4azoVQCiU+DCcczbNdhDHIiWHYhfpsJQlOedW0zL20q+vAdB3y8CAUD6E9OZVEYMtbsCtrYNWWNhAd4Dt3qdblePPJP+PgfQ8ADUdhl1dOw4oFM82cC+7kxHCeyIdfOaOU5CIiALB1LIHDnn4EAHBSfTN+dMjqgtfhTboN/AePumk5Psx7Rqh/SU3RXTs9ADkxBGIasMurQINhpJbsC7WnA6pPw66SHIP87hswaxqRalkCdXgQ0lgCTFV5U3OANyIXk9T1XZOq8eHAu4Ay1D+14I4IiV7B3EFKjqWFjApDGAWIlD2/yLYgWSaueO15/LJjKwDgBwcdgVMb81UcvZDjQ67xZyCggSDsimo4ZfyLXW971/N5TjiKhOGd8WaaDqumAWpfV37fTZHAzj2OUijjhWdDTRY5PgjUNfIy1tmIEDYQzAK46FoUSk4f11RTE4Q6CLRtzOubJYxB626HMxqHHYkVHCzuhKIwFixE8N038+Z3lrIuOTEMJ+0zTQrb4oJzlgknFOF2dReTNE60HE6kDNKWt3yrpdTBPoAx7qOVWOnENB1Pvvsu9j3upF1an2DvYs59a/gFcsxDCZJpOqTUON5KDOPoZ/4EAFjfvARf3/8wz3PYehCyZYKpKsxCQh62Da2rzTVIfre032iCCWQjBRgpAMNQ4oNItS7nvXdFhmgSSqH17AB6uIqe0bQoLwCloWjR6+di1DTAqazdaaRkhTtMJZR3Zl98irLLtpAEF8wRGCu6C+8eCpLXoM8kGfs/8Vv0pLPPfztyLfbPkK9mRALA3KG3hQSbCBhAiBvYSfFhyElvpUm7rHBW166qAygtaUj5noIwBlAGzMKqTAKAjI+BRac+P08g2BVK3XkjwLSWPk9c08tHIQCU0REooyMF1yeP8/mhVqwC2sjAlNZAwxGwgeyeNwZekWSXV+8sEZ3AthDY9o6r6q3Eh2CPxqenR5cQWLULoHVu8/Xd1KF+SEZqav19gjnDnPOUnVD+gHAGwK6sziqNZDJvUn29azuO2/AXAMDli1bippWH+M+DC4aRXLSi6BrksUTRoInqQVhlldB6O0prCDb4UF6qB0uS+HbHKowleD12pgiJY0MZ7PNdI5UkSDkBGFXU7MAOAGyrpL69XCZ27aTROBdOMFKg6XKwgnOiLBsQonGCOQAxUiX3jtlVtVnJmZRtYcW9N7g/b/zP01CREfwxQuCEIp7lO4C3syYlx6AM9AKODW2w1+tpoFoAtIQh3U55FdhAr+9YlemCEQKjaQkkMwlloJf38xEpL0PvhCL5DljJF+ECUspQP8AYnFg51D095LfIuBmBYHcymfCA2FbJwSBLD07f3eEHsS0oPskqdy3gbTa5A9GdQBAsrXug7djq6hgQTJR8t8NsWgRiGlz9fCzhKX6njI7ASo27lVNTQR7uh9rfA2JboMEIqKJA9WmLkcdHIY8MTo94lGBWMgtzmf4Qy+QCAxmPUUmC2dgKq3YBUi1LYYejoLICRiS8sPUdN7C7btmBPLBDWirbg1IG4gKllTwyAJBk2NHykl0gYplpdb3JIY/Gd459SI4juPkt38w6A9/to7LsrotqAb77RwjAKNS+LgQ2/xuBbZum5L4xSQIxDegdWyEZqXST8jj09s2FHZmp7vgJBHsbklRSz22qecnO8m8AO+JDWYFd99qzsgI7gA8M9pK4ZoTAKqv0LWPS+jqh+gR2AGBX5DgKjg21ewcC777J51alHQ2majBrG8FIsQErKOn3flhVdaC6DiarMJoWcXXPnMCOanp2OeskUQZ6oA70gFAHhFEoI4P4wIriCb5pRWTfBTNEoXvT63fEMkv2T+zKmryZwAyAXSjB63XNAr9jsgIG4juYfAKqakgtWoGXd+xw7ZYTDMNsXMh/Hwx73ofy6AjkoQHoO7bwpD4KtAGV6D96Pnd0BHr3Dki2lQ4sRyEbSTi6v/CJ3t3OE3aCecmc2rlTBnrzbiACuLPWiGW69eCP93XiIy8+BQC4bd9DcXHrcvc5TNNg2Db0nCDNCUdKWgcNReAECo86kI0k5O7JCZrQQAjK0ORLC0Ak1wKqvR0Fd9uoHuQSvxnYZRVuxon3+/mvoZSsndbbCaoH8puUKfUcWuwSEKpxgjmAY/OymmLHyUpW6fez29/FOb/5HwDA4Y0L8b/Hnwm5uz3/9JEyKCODvBQxAyYrsGobocSHfHftC5WQ27Hskky9Y9vOKgLbgtSxDU60HFJyDCTthBQ6J8DthdG0CPJogtslSiGPjhR8jhOKwK6oAbFN3ktT6Pyy4iv3XQrKyGDeYytqa2Gkx9PsEeQ5lYMVzCKccNQNWnJh6eHlmUiOXXoZZ3IMZstSPqIlMcLVtMurwRQV0ta3Sx934nM9nqhvglLAX5lAtkwEtr2Df7S1YcWaDwKM5mspeNzvBIDek2+D89ZCCFf5nCJedkgyDaSal4KODEBNi83kovZ38VL63A0HxqAM9kEe5e97c3n5lNcm2DuZU98aUmos7zFCKdS0hKwyxEVIfte13Q3s7jngfVmBHQEQaHsXfaPZapZOOMrrq0uBEBjNS2BV1cIJhqd8U7OM/zvBMN/pckqXAZ/ALq90a+ELCTjYkTJPhbyJ9w2UehqZiTVSRQENhGCHY3ACITg+PYmE0ZLn7GUxhRJQgWBvQ+3vKWnGpZUxFPt//vW0G9h9br/34vcHrUKgux2MkCw7YVVUc8Uzj4HaTqQMga3vTKrPdgLCGOSMMk9ipvLKwwkAJTHsZpdLgckylJFBMEWBWbcAZtMiJBfv43u8VVYJo2UpaCAEtae0kvZdYy/YNbMmb/MFgulgorLG83c+VTal3jHy+BiIkYITLefVVXVNYHoA8lgCNBCEowXg6IHCu4eyDOZzRd6XFy86y24CyTRw1JIlgCx7iuSV7P/lrjGt7lnSwPIciGVB7WmH5NOKw2QJVkOLby80YcxTFVTrbudjqpJjUEbjOOPggyElRkqaQSiYHcypnTumBwEPp0kd7IMdqwBhFD9v34wr33gBAPDjQ1ZjXboEMzP7QwA0lZfDrK7nA9BVbfK10rLiqkpKiWHIHfmBZyHsaBms6gYebDFA79zmeZxX1ooSAmgBgFHYsYqs0gc+PD0/sGKEK+bJHnOq3FIntlOgIe8YAMS23ZmAVkU1rLomBN79NyS7xHJWSeZD4D12I/hJbSBQ0qkEgr2WYj2zVJJgV9fzMSgALvn9T/HnzW8CAO57/2k4Qd1ZhkkY2+n8SDKfNQfAqm4Ak2QoI0MAAexYJZShvhIFXHwctMxyoymUSHv2+jkOpMQIkBiBMjKE1KLlgKaDqppnGZMyGgcdGQQIKcmJLCYAU/T55ZXQ+rLnS73d04NF++65vKjfcHiBYHczmZmSmVBFBRgDcWwQeN/7BAzKUD+sjAHgal8X1IyWkUK7gFZVHayKaoTefdN//eOjmEzhd0uBHSy7sgZwHGgD3SWdy6hrAhQFTnBqPb8kOY5A2ybf3mWqB1y/1GxoAQOBmiMaw4D82Xe2BTknSS9JktvS5ASCMBtawXThbM1m5tTOnVVVl1aJy0cejeN7O7a4gd0v/98xbmAHeBsQeXwMTrR8l5pgAUwqG2KHo0i1LIW5YBGYHoATLfdVx7SjZUg1LQbLKBdgAKz6FqQWrUBq8T58DktGrbhV0+CZ6SKMQevvBg3mv1Y7mlbhk2XQcGmqbcrQAGAYIEV2GqmiuCULRvOSwmqYkxzQKRDsjdAiQ6mtqno+tgTA8nuudwO7Jz56NY6vrMs73k1KUQda13YQIwkQAruqDqnFK5FatBJOJFq6gItHHzAjklveDvBEGp1kuaOXbclEsgzI8WEAgFW7wLunx7GhdW33zdZnYkfLdllQwK6s5d8rsgImSbDKq/D4O+/s0jknC9GEkyWYGaYsicQYaDBUtDQ7q6zTcaDk9Pz6lolLMqyqOkhFKiCYpvtWEHkxnCx8PrumHk6BAd8MAFV1GA0tcCqquXjcFMWc9M5tnoEdA6+yMjJF8gBYNfV5NtmurM0rSyeOU3B8hJxK+m4mCGYPc2vnTtNh1jRA7+3I+93X33gB//3yswCA37/vP7Gqsnbn89L/zxu+OVWFtRxIkbKaiUG1dlklrLqmvMZd5rOd70TKwCIxpBbtAzk+CEIp7Fg538H0gYajcKKxvL66zGvZ0TK39+3f3d1YtPxA9/dGQzP0jm28r6bAayJgkMxkwRIwBsBoWQpWqvNCp/xVIxDsNVhVdbzHjOXvfjEANFaOUdPAft/+L/fxNz/1FUQ0HaynsDAAASAnRmDn2AAuLFCop06CXVEFJ1IGGoqAjgxC7euEZNugqg6zvinbSSEEVnU99K7ifcMMXOTFqmuCND7K5clNw9MGSZYJB4ATLUNq0UqovR2ec7MkMwU7UlZwAHzJdqUQhMCqacgStbGEsJNgnuDVV1fS8xTFswooFydDfZc4dkkl41QPwKxvBiTJFXHyXAMhXHQpEIS09Z2igiYMBM9t24Z1qwpf32xcCH37u3nJMqrqSC1eOT0CSLbtu16zocUdW5OFoiK1cAXvqbZNOOEYaChfJ4JpOqimFxSZkYwUiJESu3ezmDkV3AFchpsO9WXdGDe+8yq+t+XfAIBHzrkChygqWH8PCKNgkgyzbgHknNpsy3H4Nvw0wIJhYKgv73FH02FXN3CpbknybdB3ouWgAz1Zr4mqujtSgKlqnupUwfUUqP1misIDTMcBCPCnJ57AZf+xducBigqjdRlIKgllZJAbfkrznCyqKKDhmG95FQO4ol6JDhgDgCI7HgLBbIAFQkgtWg5leADS+CikVAoELG2LmrB5dARr7rsLAKDLCt6+4mZI6YoEu6Ka33eFAgyvYd2KCqes0rdn1qqu5yMX0jhllXBiFdwOyLKnw0ILJJGyXq+mw0pXSdBwFDQcBTGSnsGdk+GMTFQu5AZ3AABJhrlgIZyRQcijI1A8HEnHw7HZm2EeEuoMKJisEwimA6oH+FzdzMcUFWZ9E/Qd2QrkuTODvUdP1UHrac8r3544loHwZFLmbE5V8/UXJjBqF8DJ9Mu8bB14siq1cJl775j1zQi0b847zo6UAemxWHZZFdqf+Kvvtd1zazpSS/aB1tkGOcHFn6iqwVjQOn3KthLxTMYxwDNgc5Flz37rLAiB0dgKvaMNkmX4XmePCUYJdgtzLriDJCHVugzqYC+kVBJXvvwsfpkO7B47/7NYXlUHG7w5llgmr0eWJDixCtChEOTROJii4oHH/4zT9zt0WpbkRPODHEYkmM1LSlNyk2UYLcugDPZCMpJ8JlxlzZRvPru8GspQf/4NTQjsiTKmIrOcWCAIK7CA/0AdkB1b3V4iJskwG1oBSYLZ0AJ9x5Y8p8WJlfO5eR5QLQA5pxSVSXLRNQkEswWmBWDVpu8fx3Ft0eNb38ZFv/8JAOD4JfvhByedn/c8bt/6QEwDkpHMHq4ry7AzHKZMzPrmtDJdHGAMDAxEkmGXVWQ5WS6EFCwpYoEgnFAkq4fQy1GwPO5zpgdh1jRA7etye3LsyposdVAgndjq73IHAme9RkLglFfxhN5AT9Fz7e1YtY3Qu3dkPeaEosLuCXY75oJFWbtRTFbSVTU6jAULofV1gdg2nEAQVkMLpPFRKPEhHhSVV/NZcvFh7kNUVIGGY7CNZN5oFat2AZxQhCeYc20LIdxf6NgK4uSLp1FFyduxsiuqoQz15ducmoaspAgNRUD1AKSMAJald+WntDtFJJgLFvGqLGrzJPV0jiyRZNhlVXk9dE6sYpfUfydggRBSi1eCmClIySS0rjaQjPU70bJpuY5g5titwd369esxODgIJX0Tf+UrX8HY2Bhuu+02GIaBD37wg7jqqqum/8KKCqt2AS75w0/x53Rg9/cLvoCWzP4LWQaTMzKihMCurIWddkR6R4sPCi8