code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4fbe3204",
   "metadata": {},
   "source": [
    "# MODS PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e059612e",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a3385cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import f \n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "fdff3a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.read_csv(\"MROZ.txt\", delim_whitespace=True, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2aa1b79",
   "metadata": {},
   "source": [
    "### Naming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "21c570b2",
   "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>laborforce</th>\n",
       "      <th>hours worked</th>\n",
       "      <th>kids below 6</th>\n",
       "      <th>kids between 6 and 18</th>\n",
       "      <th>age women</th>\n",
       "      <th>educ years of schooling</th>\n",
       "      <th>wage estimated</th>\n",
       "      <th>husbandWage</th>\n",
       "      <th>hours worked by husband</th>\n",
       "      <th>husband age</th>\n",
       "      <th>...</th>\n",
       "      <th>family income</th>\n",
       "      <th>marginal tax rate facing woman</th>\n",
       "      <th>mothers year of schooling</th>\n",
       "      <th>fathers year of schooling</th>\n",
       "      <th>rate in country of resid</th>\n",
       "      <th>live in SMSA</th>\n",
       "      <th>marginal tax rate facing woman</th>\n",
       "      <th>nwifeinc</th>\n",
       "      <th>log(wage)</th>\n",
       "      <th>expersq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1610</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>3.354</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2708</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>16310</td>\n",
       "      <td>0.7215</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10.910060</td>\n",
       "      <td>1.210154</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1656</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>1.3889</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2310</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>21800</td>\n",
       "      <td>0.6615</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>19.499980</td>\n",
       "      <td>.3285121</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>4.5455</td>\n",
       "      <td>4.04</td>\n",
       "      <td>3072</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>21040</td>\n",
       "      <td>0.6915</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>12.039910</td>\n",
       "      <td>1.514138</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0965</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1920</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>7300</td>\n",
       "      <td>0.7815</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.799996</td>\n",
       "      <td>.0921233</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1568</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>4.5918</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2000</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>27300</td>\n",
       "      <td>0.6215</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>20.100060</td>\n",
       "      <td>1.524272</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3020</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>28200</td>\n",
       "      <td>0.6215</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>.</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2056</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.7715</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>.</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2383</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>9952</td>\n",
       "      <td>0.7515</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.952000</td>\n",
       "      <td>.</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1705</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>24984</td>\n",
       "      <td>0.6215</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>24.984000</td>\n",
       "      <td>.</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3120</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>28363</td>\n",
       "      <td>0.6915</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>28.363000</td>\n",
       "      <td>.</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>753 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     laborforce  hours worked  kids below 6  kids between 6 and 18  age women  \\\n",
       "0             1          1610             1                      0         32   \n",
       "1             1          1656             0                      2         30   \n",
       "2             1          1980             1                      3         35   \n",
       "3             1           456             0                      3         34   \n",
       "4             1          1568             1                      2         31   \n",
       "..          ...           ...           ...                    ...        ...   \n",
       "748           0             0             0                      2         40   \n",
       "749           0             0             2                      3         31   \n",
       "750           0             0             0                      0         43   \n",
       "751           0             0             0                      0         60   \n",
       "752           0             0             0                      3         39   \n",
       "\n",
       "     educ years of schooling wage estimated  husbandWage  \\\n",
       "0                         12          3.354         2.65   \n",
       "1                         12         1.3889         2.65   \n",
       "2                         12         4.5455         4.04   \n",
       "3                         12         1.0965         3.25   \n",
       "4                         14         4.5918         3.60   \n",
       "..                       ...            ...          ...   \n",
       "748                       13              .         0.00   \n",
       "749                       12              .         0.00   \n",
       "750                       12              .         0.00   \n",
       "751                       12              .         0.00   \n",
       "752                        9              .         0.00   \n",
       "\n",
       "     hours worked by husband  husband age  ...  family income  \\\n",
       "0                       2708           34  ...          16310   \n",
       "1                       2310           30  ...          21800   \n",
       "2                       3072           40  ...          21040   \n",
       "3                       1920           53  ...           7300   \n",
       "4                       2000           32  ...          27300   \n",
       "..                       ...          ...  ...            ...   \n",
       "748                     3020           43  ...          28200   \n",
       "749                     2056           33  ...          10000   \n",
       "750                     2383           43  ...           9952   \n",
       "751                     1705           55  ...          24984   \n",
       "752                     3120           48  ...          28363   \n",
       "\n",
       "     marginal tax rate facing woman  mothers year of schooling  \\\n",
       "0                            0.7215                         12   \n",
       "1                            0.6615                          7   \n",
       "2                            0.6915                         12   \n",
       "3                            0.7815                          7   \n",
       "4                            0.6215                         12   \n",
       "..                              ...                        ...   \n",
       "748                          0.6215                         10   \n",
       "749                          0.7715                         12   \n",
       "750                          0.7515                         10   \n",
       "751                          0.6215                         12   \n",
       "752                          0.6915                          7   \n",
       "\n",
       "     fathers year of schooling  rate in country of resid  live in SMSA  \\\n",
       "0                            7                       5.0             0   \n",
       "1                            7                      11.0             1   \n",
       "2                            7                       5.0             0   \n",
       "3                            7                       5.0             0   \n",