ZIsFoWQq1vxtScgxUC8Cqrtt581DKDUMB48BUFVbdgmlZDtMDMFqWQe3v4uqZjDtMNBgGGEBS42khBoLqcFrp07agDA+A2DaYovJeOiLBLq8E0wIwWpaCpMZBbJtnltKBJw1FkFy6P5SBbiijCQAMTqQMVoGdRmPxSmib34KcHihKJRnGkn2n5bULBMAM2SbGIMeHICXHwDQddlkVd9zTtugbGx7DN59/HABww9En4OOHHu19Gj3ozm4jpsHtSmocVA/Cqq73D8gIgV1R4wq1ZGGZ/P52HDiRmOewcik5Bjk+jKOWLHFLdowFi6D27IAyGuc9aZW1fB5cfChd6lntXUIEwK6qgxOrBDGSYHrA25lIJ7ayXmNVHeTkKKSxBJiqwS6rLO1cezlOeTUMSYbS381L7Msqec+0YN6xp+0T343aF/LIEN59+R9Yvs/+fCC5poNGy5FKVwlN4JRV5t3XTFEgJ+KQkuM8eVXTACYrUOKD3BakEzGFoMEwkov24QqPjg0lMbIzoV1dn5foYJrOfavudkiWme7Fq+W7e4xBTgxDGh8FU3WkGhdCHerj5aKEcPEpj9L47Bfq8EoJMwUaDMOJlbvK4wD3y4DJq2CWglXfBKaqUNJjDqyKGjjFduUmAyFgehCOHsRv//IITnrfKm7/ozFYVcLuzHZ2W3DHGMO2bdvwt7/9zTVQqVQKa9euxc9+9jM0NDTg0ksvxVNPPYVjjjlm2q9/1v9+H8/v2AIA+MfHv4j6CTEAx4YSHwaow8VSplmkg1hmug6cwI6Vu/K3TNVgNrSAmAbkxAjk5BhsIkHr64I8OgKkywKs2sbpH1qbzqqBMTixcl7+EArDaFkKOTEMrWMbJJtyVc90aerECta/972wRwah9XZ61t4rQ30wmpeAhiJggZB3A7Ykwa5phF1ilas0FodsGe4aJOpAjg/C8XJKBYJJMlO2SevYmlU+qAwPINW6HJBlnPnr7+EfHVsBAL/48MdxZMtSAAAxUmn7kN6tmnBuKIWcGAaxTFiVNZMXfUo7PsQ0QIwUlMSwe7+pw/2wKmu5LUojjwxysRYA72ttBdv2jtvQrySGQRjjgi69HTAbWpBatLK0ZUgSJNuCZCThRGKeJYhM02E2tro/q907oA73uz8rQ/0wWpeBqVra2ZqlMAYpleS7J5S6Du5UJNQFs5cZsU+UQuvYBnksjv0aGoDhAajDAzDTO228oknhwY1HKaTS3w2tf6eKJBvsQ6p1GWgoDJtRMFVz20iAtJ8UH+YJp7SfpPR3Qx3sA6gDGorCbGiG6VOFkLX0UASGx/gUrbMtq9VGGe6HWdPAk1jg/b1yYhhm40L+uvJO7CCwfdPO3b7hATjxIW73pttH84IQrpo8jQmeCf8z830HgM393IYK5g67LbjbsoUHVhdeeCGGh4dx5plnYvny5WhtbUVzM++/WLduHR599NFpD+5+/trzbmD38if+C5XpOXPESCGw/V03SGF9XTAbW71LkiYDY7x3ZjQOdbjfbQpW+7t44JO+Pg+k2tx6dRUZJUyUQh3qA5MV2NWl988VQ0qOQW/f7JZusb5O0EAIZmMrmKa7pUwT5JosWZJAfAI7gKtsqv3dMNLO6HSgdbXnrUnr7USyvHrPGFXBnGYmbJM0PprXFyaZBshQP1p+9nX3sWcvuhZNaXskDw9A6955LygD3TBaloFJMnc6Jhri+7thVjfk2w3qpEUNCBcumCjjphT69ncLzppUBvt46Xda0jzPTqQfm/i3+zi4nLnfbl0mxDSyS8H6umDWN+/M7qftKrFM0HAUTNW4EEtGYAdwuXZlqD8rGJ2NKIO9WWVsymgcpGs7VxEWzBtmwj6pPR1QxvL7VtW+TmgZ9zcd6OE2KDOJ4thZ4wsArtyrd27LKoOkg71ItSyFlBznpZcZfpJVXg0t47Mvjyeg79iK1KIVpb0Ax+FzOAmBE46le3qHsw6RLBNab0eeb6H2dXoGd3J8KGv9ACCPJSCNj+7Wkm9iGny3UdML99dNEml0BHrHtqz3PdWydJfV4AV7J4SxKUy0LYGXX34ZDzzwAG688UZYloXzzz8fF198MZ555hncdRcXC3juuefwwx/+ED/+8Y9LPu83v/lNjIz4K6QBwA5q4m/2KM5Uy6FnbKGfuN9+WFmX7QCNGgZ+8NxzoJN8G+qiURy+cCEqQyEEVRUhH7GP9uFh/OqllwAAH1+1CmXBws3x/aOjuO+FFya1lkKce+ihaCgry3t8JJnEj55/Hlf+x39AKhIwUUohFejvS6RS+L9//xum46An4SF+MEk+u2aN55q+9fe/IyUG+s4pysrKcOWVV+7Ra86Ebdq/oQFr98nOLg+ZBpb/9SH3589qtVDTn3tZkvCJI49EMGcnKmma6BsbQ0tFdkLKphQ/ePZZjKfvj7poFB8+6CDXLo0aBv735ZcxMD6OgxcswPtXFHeafv7ii+iKx6HKMj7j4UQ6lMJ0nLw1AsA3nnwSjo/oS2PaHu3f0IADG7MDspRl4XvPPgtCCM44+GD3WIdSPPbOO0ikUjjjkEPyzvluXx9++/rrRV/T3szHDjsM1ZFsZ44xhu888wySwu7NCPPFPl15zDFQSuzt/Fd7O/62aZP7c00kgo8edljecYyxrD4uAHh682Yc0tSEqJ5dMZW0LE87cv8//4nuIj5FYyyG0w46CIH08xOpFGRJ8vTJHEohe/gydz3xRN5j/7F0Kf5fS0ve44+98w5e7chXZJ8O3tfaitWLF7vv2+b+fvzu9dcn7Z96cdHhh6MilB3IbR0YwG9efXWXzy3Y8xSzTbstuMvlvvvuw09/+lMceuihuPPOOwEAzz77LH784x/jRz/6UcnnGRgYBZ2iJH5g81uQrHz51+SSffN6NL7zna/jsss+m38SxqB1bYccHyppiC6TJCSXHwiYKYS2vF30eKoHS89WlUDw7Vd815lqWgR1oBdysvCA9WIKVpkiCk4gBKN5ceF5dUXwWjMDkFxxYFa9+57G9zMxz5jO90GSCKqqZlbVcE/YJmKkENy68/5/Mz6E/3j2UQBAfSSG5y/+YpYjRMwUgiXYi0xSzUvcjLK+bWPezpwTjnJxhM42T3XJTBiRkFy2n1uCFdz4mqdCpxMM59kPJxCEsXCnDSOWCWWwD1JqnEtsUyd9DeIpfZ5ctAJyYiSrzMtd06IVCG59O+95Zk1jltrn7mR32YHAxtch0WwhCQYguXS/va40c77Ywvlinwr5CblQPZBddk0pgpvf9BRBycXxUOUE/IeVM0Jg1jWBUAp5jIvdWZU1WeXbga1v5+2w+a5dC0DKEWtzgiEYrcsBZH+u5cQw9I5teedILlwBVmDW3VQhpoHAlrfy3gfDb/TBZHAchDblJ7+YrCC5bP95cz97MVtfezHbtNs85RdffBEbNmxwf2aMYcGCBejr2zkSoK+vD7W1e+YLGQCoxw1JFaXgaAAXxgDHhhwf4nNESr2mHoTavQPBLaUNvrWnOnTXsaEM9PLAc3iA92yMFxaFIYy5JaOZsAwnM2EYMBpb4WQMIM79esh8L+TUuFuuNZ0QALAnP29HIMhlJmwT0wMwq+vBADzc2eYGdmfseyj+8fHr8zLcTNHAJqGSyAgBnVB9Y8yz5FIaH0Xw3TeLB3ZAeo7Uzuv7jT2wKmtAM+wnk2Q+SmUCx4betokLGSTH3MAOgGdgxwBIY6NQhgfyfkcYhWSZMOuasmyQEwzDrti1YeV7A4TmO8cE3u+TYO4yI76TR6WM36eO5Xo/kgSzNvuepJru+Xzi8x3uO+ScMWjd7dB6OyCPJaCMDCKwbROIkR42Tp2SAzsnGIHR2AKaITjFZD5+xvP4SBnsHCEZq6p2aoEdY5Djw9C6tvMSd4+deL/ZwXIRP64kJClvwDmASX3HCGYXuy24SyQSuOOOO2AYBkZHR/Hwww/js5/9LLZu3Yq2tjY4joNHHnkERx/trQi3O7Cq68EydpQYuDRvsT4ueXgAwXffRGjTG9B6St+OZ5IEJxjmfXgepo5JMqxoOZ8Jp+owaxcUn1HiBXUQaNsEra8Tysgg9O526O2beX2139pkBU44BjmtxJT7O6NxIYwFC/HDDRvAgmEYLcuQal4Co6EFycUrYRUQN5G95lJNB6LfTjANzJRtsqvrMdS8BJe8+hwA4Pb3fxh3HX+m98GSBLOqztfByoWrZKaDLEI8v8i56Il3qSSTJG6vAiEkFy6HU5Yu+6QU8shglt2cgGo6aKQMqcX7wFiwEEZjK5JL981KGCkjg3nDfgtBAOi9HZ7PYeDiKk55FVJL9oXR0MJtUstS33lXc4Jiin6COcVM2Cfba2fI5/vWjmS0eTAGtbfT7Q2mWgCp+hakFq7wvCfJFD7LuasgjEIdTPfdEikrueQHkyQYzYvTIwD2Tdurhbxqy6/njBCYCxYi1bqc+z2LVsKqmVpfr9qzA3rnNigjg1AHehDY9g5IzhBxv5m/tMRZwAUhxPP7RDINSEUqtwSzk90mqLJmzRq8+uqrOOWUU0ApxTnnnINDDjkEt99+O6644goYhoFjjjkGa9euLX6yaYLpASQXr+QqcQ7lszyKqGVK46PQu9vdn72yq7k4gRCcWAXsWDn0rnbPY6yKGljVdYCsYNLdFJRyoQTKZcuVxMhOcYU0hUot+cDNhYAkgXg4UcSx3QZjt2+GkKwm4kLvG7FM6Ns2wqptnLaGYJZeg0Cwq8ykbdJDEXxlzck4qL4ZB6cHe/uhpAfkFsMqq4CdM1rEqmmE1rkzueNX9mSHo7Cr6rzvUydHLS59HjCWDiB1V83RyclwT+BlX6aKXVHjls8zVdv1UqW9DUL4e5tD3k6JYE4zE/bJql0AwsBVqW0bqKzhipY5u0YMgFO5c5dcGerPEgGSzBTUkX6YwZCnr8SIBIJdT1a4doUQOOEoyMhgwbvEqm7YKSglSb72ygsaDAHBqYuOkPSYmazHHJuLQGWMt6LBEOxoeZYQDNV02OXTY+ck5m1J5NHsPkxpLAG1rwuSkQINhmDWNgrRlVnIbp1zd+WVV+Y1/K1atQq///3vd+dlCyMrcMpL3x2T48NFj2Hgw2aZpoOGI3AiZSC2Ba2vC1LSe0vdLq8C0jONlPgwHwZaYCbUBMQyobdt2qkwlzZupUJVDamMmXE0HOMqUxk4JQRkTjjq6zAS8PJMqX0LUotXTmHm1MQo4tyHZ67fTjC3mCnbRAjBRw8+ovhxllm0FxbgGWm7uiHrMWWoH8rwAB95oqhgsgwplfQMtJxYhW8CRhnqzyt5IvxF8N+PxUE6thWU0HbCMS5vvgtQVYNZtwA0c8dgDsIUDSSnJ5wBu9S/LJid7HH7JEkwG5qB+iZ893vfxCc/eRVXAN/+btZ3vF1RA8g7d8q8Kn/k5DhIaszTP2B6ELYsQfZJXHk+x+Mxt02EsawxLrk4gSCs2gW7nGR254kmx0H1AK8CK7E8k5iGd1CVGM6bXWw2tsJJlEEan5iFWpk322+q+JVgMmmnfSGmAX3HFrcUXB4fRWD7ZiSX7CPs0CxD/LWKIRXOmjpaAEbLkuyGd8agt2/O201znxOOgekBqN3tUDMyOnLXdhhAwQBP7evKKlkijJXkBLpLS6+TGCmecXNsUFmGlNEMLY+PQh4ZLLgOpumw6pqg9naAMOZpgAmjkONDebsKJawy7xECeGa1BYI5h2NDGez1TZ5kwRhgWSCODabqUEYGoPV27vy9ZfqehxbYcZMTI1D7i/fOyskxd6C5FzQUgaMHIRlJN2XDFBVU0yGPj5a0J2VX1PgGdiSVhDrYC2JbcMJR7nwWUPbdm2GKAuQKfhFp1r4ewSyEELC0voDW2Zb/nZ4jRuLXq6fnjDOaeNyqquUVQLbNE+A9O1z/hao6rJp6Png8lQKTJTixCij93Xnl5K5vxah/qTmRYDYu3PVZxunRMRN+l2QZkMcTSC3ap6S5mjQQAgPJa82RbAtKf3f2HDtC4MQqdn08lwdOpAxUUbP8RwZklfDL8aG8Hl9CHciJkaID6AV7FyK480EiBLAtOKEolME+XydEMlN5SmbSWMI3sGOSBKuiGqAOlJHBvN8rQ31ZQZWcGIGcGObz78qrICXzhRK8VKombs/cddNQhKsytW30NYokrQiajMQ8fz+BXVENO1YOyUhBGh+DVoIzmL0YhzuneRkhn507gWCuwxgC298tWSSApI8nYGBEyhJDco/xeJ4VLYdd2+gdOFDqDiwvdc3yyCDksQSYosKOVbhZbWksDnlC/GBiLbYF6EHYZRVQRzx6fjPW7ISjviJTxEgi0LbJ7eORx0chj49xtd5dxbG547oHe/m8xCYIoyC2NYXqB4FgCjgONFnmu1QeO/3yWILfG+nvbLusIi+57Gc3rKq6na0digKmKDBal4GkkiCMggZCbnDjnss0spNVaaTUOOA4vELBI3BiAFLNi0sL7Cjl/yne7rA8OpL3XhBKIccHdyauHRvK8AAvZQyE0pVZadsqy3AiUU8hK62/G0xR90zgJElwImWQMuaE8hnCHaXbesGsQQR3GUjjo5BH4yCWiU8ceSRC774JJsnFP/iUZjlJhfryCKXQu7YjuWi5pwpa5he82teVNRxUGe7nWfAcm+u5a4b88IhqAViVNVAHen0Du8znl9RoKyugoQiopkMd6MlqmGaEeO8MMAa1ZweUkUEQxuCEIjAbWt0sGFVVyDmjFyghIoMtmPNIY3HPwK7QLt6EY0MYBSkxJ2JX1fkGDHJiuKTeYoCXPamDvVAyyrPUwV5QTYfR2Ap5LL8snQDuwGTPkqtIGZxYOS8r9VDznUAZ6s8TaJDH4gV3EotiW9C7tvNANe1omvVNe6QknCpyvm0nhO/oCQS7E8eB1t0OOTGMy48+GmQ4P/EMpNVbLWunwJJc+ogOP3vDAkF/ZU5J9rQRVNXy+oHd54DvgiuJEbBUEnZZhW9JodLfzXf+KQXVg6iJ5Jdv+o14cH2otKCdm9CPD0FODMFoWebubDqxCl+VYnWgZ4/tinm1CUm25c6/c2IVYAM9Wb4pkyQ40bldEj8XEd8aaZT+7qy5Skp6AGZRJ8cj6HDCUTBJ8t8Zc2xIpgknFMlrWJZsC8pAL+zyKigZjcoAz9JLTn52l8kKiMfjEwaRarxG3InGAOItouKFNJqANlGnzRjk0REQ04CUGoc8mgAkwrf6tQBoIAijaRG03k5IRhJUD8CsafTMnCm9HdnlqOOj0LrauOod4Pm+leq0CgSzGT+p8OmEarp/8MMY1P4ez19ZkTKwUBhm+1aEdR1OJAa7ogaB9s15x0rp3g27orBce17pliTDqmnwXR8xDT7vSlZ97RixreLBHaXpIJbCicRcx3MisAO4vVVGBvlsrZqGQmebFjydSMYKR/YCwTSg9Xa4Qh6EkMIKrRm9W16qvED+R5YRAsenEoiYBrSeDkhjCTBVhVVVB6YokMbH3HLuvOcYKV8VXgJuR6Uh3uurDPbCaF2WF1zK8eEsn08ykjj1wAPT91za37FtgDr5rweAnQ545Piwh6DdOKSxuFtO7kTLwch2n4T+9IlOFcPLt2IAxkyeTGeaDmNB2o8zU27Poui3m32IvxhjkBIjUHMG5paKWeshjSsrMKsboA70eAZjAM8smQ0t0LdtzDtG7e+CEw6XbAgkx86rpc56jpnihjWdfXYisaxMux/acD8+fsQRwHgCek9HfpbMQVZpKZMkmDWNPAvlo2wpjwxCHerPf3x8lM+xU/wCVQY4jm/phEAwF6AFhIpKZaLHDeDz6ZiiQB5LcKlyPQijsdX3/iRGClJu3xe4AyCnxsCog0fefBMnfeQCgBBIBWbmSbYNeaiv6OtxgmHQYBhMluHEKn37WOThAVdyHQCoh8PBJLngbh+QFg3I6KFhvQRm40I4obDnGBc5PrxHgjuvUn7uqFq73jckEBRAzlBoLAQlUlaQxAJB2JEyKBmKi0xWYFZUQ5vYEZMVLtbiNbKAMeg7triffWKZWerkvuudREAk2RaUwd7s+ZsA5ES+DxQLBJBKjEDr64SUUz00YccY+GuUR+Ow9aBvC45kmjt1QdPBrZIYyTtuMoJ4u4I0xqvSvLAzEks0EkMqEoM0Goc62Autsw00HIVZ0+D9NxTslcxvT9m2ENi+mffNFSH3xqbBEG/0z70xKYXesdV1Erx635xgGGAA0zSeBcsJZkg6W8sIyQvw/NSkUs1LoSSG3BKD7BOSrCc50XLYkeEsg+xHUFVBu9rzDJ0XhFLoPTtgEOJdZkAptB7v+m4GUnzUgRBUEcxxmKrBbGiB1rMDhFK3tLmUZAzARVKMliUgRgp6xzbe72bwUudU40KwIoGPX+nzRDYc9ihOP+ggWGYKTA+CBkOedmqCUpwwyTQKKm7yF+bk9YZIjg2qB0CMFLfNsgKjsaVo+XZuPxFJl4k7i1Z4v5aZLge3bUAEd4LdCZGAEkYUeCU5zAUL4YwMQB4bBVM12KEwAl3tO/0QSQbTvZUlpeS4b3A0nXj2MPuUWivxIU9/h2T8nzg2tP5uPjYqEssaBzFBruq4XVmbpxLKJBlmkZE404GUHIfevsU7yUakvL88SY1D37HVLfmXRgYhpcb5/EIxkmpWMK+DO62vq6TAjqoaUi1LINk2qB4s+GWvDA9kZX95QEhgR2IgtulKnAe3vs1vfr/eN4d6BjNeTpRTVgnoOmy9HpDlvEHrdnlVtiFLD+eUtr5dkmH1y/b4oQz3ewZ3xDR8y1ydsooSJH/FMF/B3Mcpq0QyWsab81UdIOk+OJ8AykkHbFQP8AZ/WeG2LSNpJFkW1IEemE2FxUaYpsMJRz13sCZQZBlseJDLeMsKzPpmaF3teaIGpcJK8BUkw/AuKZJkGEv2BbEt0ECwpN44KZUvSiXZFi/RjFVCGcmeSWVVlD46Z7cgklqC3YxVUZ1VougFlWQ4FR5JW0LglFe7I6b0be9kVd9IlgG1txPmgoXTueQsilUHUI85dXZFNVeHzHk8dzRUIZThQVi1C2CVV0MZ7nc3ALxGJdBgGEbrMi7QZxpwQmEuarUH+nmVgR7f98cur+IKqZnHDw/mq3saKUjJsWmbXSzYvczr4E4q4MCYtg1FD4CGo7Cq6wFV545WsXOO55+TgHFluMG+vJED1CegIYz6zpDLxAmGYdY18R6S+BCIacIuq3TFUOxY/oBjYqSgxIfghKOgWsB1dqgegJRK5pWJMkWZVC+QrzSxqvEhpjn1/FQP8NdQAAYA0rz+uArmEznlhVZtI1SPXW8nGIbRvCQ74eQzHiW3vzfvkskxKP3dgGPDDoQgOTYvx/RK7qTvYWksASmVhBOOQClgTwtRyjByqmmeu2pUUbhdmYSaJNUCeUktJnP1PrN+AS9jTQwDhCsb7zkJcG+VYKYJpUzB7sWuqgMkiTv1RpL33WXAAN4T7xWIMAZ5NA5pfJTfO6lk3iF+tocGQ6Cavsu7d1QPctGS4QHAsUGok6054NH7S4NhGE2LoXdtzwpG/ZJonjCehLfqm2BXVrtqmUzVeH9wfAiEUtixcrBACDQYhrmgSPXEdMAY7wVM8bl8sodfCgBWrAKWR2tRro/mUkSIT7D3MK+9ZabpXJY78zFCYDa04Ns/vw+f+ORVkz+nTwCo9ebXcAPeQiFU06EOlTj4N22IAm2b3D4bALAjMc8svTQW59vt6edllo1KtsXLTrEziLQcB7RxIdTejpz5KLzU08sQ2n4zWmQZVk19lrQxkxUYCxZlOadOtCyvNp3qwWkb5ikQzDbsiho44RjPKqfvOarx5FNemQwhnj24hQIgeaifl4JmPOboARgtSxHc/FbejrsTLc9T8y0Vll4jiAS7vApWdQn9bLICq7oeWl/2uBUlMQIaH5rUXCirup7P2Eu/JgbwfpK042rVNOyRHrtc+GiIbJVCqmqAGIMg2N0QAruyFnZlLcZffAbVOaqRTNV8h3arPTuyBNI81S39EhSEwGhanCWoAkp9tQrc86WTxBPXkYwkbLkaNBjKKmGnioJUy1LfXn0aiXnujJfa9+xEylzfhWkBOBoXcpKSY9C3b3aDJGWwF2ZDS0mJrF0m3cdYqPoCSO8wNrR4llna0fK8UV0srYwumB3M6+DOqqqDlBzLClCsmgY4sQrQUrI3jEEZ6HFvAjtWAfgM9JYs0zvzrGmgoagr6+0Ew7BjFdB7duRfzuO8TFEhx4eyAjsAUEbjsMdH825Gta8raw1eIxSoqsMq487Sfb9/COftdyiccBRKfDA9LDjGz8sY5JEBqP09PDAkBHZZZcGh5XZlLWgwDDkxAlAHVA/tlFVOYza0AmjjxyCdYVuwyPecAsF8gGk6bK2m4DHS+Ci/x3MU3iZKhbxPzKD1deaXJxkpyGMJ7nx1t0MyU2CSjPb+PjQp/mqVmVBZ4TuAhPBgNBThZUBTGFVgV9VBiQ9n2ToCQO3t5CNXSuwFYYEgkotX8v7kdKUD3Qukvq36Zv6dki4VcwJBmMLuCfYwz27dipMOOKAk20HMVFZgB3hrAlhVPrYHaYXGzNmU1IEcH3aDExoMQUqOu6IvNN2/l2sHtN7OvCSUZNtQRgZhV/mr9jJFATFz2kUkGWZ1HZ/B59h8Fl1ZJW+7iQ8DYKDhmG+/nNrXnbX7RcB9LydWsdt71qTxRNHADkhvbvishUZiMGsbofb3gFCHV1fVN898/7GgZOZ1cEfDUaRal0MZGUhvnVfkC6RkPYFCSo2DKSqYpkPt787KXGtFsthUD2SVLDAAdmUdnFg5N56MArICxec8VA9mDQRmILAra9wgKBfJSOUFd6UMRyaWAclIwaqqQzyVPl6WYVfkOJaZtfaTGPrLFI0PBjUNAANgfR0wFiza+d5LEndqfIecCwSCXIiRgt6+OXtGEZFgx8rhlFd79p0ASJcxeZfbKMMDMJqXILV4JWBb0Lq2o6WyMq/iwfO04SiMpsU7hx5Pg1PjqRZsW9x2khJ39hmD1tflJuXk0RHYlbWe5Ul7FEJgNbamgzwq7J5gRtjU1wejdRnk9P3hxCp8d2wko8Ryysl8liUZTnkVaCjCe2mDIaBS2ukPSBKC77yW9zS/fv5iugpWVR30ru15jxHHcRMtAA/QzIYWoL6pqF9CPK4p2RZ/Dbv5vi7FxwMKJPvAFYLl0TiopsOJlPHgWAipzCrm/bcHCwRhBQr3ewGAnBiB1rXdzYg7sQrIJahNZmJX1MAGV2NiRIJdUQUaTs9+kSQAPCtCA95OmFXTAMcyICdGwBSFq3UGw7wfzqOM08lVxmPUrQUvBAGgJIYhj8VRFghAToxATpc6MEnimaz0QHRXGncSBkvt78qqsSeUQuvewR3ITANSQqAoEAg4ynB/XmUAYRQ0EvMP7ABAVkAVBZJHX608Pgq1pwNWXSO07vaifXVU03mWOxx1k0Hy6AivTHAonGAIdnX91IaMMwaqKJBzSraoHpiUrZDGElklRwR88DpME3Ztw8yPHcj4LhAIZgKaHk9S9LhAqGgJIwEgjwx42iBpfJT3yTEGJ1bOd+AphdbZ5qp5MxA44QiffxkI5SlOTuC3jjw/KPf3ZZVIyTKU4QG0bduCpgMPBdUCCG57J+s4ZWQQdrScl3IWgQZDkHJbSzR9jyRs/P5uVkUNr75Il8P7vQ55ZDAr2JVT4yDUmfnkl2BSzPvgriSoA62rzc1uE6QDtElMo3LCUXdLvljdNQ1HYZdVZjkgdqyCO2lA1g6aNBqHNJ6Ao+mQMwImq6Imr0Ze62jLFxKAv2EmlOIDK1dC79jq8ds4lOF+2JFyOLHSDJ67Zg8HUbIMPs9J9JcIBJODUmjd7W4CJo9CJea2DWVkAFQPgdhxT1ugjAyAKSqUAjPtJrAqa7MESLT2LVAy1OekhAlldASphSsmHeCpPTsg52SlGZGKijHlIie9xR3U0WEo43G+tpkO8ASCWQBTVVg1jVA9yrozIVb+jrucGIHWsdV9npIYhlnTACk1njWmiYBBGUtAHhuF0bLEd8C61/WppnMl7iLQSBnMSBke/v1vcdnq90MZ9NY8UHs7YZTg61g1jXzMw8QszSnYqalCg2FY5dVQh3fOE7aj5Tw4K2H3zWusgzLcz/uQxe7drEEEdwUgqXHIowmAepctcRXJwuVJTjACq7qucLmnB2ZDC+zyaq52FAh6ZmNyBQ2YJMGqqIUTLcsL7IiRLGmuXS5NZf69KIRSqPFBqPFBWFW1sGpKy+x4vpfApIJlgWC+I40lICXHIKX8720mSXDCPs6IbSGwbWOeUFKuBDZhrGR58ImdQ2IaULu2Q/FQ7SSMQU0LDJQEY5DiQzzDn4MTiU66yb+Q6jGhFMpQX97AY4FAkM9EFZBVVQcmS2BaAFrHNkg5CSUpOcbnNWYIm3jJ86t93b4jVQgY1IEeGI0LwSQ5qwzTf9cuUtqoAcYgjSfwvtZWyIkRUJ9h3ZKZ4skyryDHcfjImvTsu9TifXh1F6VwomV7tMzaqm+CXV4JOTkOqgdBQ5NQ6HTyy1sJpVwpU4jazRpEcOfDYa2tCG7bWPAYJxwFiQ97ysYy8O1+s65pyk2oNBjyL6eybSg5GRZCKSTbgu2hauXXl8eKCCMoJd7MymAflxv2UaXKuiaRQJBtQAgArWcHzCYhICAQFEPras+bx5YL1XTeBO9zD6tD/XmKml6OlRMI8Z0sj0AtEwbAicQgx4ehdW4rnMkvoWePX9zmAajPrM3JjGhxYbRwxcIk53oKBPMRvvO2zbUZTJK5MqUk8z7bDCTqILjl30i1LAVLt5142YBiszKJZQGynFbYbOe6AooCu6wKqkewWMocYwC8DDQxjKOWLAE6tuYNIN+5Pj5zNFedl1gm9LZNO3fq+rpg1TXBnsEZmSwQgu3T4lMIJ1IGKWPXD0gPZBeB3axCFPZ7QMYSWL24yLBf8NJMv3kgRvMSnpneTepCkmV6jiHwauQlqSRUnwGlTjgGWqBfZWg8f+CvF4QxSFZpzdWOjzKdMjoCqcgsLoFgvkNS40UDOwaCVOuygrtaXrZi5/M5VFF5Vjs5nrez7uhBsAkZcFmG2dgKpqhQe/Pn8eXihEqrZNA7tvkGdgBPsE0KSqH1dRUeeOy30ykQCDiMpe/zDOVt6nDFSp8xBiR9700w2WomYOf9TkNhpBatxPiyA5Bash+c8mrvucAlJJGksQSUtBLnBPL4KKjPjp/W2QYpp0RdGejNSpRxdcxOLqAyy7BqGmBHYu5f1gmESq+yEOw1iJ27HIhlIrBjS94Qz7zjAN9eFicQmpLhmgxUD+SVJgDw3H5XRgZ9G5DVkYGCuTJNlkElGVLGTCjPc0mSK1FcDKumnqtleinfpZJilopAUADJY0hwLk5ZRdEyIBoMAz47+nasEkxRoQ32ZCljOnoQbR3taNr3QDihKC9NdxzeKytJIJbleV9nnTscLS2j7TgFkz1OOAa70l/i3AtiGb7KoADvTbH32NBygWCWQh3PpIuUGiuYOJHGx6C3bwGTZdixChAj6SqIFxNmcYJhWNU5Y5bSu0lMUUBVLW9NnuIitgVCqdtXmztGyr1eJAaSGPYcF6Xv2AKzcSGcWDk/Ryo/CU4oBTENd6dy1iDLMJsW8woGxkT/8SxlbgZ3lEIZGXT71eyySk81NTkxAnk0zpUny6vAVA3yyKDnjlipMJDsmS27C8bSJVA7pXqpHoTl5ez4vB6S838vwroOUIcb1tpGSIkRaDnloAyAEy4rLNyQiazAqm2E3tmW9yvqMyhVIBBwit0jVFF95y8BfGSCMjIAOBSOqkH2cNKIY0EZHc57XLJMbOzrw6L+Hmi0E0ySYdY2wNGr+Q7f6Ih/AgiAWbsATmXhWX3uGgrsLJq1jZMO7ACAqTpX/M0J8OxwFFbtgqmpeAoE8wnbgjLU73kfFVXOZNTt35XjwzCaFoJ0tUNybO+euVAk3c8nFw6SCIFZ3QC9q22nP6RqXARkAkqhdW3nfXHgCXKjcaFvUppGy2HEKhDwEJQjALSu7UiGo4AsgwZCkHMCPCZJszowEuJ2s5u5F9wxBn37uztvtBEu7Wq0LstqrFV7O7NUgZShfqRal/mWFJSKXV61+xtnKUVg+6aseSZUCyDVusyzDNQpq+Ay6btwSTk5BpOQLAWmCfjohCFIZjK9huK12U60HE5oAHJGZt6OlYtdO4GgCCwQAiPENwllV9b4qppJYwnoO7ZkPZfKMiQnW5zAd+QBdXDcypVuxQChDh9jEgxD7esuKNpEwMudSg3u5NS4twKe5DFzs1QkCWbtAmjd7TudQEWFVd8snBmBoAjEMhHYttHTT2KEAKx0L4OAQe3rglTA5zKr68FK8AmIaUDv3ZFlL5xILOueVgd6ssovJSMFvbMNqUUrYEfLs37nhKNu+whVVM9qBMIopOQYaCQGu6o2qxqJgStminFOgplizgV3cmIkL4Mip5KQEyM7m2BtG0rOXDhCHaiDvbDLKqEO5QcwhXBrk8sq98gsEDk+lDeoUjJTkMZHPUcS0GAYZkMLN6S2BSorBQ2qH8QyC5Y0SUYKcnw4Swrd/2QERvMSyKNxECPJ5+qIwE4gKAkaCEJO5pcCUblw4KP2d+cFhROBHQMBVVXPnTz3/FoAcs6OGgGgDPaXpMZbspAKAKZ4B1t2Vd0uSXI75VVIBcOQR0fAZIXP1hJiAQJBUZTBPv+eOsYA4u8feD6niCCSnBqHXYJfwNeV3aKiDPXDrqwDU7nypddcYslIglgmzMZW2OOV+OeTj+Gwo98PJxJzbYzRvBj69s2ePhNLq2oyVUNq0costUxWYpuKQLA7mHPBHfER9cgc3E1sPzESAzQUgVnbCNa5HbqieJYZZD5GZQVmQwvvsdtDM0ByZ9VlPu5nWp2ySh7cMgbi2AhsfbtgoJYLA8C0IByP8oPcNZTcQkwIz475CKwIBAJvzLomnkHPeIwBSC1cWdAOFVKCJGAF++WcUAR2JAa5t9Pjt6WVZE+mF9mJxOAEgm5PDsBLraajJ47pAdiiBFMgmBR+/pX7e8aKlmZmQsNRSH7zOVG6cq2XT0TA18tUFVJy3DOxxEC4KBQhoOEY/tHWhkNz/BGmB2G0LkNg6ztZAnp2JGfklCyXltgWCPYAcy64o0HvLE/mrhDTAmCynJfpmTjGrqzFD379C1x64SdBLBPKUD8kM8VnRkUrYEfL0ipRhG/77+HBjk4o4jlo0ik2y4QQgBBIY0nQQAjS+FiW2lVBeXAAWk87AGQJrOStQZ+9NeYCwWyBBUJItSyD1rsDkmnC0QMwG1sB1Xs+0wQ0GIaUowyXfeL8II0qKoyWpbx/xLaR7GhDMOM6VFbgBEK+wk3uqQFY5ZOQBicERvNSbn9TY2B6AFZFjdhlEwhmCBqMAKOFZ15SPQimKHwGZ4EEshOOwqxvAlM1z5l37vVKwAmFIY9nl5IzcMVKJiuQxxKeCX27vLKkNhqm6UgtWgFlqA/EMkFDUdgVIpAT7L3MveAuFIZVUQM1o+zSKq/OLvmTJJj1zdA629wb3gmEYGX0gthpNSWm6TA9ss1Tl1zZdWggCDtWASWd8WLgAWkpqkzy8AD07va8xxlQsI8HQFZ/HNV0z2yZZNm+u4cCgWD6YKEwjIUrSn8CpbCq6iClxn3HC9BAiM/LTJdeMkmC2dDiCgOogz1QMwI7Bp6tD/TsKHp5AkAei08uuy3LsHMV8gQCwYxgV1RDHou7voBXQpgGQrAamrn+wbaNkD3UKM3yatj1TQC49L5VXoXg1nfyBpOzEquL7MoayKNxt6poYl1epesTWLFKWHVNJZ0f4AHeZI4XCGaSORfcAYBVtwB2eRWX1Q8EPRXQnGg5kksjkMcSYLLCg789vAM3WYhpcLWn5BgYkWDFKkBDUdBQuCRVJpIah9bt7YQRYFIqof6loaUNDRUIBHsIxqD2dUIZGgBhFI5PLwgDt518Vz8B4lA+V2pip4w6UAZzepXTj5dKbrWEQCCYRUgSjJalkMZHoQz1582HAzLGDxACGol5BncskO2TSUYqb6wTAaD3tCMZLSu6Wy+PJdyyy1LLQvdkK41AsKeZk8EdwHsqnGI9FbKyU2RlFqB3bHNnshBGocaHYAZDpcntMgp9x9asMszdwUzuaAoEgnyUoX6oGUGZl7MF8LlxE46Z1yBv4ji7pLjLAFeBTiAQzF5oKALmMyMz0wuwo2V5JZeMEDiRbDvgJ7REGOMKuwXsBjENaB3bShrtlL2G3TuLWCCYSfJ18wV7JcRIeQ7blOPDJT1fGh8rOlzYD+ZRk848Ri4AyG4wFggEM45cqMcug2KBF1M1Lnc+RSbK3AUCwezH8VDmZsgWTWKBEMz6ZrD0zhtVVJiNCwEluzeYhqO+iWEv/yMTOTEy6aSTHavY/SOrBIIZRAR3swWfYMr38SkcxwgBS88CnBA/+N6zzyK5dD/YsQrX+DJZ4cM/cxw1JiuwY5WlrUcgEOwZSP69z7AzQcMA2GVVcMqK37t+A39zz+2oet5jZu2CEhYrEAhmAzQc5QPG0wkfltYyyJ0X6ZRXIbl0PySX7IvUkn09k0gsPXA8N8BzgmHQYkJxpfpAmdcTYwoEcxyRupglMFWDHYlByVGqsktUn6OBEKgezNr9Y+AjEohpcONaWQum6ZCMJKiqAYqKUcMACIHZ2ApS0wBiW6CBEEAIUoEg1IFermSnBWFV1QKK+EgJBHsTdkVVnpKcE4nBbFwIyUiCKWrJA7ztqlrIHduyz6XpgKzwmXGRGM/oywqUoX7IiWGw9Oy9yYxBEAgEez9WTQOsihpIlgGqB/yHdhOpqI2xq+rgBCN8Fie1QcMxWJW1Rddgx8qh9ndl9fMyQngiihBIqWTWCAMmybBj5SW9PoFgtiI88VmE2dgK1tftDt+1K2tK72EhBKnmxdD6uiCNJXgwV13n2VvjNkTnwFQt20ArKqw6kY0XCPZmnGg5jMZWdwCxEymDVVMPSJLvvV7oXH944w186P8dlnOufKfOrqyBXek/UF0gEMwBFAV0mpK6LBSG2bJkck+SFaRalkLt64aUGgcNBPkuYHp3jhgpqH1dO39X3ZBXFioQzDVEcDebkGRYdQumHlApKsyGluldk0Ag2OtxYhXTJh71Tm8v/nPh8mk5l0AgEOwqTA/CbFrk87uA7+8EgrmK6LkTCAQCgUAgEAgEgjmACO4EAoFAIBAIBAKBYA4ggjuBQCAQCAQCgUAgmAOI4E4gEAgEAoFAIBAI5gAiuBMIBAKBQCAQCASCOYAI7gQCgUAgEAgEAoFgDiCCO4FAIBAIBAKBQCCYA8y6OXeSRPbIdcrKyvbYtfZmxPuwE/FecKbzfZhL7+dcei2FmO/3wXx//cD8eQ/m0mss9bXMl7+tF/P1tc/X1w3M3tdebM2EMcb20FoEAoFAIBAIBAKBQLCbEGWZAoFAIBAIBAKBQDAHEMGdQCAQCAQCgUAgEMwBRHAnEAgEAoFAIBAIBHMAEdwJBAKBQCAQCAQCwRxABHcCgUAgEAgEAoFAMAcQwZ1AIBAIBAKBQCAQzAFEcCcQCAQCgUAgEAgEcwAR3AkEAoFAIBAIBALBHEAEdwKBQCAQCAQCgUAwBxDBnUAgEAgEAoFAIBDMAURwl8P69etxwgkn4OSTT8bJJ5+MV199daaXtEcZHR3FiSeeiB07dgAAnnvuOaxbtw7HHXccvvGNb8zw6vYsue/Fddddh+OOO879bDz22GMzvMLdz7e+9S2ccMIJOOGEE3DHHXcAmN+fifnEH/7wB3zoQx/Ccccdh5///Oe+xz355JM49thj9+DK9gzFXv+WLVuwfv16nHTSSbjoooswMjIyA6vcvRR7D9588018+MMfxkknnYRLL70U8Xh8BlYpmA5Kvd/nIvPN75vPft688euYwIVSylavXs0sy5rppcwIr7zyCjvxxBPZfvvtx9rb21kymWTHHHMM2759O7Msi1144YXsySefnOll7hFy3wvGGDvxxBNZT0/PDK9sz/Hss8+ys846ixmGwUzTZOeffz77wx/+MG8/E/OJ7u5utmbNGjY0NMTGxsbYunXr2KZNm/KO6+vrY2vXrmVr1qyZgVXuPoq9fkopO+6449hTTz3FGGPszjvvZHfcccdMLXe3UMpn4CMf+Yh7/992223s61//+kwsVbCLlHq/z0Xmm983n/28+eTXiZ27DLZs2QIAuPDCC3HSSSfh/vvvn+EV7Vl+/etf46abbkJtbS0A4LXXXkNrayuam5uhKArWrVuHRx99dIZXuWfIfS+SySQ6OzvxxS9+EevWrcM999wDSukMr3L3UlNTg2uvvRaapkFVVSxZsgTbtm2bt5+J+cRzzz2Hww8/HOXl5QiFQjj++OM9/8433HADLr/88hlY4e6l2Ot/8803EQqFcPTRRwMAPvGJT+Dcc8+dqeXuFkr5DFBKMTY2BoDbyEAgMBNLFewipd7vc5H55vfNZz9vPvl1ykwvYG8iHo9j1apVuPHGG2FZFs4//3wsWrQIRx555EwvbY9wyy23ZP3c29uLmpoa9+fa2lr09PTs6WXNCLnvRX9/Pw4//HDcdNNNiEajuPTSS/Hggw/izDPPnKEV7n6WLVvm/nvbtm3405/+hPPOO2/efibmE173/muvvZZ1zE9/+lPsu+++OOigg/b08nY7xV7/9u3bUV1djS9+8Yt46623sHjxYtx4440zsdTdRimfgWuvvRYXXnghbr31VgSDQfz617/e08sUTAOl/K3nKvPN75vPft588uvEzl0GhxxyCO644w5Eo1FUVlbi9NNPx1NPPTXTy5oxKKUghLg/M8ayfp5PNDc349vf/jZqa2sRDAaxfv36efPZ2LRpEy688EJ84QtfQHNzs/hMzAOK3fsbN27EX/7yF1x22WUzsbzdTrHXb9s2XnjhBXzkIx/Bww8/jObmZtx+++0zsdTdRrH3IJVK4frrr8d9992HZ555Bueccw6uueaamViqYBeZz9/1893vm89/+7ns14ngLoMXX3wRGzZscH9mjEFR5u/mZn19Pfr6+tyf+/r63O3s+cY777yDP//5z+7P8+Wz8a9//Qsf+9jH8LnPfQ6nnnqq+EzME4r9nR999FH09fXhwx/+MC655BL09vbinHPOmYml7haKvf6amhq0trbigAMOAACceOKJc26no9h7sHHjRui6jgMPPBAAcNZZZ+GFF17Y4+sU7Drz2a7Pd79vPv/t57JfJ4K7DBKJBO644w4YhoHR0VE8/PDD+MAHPjDTy5oxDjroIGzduhVtbW1wHAePPPKI22My32CM4dZbb8XIyAgsy8KvfvWrOf/Z6Orqwqc+9SncddddOOGEEwCIz8R84YgjjsCGDRswODiIZDKJv/zlL1l/509/+tP485//jN/97nf4wQ9+gNraWvziF7+YwRVPL8Ve/yGHHILBwUG8/fbbAIAnnngC++2330wtd7dQ7D1obW1Fd3e327P017/+1Q12BbOLYn/rucx89/vm83f6XPbr5kaIOk2sWbMGr776Kk455RRQSnHOOefgkEMOmellzRi6ruP222/HFVdcAcMwcMwxx2Dt2rUzvawZYeXKlbjkkkvwkY98BLZt47jjjsOJJ54408varfzoRz+CYRhZ5WZnn322+EzMA+rq6nDVVVfh/PPPh2VZOP3003HggQfi4x//OD796U/PeSe+lNf/7W9/GzfccAOSySTq6+vdUSFzhVLeg9tuuw1XXnklGGOoqqrCrbfeOtPLFkwBv7/1fGC++33z2c+by34dYYyxmV6EQCAQCAQCgUAgEAh2DVGWKRAIBAKBQCAQCARzABHcCQQCgUAgEAgEAsEcQAR3AoFAIBAIBAKBQDAHEMGdQCAQCAQCgUAgEMwBRHAnEAgEAoFAIBAIBHMAEdztZnbs2IEVK1bgf//3f7Me/9GPfoRrr7122q5z7LHH4vXXX5+28xVidHQUZ599Nk444QT85S9/2SPXnA4cx8EnP/lJHH/88bj//vt36VzXXnstfvSjH03TyoD169fj0UcfRU9PD84+++xpO69AMJPsSbu0q/T09ODaa6/FunXrcNJJJ+GMM87A448/PtPL8iTTTrS3t+OKK67Ie1wg2JuwLAurV6/GxRdf7D72j3/8Y5ek51esWIHBwcFJPWfiu3YucsMNN+CNN94AAFx//fV47rnnpuW8DzzwAH7wgx9My7lmksz3Z64j5tztASRJwte+9jUceuihWLx48UwvZ5d56623MDAwgMcee2ymlzIpenp68Mwzz+CVV16BLMszvRxP6urq8Mtf/nKmlyEQzCsGBwdx9tln4zOf+Qxuu+02EELw9ttv44ILLkAwGMSRRx4500vMItNOdHZ2YuvWrXmPCwR7E4899hhWrlyJN954A5s3b8aSJUtmeklzjueeew5nnXUWAOCWW26ZtvN+5CMfmbZzzSSZ789cRwR3e4BAIIALLrgAV199NX75y19C07Ss31977bVYtmwZLrrooryfjz32WJx44ol4/vnnMTIygosvvhgvvfQS3nzzTSiKgu9+97uoq6sDAPziF7/A22+/DdM0ccEFF+D0008HADzxxBP47ne/C8uyEAgEcM011+CQQw7Bvffei1deeQW9vb1YsWIF7rrrrqx1Pf744/jWt74FSinC4TCuu+46RCIRfPGLX0RPTw9OPvlk/OpXv0IgEADAnYwTTzwRTz31FKLRKBhjWLt2Le6++24sWLAAt9xyCzZu3AjLsrBq1Sp84QtfgKIoePDBB/GrX/0KlmVhZGQEH//4x3HOOefgoYcewoMPPohkMolIJIKvf/3ruOaaazA0NAQAOOaYY3DllVfmvd8vvvgi7rjjDiSTSaiqiiuvvBLvec97cPHFF8O2bZx22mm499570dLSkvWc22+/HZRSAMCll16K448/HmNjY/jqV7+Kl156CbIs4/3vfz+uuuoqAMDLL7+Ms88+G/39/Vi2bBn++7//G6FQyPP6Rx99NADg29/+Nv74xz9ClmUsWrQIN954I2pqatx17NixA+vWrcPLL7+Me++9Fx0dHejr60NHRwfq6upw5513ora2Fq+99hq+9KUvwbIstLS0oLOzE9deey3e9773Te1DKhDsZg444ABccsklePbZZ9Hb24uLL74Y55xzDgDg+9//Ph5++GEoioLW1lbcfvvtiEajvvfL+vXrsd9+++GVV17B4OAgzjzzTPT39+OFF15AMpnEN7/5TaxYsQKJRMLX7mTyi1/8Au95z3twyimnuI+tXLkS99xzD2KxGABvu3L00UfjoYcewl/+8hdQStHZ2Ym6ujqceeaZuP/++7Ft2zZccMEFuPDCC/HQQw/h0UcfzTru9ttvR11dHbq7u/GlL30JHR0dYIzhlFNOce3VzTffjJdeegmqqqKpqQm33XYbhoaGsG7dOrz44ou44YYb0NPTg4suughf/vKXXfthWRZuv/12bNiwAbIs48ADD3Rt+LHHHotTTz0VGzZsQFdXF04++WRPWyoQTBcPPPAAPvShD6GlpQU/+clP8JWvfCXr937ftaOjo/jyl7+Mt99+G4QQHHXUUfjsZz/r3sP33nsvXn31VQwPD+Oiiy7CueeeC6D4d20ufX19uOmmm7BlyxZIkoSzzz4b559/vu+9uWPHDnzsYx/DMcccg1dffRXxeByf//zn8YEPfACbN2/G9ddfD9M0wRjD6aefjnPPPRf33nsvhoaG8F//9V/u2id+LtWmrV+/Hvvuuy/+9a9/YWhoCCeffDI+/elP4xvf+AZ6e3tx9dVX44477sBdd92Fc889F2vXrvX05Q488MCCPkYmmess1XYce+yxOOGEE/Dss88ikUjgggsucO19qT7p7bffjjvvvBNPPvkkZFnGIYccgptuugmapuG73/2ua3cXLFiAm266CXV1dVi/fj0OPvhgvPTSS+jq6sKqVatw88034+677856fxhjuPPOO2GaJvr6+nDEEUfg1ltvBQA89NBD+MEPfoBAIIDDDz8cP/3pT/Hvf/8bAHyvu9fBBLuV9vZ2dvDBBzPHcdi5557Lbr/9dsYYYz/84Q/ZNddcwxhj7JprrmE//OEP3edk/rxmzRp26623MsYY++Mf/8hWrlzJ3nrrLcYYY5dddhn77ne/6x530003McYY6+7uZqtWrWIbN25kW7duZSeeeCIbHBxkjDG2ceNGduSRR7KxsTF2zz33sOOPP55ZlpW37nfffZcdccQRbPv27Ywxxp577jl25JFHskQiwZ5//nl2wgkneL7eT37yk+z+++93n3PmmWcyxhi79tpr2U9/+lPGGGO2bbOrr76a/eAHP2Cjo6PszDPPdNf38ssvs4MPPpgxxthvfvMb9t73vpclEgnGGGPf+ta32I033sgYY2xsbIxdeeWVLB6PZ11/cHCQrVq1ir3yyivu6z3ssMPY9u3b3b+FF+effz575JFHGGOMvfXWW+xLX/oSY4yxW2+9lV111VXMtm1mGAY799xz2fPPP8+uueYadvrpp7Px8XFm2zY79dRT2cMPP1zw+g8++CA766yz2NjYGGOMsXvuuYddeOGFjDHGzjvvPPanP/0pa4333HMP+8///E/39V966aXs7rvvZpZlsaOPPpo9+eSTjDHGNmzYwFasWMGef/55z9cmEMwUa9asYa+99hpjjLHly5ezn/3sZ4wxxl5//XW2//77s1QqxR5//HF23HHHseHhYcYYv+e+853vFL1fLr/8csYYY6+88gpbvnw5++tf/8oYY+yWW25hN9xwA2PM3+7kcumll7p2y4tC9/VvfvMbduihh7LOzk7mOA770Ic+xK644grmOA5766232AEHHMAcx2G/+c1v2MEHH8y2bNnCGGPszjvvZFdccQVjjLFzzz2X/fjHP2aMMRaPx9m6devYI488wv75z3+ytWvXMkopY4yxO+64g/3rX//KshOZ9jjz8bvvvptdfvnlzDRN5jgOu/baa137uWbNGve7qLu7mx1wwAGurRcIpptNmzax/fbbjw0ODrJXX32VHXjggWxwcDDrs+v3XfuFL3yB3XzzzYxSygzDYBdeeCH7/ve/zxjjNuVHP/oRY4yxN998k+2///7MNM2Svmtz+dSnPsW+9rWvMcb4PXjCCSewbdu2+d6b7e3tbPny5eyJJ55gjDH26KOPsv/4j/9gjDF23XXXuWvs7e1lV155JXMch91zzz3sy1/+snvNzJ9LtWnnnXce+/jHP85M02QjIyPs+OOPd9eQaW8nXmchX87Px8glc52l2o41a9awG2+8kVFKWVdXF3vf+97H3n777Un5pD/5yU/Yueeey5LJJHMch33mM59hDz/8MHv44YfZlVde6R73y1/+kl188cXu6/70pz/NHMdhiUSCrV69mm3YsCHv/bnqqqtcn2l0dJS9733vY6+//jrbtGkTW7VqFevq6mKMMXbvvfey5cuXM8ZYwevubYiduz2EJEm48847ccopp2D16tWTeu5xxx0HAGhubkZ1dTVWrlwJAGhpacHIyIh73ESvRV1dHY488kg3Y9vb24uPfexj7nGEEGzfvh0AcPDBB+dlsQHg+eefx+GHH47m5mYAwKpVq1BZWYk33ngDhBDftZ577rm48847ce655+JXv/qVu53/5JNP4vXXX8eDDz4IAEilUgCAcDiM733ve3jqqaewbds2vP322xgfH3fPt2LFCkQiEQDAUUcdhUsuuQRdXV044ogj8LnPfQ7RaDTr+q+99hpaWlpw0EEHAQCWLVuG97znPXjhhRcK7mp98IMfxFe+8hU88cQTOOKII/DZz34WAN/Gv+666yDLMmRZdnv1Hn74Ybz//e9HMBh0rzM4OFjw+k8//TROO+00hEIhAMD555+P733vezBN03ddhx12mPv69913X4yMjGDjxo0A+M4lABx++OFYtmyZ7zkEgr2F//zP/wQA7LfffjBNE+Pj49iwYQPWrl2LsrIyAMB1110HAPjMZz5T8H75wAc+AACujTrqqKMAcLv4wgsvAPC3O7kQQsAY8113ofuaEIIDDjgADQ0NAICmpiasXr0akiShubkZhmEgmUwCAI488kgsWrQIAHDmmWfi5JNPxvj4OF566SX8+Mc/BgBEo1GcdtppePrpp3H99ddDlmWcccYZWL16NY4//ngceOCB2LFjR9H3+umnn8ZVV10FVVUB8F6jT33qU3l/i7q6OlRVVWFkZMR9LwWC6eSBBx7AmjVrUFFRgYqKCjQ1NeHXv/41Dj74YPcYv+/aK6+8Eg888AAIIdA0DWeffTZ+8pOf4JJLLgEAt2dvn332gWmaGB0dndJ37XPPPYfPf/7zAPg9+MgjjxS8Nw866CCoqup+D++7774YHh4GwG3TNddcg9deew2rVq3CDTfcAEkqLnFRik0DgLPOOguqqkJVVaxduxbPPPMM1qxZ43nOQr4c4O1jFKNU23HOOeeAEIL6+nocddRRePbZZ6Hresk+6XPPPYeTTz7ZrQ775je/CYB/N7z++uv48Ic/DACglLo2FgDWrFkDSZIQiUTQ2trq+Zpuv/12PP300/je976HLVu2wDAMjI+P48UXX8SRRx6J+vp6AMB5552He++9FwDwt7/9reB19yZEcLcHaWhowJe//GVcc801WeU/uY6FZVlZz8ss45z4ovYi03hQSqEoChzHwapVq9ybAgC6urpQW1uLxx57zDV+uVBK84I4xhhs2y64hiOOOALJZBIbNmzAiy++iK997Wvu+e6++263zj4ej4MQgu7ubpx11lk488wzceihh2Lt2rX429/+5p4vc30HHngg/vrXv2LDhg14/vnnccYZZ+B//ud/sP/++7vHOI7ju+5CnH322VizZg2effZZ/P3vf8e3vvUtPProo1AUJet8XV1drqHJDIon/oaFrp/7nlJKi65r4lqZ15BlOc8R3Vt7CAWCTHRdBwD3Ppj4PGfeF/F4HPF4vOj9klve7mWX/OxOLgcffDBeeeUVnHfeeVmP//KXv0QymURra2tBe5i7Fq+EGZB9n1JKIcsyKKV59/PEa43FYvjd736Hl156Cc8//zyuvPJKXHTRRa5DWQiv9y/zu2XibwEUD24FgqkyPj6O3/3ud9A0DcceeywALsp2//33Z313+33XFrMDE/dapk2Zyndt7vXb29tRXl7ue28C3OZM+F2Zz12zZg3+/Oc/47nnnsOGDRvw7W9/Gw899NCkfL2J8/utdQLGWMHAsZAvB3j7GMUo1XZkrpNSCkmSQCkt2SfNtaP9/f2glIJSmlXWb5pmVgBXyms677zzsGLFChx11FH44Ac/iFdffdXTv8q12YWuuzch1DL3MGvXrsXRRx+Nn/zkJ+5jFRUVbhalp6cnK0MzGR5++GEAvPdtw4YNWLVqFVatWoVnn30WmzdvBgA89dRTOOmkk3wz2BOsWrUKzzzzDNrb2wHAra+eyFz7QQjBOeecg+uvvx4nnniiawRWr16N++67D4wxmKaJT37yk7j//vvxxhtvoLKyEpdddhlWr17tBnaO4+Sd+6677sJ3vvMdvP/978f111+PpUuXYtOmTVnHHHzwwdiyZQtee+01AMCmTZvwz3/+E4cddljBdZ999tl46623cNppp+Hmm29GPB5HX18fVq1ahYcffhiUUpimiU9/+tP45z//6XueQtc/6qij8Jvf/MbdmfzZz36G9773vXkGvRhLliyBpml4+umnAfBdhY0bNxbcURUI9laOOOIIPPbYYxgdHQXA+zvuu+++ablf/OxOLmeddRZeeOEF/P73v3e/2N944w3cc889WL58+ZTtSi7PP/88enp6APDAcc2aNYhEIjjooIPw85//HACQSCTw29/+FkcccQT+9re/4WMf+xgOOeQQXHHFFTjllFPy1N5kWc5zEgGe9X/ggQdgWRYopfj5z3++1wnDCOY+f/jDH1BeXo6///3veOKJJ/DEE0/g8ccfx/j4eJbSpd937erVq3H//fe79/Cvf/1rHHHEEQWvORXbsWrVKvzmN78BwO/Bj370o2hra/O9Nwvxuc99Dv/3f/+HE044ATfddBMikQi2b9+OiooKvPnmm2CMYXR0NCuRPRl+//vfg1KKkZER/OlPf3KDZlmW84LYqfpy08Fvf/tbANwnffbZZ3H00UdPyiddtWoVHnnkEZimCUopvvSlL+GPf/wjVq9ejQcffND9zrj77rvxhS98oeh6Jt6feDyO119/HVdffTWOO+44dHd3Y/v27aCUYvXq1diwYYNrpzOV7qd63ZlA7NzNADfccAP+9a9/uT+vX78eV199NY4//ng0NTXh8MMPn9J5DcPAqaeeCsuycMMNN7jlP1/5ylfw2c9+FowxV4QlHA4XPNfSpUtx00034fLLL4fjOAgEAvje976XVwbpxamnnoqvfe1rWapE119/PW655RasW7cOlmXhiCOOcAUDHnzwQaxduxaEEBx22GGorKxEW1tb3nk/+tGP4tprr8WJJ54ITdOwYsUKnHDCCVnHVFZW4u6778bNN9+MVCoFQghuu+02LFq0qGAp09VXX41bb70V3/zmN0EIweWXX46mpiZcfvnluOWWW3DyySfDcRx86EMfwnHHHYcnnnjC8zyFrt/a2oquri6cccYZoJSitbU1T8SmFBRFwb333oubbroJX//617Fw4UJUV1dnZasEgtnCMcccg3fffdct4V66dCluvvlmhEKhXb5f/OxOLuXl5fjZz36GO++8E9///vchSRKCwSBuueUWNyDyu69ffvnlktdTV1eHz3/+8+jr68PSpUtdUYm77roLX/nKV/DQQw/BNE2sW7cOp512GiilePrpp3HiiSciFAqhrKwMN998c9Y5ly5dCl3Xcfrpp+Mb3/iG+/gnP/lJfO1rX8Mpp5wC27Zx4IEH4sYbb5zU+ycQ7CoPPPAALrjggqwdkFgshvXr1+O+++5zH/P7rn3ve9+Lr371q+49fNRRR+ETn/hEwWuefvrpk7Yd//Vf/4UvfelLWLduHRhjuPTSS7H//vv73psdHR2+57rssstw/fXX41e/+pUrDvPe974X++yzD/7+97/juOOOQ11dHQ477LAp7ZinUimcfvrpGBsbwznnnINVq1YB4GWdn//85/GlL33JPXZXfLldZceOHTjttNOQSqVwww03uGrxpfqkZ599Njo6OnDaaaeBMYbDDjsM69evhyRJ6OnpwZlnnglCCBoaGnD77bcXXU/m+3PJJZfg1FNPRSgUQl1dHd7znvegra0Nq1atwnXXXYeLLroImqZhn332cdtvzjjjjClddyYgTNRiCKaZP/7xj3j44Yfxwx/+cKaXMmf52te+hosuugjV1dWuYtXjjz/uKvsJBIK9i4ceegh//vOf8f3vf3+mlyIQCGYp69evd1Uw92aOPfZY3H333TjggANmeimTor29Hb/73e9w2WWXQZIk/OUvf8H//M//5M2q3tsRO3eCaWX9+vUYHBzEd77znZleypxmwYIF+NjHPgZFUcAYw1e/+lUR2AkEAoFAIBBMkfr6evT29mLdunWQZRnRaNQdkTCbEDt3AoFAIBAIBAKBQDAHEIIqAoFAIBAIBAKBQDAHEMGdQCAQCAQCgUAgEMwBRHAnEAgEAoFAIBAIBHMAEdwJBAKBQCAQCAQCwRxABHcCgUAgEAgEAoFAMAf4/wFVKuzElBSFEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the graph when these two factors (Income and schooling) affect schooling\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,3,1)\n",
    "plt.scatter(x, y,facecolors='#f4c2c2', edgecolors='none' )\n",
    "plt.plot(x,life_pred_sc, color=\"#097969\")\n",
    "plt.xlabel(\"Number of years of schooling\")\n",
    "plt.ylabel(\"Life expectancy in years\")\n",
    "plt.title(\"Schooling vs Life Expectancy\")\n",
    "plt.grid(color = \"grey\",linewidth=1)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.scatter(x_new_income, y_inc,facecolors='#f4c2c2', edgecolors='none' )\n",
    "plt.plot(x_new_income,life_pred_income, color=\"#097969\")\n",
    "plt.xlabel(\"Income Composition\")\n",
    "plt.ylabel(\"Life expectancy in years\")\n",
    "plt.title(\"Income vs Life Expectancy\")\n",
    "plt.grid(color = \"grey\",linewidth=1)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.scatter(x_alcohol, y,facecolors='#f4c2c2', edgecolors='none' )\n",
    "plt.plot(x_alcohol,life_pred_alcohol, color=\"#097969\")\n",
    "plt.xlabel(\"Alcohol consumption in percentage\")\n",
    "plt.ylabel(\"Life expectancy in years\")\n",
    "plt.title(\"Alcohol conusmption vs Life Expectancy\")\n",
    "plt.grid(color = \"grey\",linewidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0e5526",
   "metadata": {},
   "source": [
    "### Modelling and Regression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "9bd040bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Income composition of resources'>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_lifeexp['Income composition of resources'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4b7688",
   "metadata": {},
   "source": [
    "#### Insight-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "e3529145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.533\n",
      "Model:                            OLS   Adj. R-squared:                  0.533\n",