       "4                           14                       9.5             1   \n",
       "..                         ...                       ...           ...   \n",
       "748                         10                       9.5             1   \n",
       "749                         12                       7.5             0   \n",
       "750                          3                       7.5             0   \n",
       "751                         12                      14.0             1   \n",
       "752                          7                      11.0             1   \n",
       "\n",
       "     marginal tax rate facing woman   nwifeinc  log(wage)  expersq  \n",
       "0                                14  10.910060   1.210154      196  \n",
       "1                                 5  19.499980   .3285121       25  \n",
       "2                                15  12.039910   1.514138      225  \n",
       "3                                 6   6.799996   .0921233       36  \n",
       "4                                 7  20.100060   1.524272       49  \n",
       "..                              ...        ...        ...      ...  \n",
       "748                               5  28.200000          .       25  \n",
       "749                              14  10.000000          .      196  \n",
       "750                               4   9.952000          .       16  \n",
       "751                              15  24.984000          .      225  \n",
       "752                              12  28.363000          .      144  \n",
       "\n",
       "[753 rows x 22 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0 = df0.rename(columns={0: 'laborforce' \n",
    "                          ,1: \"hours worked\"\n",
    "                          , 2: \"kids below 6\"\n",
    "                          ,3: 'kids between 6 and 18'\n",
    "                          , 4: 'age women'\n",
    "                          , 5: 'educ years of schooling'\n",
    "                          , 6: 'wage estimated'\n",
    "                          , 7: 'husbandWage'\n",
    "                          , 8: 'hours worked by husband'\n",
    "                          , 9: 'husband age'\n",
    "                          , 10: 'huband year of schooling'\n",
    "                          , 11: 'husbands hourly wage'\n",
    "                          , 12: 'family income'\n",
    "                          , 13: 'marginal tax rate facing woman'\n",
    "                          , 14:'mothers year of schooling'\n",
    "                          , 15: 'fathers year of schooling'\n",
    "                          , 16:'rate in country of resid'\n",
    "                          , 17: 'live in SMSA'\n",
    "                          , 18: 'marginal tax rate facing woman'\n",
    "                          , 19: 'nwifeinc'\n",
    "                          , 20: 'log(wage)'\n",
    "                          , 21: 'expersq'})\n",
    "df0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aff6fa2",
   "metadata": {},
   "source": [
    "### Question  1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1c5302",
   "metadata": {},
   "source": [
    "Let's transform \".\" into real values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "86d1b0cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "s,df = df0['wage estimated'],df0.copy()\n",
    "empty_rows = s[s==\".\" ].index\n",
    "df = df.drop(empty_rows)\n",
    "df0=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4b4db921",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We removed  0  where the wage data was null or negative\n"
     ]
    }
   ],
   "source": [
    "print(\"We removed \",len(df0)-len(df),\" where the wage data was null or negative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da603a0",
   "metadata": {},
   "source": [
    "### Question  2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce6b347",
   "metadata": {},
   "source": [
    "#### For all women:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "445c665d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics of women salaries whose husband earn more the median are:\n"
     ]
    },
    {
     "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>wage estimated</th>\n",
       "      <th>age women</th>\n",
       "      <th>educ years of schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>428.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.177682</td>\n",
       "      <td>41.971963</td>\n",
       "      <td>12.658879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.310282</td>\n",
       "      <td>7.721084</td>\n",
       "      <td>2.285376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.128200</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.262600</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.481900</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.970750</td>\n",
       "      <td>47.250000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wage estimated   age women  educ years of schooling\n",
       "count      428.000000  428.000000               428.000000\n",
       "mean         4.177682   41.971963                12.658879\n",
       "std          3.310282    7.721084                 2.285376\n",
       "min          0.128200   30.000000                 5.000000\n",
       "25%          2.262600   35.000000                12.000000\n",
       "50%          3.481900   42.000000                12.000000\n",
       "75%          4.970750   47.250000                14.000000\n",
       "max         25.000000   60.000000                17.000000"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wage estimated']= pd.to_numeric(df['wage estimated'])\n",
    "print('The statistics of women salaries whose husband earn more the median are:')\n",
    "df[['wage estimated', 'age women', 'educ years of schooling']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b999348",
   "metadata": {},
   "source": [
    "#### For women whose husband earn more than the median wage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "1da2ea7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics of women salaries whose husband earn more the median are:\n"
     ]
    },
    {
     "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>wage estimated</th>\n",
       "      <th>age women</th>\n",
       "      <th>educ years of schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.232559</td>\n",
       "      <td>41.841121</td>\n",
       "      <td>13.135514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.937990</td>\n",
       "      <td>7.338431</td>\n",
       "      <td>2.238240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.382600</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.576825</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.543150</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.863100</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wage estimated   age women  educ years of schooling\n",
       "count      214.000000  214.000000               214.000000\n",
       "mean         5.232559   41.841121                13.135514\n",
       "std          2.937990    7.338431                 2.238240\n",
       "min          0.382600   30.000000                 7.000000\n",
       "25%          3.576825   35.000000                12.000000\n",
       "50%          4.543150   43.000000                12.000000\n",
       "75%          5.863100   47.000000                15.000000\n",
       "max         25.000000   58.000000                17.000000"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The statistics of women salaries whose husband earn more the median are:')\n",
    "df[['wage estimated', 'age women', 'educ years of schooling']][df['husbandWage']> np.median(df['husbandWage'])].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796b7322",
   "metadata": {},
   "source": [
    "#### For women whose husband earn more than the median wage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "0ea9939f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The statistics of women salaries whose husband earn more the median are:\n"
     ]
    },
    {
     "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>wage estimated</th>\n",
       "      <th>age women</th>\n",
       "      <th>educ years of schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.122804</td>\n",
       "      <td>42.102804</td>\n",
       "      <td>12.182243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.331609</td>\n",
       "      <td>8.100814</td>\n",
       "      <td>2.237005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.128200</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.697225</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.369150</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.328950</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wage estimated   age women  educ years of schooling\n",
       "count      214.000000  214.000000               214.000000\n",
       "mean         3.122804   42.102804                12.182243\n",
       "std          3.331609    8.100814                 2.237005\n",
       "min          0.128200   30.000000                 5.000000\n",
       "25%          1.697225   35.000000                12.000000\n",
       "50%          2.369150   41.000000                12.000000\n",
       "75%          3.328950   48.000000                13.000000\n",
       "max         25.000000   60.000000                17.000000"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The statistics of women salaries whose husband earn more the median are:')\n",
    "df[['wage estimated', 'age women', 'educ years of schooling']][df['husbandWage']<=np.median(df['husbandWage'])].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc152ea8",
   "metadata": {},
   "source": [
    "### Question  3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "5360dfd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAF1CAYAAAAQgExAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAkpUlEQVR4nO3dfbRlZ10n+O/PFC/hTRMht8tAd8GYVmwzoHOlsZnWawcwGtvENRMWTLQrDq6ye3zB7vSSQns16pJeoRUXtK2zulrQ6hGxIy8mGl9Il17QGUUIorwEDEIZYsoUImkotMGC3/xxdulN5VblUvfcek6d+nzWuuuc/exz9vndp3bd53zPfvY+1d0BAACAET5ndAEAAACcv4RSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAWApVdbiqnjmH7Ty7qn5xDiXNRVX9WFX989F1wE4RSgEA4P7+XZIbRxexwY8k+f6qeujoQmAnCKUAADCpqq9I8rnd/bujazmhu48keW+SbxxdC+wEoRTOsqr61qr6pQ3L76+qmzYsf6iqnlpVr5juf6yqbq+qf7zhMRdW1cGq+mhV3VFV31tVd29Y/wVV9bqq+nBVfbCqvvvs/YYAMFZVPayqXl5V90w/L6+qh21Y/71VdWRa921V1VX1hdPqr0vypg2P/cGq+vHp/kOq6hNV9e+n5Qur6n9U1UXT8i9U1Z9V1X+vqjdX1T/YsJ3Pr6pfmsb1t1bVD1fVb29Y/8VVdVtV/UVVva+qnnPSr7We5Ko5dxUsBKEUzr43JfnHVfU5VbU7yUOSPCNJqupJSR6V5A+TvDXJU5NcnOTnkvxCVT182saLk+xJ8qQkz0ryzSc2XlWfk+SXkvxBkkuTXJHke6rqa3f6FwOABfH9SZ6e2Tj6lCRPS/JvkqSqrkzyr5I8M8kXJvnqk557eZL3bVh+U5K16f5XJPmzDc/5yiTv6+6PTsu/muSyJJckeXuSV2/Yzk8k+USSv5Nk7/STqaZHJrkts/H+kiTPS/KTG0Ntkjum3wWWjlAKZ1l3fyDJxzMbKL86ya8n+dOq+uJp+be6+zPd/bPd/ZHuPt7dL0vysCRfNG3mOUn+XXd/tLvvTvIfNrzEVyR5XHf/UHd/anq9/5zkuWflFwSA8a5L8kPdfbS7P5zkB5N8y7TuOUl+urvf3d1/Oa3b6PMyG6dP+J0kl1XV5yf5qiSvTHJpVT0qs3H7b46qdveruvvj3f3JJD+Q5ClV9blVdUGS/y3Ji7v7L7v7PUkObniNb0hyuLt/ehr3357kdUn+9w2P+fhUGyydXaMLgPPUiU9dv3C6f19mA9tXTsupqhuSfFuSL0jSSR6T5LHT878gyYc2bG/j/b+X5Auq6r4NbRck+a35/goAsLC+IMmfbFj+k6ntxLq3bVi3cQxNko8mefSJhe7+q6p6W2bj9FcleUlmHyw/Y2o7MbX3gmndtUkel+Qz0yYem+TCzN53n27s/ocnjd27kvw/G5Yfndn7BVg6QimM8aYk/zTJEzO7wt99mX2q+5VJ/uN0/ugLM5t6++7u/kxVfTRJTc8/kuTxSd4zLT9hw7Y/lOSD3X3ZTv8SALCg7sks6L17Wv67U1vyt2PoCRvH0GR2Cs3fP6ntTUn+SZIvy+z0mjcl+drMpgW/eXrM/5Hk6symBR9O8rmZBdxK8uEkx6fX/aNNXvdDSd7U3c86ze/05MxOzYGlY/oujPGmJF+T5MJp+u1vJbkyyecn+f3MPg09ntkgtquq/m1mR0pPuCnJi6rqoqq6NMl3blj3e0k+VlUvnC7AcEFVfel0NUEAOB+8Jsm/qarHVdVjk/zbJD87rbspybdW1ZOr6hHTuo1+JQ88z/RNSf5Zkvd096cyu+jQt2X2IfCHp8c8Osknk3wkySMy+9A5SdLdn07y+iQ/UFWPmE7Z+Wcbtv/LSf5+VX3LdDGlh1TVV1TVkzc85qszO2cVlo5QCgN09x8lOZZpSm13fyzJB5L8v9PA9euZDTx/lNmUo/+R+0/z+aEkdyf5YJL/luS1mQ2EJwa+f5rZ1KIPJvnzJD+V2Se2AHA++OHMpuj+YZJ3ZnbRoR9Oku7+1cyuxfCbSd6f2Tmjyd+Oo29P8t+r6h9u2N7/l9kU3BNHRd+T2dj85g2P+S+Zjdl/Oq0/+StlvjOzsfjPMpuW+5oNr/nxJM/O7PoP90yPeWlm15PIdGHEL0nyi591T8A5oLp7dA3ANlXVv0jy3O4++ZNdAOA0pqOR70rysO4+PrU9O8n/1d3X7ODrvjTJ3+nuvVt47MuS/HF3/+RO1QMjCaVwDpo+MX1SpisCJrk1yX/s7pePrAsAzgVV9U2ZjZ2PzOwquJ/ZyQA6veYXJ3loZkduvyKzacLf1t2/uJOvC+cC03fh3PTQJP8ps8vD/0aSm5P49BQAtubbM7tuwx8n+XSSf3EWXvPRmZ1X+onMzmt9WWbjN5z3HCkFAABgGEdKAQAAGEYoBQAAYJhdowtIksc+9rG9Z8+ebW3jE5/4RB75yEfOp6Alpp+2Rj9tjX7aOn21NfPqp9tvv/3Pu/txcyjpvDWPsRkATjjd2LwQoXTPnj1529vetq1trK+vZ21tbT4FLTH9tDX6aWv009bpq62ZVz9V1Z9sv5rz2zzGZgA44XRjs+m7AAAADCOUAgAAMIxQCgBLpKr+ZVW9u6reVVWvqaqHV9XFVXVbVd053V40uk4AOEEoBYAlUVWXJvnuJKvd/aVJLkjy3CT7kxzq7suSHJqWAWAhCKUAsFx2JbmwqnYleUSSe5JcneTgtP5gkmvGlAYAD7QQV98FALavu/+0qn40yV1J/irJG7v7jVW10t1HpsccqapLNnt+Ve1Lsi9JVlZWsr6+fpYqB+B8JpQCwJKYzhW9OskTk9yX5Beq6pu3+vzuPpDkQJKsrq62rzEC4GwwfRcAlsczk3ywuz/c3X+d5PVJ/lGSe6tqd5JMt0cH1ggA9yOUAsDyuCvJ06vqEVVVSa5IckeSW5LsnR6zN8nNg+oDgAcwfRcAlkR3v6WqXpvk7UmOJ/n9zKbjPirJTVX1/MyC67XjqgSA+xNKAWCJdPeLk7z4pOZPZnbUFAAWjum7AAAADCOUAgAAMIzpu6ewZ/+tc9vW4Ruvmtu2AADgbJnXe2LvhzkdR0oBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAY5kFDaVW9qqqOVtW7NrRdXFW3VdWd0+1FG9a9qKreX1Xvq6qv3anCAQAAOPdt5UjpzyS58qS2/UkOdfdlSQ5Ny6mqL0ny3CT/YHrOT1bVBXOrFgAAgKXyoKG0u9+c5C9Oar46ycHp/sEk12xo//nu/mR3fzDJ+5M8bT6lAgAAsGzO9JzSle4+kiTT7SVT+6VJPrThcXdPbQAAAPAAu+a8vdqkrTd9YNW+JPuSZGVlJevr69t64WPHjm17GxvdcPnxuW1rnnVt17z7aVnpp63RT1unr7ZGPwHA+edMQ+m9VbW7u49U1e4kR6f2u5M8YcPjHp/kns020N0HkhxIktXV1V5bWzvDUmbW19ez3W1sdP3+W+e2rcPXrc1tW9s1735aVvppa/TT1umrrdFPAHD+OdPpu7ck2Tvd35vk5g3tz62qh1XVE5NcluT3tlciAAAAy+pBj5RW1WuSrCV5bFXdneTFSW5MclNVPT/JXUmuTZLufndV3ZTkPUmOJ/mO7v70DtV+ztgzp6Ouh2+8ai7bAQAAWBQPGkq7+3mnWHXFKR7/kiQv2U5RAAAAnB/OdPouAAAAbJtQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikALImq+qKqeseGn49V1fdU1cVVdVtV3TndXjS6VgA4QSgFgCXR3e/r7qd291OT/C9J/jLJG5LsT3Kouy9LcmhaBoCFIJQCwHK6Iskfd/efJLk6ycGp/WCSa0YVBQAn2zW6AABgRzw3yWum+yvdfSRJuvtIVV2y2ROqal+SfUmysrKS9fX1s1EnsMBuuPz4XLbj7wmnI5QCwJKpqocm+cYkL/psntfdB5IcSJLV1dVeW1ubf3HAOeX6/bfOZTuHr1uby3ZYTqbvAsDy+bokb+/ue6fle6tqd5JMt0eHVQYAJxFKAWD5PC9/O3U3SW5Jsne6vzfJzWe9IgA4BdN3AWCJVNUjkjwrybdvaL4xyU1V9fwkdyW5dkRtwNmxZ05TbuFsEUoBYIl0918m+fyT2j6S2dV4AWDhmL4LAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMP4ShgAAFgAvl+U85UjpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAskar6vKp6bVW9t6ruqKqvrKqLq+q2qrpzur1odJ0AcIJQCgDL5RVJfq27vzjJU5LckWR/kkPdfVmSQ9MyACwEoRQAlkRVPSbJVyV5ZZJ096e6+74kVyc5OD3sYJJrRtQHAJvZNboAtm7P/lu3vY0bLj+e6/ffmsM3XjWHigBYME9K8uEkP11VT0lye5IXJFnp7iNJ0t1HquqSgTUCwP0IpQCwPHYl+fIk39Xdb6mqV+SzmKpbVfuS7EuSlZWVrK+v70iRwOZuuPz46BJ2jL8nnM62QmlV/csk35akk7wzybcmeUSS/5pkT5LDSZ7T3R/dVpUAwFbcneTu7n7LtPzazELpvVW1ezpKujvJ0c2e3N0HkhxIktXV1V5bWzsLJQMnXD+HWXGL6vB1a6NLYIGd8TmlVXVpku9OstrdX5rkgiTPjYspAMAQ3f1nST5UVV80NV2R5D1Jbkmyd2rbm+TmAeUBwKa2O313V5ILq+qvMztCek+SFyVZm9YfTLKe5IXbfB0AYGu+K8mrq+qhST6Q2Symz0lyU1U9P8ldSa4dWB8A3M8Zh9Lu/tOq+tHMBre/SvLG7n5jVW3pYgrzPm/l2LFjc52rvqxz+lcunP1u5vWf3rz3p2Wln7ZOX22Nftq+7n5HktVNVl1xlksBgC0541A6ffH21UmemOS+JL9QVd+81efP+7yV9fX1zPPcl2Wd03/D5cfzsnfuMq//Qcx7f1pW+mnr9NXW6CcAOP9s53tKn5nkg9394e7+6ySvT/KPMl1MIUlOdzEFAAAA2E4ovSvJ06vqEVVVmU0LuiMupgAAAMAWbeec0rdU1WuTvD3J8SS/n9l03EfFxRQAAADYgm1dfbe7X5zkxSc1fzIupgAAAMAWbGf6LgAAAGyLUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwu0YXAADMT1UdTvLxJJ9Ocry7V6vq4iT/NcmeJIeTPKe7PzqqRgDYyJFSAFg+X9PdT+3u1Wl5f5JD3X1ZkkPTMgAsBKEUAJbf1UkOTvcPJrlmXCkAcH9CKQAsl07yxqq6var2TW0r3X0kSabbS4ZVBwAncU4pACyXZ3T3PVV1SZLbquq9W33iFGL3JcnKykrW19d3qERgMzdcfnx0CTvG3xNORygFgCXS3fdMt0er6g1Jnpbk3qra3d1Hqmp3kqOneO6BJAeSZHV1tdfW1s5S1UCSXL//1tEl7JjD162NLoEFZvouACyJqnpkVT36xP0kz07yriS3JNk7PWxvkpvHVAgAD+RIKQAsj5Ukb6iqZDbG/1x3/1pVvTXJTVX1/CR3Jbl2YI0AcD9CKQAsie7+QJKnbNL+kSRXnP2KAODBmb4LAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADLOtUFpVn1dVr62q91bVHVX1lVV1cVXdVlV3TrcXzatYAAAAlst2j5S+IsmvdfcXJ3lKkjuS7E9yqLsvS3JoWgYAAIAHOONQWlWPSfJVSV6ZJN39qe6+L8nVSQ5ODzuY5JrtlQgAAMCy2rWN5z4pyYeT/HRVPSXJ7UlekGSlu48kSXcfqapLNntyVe1Lsi9JVlZWsr6+vo1SkmPHjm17GxvdcPnxuW1rkaxcOPvd5tlXy2je+9Oy0k9bp6+2Rj8BwPlnO6F0V5IvT/Jd3f2WqnpFPouput19IMmBJFldXe21tbVtlJKsr69nu9vY6Pr9t85tW4vkhsuP52Xv3JXD162NLmWhzXt/Wlb6aev01dboJwA4/2znnNK7k9zd3W+Zll+bWUi9t6p2J8l0e3R7JQIAALCszjiUdvefJflQVX3R1HRFkvckuSXJ3qltb5Kbt1UhAAAAS2s703eT5LuSvLqqHprkA0m+NbOge1NVPT/JXUmu3eZrAAAAsKS2FUq7+x1JVjdZdcV2tgsAAOeCPUt6HRI4m7b7PaUAAABwxoRSAAAAhhFKAQAAGGa7FzoCAAA4rXmde3v4xqvmsh0WiyOlAAAADCOUAgAAMIxQCgAAwDBCKQAsmaq6oKp+v6p+eVq+uKpuq6o7p9uLRtcIACcIpQCwfF6Q5I4Ny/uTHOruy5IcmpYBYCEIpQCwRKrq8UmuSvJTG5qvTnJwun8wyTVnuSwAOCWhFACWy8uTfG+Sz2xoW+nuI0ky3V4yoC4A2JTvKQWAJVFV35DkaHffXlVrZ/D8fUn2JcnKykrW19fnWh8soxsuPz66hPOKv0vLSSgFgOXxjCTfWFVfn+ThSR5TVT+b5N6q2t3dR6pqd5Kjmz25uw8kOZAkq6urvba2dpbKhnPX9ftvHV3CeeXwdWujS2AHmL4LAEuiu1/U3Y/v7j1JnpvkN7r7m5PckmTv9LC9SW4eVCIAPIBQCgDL78Ykz6qqO5M8a1oGgIVg+i4ALKHuXk+yPt3/SJIrRtYDAKfiSCkAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAyza3QBAAAAW7Fn/61z29bhG6+a27bYHkdKAQAAGEYoBQAAYBihFAAAgGGcUwoAwHlnnucmAtvjSCkAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpACyJqnp4Vf1eVf1BVb27qn5war+4qm6rqjun24tG1woAJwilALA8Ppnkn3T3U5I8NcmVVfX0JPuTHOruy5IcmpYBYCFsO5RW1QVV9ftV9cvTsk9jAWCAnjk2LT5k+ukkVyc5OLUfTHLN2a8OADY3j+8pfUGSO5I8Zlo+8WnsjVW1f1p+4RxeBwB4EFV1QZLbk3xhkp/o7rdU1Up3H0mS7j5SVZec4rn7kuxLkpWVlayvr5+lquHsu+Hy46NLYDB/4xbHtkJpVT0+yVVJXpLkX03NVydZm+4fTLIeoRQAzoru/nSSp1bV5yV5Q1V96Wfx3ANJDiTJ6upqr62t7UiNsAiu33/r6BIY7PB1a6NLYLLd6bsvT/K9ST6zoe1+n8Ym2fTTWABg53T3fZl9MHxlknuraneSTLdHx1UGAPd3xkdKq+obkhzt7turau0Mnj/XKULHjh2b6yH4ZZ3SsXLh7HczXeH05r0/LSv9tHX6amv00/ZU1eOS/HV331dVFyZ5ZpKXJrklyd4kN063N4+rEgDubzvTd5+R5Bur6uuTPDzJY6rqZzN9Gjuds3LKT2PnPUVofX0985xmtKxTOm64/Hhe9s5dpis8iHnvT8tKP22dvtoa/bRtu5McnM4r/ZwkN3X3L1fV7yS5qaqen+SuJNeOLBIANjrjUNrdL0ryoiSZjpT+6+7+5qr6kfg0FgDOuu7+wyRftkn7R5JccfYrAoAHN4+r757sxgz8NHbPkh7hnLd59dPhG6+ay3YAAIDz01xCaXevZ3YxBZ/GAgAAsGXbvfouAAAAnDGhFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAlkRVPaGqfrOq7qiqd1fVC6b2i6vqtqq6c7q9aHStAHCCUAoAy+N4khu6+8lJnp7kO6rqS5LsT3Kouy9LcmhaBoCFIJQCwJLo7iPd/fbp/seT3JHk0iRXJzk4PexgkmuGFAgAm9g1ugAAYP6qak+SL0vyliQr3X0kmQXXqrrkFM/Zl2RfkqysrGR9ff3sFAsD3HD58dElMJi/cYtDKAWAJVNVj0ryuiTf090fq6otPa+7DyQ5kCSrq6u9tra2YzXCaNfvv3V0CQx2+Lq10SUwMX0XAJZIVT0ks0D66u5+/dR8b1XtntbvTnJ0VH0AcDKhFACWRM0Oib4yyR3d/WMbVt2SZO90f2+Sm892bQBwKqbvAsDyeEaSb0nyzqp6x9T2fUluTHJTVT0/yV1Jrh1THgA8kFAKAEuiu387yalOIL3ibNYCAFtl+i4AAADDCKUAAAAMY/ouAADnhD2+xgWWkiOlAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMc8ahtKqeUFW/WVV3VNW7q+oFU/vFVXVbVd053V40v3IBAABYJts5Uno8yQ3d/eQkT0/yHVX1JUn2JznU3ZclOTQtAwAAwAOccSjt7iPd/fbp/seT3JHk0iRXJzk4Pexgkmu2WSMAAABLatc8NlJVe5J8WZK3JFnp7iPJLLhW1SWneM6+JPuSZGVlJevr69uq4dixY1lfX88Nlx/f1naW3cqFmWsf/firb57Ldi6/9HPnsp15ObE/cXr6aev01dboJwA4/2w7lFbVo5K8Lsn3dPfHqmpLz+vuA0kOJMnq6mqvra1tq4719fWsra3l+v23bms7y+6Gy4/nZe+cy2cRc3X4urXRJdzPif2J09NPW6evtkY/AcD5Z1tX362qh2QWSF/d3a+fmu+tqt3T+t1Jjm6vRAAAAJbVdq6+W0lemeSO7v6xDatuSbJ3ur83yXzmdwIAALB0tjOP8xlJviXJO6vqHVPb9yW5MclNVfX8JHcluXZbFQIAALC0zjiUdvdvJznVCaRXnOl2AQAAOH8s3hVvOC/tmeMFqg7feNXctgUAAOwsoRQAADjvzOugiAMi27etq+8CAADAdgilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikALJGqelVVHa2qd21ou7iqbquqO6fbi0bWCAAbCaUAsFx+JsmVJ7XtT3Kouy9LcmhaBoCFIJQCwBLp7jcn+YuTmq9OcnC6fzDJNWezJgA4nV2jCwAAdtxKdx9Jku4+UlWXbPagqtqXZF+SrKysZH19/exVCFtww+XHR5cAD+Bv5fYJpQBAkqS7DyQ5kCSrq6u9trY2tiA4yfX7bx1dAjzA4evWRpdwzjN9FwCW371VtTtJptujg+sBgL8hlALA8rslyd7p/t4kNw+sBQDuRygFgCVSVa9J8jtJvqiq7q6q5ye5McmzqurOJM+algFgITinFACWSHc/7xSrrjirhQDAFjlSCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADD+EoYAAB21J79t44uAVhgQilLZx4D3w2XH8/a9ksBAAAehOm7AAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMr4QBAABYIvP8buDDN141t22diiOlAAAADCOUAgAAMIxQCgAAwDDOKQUA4AHmeU4aLLNz7fzNReRIKQAAAMMIpQAAAAwjlAIAADCMc0rhFJwfAMC5yLmgcO46X///OlIKAADAMEIpAAAAwwilAAAADOOcUgBgRy3iOVKLeK7/IvYTwNngSCkAAADDOFIKZ8G8Pv1exE/2F9Ey9/ciXhV6njX9zJWPnNu2AIBzgyOlAAAADLNjR0qr6sokr0hyQZKf6u4bd+q1AIAHZ2yeP+eBAmzfjoTSqrogyU8keVaSu5O8tapu6e737MTrwfli0d783HD58Vy/gNNJ2ZpF25/YWcZmABbVTk3ffVqS93f3B7r7U0l+PsnVO/RaAMCDMzYDsJB2KpRemuRDG5bvntoAgDGMzQAspJ06p7Q2aev7PaBqX5J90+KxqnrfNl/zsUn+fJvbWHrfrZ+2RD9tzbz7qV46ry3Nx5zrsU9twde8dG799PfmsI1lM2JsPpXh/x8W7e/NZHi/LCB9sjn9sjn9srlt9csc/16ecmzeqVB6d5InbFh+fJJ7Nj6guw8kOTCvF6yqt3X36ry2t6z009bop63RT1unr7ZGP+2osz42n4p/583plwfSJ5vTL5vTL5s7F/plp6bvvjXJZVX1xKp6aJLnJrllh14LAHhwxmYAFtKOHCnt7uNV9Z1Jfj2zy86/qrvfvROvBQA8OGMzAItqx76ntLt/Jcmv7NT2N7Hj042WhH7aGv20Nfpp6/TV1uinHTRgbD4V/86b0y8PpE82p182p182t/D9Ut394I8CAACAHbBT55QCAADAgzrnQ2lVXVlV76uq91fV/tH1LLKqOlxV76yqd1TV20bXsyiq6lVVdbSq3rWh7eKquq2q7pxuLxpZ4yI4RT/9QFX96bRPvaOqvn5kjYugqp5QVb9ZVXdU1bur6gVTu31qg9P0k33qPFBVP1JV762qP6yqN1TV542uaRFU1bXT/4fPVNVCXynzbPAe74E2G4s59Zhyvquqh1fV71XVH0z98oOjazqVc3r6blVdkOSPkjwrs0vdvzXJ87r7PUMLW1BVdTjJanf7/qYNquqrkhxL8l+6+0untn+f5C+6+8ZpILyou184ss7RTtFPP5DkWHf/6MjaFklV7U6yu7vfXlWPTnJ7kmuSXB/71N84TT89J/appVdVz07yG9PFl16aJOfz/4cTqurJST6T5D8l+dfdfd5+gOw93uY2G4s59Zhif6lK8sjuPlZVD0ny20le0N2/O7i0BzjXj5Q+Lcn7u/sD3f2pJD+f5OrBNXGO6e43J/mLk5qvTnJwun8wszfL57VT9BMn6e4j3f326f7Hk9yR5NLYp+7nNP3EeaC739jdx6fF383sO1PPe919R3e/b3QdC8J7vE0YizdnTNlczxybFh8y/SzkEclzPZRemuRDG5bvjh3wdDrJG6vq9qraN7qYBbfS3UeS2R+6JJcMrmeRfec0Be9V5/uU1JNV1Z4kX5bkLbFPndJJ/ZTYp843/2eSXx1dBAvHezzOyCZjynmtqi6oqnckOZrktu5eyH4510NpbdK2kOl/QTyju788ydcl+Y5pCghsx/+d5H9K8tQkR5K8bGg1C6SqHpXkdUm+p7s/NrqeRbVJP9mnlkRV/beqetcmP1dveMz3Jzme5NXjKj27ttIvJPEejzNg7H2g7v50dz81sxkpT6uqhZz2vWPfU3qW3J3kCRuWH5/knkG1LLzuvme6PVpVb8hsasybx1a1sO6tqt3dfWQ6T+Ho6IIWUXffe+J+Vf3nJL88sJyFMZ238bokr+7u10/N9qmTbNZP9qnl0d3PPN36qtqb5BuSXNHn8gUuPksP1i/8De/x+KycYuxl0t33VdV6kiuTLNyFss71I6VvTXJZVT2xqh6a5LlJbhlc00KqqkdOJ36nqh6Z5NlZwB1ygdySZO90f2+SmwfWsrCmcHXCN8U+deKiAq9Mckd3/9iGVfapDU7VT/ap80NVXZnkhUm+sbv/cnQ9LCTv8diy04y957WqetyJq5tX1YVJnpnkvUOLOoVz+uq7STJ9XcDLk1yQ5FXd/ZKxFS2mqnpSkjdMi7uS/Jy+mqmq1yRZS/LYJPcmeXGSX0xyU5K/m+SuJNd293l9YYFT9NNaZtMsO8nhJN9+4rzJ81VV/a9JfivJOzO7imaSfF9m57bYpyan6afnxT619Krq/UkeluQjU9Pvdvc/H1jSQqiqb0ry40kel+S+JO/o7q8dWtRA3uM90GZjcXe/cmhRC+BUY0p3/8q4qsarqv85s4srXpDZwcibuvuHxla1uXM+lAIAAHDuOten7wIAAHAOE0oBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGH+fwihKIivvNp1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (16, 6)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(df['wage estimated'],20)\n",
    "plt.title('wage')\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(1,2,2) \n",
    "df['log(wage)']= np.log(df['wage estimated'])\n",
    "plt.hist(df['log(wage)'],20)\n",
    "plt.title('log(wage)')\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7455d84a",
   "metadata": {},
   "source": [
    "On constate que les deux variables semblent suivre une loi gaussienne de moyenne approximative 3 et 1.2 respectivement.\n",
    "Ce résultat est normal puisque tout échantillon de donné converge vers une telle loi lorsque n devient assez grand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26eb05f9",
   "metadata": {},
   "source": [
    "### Question  4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "3c229b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La correlation entre ces deux variables est: \n",
      " 0.550732782239588\n",
      "La p-value de rejet \n",
      " 2.580005695516474e-35\n"
     ]
    }
   ],
   "source": [
    "motheduc = df['mothers year of schooling']\n",
    "fatheduc = df['fathers year of schooling']\n",
    "print('La correlation entre ces deux variables est: \\n', spearmanr(df['mothers year of schooling'], df['fathers year of schooling'])[0])\n",
    "print('La p-value de rejet \\n', spearmanr(df['mothers year of schooling'], df['fathers year of schooling'])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839fe5de",
   "metadata": {},
   "source": [
    "On remarque une corrélation assez forte, ou du moins non-négligeable. Cela peut s'expliquer par le fait que les membres d'un couple sont souvents issus d'un niveau d'études assez similaire à cause de la non-mixité des classes sociales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf4714a",
   "metadata": {},
   "source": [
    "Cette corrélation peut donc introduire un biais de multicolinéarité.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1b4608",
   "metadata": {},
   "source": [
    "Cepandant, puisque la p-value vaut environ 0 (<1%), on rejette l'hypothèse \"présence de multicolinérarité\" entre les 2 variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cad703a",
   "metadata": {},
   "source": [
    "### Question  5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d0f995",
   "metadata": {},
   "source": [
    "#### Wage and education / expersq / education of the father"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5fb5a8d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1224 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (16, 17)\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.scatter(df['educ years of schooling'],df['wage estimated'])\n",
    "plt.grid()\n",
    "plt.title('Wage and education')\n",
    "fit1 = np.polyfit(df['educ years of schooling'],df['wage estimated'] , 1)\n",
    "tt1=np.linspace(4,17,100)\n",
    "plt.plot(tt1,fit1[0]*tt1+fit1[1], 'r')\n",
    "plt.xlabel('education')\n",
    "plt.ylabel('wage')\n",
    "\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.scatter(df['expersq'],df['wage estimated'])\n",
    "plt.grid()\n",
    "plt.title('Wage and expersq')\n",
    "fit2 = np.polyfit(df['expersq'],df['wage estimated'] , 1)\n",
    "tt2=np.linspace(0,1500,100)\n",
    "plt.plot(tt2,fit2[0]*tt2+fit2[1], 'r')\n",
    "plt.xlabel('experience')\n",
    "plt.ylabel('wage')\n",
    "\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.scatter(df['fathers year of schooling'],df['wage estimated'])\n",
    "plt.title(\"Wage and the father's year of schooling\")\n",
    "fit3 = np.polyfit(df['fathers year of schooling'],df['wage estimated'] , 1)\n",
    "tt3=np.linspace(0,17,100)\n",
    "plt.plot(tt3,fit3[0]*tt3+fit3[1], 'r')\n",
    "plt.xlabel('fathers year of schooling')\n",
    "plt.ylabel('wage')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964254b0",
   "metadata": {},
   "source": [
    "Il ne s'agit ici pas d'un effet \"toute chose étant égale par ailleurs\", puisque pour la variation de chacune des données, les autres données ne sont pas constantes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea78296",
   "metadata": {},
   "source": [
    "### Question  6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bf224f",
   "metadata": {},
   "source": [
    "L'hypothèse est que: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c78898",
   "metadata": {},
   "source": [
    "                E(U|X) = 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742b6a2d",
   "metadata": {},
   "source": [
    "                Since:    E(𝛽_hat) = E(𝛽) + (x-x')E(U|X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d5a188",
   "metadata": {},
   "source": [