      "Method:                 Least Squares   F-statistic:                     3357.\n",
      "Date:                Sat, 03 Dec 2022   Prob (F-statistic):               0.00\n",
      "Time:                        19:18:00   Log-Likelihood:                -9623.2\n",
      "No. Observations:                2938   AIC:                         1.925e+04\n",
      "Df Residuals:                    2936   BIC:                         1.926e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     41.3403      0.497     83.104      0.000      40.365      42.316\n",
      "Schooling      2.2986      0.040     57.940      0.000       2.221       2.376\n",
      "==============================================================================\n",
      "Omnibus:                      375.706   Durbin-Watson:                   0.229\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              563.330\n",
      "Skew:                          -0.923   Prob(JB):                    4.73e-123\n",
      "Kurtosis:                       4.092   Cond. No.                         53.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# OLS Regression results: to find the p-value, and constants\n",
    "\n",
    "x_const = sm.add_constant(x) # Adding intercept\n",
    "est = sm.OLS(y, x_const)\n",
    "estimate = est.fit()\n",
    "#print(estimate.summary())\n",
    "print(estimate.summary(xname=[\"Intercept\",\"Schooling\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72f903f",
   "metadata": {},
   "source": [
    "Life expectancy is highly affected by schooling  with a p-value of < 0.05\n",
    "Therefore, every 1-year increase in schooling increases life    expectancy by 2.3 years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68f1ccb",
   "metadata": {},
   "source": [
    "#### Insight-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "3faeb6c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For income vs Life expectancy\n",
    "lr_income = LinearRegression()\n",
    "lr_income.fit(x_income, y)\n",
    "life_pred_income = lr_income.predict(x_income)\n",
    "\n",
    "# For alcohol vs life expectancy\n",
    "lr_alcohol = LinearRegression()\n",
    "lr_alcohol.fit(x_alcohol, y)\n",
    "life_pred_alcohol = lr_alcohol.predict(x_alcohol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "ffafd81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing the significance of schooling on life expectancy in developed and undeveloped countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "a538787c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lifeexp_final = pd.get_dummies(df_lifeexp, columns=[\"Status\"])\n",
    "df_lifeexp_final.drop(\"Status_Developing\", inplace=True, axis=1)\n",
    "df_lifeexp_final[\"Schooling:Developed\"] = df_lifeexp_final[\"Schooling\"]*df_lifeexp_final[\"Status_Developed\"]\n",
    "x_categorical = df_lifeexp_final.loc[:,[\"Schooling\",\"Status_Developed\",\"Schooling:Developed\"]].values\n",
    "# 2426 Developing countries and 512 Developed Countries, 1- Developed, 0-developing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "4536b8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.564\n",
      "Model:                            OLS   Adj. R-squared:                  0.564\n",
      "Method:                 Least Squares   F-statistic:                     1266.\n",
      "Date:                Sat, 03 Dec 2022   Prob (F-statistic):               0.00\n",
      "Time:                        19:18:01   Log-Likelihood:                -9523.0\n",
      "No. Observations:                2938   AIC:                         1.905e+04\n",
      "Df Residuals:                    2934   BIC:                         1.908e+04\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept              41.8293      0.557     75.151      0.000      40.738      42.921\n",
      "Schooling               2.2159      0.047     46.895      0.000       2.123       2.309\n",
      "Status_Developed       26.1695      2.257     11.597      0.000      21.745      30.594\n",
      "Schooling:Developed    -1.4945      0.148    -10.129      0.000      -1.784      -1.205\n",
      "==============================================================================\n",
      "Omnibus:                      399.230   Durbin-Watson:                   0.240\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              614.852\n",
      "Skew:                          -0.957   Prob(JB):                    3.07e-134\n",
      "Kurtosis:                       4.167   Cond. No.                         260.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "x_const_categorical = sm.add_constant(x_categorical) # Adding intercept\n",
    "est = sm.OLS(y, x_const_categorical)\n",
    "estimate = est.fit()\n",
    "# #print(estimate.summary())\n",
    "print(estimate.summary(xname=[\"Intercept\",\"Schooling\",\"Status_Developed\", \"Schooling:Developed\"]))\n",
    "# R2 is very low (0.185) which means there does not exist strong linear relationship between GDP and Life expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7fe1148",
   "metadata": {},
   "source": [
    "---> From the above analysis, we can understand that:\n",
    "\n",
    "y = 41.8293  + 2.2159*Schooling + 26.1695*Developed -1.4945*Schooling*Developed\n",
    "\n",
    "Developing: The average LE in a developing country is 41.8 years, and with 1 year increase in schooling the LE increases by 2.2 years <br>\n",
    "Developed: The average LE in a developed country is 68 years, and with 1 year increase in schooling the LE increases by 0.7 years <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "49faf97a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####################\n",
    "### FOR PPT - 2\n",
    "# Graph\n",
    "\n",
    "Schooling = np.linspace(0,20,100)\n",
    "l_exp_undev = 45.197 + 1.9477*Schooling # Developing\n",