    "                                            E(𝛽_hat) = E(𝛽)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7045d086",
   "metadata": {},
   "source": [
    "Le biais de variable omise est le biais lié à la non prise en compte dans le modèle d'une varibale importante."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4fa62c",
   "metadata": {},
   "source": [
    "### Question  7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "924a68bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAFlCAYAAACDRTcUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABA0ElEQVR4nO3deZhU5YHv8e9b1c2+ySIiCM0miOyryiKC+4ZRo9G5RvRGnCRmMpMZb8xkJjHm3sRJMtkTEyNuGY1JNC4RjUYWBVdQkUWUXUGRVWWH7q73/lEtKmt3092nq+r7eZ5+Tld1VZ1fOKnu+vme874hxogkSZIkSbUllXQASZIkSVJ+s3hKkiRJkmqVxVOSJEmSVKssnpIkSZKkWmXxlCRJkiTVKounJEmSJKlWFdXlztq2bRtLSkrqcpeSJEmSpDry8ssvb4gxttv7/jotniUlJcyZM6cudylJkiRJqiMhhLf2d7+n2kqSJEmSapXFU5IkSZJUqyyekiRJkqRaVafXeEqSJEnSR0pLS1m9ejU7d+5MOoqqqFGjRnTq1Ini4uJKPd7iKUmSJCkRq1evpnnz5pSUlBBCSDqOKinGyMaNG1m9ejVdu3at1HM81VaSJElSInbu3EmbNm0snTkmhECbNm2qNFJt8ZQkSZKUGEtnbqrqcbN4SpIkSVId+ta3vsVTTz2VdIw65TWekiRJklSHbrrppqQjUFZWRlFR3dVBRzwlSZIkFazvfve79O7dm9NOO43LLruMH/3oRwD87ne/Y9iwYQwYMICLLrqI7du3AzBx4kTuv//+Pc9v1qwZAGvWrGHMmDEMHDiQvn37MnPmTMrLy5k4cSJ9+/alX79+/OQnP9nnNW666SaGDRtG3759mTRpEjFGAMaOHcvXv/51hg8fzrHHHsvMmTP3m/8HP/gB/fr1Y8CAAdxwww17njtnzhwANmzYQElJCQB33nknn/3sZznvvPM4/fTTufTSS3nsscf2vNbEiRN54IEHKC8v5/rrr2fYsGH079+f3/72t4f97+yIpyRJkqTkPX4DvDe/Zl/zqH5w1s0H/PGcOXN44IEHePXVVykrK2Pw4MEMGTIEgAsvvJBrrrkGgP/4j/9g8uTJfOUrXznga917772cccYZfPOb36S8vJzt27czd+5c3nnnHRYsWADABx98sM/zrrvuOr71rW8BcMUVV/Doo49y3nnnAdlRyZdeeonHHnuM73znO/ucnvv444/z0EMP8eKLL9KkSRM2bdp0yH+S559/nnnz5tG6dWsefPBB/vjHP3L22Weze/dupk6dyi233MLkyZNp2bIls2fPZteuXYwcOZLTTz+90jPY7o8jnpIkSZIK0qxZs5gwYQKNGzemefPmewofwIIFCxg9ejT9+vXjnnvuYeHChQd9rWHDhnHHHXdw4403Mn/+fJo3b063bt1Yvnw5X/nKV/jb3/5GixYt9nne9OnTGTFiBP369WPatGmf2s+FF14IwJAhQ1i5cuU+z33qqae46qqraNKkCQCtW7c+5P/m0047bc/jzjrrLKZNm8auXbt4/PHHGTNmDI0bN+bJJ5/k7rvvZuDAgYwYMYKNGzeyZMmSQ772wTjiKUmSJCl5BxmZrC0fnda6PxMnTuShhx5iwIAB3HnnncyYMQOAoqIiMpnMnufv3r0bgDFjxvDMM88wZcoUrrjiCq6//no+//nP89prr/HEE0/wq1/9ij/96U/cfvvte/axc+dOvvSlLzFnzhyOOeYYbrzxxk8tUdKwYUMA0uk0ZWVl+82/v9llP5lx7yVPmjZtuuf7Ro0aMXbsWJ544gn++Mc/ctlll+153V/84hecccYZB/7HqyKLp1QgSm6YUu3nrrz5nBpMIkmSVD+MGjWKa6+9lm984xuUlZUxZcqUPafXbtmyhQ4dOlBaWso999xDx44dASgpKeHll1/mkksu4eGHH6a0tBSAt956i44dO3LNNdewbds2XnnlFc4++2waNGjARRddRPfu3Zk4ceKn9v9RKWzbti1bt27l/vvv5+KLL650/tNPP52bbrqJyy+/fM+ptq1bt96Tcfjw4Z+6HnV/Pve5z3HbbbcxZ84c7rzzTgDOOOMMbrnlFsaNG0dxcTGLFy+mY8eOnyqtVWXxlCRJklSQhg0bxvnnn8+AAQPo0qULQ4cOpWXLlkB20qERI0bQpUsX+vXrx5YtWwC45pprmDBhAsOHD2f8+PF7ytiMGTP44Q9/SHFxMc2aNePuu+/mnXfe4aqrrtoz+vj973//U/tv1aoV11xzDf369aOkpIRhw4ZVKf+ZZ57J3LlzGTp0KA0aNODss8/me9/7Hv/2b//GJZdcwu9//3vGjRt30Nc4/fTT+fznP8/5559PgwYNAPjCF77AypUrGTx4MDFG2rVrx0MPPVSlbHsLBxte3vOgEFoBtwF9gQhcDbwJ/BEoAVYCl8QY3z/Y6wwdOjR+NLuSpLrliKckSapvFi1axHHHHZdohq1bt9KsWTO2b9/OmDFjuPXWWxk8eHCimXLF/o5fCOHlGOPQvR9b2cmFfgb8LcbYGxgALAJuAKbGGHsCUytuS5IkSVLOmDRpEgMHDmTw4MFcdNFFls5acshTbUMILYAxwESAGONuYHcIYQIwtuJhdwEzgK/XRkhJkiRJqg333ntv0hEKQmVGPLsB64E7QgivhhBuCyE0BdrHGNcAVGyP3N+TQwiTQghzQghz1q9fX2PBJUmSJEm5oTLFswgYDNwSYxwEbKMKp9XGGG+NMQ6NMQ5t165dNWNKkiRJknJVZYrnamB1jPHFitv3ky2ia0MIHQAqtutqJ6IkSZIkKZcdsnjGGN8DVoUQelXcNR54HXgEuLLiviuBh2sloSRJkiQpp1V2VtuvAPeEEOYBA4HvATcDp4UQlgCnVdyWJEmSpJyxevVqJkyYQM+ePenevTtf/epX2b17N5Bdm/Pcc8/d5zmPPvoogwYNYsCAAfTp04ff/va3dR075xxyVluAGONcYJ+1WMiOfkqSJEnSYTucdcf351BrkccYufDCC/niF7/Iww8/THl5OZMmTeKb3/wmP/zhD/f7nNLSUiZNmsRLL71Ep06d2LVrFytXrqzR3PmosiOekiRJkpRXpk2bRqNGjbjqqqsASKfT/OQnP+H2229n+/bt+33Oli1bKCsro02bNgA0bNiQXr167fO4TZs2ccEFF9C/f39OOOEE5s2bB8CNN97I1VdfzdixY+nWrRs///nP93nun/70J772ta8B8LOf/Yxu3boBsGzZMkaNGgXATTfdxLBhw+jbty+TJk0ixgjA7Nmz6d+/PyeeeCLXX389ffv2BaC8vJzrr7+eYcOG0b9//zofpbV4SpIkSSpICxcuZMiQIZ+6r0WLFnTu3JmlS5fu9zmtW7fm/PPPp0uXLlx22WXcc889ZDKZfR737W9/m0GDBjFv3jy+973v8fnPf37Pz9544w2eeOIJXnrpJb7zne9QWlr6qeeOGTOGmTNnAjBz5kzatGnDO++8w6xZsxg9ejQA1113HbNnz2bBggXs2LGDRx99FICrrrqK3/zmNzz//POk0+k9rzl58mRatmzJ7NmzmT17Nr/73e9YsWJFNf7VqsfiKUmSJKkgxRgJIVT6/o/cdtttTJ06leHDh/OjH/2Iq6++ep/HzJo1iyuuuAKAcePGsXHjRj788EMAzjnnHBo2bEjbtm058sgjWbt27aeee9RRR7F161a2bNnCqlWruPzyy3nmmWeYOXPmnuI5ffp0RowYQb9+/Zg2bRoLFy7kgw8+YMuWLZx00kkAXH755Xte88knn+Tuu+9m4MCBjBgxgo0bN7JkyZIq/otVX6Wu8ZQkSZKkfHP88cfzwAMPfOq+zZs3s2rVKrp3787GjRsP+Nx+/frRr18/rrjiCrp27cqdd975qZ9/dOrrJ31UZhs2bLjnvnQ6TVlZ2T6PPfHEE7njjjvo1asXo0eP5vbbb+f555/nv//7v9m5cydf+tKXmDNnDscccww33ngjO3fu3O8+P5nnF7/4BWecccYBH1ObHPGUVH9lMrDzw6RTSJKkPDV+/Hi2b9/O3XffDWSvg/zXf/1XJk6cSJMmTfb7nK1btzJjxow9t+fOnUuXLl32edyYMWO45557gOzsuG3btqVFixaVzjZmzBh+9KMfMWbMGAYNGsT06dNp2LAhLVu2ZOfOnQC0bduWrVu3cv/99wNwxBFH0Lx5c1544QUA7rvvvj2vd8YZZ3DLLbfsOa138eLFbNu2rdJ5DpcjnpLqpw9Wwb2XwLrXoV1v6HISdD4JupwILTslnU6SJOWBEAIPPvggX/rSl/jud79LJpPh7LPP5nvf+96ex0ydOpVOnT7+7PGHP/yBH/zgB1x77bU0btyYpk2b7jPaCdlJhK666ir69+9PkyZNuOuuu6qUbfTo0axatYoxY8aQTqc55phj6N27NwCtWrXimmuuoV+/fpSUlDBs2LA9z5s8eTLXXHMNTZs2ZezYsbRs2RKAL3zhC6xcuZLBgwcTY6Rdu3Y89NBDVcp0OMLBhmNr2tChQ+OcOXPqbH+SPnY405MfairyGvfuXLj3UijdDsO+AO/Ng7dfhN1bsj9v1fnjEtp9XPa2JEnKOYsWLeK4445LOkZe2bp1K82aNQPg5ptvZs2aNfzsZz+rlX3t7/iFEF6OMe6zFKcjnpLql8VPwp8nQpPWcMUT0L5P9v7yMli7AN5+Ht56DpZNhXn3QbohXP036Dg40diSJEn1wZQpU/j+979PWVkZXbp02e9obBIsnpLqj9mT4bF/g6P6weV/guZHffyzdBEcPTD7dcIXIUZY/yb8z0XZonrtM9C4VTK5JUmS6olLL72USy+9NOkY+3ByIUnJy2Tg79+CKV+DHqfCxMc+XTr3JwQ4sjd89g7Y/A48/OVsGZUkSVK944inpEOq1etDS3fCQ/8ICx+EoVfDWT/Mjm5W1jHD4dQb4cn/gBd/kx0NlSRJOeNQa2aqfqrqXEGOeEpKzvZNcPeEbOk89Ttwzo+rVjo/cuJ10OtsePI/YfXLNZ9TkiTVikaNGrFx48YqlxglK8bIxo0badSoUaWf44inpGRkyrOlc/2bcPEd0PfC6r9WCHDBr+E3Yyqu93w6OzmRJEmq1zp16sTq1atZv3590lFURY0aNfrUMjOHYvGUlIz5f84uk3LhbYdXOj/S+Aj47J1w+xnZ6z0/d2+2kEqSpHqruLiYrl27Jh1DdcBTbSXVvbLdMP17cFR/6HtRzb1upyFw2k3w5mPw/K9q7nUlSZJ0WCyekureK3fBB2/B+G9DqoZ/DZ3wReh9Ljz1bVg1u2ZfW5IkSdVi8ZRUt3Zvg6d/AF1GQo/xNf/6IcCEX0GLjtnrPbdvqvl9SJIkqUosnpLq1ou/hW3rYPy3au8azMatstd7blsHD/5jdp1QSZIkJcbiKanu7Hgfnv0p9DwDOp9Qu/vqOBhO/7+w5Al449Ha3ZckSZIOyuIpqe48+3PY+SGM/8+62d+wL0CrzvDCLXWzP0mSJO2XxVNS3diyFl78DfS9GI7qVzf7TKVh+LXw9nPw7ty62ackSZL2YfGUVDdm/gjKdsEp/163+x18BTRoli29kiRJSoTFU1Lte38lzLkjWwLbdK/bfTdqCQMvhwUPZEddJUmSVOcsnpJq34ybs6e9nvz1ZPY/4h+hfDfMuT2Z/UuSJBU4i6ek2rVuEbx2Hwy/BlocnUyGNt2zM+nOmZw93VeSJEl1qijpAJLy299+8RVGphoxelpfPpg2pUrPXXnzOTUX5IQvwu8vyJ5yO/DymntdSZIkHZIjnpJqzYCwlDPTs/ld2Tl8QPNkw3QbC+2Ogxd+DTEmm0WSJKnAWDwl1Zrri/7IhtiCyeVnJR0FQoAT/hHemw9vPZd0GkmSpIJi8ZRUK3qFtxmVXshvys5jG42TjpPV/1Jo3Do76ilJkqQ6Y/GUVCs+k55FaUzzQPnopKN8rLgxDL0K3piSXeJFkiRJdcLJhSTVuBQZLkg/y4zMAN6nRbVfp+SGqk1G9EkHnJho2Bfg2Z/BS7+DM/5ftV9fkiRJleeIp6Qad2JqIUeF93mwfFTSUfbV4mjocwG8cjfs2pJ0GkmSpIJg8ZRU4z6TfpbNsQlTM4OTjrJ/J3wJdm2GuX9IOokkSVJBsHhKqlGN2MWZqZeYUj6CXTRIOs7+dRoCnYbBi7dAJpN0GkmSpLxn8ZRUo05PzaFZ2MlD9fE020864YuwaTkseTLpJJIkSXnP4impRl2YnsXq2JaXYq+koxzccedD86Ozo56SJEmqVRZPSTWmLR8yOjWPh8pHEuv7r5d0MQy/BpbPgLWvJ51GkiQpr9XzT4aScsn56edIh1g/Z7PdnyETId0A5t6TdBJJkqS8ZvGUVGM+k57JvExXlsWOSUepnCatodsp8PojEGPSaSRJkvKWxVNSjegRVtMvtTJ3Rjs/0mcCfPg2vPtq0kkkSZLylsVTUo34THoWZTHFX8tPSjpK1fQ6C1JF8PrDSSeRJEnKWxZPSYctkGFC+jlmZvqxgZZJx6maJq2h6xhY5Om2kiRJtaVSxTOEsDKEMD+EMDeEMKfivtYhhL+HEJZUbI+o3aiS6qsRqTfoFDbwYPnopKNUT58J2TU91y5IOokkSVJeqsqI5ykxxoExxqEVt28ApsYYewJTK25LKkCfSc1ia2zEk5khSUepnt7nQkh5uq0kSVItOZxTbScAd1V8fxdwwWGnkZRzGrKbs9Iv8rfMcHbSMOk41dO0LXQZmZ3dVpIkSTWussUzAk+GEF4OIUyquK99jHENQMX2yP09MYQwKYQwJ4QwZ/369YefWFK9cmrqFVqEHfwl12az3VufCbDhTVj3RtJJJEmS8k5li+fIGONg4CzgyyGEMZXdQYzx1hjj0Bjj0Hbt2lUrpKT66zPpmayJrXkh0yfpKIen97lAyE4yJEmSpBpVqeIZY3y3YrsOeBAYDqwNIXQAqNiuq62Qkuqn1mzm5NQ8Hi4fSSbXJ8lu0QE6n+B1npIkSbXgkJ8UQwhNQwjNP/oeOB1YADwCXFnxsCsBP61JBebc9PMUh3IeLB+ZdJSacdz52ZltNy5LOokkSVJeqcwQRXtgVgjhNeAlYEqM8W/AzcBpIYQlwGkVtyUVkAvTs3g904U3Y+eko9SM487Lbh31lCRJqlFFh3pAjHE5MGA/928ExtdGKEn1X9ewhoGpZfzf0n9IOkrNaXUMdByaLZ6jv5Z0GkmSpLyR4xdlSUrKaak5AEwpPyHhJDWsz/mwZi68vzLpJJIkSXnD4impWsanX+X1TBfW0CbpKDXruPOz20V/TTaHJElSHrF4SqqyFmxlSFjMtMzApKPUvNZd4aj+XucpSZJUgyyekqrs5NQ8ikKGaeWDko5SO/pMgNWz4cN3kk4iSZKUFyyekqrslPRcNsbmzI09ko5SO/pckN16uq0kSVKNsHhKqpIUGU5JzWVGZgCZfP0V0rYHHNnH020lSZJqSJ5+apRUWwaFJRwRtjKtfHDSUWpXnwnw9vOwZW3SSSRJknKexVNSlYxLv0pZTDEz0y/pKLWrzwQgwhuebitJknS4LJ6SqmRc6lVmZ3qzmaZJR6ld7XpDm56ebitJklQDLJ6SKu1oNnBcalV+LqOytxCyo54rZ8G2DUmnkSRJymkWT0mVNi79KgDTMnm6jMre+pwPMQNvTEk6iSRJUk6zeEqqtHGpV1mZac+yeHTSUerGUf2hVReLpyRJ0mGyeEqqlEbs4qTUQqZnBgIh6Th1IwToeRqsnAllu5JOI0mSlLMsnpIq5aTUQhqFUqZm8nwZlb31OBVKt2eXVpEkSVK1WDwlVcq41Ktsiw15KdM76Sh1q2Q0pIph6dSkk0iSJOUsi6ekSoiMS7/KzEx/dlOcdJi61bAZdD7B4ilJknQYLJ6SDum48DZHh01MLZTZbPfW41RYtxA2v5t0EkmSpJxk8ZR0SKekssuozCgfmGyQpPQ4NbtdNi3ZHJIkSTnK4inpkManX+W1TDfW0yrpKMlofzw0OwqWPpV0EkmSpJxk8ZR0UEewmUFhacUyKgUqBOgxHpZNh0x50mkkSZJyjsVT0kGNTb1GKkSmlhfYMip76z4Odn4A77ySdBJJkqScY/GUdFDj06+yPrZkQSxJOkqyuo8DgqfbSpIkVYPFU9IBFVHGmNQ8ppUPIhb6r4smraHjEFjmsiqSJElVVeCfJCUdzNDUYlqE7Uwr1GVU9tZjPLzzMmzflHQSSZKknGLxlHRAp6ReZXdMMyvTN+ko9UOPUyFmYPn0pJNIkiTlFIunpAMan3qVFzJ92EbjpKPUD0cPhkatYKnreUqSJFWFxVPSfnUOa+mRerewl1HZW7oIuo3NTjAUY9JpJEmScobFU9J+nZKaC+D1nXvrcSpsfQ/WLkw6iSRJUs6weErar1Gp+azMtOeteFTSUeqX7uOyW2e3lSRJqjSLp6R9FFHGCalFTiq0Py07wpF9XM9TkiSpCiyekvYxICyjedjBrEy/pKPUTz3Gw9svwK6tSSeRJEnKCRZPSfsYnZ5PJgaez/RJOkr91H08lO+GlbOSTiJJkpQTLJ6S9jEytYB5sSsf0izpKPVT5xOhuImn20qSJFWSxVPSpzRjO4PCUp71+s4DK24EJaOdYEiSJKmSLJ6SPmVEahFFIeP1nYfSYzxsWp79kiRJ0kFZPCV9yqjUAnbEBryS6Zl0lPqtx6nZ7VJHPSVJkg7F4inpU0amFjA704tdNEg6Sv3Wuhu06mLxlCRJqgSLp6Q92rOJY1PvuH5nZYSQHfVc8QyU7U46jSRJUr1m8ZS0x8jUAgCv76ysHqdC6TZY9ULSSSRJkuo1i6ekPUamF7AxNmdR7Jx0lNzQdTSkilxWRZIk6RAsnpIqREalFvBc5niivxoqp2Hz7JqeXucpSZJ0UH66lARAz/AO7cMHzPQ026rpdjKsXQBb1yedRJIkqd6qdPEMIaRDCK+GEB6tuN06hPD3EMKSiu0RtRdTUm0blZoPwLPlTixUJV3HZrcrn0kyhSRJUr1WlRHPrwKLPnH7BmBqjLEnMLXitqQcNSq1gBWZ9rxDu6Sj5JajB0HDFrD86aSTSJIk1VuVKp4hhE7AOcBtn7h7AnBXxfd3ARfUaDJJdaaIMk5Ive5sttWRLoKSUbDC4ilJknQgRZV83E+B/wM0/8R97WOMawBijGtCCEfu74khhEnAJIDOnZ0pU6qPBoWlNA278mr9zpIbplT7uStvPqdqT+h6Mrz5GLz/FhzRpdr7lSRJyleHHPEMIZwLrIsxvlydHcQYb40xDo0xDm3XzlP4pPpoVHoB5THwfKZP0lFyU7eTs1tHPSVJkvarMqfajgTODyGsBO4DxoUQ/gdYG0LoAFCxXVdrKSXVqpGpBcyP3dhMs6Sj5KZ2vaFZe6/zlCRJOoBDFs8Y4zdijJ1ijCXA54BpMcb/BTwCXFnxsCuBh2stpaRa05ztDAxL8+o02zoXAnQdAyuegRiTTiNJklTvHM46njcDp4UQlgCnVdyWlGNGpBZRFDJOLHS4up4M29bBukWHfqwkSVKBqezkQgDEGGcAMyq+3wiMr/lIkurSyNQCdsQGvJLpmXSU3PbJ6zzbe62sJEnSJx3OiKekPDA6NZ+XMr3ZTXHSUXJbq85wRFev85QkSdoPi6dUwI5iIz1S7zLT02xrRreT4a1nobws6SSSJEn1isVTKmAjUwsBeNaJhWpG15Nh12Z499Wkk0iSJNUrFk+pgI1Kz2dDbMEb8Ziko+SHrmOy2xUzEo0hSZJU31RpciFJ+SQyKrWAZzN9if43qE8puWFKtZ+7sqRf9jrPMdfXYCJJkqTc5qdNqUAdG1bTLnzo+p01revJsOpF2L096SSSJEn1hsVTKlCjU/MBeLbc4lmjuo2F8t2w6oWkk0iSJNUbFk+pQI1MLWB55ijepW3SUfJL5xMhVeSyKpIkSZ9g8ZQKUBFlDE+9wXOZ45OOkn8aNoNOw2CFxVOSJOkjFk+pAPUPy2kWdrqMSm3pejK8Oxd2vJ90EkmSpHrB4ikVoJGpBQA8n+mTcJI81e1kIMLKWUknkSRJqhcsnlIBOin1OgszXfiA5klHyU8dh0JxE6/zlCRJqmDxlApMI3YxOLXY02xrU1ED6HKS13lKkiRVsHhKBWZoajENQ5kTC9W2rifDhsWw+d2kk0iSJCXO4ikVmJNSCymNaV7K9E46Sn7rdnJ2u+KZZHNIkiTVAxZPqcCclFrA3Nid7TRKOkp+a98PGrf2Ok9JkiQsnlJBacE2+oUVPOf1nbUvlYKuY7LXecaYdBpJkqREWTylAjIitYh0iDxX7vWddaLbybD5Hdi4LOkkkiRJibJ4SgXkpNRCdsQGvBp7JB2lMHT96DrPGYnGkCRJSprFUyogJ6UWMjvTi90UJx2lMLTuBi2P8TpPSZJU8CyeUoFoxwf0Sq12GZW6FEJ21HPlTMhkkk4jSZKUGIunVCBOTC0E4FknFqpb3U6GHe/De/OSTiJJkpQYi6dUIE5KLeTD2ISFsSTpKIWl65jsdvmMRGNIkiQlyeIpFYiRqYW8kOlDxrd93Wp+FLQ7LrusiiRJUoHyE6hUCDat4JjUep71+s5kdDsZ3noeynYlnUSSJCkRFk+pEKx4BvD6zsR0PRnKdsDq2UknkSRJSoTFUyoEK55mbWzFsnh00kkKU8lICCmv85QkSQXL4inluxhhxTMVy6iEpNMUpkYt4ejBrucpSZIKlsVTynfrFsG29a7fmbRuY+Gdl2Hn5qSTSJIk1TmLp5TvKmZTfa7c4pmobidDLIe3nks6iSRJUp2zeEr5bsUzcERX3qFd0kkKW6fhUNTI6zwlSVJBsnhK+ay8DFbOyo62KVnFjaDzCa7nKUmSCpLFU8pna+bCrs3QdUzSSQTZ6zzXvQ5b1yWdRJIkqU5ZPKV89tHoWldHPOuFj45DxbqqkiRJhcLiKeWz5U9D+77QtG3SSQTQYUB2aZXl05NOIkmSVKcsnlK+Kt0Jq170NNv6JJWGktGw/Jns+qqSJEkFwuIp5avVL0HZTk+zrW+6jYUP34b3VySdRJIkqc5YPKV8tfxpCGnoclLSSfRJ3cZmt8ud3VaSJBUOi6eUr1Y8DR0HQ6MWSSfRJ7XpAc2Pdj1PSZJUUCyeUj7a+SG888rHo2uqP0LIrqu64hnIZJJOI0mSVCcsnlI+WjETYjl0OyXpJNqfbmNhxyZYuyDpJJIkSXXC4inlo+XTobgpdBqWdBLtz0cTPnm6rSRJKhCHLJ4hhEYhhJdCCK+FEBaGEL5TcX/rEMLfQwhLKrZH1H5cSZWybBqUjIKiBkkn0f606ABtj81ehytJklQAKjPiuQsYF2McAAwEzgwhnADcAEyNMfYEplbclpS099+CTcuhu6fZ1mtdT4a3noOy3UknkSRJqnWHLJ4xa2vFzeKKrwhMAO6quP8u4ILaCCipipZPz267j0s2hw6u28lQuh3emZN0EkmSpFpXqWs8QwjpEMJcYB3w9xjji0D7GOMagIrtkQd47qQQwpwQwpz169fXUGxJB7Rsena5jrbHJp1EB1MyCkLK6zwlSVJBqFTxjDGWxxgHAp2A4SGEvpXdQYzx1hjj0Bjj0Hbt2lUzpqRKyZRnrxvsfkp22Q7VX42PgA4DYbnXeUqSpPxXpVltY4wfADOAM4G1IYQOABXbdTUdTlIVrXkNdrzvMiq5otvJ2VNtd2099GMlSZJyWGVmtW0XQmhV8X1j4FTgDeAR4MqKh10JPFxLGSVV1kfXd3Ybm2gMVVLXkyFTlp1kSJIkKY9VZsSzAzA9hDAPmE32Gs9HgZuB00IIS4DTKm5LStKy6dC+HzTztPac0PkESDf0Ok9JkpT3ig71gBjjPGDQfu7fCIyvjVCS9q/khikH/FljdvJaw+e4o/xMvn+Qx6keKW4MnUe4nqckScp7VbrGU1L9NSL1Bg1COTMz/ZOOoqroejKsXQBbnfVbkiTlL4unlCdGpeazKxYzO9Mr6Siqio8mgvJ0W0mSlMcsnlKeGJVawEuZXuyiQdJRVBVHD8wurbJsatJJJEmSao3FU8oD7Xif3qlVzMr0SzqKqiqVhu7jYOlUyGSSTiNJklQrLJ5SHhiVWgBg8cxV3cfDtnXZaz0lSZLykMVTygOj0vPZEFvweuycdBRVR4+KCcI93VaSJOUpi6eU8yKjUwt4LnM80bd0bmp+FLTvmz3dVpIkKQ/5KVXKcb3CKo4MHzDT02xzW4/x8PYLsGtr0kkkSZJqnMVTynGjUvMBmFlu8cxp3cdDphRWzkw6iSRJUo2zeEo5bnRqAUszR/MebZKOosPR+QQobgJLn0o6iSRJUo2zeEo5rAGljEgt8jTbfFDUELqO8TpPSZKUlyyeUg4bklpM47CbWZm+SUdRTeg+Ht5fARuXJZ1EkiSpRlk8pRw2KjWf0pjmhUyfpKOoJuxZVmVasjkkSZJqmMVTymGjU/N5JfZkG42TjqKa0KY7HFHi6baSJCnvWDylHNWKLfQNK5lV7mm2eaX7eFjxDJTtTjqJJElSjbF4SjlqZGohqRCZ5cRC+aXHqVC6DVa9kHQSSZKkGmPxlHLUqNR8NscmzIvdko6imtR1NKSKPN1WkiTlFYunlJMio9PzeS5zPOWkkw6jmtSwOXQ+0eIpSZLyisVTykHdw7t0ChtcvzNfdR8Ha+fDlrVJJ5EkSaoRFk8pB52SmgvA9PKBieZQLXFZFUmSlGcsnlIOGp96lUWZzrxL26SjqDa07wdNj4SlTyWdRJIkqUZYPKUc04JtDEu9wbTMwKSjqLakUtnTbZdPh0wm6TSSJEmHzeIp5ZjRqfkUhQxTywcnHUW1qcepsH0jrJmbdBJJkqTDZvGUcsy49Ctsis2YG3skHUW1qfspQHB2W0mSlBcsnlIOSZFhbOo1ZmQGkvHtm9+atoUOA2CZxVOSJOU+P7lKOWRgWEqbsIVp5YOSjqK60ONUWPUS7Pww6SSSJEmHxeIp5ZBx6VcpiymeyfRPOorqQo/xEMth+dNJJ5EkSTosFk8ph4xLzWVO7MVmmiYdRXWh0zBo2MLTbSVJUs6zeEq54sPV9Em9xVRPsy0c6WLoOiY7wVCMSaeRJEmqNounlCsWPwHAtIzFs6D0GA8froINS5JOIkmSVG0WTylXLHmStzJHsiwenXQS1aUep2a3S55MNockSdJhsHhKuaB0Byx/umK0MySdRnWpVWdo3xfefDzpJJIkSdVm8ZRywYqZULbD02wLVa+z4e3nYNvGpJNIkiRVi8VTygWL/wbFTXkxc1zSSZSE3udAzGT/fyBJkpSDLJ5SfRdjdmKh7qewm+Kk0ygJHQZAi07wxpSkk0iSJFWLxVOq79a9DptXw7FnJJ1ESQkhO+q5bBrs3p50GkmSpCqzeEr13UenV/Y8PdkcSlbvs6FsByyfnnQSSZKkKrN4SvXd4iegw0BoflTSSZSkLiOhUUtPt5UkSTnJ4inVZ9s2wurZnmYrSBfDsWdml1UpL0s6jSRJUpVYPKX6bOlT2dlMLZ6C7LIqOzbBqheSTiJJklQlFk+pPlvyBDQ9Ejq4fqeAHuMh3RDeeCzpJJIkSVVi8ZTqq/LS7Ihnz9Mh5VtVQMPm0G0svPFodpkdSZKkHHHIT7MhhGNCCNNDCItCCAtDCF+tuL91COHvIYQlFdsjaj+uVEBWvQg7P/Q0W31a73Pgg7dg7cKkk0iSJFVaZYZRyoB/jTEeB5wAfDmE0Ae4AZgaY+wJTK24LammLH4CUsXQ/ZSkk6g+6XUWEJzdVpIk5ZRDFs8Y45oY4ysV328BFgEdgQnAXRUPuwu4oJYySoVp8RNQMjJ7eqX0kWZHwjHD4U2LpyRJyh1VunAshFACDAJeBNrHGNdAtpwCRx7gOZNCCHNCCHPWr19/mHGlArFpBWx4M7t8hrS33ufAmtfgg1VJJ5EkSaqUShfPEEIz4AHgn2OMmyv7vBjjrTHGoTHGoe3atatORqnwLHoku7V4an96n5vdvunstpIkKTdUqniGEIrJls57Yox/qbh7bQihQ8XPOwDraieiVIAW/AWOHgytuyadRPVRm+7Qtld2dltJkqQcUJlZbQMwGVgUY/zxJ370CHBlxfdXAg/XfDypAG1cBmvmwvGfSTqJ6rPe58DKZ2H7pqSTSJIkHVJlRjxHAlcA40IIcyu+zgZuBk4LISwBTqu4Lelwvf5Qdmvx1MH0PhdiOSz5e9JJJEmSDqnoUA+IMc4CwgF+PL5m40hiwYPQaTi0OibpJKrPjh4EzTtkT7cdcGnSaSRJkg6qSrPaSqplG5bA2vnQ98Kkk6i+S6Wya3ounQqlO5JOI0mSdFAWT6k+WfAXIECfCUknUS7ofQ6UboPlTyedRJIk6aAsnlJ9svBB6HwitDg66STKBSVjoGELeHNK0kkkSZIOyuIp1RfrFsH6RZ5mq8oragA9T4M3H4dMedJpJEmSDsjiKdUXC/4CIQXHnZ90EuWSXmfDtvWwenbSSSRJkg7I4inVBzHCwr9Al5HQvH3SaZRLep4GqWJY9Nekk0iSJB2QxVOqD96bDxuXepqtqq5RS+gxPjti7um2kiSpnrJ4SvXBwgchpOE4Z7NVNfS/FLa8CyueSTqJJEnSflk8paR9dJptt5OhaZuk0ygX9ToLGraEeX9MOokkSdJ+FSUdQCo0JTd8eumLfmE5f224kuvXnc6fb3BZjFy39/GtipU3n1O9JxY3huMnwPwH4Jz/hgZNq51BkiSpNjjiKSXs3PTzlMY0T5YPTTqKcln/z0HpNlj0aNJJJEmS9mHxlBIVOSf9IjMz/fiQZkmHUS7rfCK07Azz7ks6iSRJ0j4snlKCBoWldAobeLT8hKSjKNelUjDgUlg+AzavSTqNJEnSp1g8pQSdm36BXbGIv2c8zVY1oP/nIGZg/p+TTiJJkvQpFk8pIYEMZ6df5JnMALbQJOk4ygdte0DHIc5uK0mS6h2Lp5SQIWExHcIm/upptqpJAy6DtQvgvQVJJ5EkSdrD4ikl5Nz0C+yMxUzNDE46ivLJ8RdCqshJhiRJUr1i8ZQSkCLD2emXmJYZxDYaJx1H+aRpG+h5Osz7M2TKk04jSZIEWDylRIxILeLI8AFTPM1WtWHA52Dre9kZbiVJkuoBi6eUgIvSM9kaGzEtMzDpKMpHx54JjVo6yZAkSao3LJ5SHWvJVs5NPc+D5aPYQaOk4ygfFTWE4z8Di/4Ku7YmnUaSJMniKdW1i9PP0CiUcm/5+KSjKJ8NuAxKt2fLpyRJUsIsnlJdipHL01N5OdOTRbFL0mmUz44ZAUeUOLutJEmqFyyeUl1aOZPuqTXcU+Zop2pZCND/Ulj+NGx+N+k0kiSpwFk8pbo053Y+iE2ZknE2W9WB/pcCEeb9KekkkiSpwFk8pbqydR0s+iv3l49hFw2STqNC0KY7dBqend02xqTTSJKkAmbxlOrKq7+HTJmTCqluDbgU1r0O781POokkSSpgFk+pLmTK4eU7oWQ0y+PRSadRITn+QkgVw2tOMiRJkpJj8ZTqwrJp8MHbMPTqpJOo0DRpDb3OzM5uW7oj6TSSJKlAWTylujDndmjaDnqfm3QSFaIR/wjbN2av9ZQkSUqAxVOqbR+uhsV/g0FXQJGTCikBXUZChwHw/K8hk0k6jSRJKkAWT6m2vXJ3dkbRIVcmnUSFKgQ48TrY8CYsm5p0GkmSVIAsnlJtKi+Fl++CHqfCESVJp1Eh63MBND8anv9l0kkkSVIBsnhKtWnx32Dre04qpOQVNYDh18DyGfDegqTTSJKkAmPxlGrTnNuhRUfoeXrSSSQYMhGKm8ALtySdRJIkFZiipANIuajkhimHfEznsJZnGk7jx6UX8/NvPlEHqaRDaNIaBv4DvHIXjP8WNG+fdCJJklQgHPGUasnl6WmUxRR/LB+bdBTpYyd8MXvt8ezbkk4iSZIKiMVTqgUNKOWz6Rk8lRnCWlonHUf6WJvu0OvsbPEs3ZF0GkmSVCAsnlItODM1mzZhC/eUj086irSvE78MOzbBa/clnUSSJBUIi6dU4yJfKJrCykx7ZmX6Jh1G2leXk6DDQHjh15DJJJ1GkiQVAIunVMNOTb1C/9QKflU+gehbTPVRCHDidbBhMSx9Kuk0kiSpABzyU3EI4fYQwroQwoJP3Nc6hPD3EMKSiu0RtRtTyg2BDF8rup+Vmfb8pXx00nGkAzv+Amh+NDz/y6STSJKkAlCZ4Zg7gTP3uu8GYGqMsScwteK2VPBOT82hT+otflZ2IeWkk44jHVi6GEZMghVPw3sLDv14SZKkw3DI4hljfAbYtNfdE4C7Kr6/C7igZmNJuSeQ4V+KHmBZpgOPZE5KOo50aEMmQnGT7LWekiRJtai6F6C1jzGuAajYHllzkaTcdHbqJXqnVvGzsosc7VRuaHwEDPpfMO9PsOW9pNNIkqQ8VlTbOwghTAImAXTu3Lm2dyclIkWGfy56gMWZjjyaOSHpOFLljfhHMi/+jl/+19f5cdklVX76ypvPqYVQkiQp31R3xHNtCKEDQMV23YEeGGO8NcY4NMY4tF27dtXcnVS/nZd6jp6pd/hp2UVknMlWuaRNd/6eGcIV6adoxvak00iSpDxV3U/IjwBXVnx/JfBwzcSRck+acr5a9BcWZY7h8czwpONIVfbLsgs4Imzly0X+KpckSbWjMsup/AF4HugVQlgdQvjfwM3AaSGEJcBpFbelgnRB6lm6pd7jp2UXu26nctL82I0HykdxdfpxOoUDnsAiSZJUbZWZ1fayGGOHGGNxjLFTjHFyjHFjjHF8jLFnxXbvWW+lglBEGf9U9BcWZEp4IjM06ThStf2w9FIypLih6L6ko0iSpDzk8Ix0GC5Mz6RLah0/KbsICEnHkartPdrw2/JzOTf9AkPCm0nHkSRJecbiKVVTMWX8U9GDvJbpxtTM4KTjSIftt2Xn8l48gv8s/j2BTNJxJElSHrF4StX02fTTdAob+EnZxTjaqXywg0b8oPRSBqaWMyH1XNJxJElSHrF4StXQgFKuK3qQVzI9mJEZkHQcqcY8mBnFvExX/k/xfTRiV9JxJElSnrB4StVwaXo6R4dN/LjsszjaqXwSSfHd0is4OmxiUnpK0nEkSVKeKEo6gJSUkhuq96G6FVv4e8O/8FKmF7MyfWs4lZS82bE3j5UP5x+L/sp95aewjiOSjiRJknKcI55SFX27+G5asY1vl07E0U7lq++XXUaacq4v+mPSUSRJUh6weEpVMD71Mp9JP8uvyyewKHZJOo5Ua1bF9txRfiYXpWdyfFiRdBxJkpTjLJ5SJbVgK98rnsyizDH8suyCpONIte5XZRewieZ8q/j3QEw6jiRJymEWT6mS/rPof2jDZq4vvZZSL49WAdhCE35SdjEjUm9wRmp20nEkSVIO89OzVAljU3P5bNEz/LJsAgtit6TjKE9Vd8IrgJU3n1ODST52X/kpfD