    "l_exp_dev = 68.619 + 0.683*Schooling # Developed\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(Schooling, l_exp_dev, label=\"Developed\")\n",
    "plt.plot(Schooling, l_exp_undev, label=\"Developing\")\n",
    "plt.xlabel(\"Number of Schooling years\")\n",
    "plt.ylabel(\"Life expectancy\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5ed9bb",
   "metadata": {},
   "source": [
    "From the above graph (WHO data) we can deduce that:\n",
    "1. The rate at which the life expectancy increases w.r.t schooling is slower in developed countries than developing.\n",
    "2. People in developing countries have to mandatorily take schooling in order to live longer\n",
    "3. People in developed countries do not need to do schooling mandatorily to bring their life expectancy longer, there may be other factors that are driving them to live longer as we can see that people with no schooling live up to > 60 years, but for a person in developing country, he needs to do 10 years of schooling to bring his lifeline above 60 years\n",
    "4. A person who does 8 years of schooling in developed country live 14 years longer than a person who does same years of schooling in undeveloped countries.\n",
    "\n",
    "Hence we can deduce, that:\n",
    "Schooling has a major impact on life expectancy for developing countries than developed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192205fa",
   "metadata": {},
   "source": [
    "#2\n",
    "\n",
    "-> The average life expectancy in a developed country w/o education is higher than that of a developing country w/o education. <br>\n",
    "-> However, we see a greater influence of education in a developing country as opposed  to a developed country. <br>\n",
    "-> Reason being, chances of accessing most of new age amenities increases with education in developing countries, whereas, people already have access to better healthcare, better pharma, better standard of living by default in developed countries. <br>\n",
    "-> A developing country is constantly educating themselves, making changes towards advancement and striving to make their amenities better, in turn offering a better chance at healthcare, hence we can expect better effect of schooling on LE in a developing country over a already developed country. <br>\n",
    "-> We can expect the healthcare to be saturated in an already developed country."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a063f0b",
   "metadata": {},
   "source": [
    "#### Insight-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "ddfccffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.606\n",
      "Model:                            OLS   Adj. R-squared:                  0.605\n",
      "Method:                 Least Squares   F-statistic:                     749.9\n",
      "Date:                Sat, 03 Dec 2022   Prob (F-statistic):               0.00\n",
      "Time:                        19:18:01   Log-Likelihood:                -9376.6\n",
      "No. Observations:                2938   AIC:                         1.877e+04\n",
      "Df Residuals:                    2931   BIC:                         1.881e+04\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================================\n",
      "                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Intercept                          41.0600      0.536     76.654      0.000      40.010      42.110\n",
      "Schooling                           1.6755      0.062     27.053      0.000       1.554       1.797\n",
      "Status_Developed                   25.0638      2.400     10.444      0.000      20.358      29.769\n",
      "Alcohol                            -0.1867      0.040     -4.653      0.000      -0.265      -0.108\n",
      "Income composition of resources    12.9896      0.790     16.442      0.000      11.441      14.539\n",
      "Schooling:Developed                -1.3622      0.143     -9.514      0.000      -1.643      -1.081\n",
      "Alcohol:Developed                  -0.0923      0.095     -0.967      0.334      -0.280       0.095\n",
      "==============================================================================\n",
      "Omnibus:                      355.073   Durbin-Watson:                   0.271\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              612.238\n",
      "Skew:                          -0.811   Prob(JB):                    1.13e-133\n",
      "Kurtosis:                       4.540   Cond. No.                         319.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df_lifeexp_final[\"Alcohol:Developed\"] = df_lifeexp_final[\"Alcohol:Developed\"] = df_lifeexp_final[\"Alcohol\"]*df_lifeexp_final[\"Status_Developed\"]\n",
    "x_final = df_lifeexp_final.loc[:,[\"Schooling\",\"Status_Developed\",\"Alcohol\",\"Income composition of resources\",\"Schooling:Developed\",\"Alcohol:Developed\"]].values\n",
    "x_const_final = sm.add_constant(x_final) # Adding intercept\n",
    "est = sm.OLS(y, x_const_final)\n",
    "estimate = est.fit()\n",
    "# #print(estimate.summary())\n",
    "print(estimate.summary(xname=[\"Intercept\",\"Schooling\",\"Status_Developed\",\"Alcohol\",\"Income composition of resources\",\"Schooling:Developed\",\"Alcohol:Developed\"]))\n",
    "# R2 is very low (0.185) which means there does not exist strong linear relationship between GDP and Life expectancy\n",
    "# 2426 Developing countries and 512 Developed Countries, 1- Developed, 0-developing"
   ]
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    "When controlling for country status, alcohol and income composition of a country, the effect of schooling on life expectancy is significant. Also, when controlled for income composition, country status, and alcohol consumption, for every unit increase in alcohol consumption, life expectancy decreases"
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