79JP9edC/Tdw9iN8W1sh9JkpTfHPGUDqE52/l+8W0sznTk52UXJh1HqlPlpLmp7Aq6pNbxjaJ7k44jSZJylMVTOoR/L7qHI3mf60uvdbRHBenZTD8ml53FVUVPcEbqpaTjSJKkHGTxlA5iVGo+lxVN53fl5/Ja7JF0HCkxN5ddxtxMN35YfCudwrqk40iSpBxj8ZQOoCk7uLn4dyzLdOAnZRclHUdKVClFXFf6T0Tgl8U/p5iypCNJkqQcYvGUDuAbRfdyNBu5vvRadtEg6ThS4lbHI7m+9FoGppZ7vackSaoSi6e0HyemFvK/iqZye/mZvBKPTTqOVG88mRnG7WVncnXR3zjdJVYkSVIlWTylvbRiC/9VdCsrMu35UdklSceR6p3vl13Oa5lu/LD4t/D+yqTjSJKkHGDxlD6hEbuY3OBHtA8f8K+lX2QnDZOOJNU72es9v0IA+PNVULY76UiSJKmes3hKFdKU84viXzIoLOWfSr/sKbbSQayK7bm+dBK8+wo8dWPScSRJUj1XlHQAqX6IfLfoDk5Lv8y3Sq/kiczwpANJVVJyw5Q63+cTmeEw/Fp44VdQMhJ6n1PnGSRJUm5wxFMCvpJ+kMuLpvHrsvO5u/yMpONIueP070KHgfDQF2HTiqTTSJKkesriqYJ3SXo6/1p8Pw+Uj+YHZZcmHUfKLUUN4bN3AAHuOg82Lks6kSRJqocsnipop6Re5XtFk3m6vD9fL70GstOlSKqK1t3gykdg9za48xxYvzjpRJIkqZ6xeKpgDQxL+XXxz3g9duFLpV+lzEueperrMAAmPgqZsmz5XPt60okkSVI94idtFaYNS5nc4Iesi624evf/YRuNk04k5b72x8PEx7Kn3N51Lnz+YTiq3wEffjgTIq282YmMJEnKJY54qvC8/xb8z4UAXFn6dTbQMuFAUh5pdyxc9RgUNYI7z4V3Xkk6kSRJqgcsniosy6bDrSfDjg+4evf1rIwdkk4k5Z823bPls1ELuHsCrHop6USSJClhFk8VhhjhuV9kRzqbtYdJ03kt9kg6lZS/jiiBqx6Hpm3h95+Blc8mnUiSJCXI4qn8t3sbPPC/4cn/gN7nwheeyo7ISKpdLTtlr/lscTTcczEsfiLpRJIkKSEWT+W3TStg8umw4C8w/ttwyd3QsHnSqaTC0aIDTJwCrbvDvZfAo/8Cu7YmnUqSJNUxZ7VV/lo6Fe6/GojwD/dDz1OTTiQVpmZHwhf+DtP+Lzz/K1g2DS64JelUOcUZgCVJuc4RT+WfGGHWT7Kn9rXoCJNmWDqlpBU3hjP+X3b0M0a442y+UXQPDdmddDJJklQHLJ7KL6tmwx1nwVM3Qp8J2VGW1t2STiXpIyUj4YvPwZCJXFs0hb82+CZ9w/KkU0mSpFpm8VR+2LgM/vR5mHxq9vvzfgYX3wENmiadTNLeGjaD837Klbu/TouwnQcbfJuvph+giLKkk0mSpFriNZ7Kbds2wNM/gDmTId0Qxn4DTrwu+8FWUr32dGYAp+/6L75TfBf/UvwA56Rf4Ddl5/HXzEmU+udJkqS84l925abd2+HFW2DWT7PLpQz+PIy9AZoflXQyqaAczqQ3AJtpxr+Ufpm/lQ/nX4ru58cNfsO/xT8xuews7isfxzYa11BSSXtz0iodiP/fUG2weCq3bF0P8/8Mz/0CtrwLvc6GU2+Edr2STibpMDyRGcYTu4cyNvUa16Yf5T+L7+Gfih7k9+WncWfZmWygZdIRJUnSYbB4qv4r3QFvTIF5f8wukRLLodNwuOi27EQlkvJEYEZmIDMyAxlYtpRJRY/ypfQjXJN+jAfKRzO5/CyWxY5Jh5QkSdVg8VT9lMnAW7PgtT/C6w/D7i3ZpVFO+goM+BwceVzSCSXVormxB18q/WdKwhquST/GxelnuLxoGoszHXkqMwTebgudhkIqnXRUSZJUCRZP1R9b18OqF+Ct52HRI/DhKmjQLLssSv9LoWQ0pJyIWSokK2MHvln2v/lJ2cWcn36O8alXuCY9BW5/BJq0gZ5nQK8zofs4aNg86biSJOkALJ5KRoywYQmseoE//+XPDEktplvqPQB2xWKey/ThwfIJPLlzCDtfaAgvbAUeTzazpMRsoCW3l5/F7eVn0YJtnJx6jfHlrzB27sO0eu1edsUiXswcx2uxOwszJSyMXVgVjwTCPq91OBNfOOGG6pvDneArCb6PKs9/K+WTwyqeIYQzgZ8BaeC2GOPNNZJK+SOTgc2rs2trbloGm1ZkC+c7c2D7RgDGp5vxcqYX95WewpxMLxbEruymOOHgkuqrzTTlr5mT+GvmJNKUMzQsZnz6FUan5vHF1CMUFWWyj4uNWRS7sDBTwusV27di+4TTS5JUmKpdPEMIaeBXwGnAamB2COGRGOPrNRVO9VzZruw6mtvWf2K7HrauzRbMj4pm+a6Pn1PUCFp3g2PPhGNGQOcTGfzfi9nfqIQkHUo5aV6Mx/Fi2XHAP9CQ3RwbVnN8aiXHh5Ucn1rJ59LTaRI+8Xvo5q9By04ff7XoCC2PyX7ftB00PgIatYS0JwVJklRTDuev6nBgaYxxOUAI4T5gAmDxrGkxZr/45DZzgK8ImbKKr/K9thVf5aXZMli2C8p3Q9lOKNv98X1lO2HXVthd8bXn+22wawvs2pwtmrs27z9vUWM4ogTa9ICep2eLZpvu0Lo7NO+wn+s0l9Tuv5+kgrGLBsyP3Zhf3m3PfSkydA1rOC68TaewnhuGNYMP34EPV8PbL8DOD/b/Yg2aZ0to45YVZbQVPyj6kB00YAcN2UkDdsSGH9+ODdhFMbspoowiSmMRpaQppYgy0rD+TUgVZSdECum9tqmPb4eQvU3FNgRSZMj+9g/4H+okSbkoxBir98QQLgbOjDF+oeL2FcCIGON1B3rO0KFD45w5c6q1v1q3+mW467xDPOgQ/1b7/beMB3hMPMjtTxTMJBU3yU7u06ApNGyW/RDWoGl2Ao+m7aBZu+x2z1fb7LZBs+wHp0rKxetTJOWufa572rUVNlcU0e0bYcf7sOOD7HbnB5+6vWb9ehqxm8bsolEoTSD9xzIxfKKMZrcf/0X56HdwoGFRxX/s+9Tv5b1+R+/zO/swy20V/gbo8GzdVZbIfps1rP7YxeFkPpz95qKk/q08RvVY49bwL/OTTnFQIYSXY4xD977/cP6fsb+/Kvs0pRDCJGBSxc2tIYQ3D2OfdaEtsCHpEPXDAUY0c4fHMr94PPNLYscz/FcSe817vj/zi8czv3g888q7bflaqO/Hs8v+7jyc4rkaOOYTtzsB7+79oBjjrcCth7GfOhVCmLO/hq7c47HMLx7P/OLxzC8ez/zi8cwvHs/8ksvH83AWRZwN9AwhdA0hNAA+BzxSM7EkSZIkSfmi2iOeMcayEMJ1wBNkl1O5Pca4sMaSSZIkSZLywmFd/RtjfAx4rIay1Bc5c1qwDsljmV88nvnF45lfPJ75xeOZXzye+SVnj2e1Z7WVJEmSJKkyDucaT0mSJEmSDsniCYQQPhtCWBhCyIQQhu71s2+EEJaGEN4MIZyRVEZVTwjhxhDCOyGEuRVfZyedSVUXQjiz4j24NIRwQ9J5dHhCCCtDCPMr3pP1dHFnHUgI4fYQwroQwoJP3Nc6hPD3EMKSiu0RSWZU5R3gePq3M0eFEI4JIUwPISyq+Gz71Yr7fY/moIMcz5x8j3qqLRBCOA7IAL8F/i3GOKfi/j7AH4DhwNHAU8CxMcbypLKqakIINwJbY4w/SjqLqieEkAYWA6eRXcZpNnBZjPH1RIOp2kIIK4GhMcb6vg6Z9iOEMAbYCtwdY+xbcd8PgE0xxpsr/uPQETHGryeZU5VzgON5I/7tzEkhhA5AhxjjKyGE5sDLwAXARHyP5pyDHM9LyMH3qCOeQIxxUYzxzf38aAJwX4xxV4xxBbCUbAmVVHeGA0tjjMtjjLuB+8i+NyUlIMb4DLBpr7snAHdVfH8X2Q9GygEHOJ7KUTHGNTHGVyq+3wIsAjriezQnHeR45iSL58F1BFZ94vZqcvhgF7DrQgjzKk4n8tSS3OP7MP9E4MkQwsshhElJh1GNaB9jXAPZD0rAkQnn0eHzb2eOCyGUAIOAF/E9mvP2Op6Qg+/RgimeIYSnQggL9vN1sJGTsJ/7PDe5njnEsb0F6A4MBNYA/51kVlWL78P8MzLGOBg4C/hyxal+kuoP/3bmuBBCM+AB4J9jjJuTzqPDs5/jmZPv0cNaxzOXxBhPrcbTVgPHfOJ2J+DdmkmkmlLZYxtC+B3waC3HUc3zfZhnYozvVmzXhRAeJHs69TPJptJhWhtC6BBjXFNxTdK6pAOp+mKMaz/63r+duSeEUEy2pNwTY/xLxd2+R3PU/o5nrr5HC2bEs5oeAT4XQmgYQugK9AReSjiTqqDil+tHPgMsONBjVW/NBnqGELqGEBoAnyP73lQOCiE0rZgggRBCU+B0fF/mg0eAKyu+vxJ4OMEsOkz+7cxdIYQATAYWxRh//Ikf+R7NQQc6nrn6HnVWWyCE8BngF0A74ANgbozxjIqffRO4GigjO7z9eFI5VXUhhN+TPQ0hAiuBaz+6xkG5o2Ka8J8CaeD2GOP/SzaRqiuE0A14sOJmEXCvxzO3hBD+AIwF2gJrgW8DDwF/AjoDbwOfjTE6YU0OOMDxHIt/O3NSCGEUMBOYT3bFBoB/J3tdoO/RHHOQ43kZOfgetXhKkiRJkmqVp9pKkiRJkmqVxVOSJEmSVKssnpIkSZKkWmXxlCRJkiTVKounJEmSJKlWWTwlSZIkSbXK4ilJkiRJqlUWT0mSJElSrfr/24WMjx7z/5AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         wage estimated   R-squared:                       0.127\n",
      "Model:                            OLS   Adj. R-squared:                  0.114\n",
      "Method:                 Least Squares   F-statistic:                     10.18\n",
      "Date:                Fri, 19 Nov 2021   Prob (F-statistic):           1.59e-10\n",
      "Time:                        15:42:35   Log-Likelihood:                -1090.1\n",
      "No. Observations:                 428   AIC:                             2194.\n",
      "Df Residuals:                     421   BIC:                             2223.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -2.2446      0.926     -2.425      0.016      -4.064      -0.425\n",
      "x1             0.3762      0.327      1.152      0.250      -0.266       1.018\n",
      "x2             0.4617      0.070      6.569      0.000       0.324       0.600\n",
      "x3             0.0006      0.001      1.026      0.305      -0.001       0.002\n",
      "x4             0.0148      0.015      0.954      0.340      -0.016       0.045\n",
      "x5             0.0203      0.396      0.051      0.959      -0.758       0.798\n",
      "x6            -0.0700      0.124     -0.562      0.574      -0.315       0.175\n",
      "==============================================================================\n",
      "Omnibus:                      343.215   Durbin-Watson:                   2.058\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6326.629\n",
      "Skew:                           3.359   Prob(JB):                         0.00\n",
      "Kurtosis:                      20.596   Cond. No.                     2.21e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.21e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "4086.2143777901156\n"
     ]
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (16, 6)\n",
    "a = np.ones(len(df['wage estimated']))\n",
    "\n",
    "stack =np.column_stack((a, \n",
    "                        df['live in SMSA'], \n",
    "                        df['educ years of schooling'], \n",
    "                        df['expersq'], \n",
    "                        df['nwifeinc'], \n",
    "                        df['kids below 6'], \n",
    "                        df['kids between 6 and 18']))\n",
    "\n",
    "mod0 = sm.OLS(df['wage estimated'], stack)\n",
    "mod0 = mod0.fit()\n",
    "res0 = mod0.resid\n",
    "SSR0 = res0.T@res0\n",
    "\n",
    "\n",
    "plt.hist(res0,'auto', label='OLS on wage')\n",
    "\n",
    "\n",
    "x = np.linspace(-10, 25, 100)\n",
    "y = norm.pdf(x,0,2)*290  ## We mutliply to normalize the 2 curves\n",
    "plt.plot(x,y, label = 'gaussian curve')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(mod0.summary())\n",
    "print(SSR0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112e3c07",
   "metadata": {},
   "source": [
    "A partir de l'histogramme, nous pouvons interpréter que les résidus suivent une lois gaussienne centrée en 0.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be8fdca",
   "metadata": {},
   "source": [
    "Cependant, il y a une lègère erreur introduisant une asymétrie pour des valeurs d'abscisse négatives proches de 0, et de plus, certaines valeurs me semblent incohérentes puisque la courbe de la gaussienne n'est pas parfaite: certains points semblent très éloignés des autres -ce qui est inhabituel pour une telle loi-."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "371f71c2",
   "metadata": {},
   "source": [
    "### Question  8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c6944552",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAFlCAYAAACDRTcUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABIU0lEQVR4nO3dd5iU1cGG8ftso4MCqyIgYC+AgBQbiFhQ7JrYErtgYkxMrPhpYotKLDHGGHvvRmKJXQEFrKAigthFBQ1SpLct5/tjVoIKssDunpnd+3ddc83MO/POPODI7jPnfc8JMUYkSZIkSaoueakDSJIkSZJqN4unJEmSJKlaWTwlSZIkSdXK4ilJkiRJqlYWT0mSJElStbJ4SpIkSZKqVUFNvlnLli1j+/bta/ItJUmSJEk15M0335wRYyz+4fYaLZ7t27dn7NixNfmWkiRJkqQaEkL4fEXbPdRWkiRJklStLJ6SJEmSpGpl8ZQkSZIkVasaPcdTkiRJkr5TUlLClClTWLx4ceooWk3169enTZs2FBYWVur5Fk9JkiRJSUyZMoUmTZrQvn17Qgip46iSYozMnDmTKVOm0KFDh0rt46G2kiRJkpJYvHgxLVq0sHTmmBACLVq0WK2RaounJEmSpGQsnblpdf+7WTwlSZIkqQb96U9/4oUXXkgdo0Z5jqckSZIk1aCLLroodQRKS0spKKi5OuiIpyRJkqQ66+KLL2bLLbdkjz324IgjjuDKK68E4Oabb6ZHjx5su+22HHLIISxcuBCAY489locffnjZ/o0bNwbg66+/pk+fPnTp0oWOHTsyatQoysrKOPbYY+nYsSOdOnXi6quv/tFrXHTRRfTo0YOOHTsyaNAgYowA9O3bl7PPPpuePXuy+eabM2rUqBXmv/zyy+nUqRPbbrstgwcPXrbv2LFjAZgxYwbt27cH4I477uDnP/85++23H3vuuSeHHXYYTz311LLXOvbYYxk6dChlZWWceeaZ9OjRg86dO3PjjTeu9d+zI56SJEmS0nt6MPz33ap9zQ06wd5DVvrw2LFjGTp0KG+//TalpaV069aN7bbbDoCDDz6YgQMHAnDeeedx66238tvf/nalr3XffffRv39/zj33XMrKyli4cCHjxo1j6tSpTJgwAYDZs2f/aL9TTjmFP/3pTwAcddRRPPHEE+y3335AZlTyjTfe4KmnnuLCCy/80eG5Tz/9NI8++iivv/46DRs2ZNasWav8K3n11VcZP348zZs355FHHuHBBx9kwIABLF26lGHDhnH99ddz66230qxZM8aMGcOSJUvYaaed2HPPPSs9g+2KOOIpSZIkqU4aPXo0BxxwAA0aNKBJkybLCh/AhAkT6N27N506deLee+9l4sSJP/laPXr04Pbbb+eCCy7g3XffpUmTJmy88cZ8+umn/Pa3v+WZZ56hadOmP9pvxIgR9OrVi06dOjF8+PDvvc/BBx8MwHbbbcfkyZN/tO8LL7zAcccdR8OGDQFo3rz5Kv/Me+yxx7Ln7b333gwfPpwlS5bw9NNP06dPHxo0aMBzzz3HXXfdRZcuXejVqxczZ87ko48+WuVr/xRHPCVJkiSl9xMjk9Xlu8NaV+TYY4/l0UcfZdttt+WOO+7gxRdfBKCgoIDy8vJl+y9duhSAPn36MHLkSJ588kmOOuoozjzzTI4++mjeeecdnn32Wa677joeeughbrvttmXvsXjxYk4++WTGjh1L27ZtueCCC763REm9evUAyM/Pp7S0dIX5VzS77PIZf7jkSaNGjZbdrl+/Pn379uXZZ5/lwQcf5Igjjlj2utdeey39+/df+V/earJ4SpJUw9oPfnKN9ps8ZJ8qTiJJddvOO+/MSSedxDnnnENpaSlPPvnkssNr582bR6tWrSgpKeHee++ldevWALRv354333yTQw89lMcee4ySkhIAPv/8c1q3bs3AgQNZsGABb731FgMGDKCoqIhDDjmETTbZhGOPPfZ77/9dKWzZsiXz58/n4Ycf5mc/+1ml8++5555cdNFFHHnkkcsOtW3evPmyjD179vze+agrcvjhh3PLLbcwduxY7rjjDgD69+/P9ddfT79+/SgsLOTDDz+kdevW3yutq8viKUmSJKlO6tGjB/vvvz/bbrst7dq1o3v37jRr1gzITDrUq1cv2rVrR6dOnZg3bx4AAwcO5IADDqBnz57stttuy8rYiy++yBVXXEFhYSGNGzfmrrvuYurUqRx33HHLRh8vu+yy773/Ouusw8CBA+nUqRPt27enR48eq5V/r732Yty4cXTv3p2ioiIGDBjApZdeyhlnnMGhhx7K3XffTb9+/X7yNfbcc0+OPvpo9t9/f4qKigA48cQTmTx5Mt26dSPGSHFxMY8++uhqZfuh8FPDy1Wte/fu8bvZlSRJqqsc8ZSkjEmTJrHVVlslzTB//nwaN27MwoUL6dOnDzfddBPdunVLmilXrOi/XwjhzRhj9x8+1xFPSZIkSXXWoEGDeO+991i8eDHHHHOMpbOaVLp4hhDygbHA1BjjviGE5sCDQHtgMnBojPHb6ggpSZIkSdXhvvvuSx2hTlid5VROBSYtd38wMCzGuBkwrOK+JEmSJEnfU6niGUJoA+wD3LLc5gOAOytu3wkcWKXJJEmSJEm1QmVHPP8GnAWUL7dt/Rjj1wAV1+tVbTRJkiRJUm2wyuIZQtgX+CbG+OaavEEIYVAIYWwIYez06dPX5CUkSZIkSTmsMiOeOwH7hxAmAw8A/UII9wDTQgitACquv1nRzjHGm2KM3WOM3YuLi6sotiRJkiStvSlTpnDAAQew2Wabsckmm3DqqaeydOlSILM257777vujfZ544gm6du3Ktttuy9Zbb82NN95Y07FzzipntY0xngOcAxBC6AucEWP8ZQjhCuAYYEjF9WPVF1OSJElSbbem6xyvzKrWP44xcvDBB/PrX/+axx57jLKyMgYNGsS5557LFVdcscJ9SkpKGDRoEG+88QZt2rRhyZIlTJ48uUpz10arM6vtDw0B9gghfATsUXFfkiRJknLC8OHDqV+/PscddxwA+fn5XH311dx2220sXLhwhfvMmzeP0tJSWrRoAUC9evXYYostfvS8WbNmceCBB9K5c2e23357xo8fD8AFF1zA8ccfT9++fdl44435+9///qN9H3roIU477TQArrnmGjbeeGMAPvnkE3beeWcALrroInr06EHHjh0ZNGgQMUYAxowZQ+fOndlhhx0488wz6dixIwBlZWWceeaZ9OjRg86dO9f4KO1qFc8Y44sxxn0rbs+MMe4WY9ys4npW9USUJEmSpKo3ceJEtttuu+9ta9q0KRtttBEff/zxCvdp3rw5+++/P+3ateOII47g3nvvpby8/EfPO//88+natSvjx4/n0ksv5eijj1722Pvvv8+zzz7LG2+8wYUXXkhJScn39u3Tpw+jRo0CYNSoUbRo0YKpU6cyevRoevfuDcApp5zCmDFjmDBhAosWLeKJJ54A4LjjjuOGG27g1VdfJT8/f9lr3nrrrTRr1owxY8YwZswYbr75Zj777LM1+FtbM2sz4ilJkiRJOSvGSAih0tu/c8sttzBs2DB69uzJlVdeyfHHH/+j54wePZqjjjoKgH79+jFz5kzmzJkDwD777EO9evVo2bIl6623HtOmTfvevhtssAHz589n3rx5fPnllxx55JGMHDmSUaNGLSueI0aMoFevXnTq1Inhw4czceJEZs+ezbx589hxxx0BOPLII5e95nPPPcddd91Fly5d6NWrFzNnzuSjjz5azb+xNbfKczwlSZIkqTbaZpttGDp06Pe2zZ07ly+//JJNNtmEmTNnrnTfTp060alTJ4466ig6dOjAHXfc8b3Hvzv0dXnfldl69eot25afn09paemPnrvDDjtw++23s8UWW9C7d29uu+02Xn31Va666ioWL17MySefzNixY2nbti0XXHABixcvXuF7Lp/n2muvpX///it9TnVyxFOSJElSnbTbbruxcOFC7rrrLiBzHuTpp5/OscceS8OGDVe4z/z583nxxReX3R83bhzt2rX70fP69OnDvffeC2Rmx23ZsiVNmzatdLY+ffpw5ZVX0qdPH7p27cqIESOoV68ezZo1Y/HixQC0bNmS+fPn8/DDDwOw7rrr0qRJE1577TUAHnjggWWv179/f66//vplh/V++OGHLFiwoNJ51pYjnpIkSZLqpBACjzzyCCeffDIXX3wx5eXlDBgwgEsvvXTZc4YNG0abNm2W3b///vu5/PLLOemkk2jQoAGNGjX60WgnZCYROu644+jcuTMNGzbkzjvvXK1svXv35ssvv6RPnz7k5+fTtm1bttxySwDWWWcdBg4cSKdOnWjfvj09evRYtt+tt97KwIEDadSoEX379qVZs2YAnHjiiUyePJlu3boRY6S4uJhHH310tTKtjfBTw7FVrXv37nHs2LE19n6SJGWjNV0uYFXLAkhSrpk0aRJbbbVV6hi1yvz582ncuDEAQ4YM4euvv+aaa66plvda0X+/EMKbMcbuP3yuI56SJEmSVEs8+eSTXHbZZZSWltKuXbsVjsamYPGUJEmSpFrisMMO47DDDksd40ecXEiSJEmSVK0snpIkSZKSqck5Z1R1Vve/m8VTkiRJUhL169dn5syZls8cE2Nk5syZ1K9fv9L7eI6nJEmSpCTatGnDlClTmD59euooWk3169f/3jIzq2LxlCRJkpREYWEhHTp0SB1DNcBDbSVJkiRJ1criKUmSJEmqVhZPSZIkSVK1snhKkiRJkqqVxVOSJEmSVK0snpIkSZKkamXxlCRJkiRVK4unJEm5JMbUCSRJWm0FqQNIkqQfy6eMdmEam4WpbBqmslneFLjhMpjxMdRrAutvAxt0hPU7Zm633AIKilLHliRphSyekiRlia3DZE4oeJptwmQ6hK+pF0qXPTYltoTGXaB9b1g8B6ZNgNdvgrIlmSfkFWTK5/rbwCb9oNPPId8f85Kk7OBPJEmSEtsyfMHvC4ayV/4Y5saGvF6+JSPKu/BxeWs+iq35JG7IAhow+Zf7fH/HslKY9Qn8912YNjFzmTwK3n0IRl0Fu54DWx8EeZ5ZI0lKy+IpSVIiW4QvOLXg3wzIf4O5sQF/Kz2Y20r3Zi6NKvcC+QVQvEXm0ulnmW0xwvtPwPBL4OHjYf2rod95sHl/CKH6/jCSJP0Ei6ckSTVsszCFUwuGsm/+68yLDbim9CBuLd2buTRe+xcPAbbaD7YYABOGwohL4f7DoE1P2O2P0KHP2r+HJEmryeIpSdIaaD/4ydXepyVz+FPhXTxb9BoLqce1pQdyS+kA5lRF4fyhvHzofChscxCMuxdeuhzu3A867AK7nw+tt6v695QkaSUsnpIk1YCtw2RuLrqKFszl+rL9uLl0H2bTpPrfOL8QtjsWOh8OY2/LnPt5yx4w4ArocUL1v78kSVg8JUmqdv3z3uDqwuuZQyMOWXoBE2OHmg9RWB92OBm6/hKGnghPngYzPoL+l2RGRyVJqkZOcydJUrWJnJL/CDcW/Y0PYlv2X3JxmtK5vPpN4Yj7YfvfwOvXw/2Hw+K5aTNJkmq9VRbPEEL9EMIbIYR3QggTQwgXVmy/IIQwNYQwruIyoPrjSpKUG+qxlL8X/oMzCv/Fv8t25vCl5zGddVPHysjLh70uhX2vho+HwW394dvPU6eSJNVilTnUdgnQL8Y4P4RQCIwOITxd8djVMcYrqy+eJEm5Z31mcVPRX+kUPmNIyeHcULYfkIVLmXQ/HtbtAA8dA7fsBoffB217pk4lSaqFVjniGTPmV9wtrLjEak0lSVKO6hw+4fF657FJ+IpBJadxQ9n+ZGXp/M4mu8KJL0BRY7hjX3j34dSJJEm1UKXO8Qwh5IcQxgHfAM/HGF+veOiUEML4EMJtIYQsOX5IkqQ0+ua9zUNFF7E0FnLI0gt4oTxHliwp3hxOHJZZYmXoCTDiMoh+xyxJqjqVmtU2xlgGdAkhrAM8EkLoCFwPXExm9PNi4Crg+B/uG0IYBAwC2GijjaomtSRJWWbb8DH/LPw7H8Y2HLv0bGbRNHWk1dOoBRz9KDzxB3hpCOQX0v6prdbopSYP2adqs0mSct5qzWobY5wNvAjsFWOcFmMsizGWAzcDKzwpJMZ4U4yxe4yxe3Fx8drmlSQp67QL/+W2oiuYHptx3NKzcq90fqegHhxwHXQ+DIZfzIF5o1MnkiTVEpWZ1ba4YqSTEEIDYHfg/RBCq+WedhAwoVoSSpKUxVowhzsL/wLAMSVnM5NmiROtpRBg/39A+95cXngjO+RNTJ1IklQLVGbEsxUwIoQwHhhD5hzPJ4DLQwjvVmzfFfhDNeaUJCnrNGAxtxZdwfrhW05YeiaTY6tV75QLCorgsHuYHDfgxsKr2SxMSZ1IkpTjVnmOZ4xxPNB1BduPqpZEkiTlgHzK+EfhtXQKnzGo5DTGxU1TR6paDdbh2KVn80i9P3FH0V84aMlFfJMt65BKknLOap3jKUmSgBi5uOA2dst/mz+WHs+wXJm9djV9RUuOX3oW6zCf24supxGLUkeSJOUoi6ckSavrpcs5smAE15YeyH1lu6VOU60mxvb8puRUtghf8s/CayigNHUkSVIOqtRyKpIkqcJbd8OLl/JwWR+uKv156jSV0n7wk2u1/4vlXTiv9HiGFN7CxfF2zik9EQhVE06SVCdYPCVJqqyPh8F/ToVN+jF44rHUpfL1QFk/WocZ/LbgUabEYq4rOzB1JElSDvFQW0mSKmPef+HfA2G9reDQuyitg9/dXlX6c/5dtjNnFj5E/7wxqeNIknKIxVOSpFUpL4dHfgVLF8LPbod6TVInSiRwdskgxpd3YEjhzazHt6kDSZJyhMVTkqRVee06+HQE7D0EijdPnSapEgr4fclvqEcJVxVeT6A8dSRJUg6weEqS9FO+fgdeuBC23Be6HZM6TVb4NG7IRaVH0Tt/AsfnP506jiQpB1g8JUlamaUL4OEToFEx7H8thLozmdCqPFC2K8+VbcdZBQ+ydZicOo4kKctZPCVJWpln/w9mfgwH3wgNm6dOk2UCZ5cMZA6NuabwOuqzJHUgSVIWq3tT8kmSVBmT/gNv3gE7/R469EmdBlj79Tir2rc05fSSX3F30RDOKbiP80uPSx1JkpSlHPGUJOmH5kyFx38LG3aFXc9NnSarjSrvzC2le3NMwfP0y3srdRxJUpayeEqStLzyMnjkJChdCofcCgVFqRNlvStKD2NS+UZcXngTLZmTOo4kKQtZPCVJWt4rf4fJo2DA5dBik9RpcsISivhdySk0ZhGXF94IMaaOJEnKMhZPSZK+M/VNGP5n2PpA6PKL1GlyykexDZeWHkm//HEw5pbUcSRJWcbiKUkSQOkSeORX0HgD2O9vLp2yBu4q25PhZV3gufPgm0mp40iSsojFU5IkgJf/DjM+hP2ugQbrpk6TowJnlZwERY3gP6dCeXnqQJKkLGHxlCRp1qcw6krY5iDYbPfUaXLaDJrBHhfBl6/DuHtSx5EkZQmLpySpbosRnjoT8gqh/2Wp09QO2x4JG+0Az/8JFsxMnUaSlAUsnpKkuu29R+HjF6DfedC0Veo0tUNeHuzzV1gyD174U+o0kqQsYPGUJNVdi+fC04Oh1bbQ48TUaWqX9beG7U+Gt++BL15LnUaSlJjFU5JUd424BOZPg32vhvyC1Glqn13OhqZt4InToKwkdRpJUkIWT0lS3fTVOHjjpsxIZ+vtUqepneo1hr2HwDcT4fUbUqeRJCVk8ZQk1T3lZfDEH6BRMez2x9Rparct94XN+sOIy2DOlNRpJEmJWDwlSXXP2Nvgq7eg/6VQv1nqNLVbCDDgcojl8Mw5qdNIkhKxeEqS6pZ502DYRbBxX+h4SOo0dcO67aHPGTDpcfjo+dRpJEkJWDwlSXXLc+dC6ZLMch8hpE5Td+z4O2i5OTx1BpQsSp1GklTDLJ6SpLrjkxHw7r+g92nQYpPUaeqWgiIYcCV8OxlGXZU6jSSphlk8JUl1Q+lSePJ0aL4J7PT71Gnqpo13gU6HwsvXwIyPUqeRJNWgVRbPEEL9EMIbIYR3QggTQwgXVmxvHkJ4PoTwUcX1utUfV5KkNTT2Vpj1Cez9FyisnzpN3dX/EihoAE+fnTqJJKkGVWbEcwnQL8a4LdAF2CuEsD0wGBgWY9wMGFZxX5Kk7LNoNrz0F9h4V9h099Rp6rbG68EuZ8Inw+CT4anTSJJqyCqLZ8yYX3G3sOISgQOAOyu23wkcWB0BJUlaa6OuypTPPS92QqFs0HMQrLMRPPenzJqqkqRar1LneIYQ8kMI44BvgOdjjK8D68cYvwaouF6v2lJKkrSmvv0cXr8BuvwCNuiUOo0ACurBbufDtHdh/IOp00iSakClimeMsSzG2AVoA/QMIXSs7BuEEAaFEMaGEMZOnz59DWNKkrSGhl0EIR/6nZs6iZbX8RDYsBsM/7PLq0hSHbBas9rGGGcDLwJ7AdNCCK0AKq6/Wck+N8UYu8cYuxcXF69dWkmSVseUN2HCw7DjKdB0w9RptLwQYM8/w9yp8No/U6eRJFWzysxqWxxCWKfidgNgd+B94HHgmIqnHQM8Vk0ZJUlafTHCc+dBo2LY6dTUabQi7XeCLQbAqKthvkdFSVJtVpkRz1bAiBDCeGAMmXM8nwCGAHuEED4C9qi4L0lSdvjgKfjiFdj1/6Bek9RptDK7XwglCzOzDkuSaq2CVT0hxjge6LqC7TOB3aojlCRJa6WsBJ7/E7TcAroenTqNfkrx5rDdsfDm7dDrV9By09SJJEnVYLXO8ZQkKSe8eQfM/DizfEr+Kr9jVWp9B0NBfXjh/NRJJEnVxOIpSapdFs+BFy+D9r1hsz1Tp1FlNF4Pdvo9vP8EfP5q6jSSpGrg18CSpFqh/eAnATir4AFOLpjJPh/sxcRznlrlfpOH7FPd0VQZO/wGxt6amRDqxBcys95KkmoNRzwlSbXGhszghPynGVq2MxNjh9RxtDqKGkK/82DqWJj4SOo0kqQqZvGUJNUapxc+RASuKjk0dRStiW2PgPW2gWEXQumS1GkkSVXI4ilJqhW2DpM5JH80t5XtzVe0TB1HayIvH/a8CL6dDGNuTZ1GklSFLJ6SpFrhDwUPMyc25IbS/VJH0drYdHfYeFcYeQUsmZc6jSSpilg8JUm5b8pY9sh/ixtL92UujVKn0dra7Y+waBa8dkPqJJKkKmLxlCTlvuF/ZmZswh1le6VOoqrQejvYYh945VpY9G3qNJKkKmDxlCTltskvw6cj+Gfp/iykfuo0qiq7/h8smQOvXpc6iSSpClg8JUm5K0YY/mdovAH3lO2ROo2q0gYdYZuD4LXrYcHM1GkkSWvJ4ilJyl2fjoAvXoE+Z7CEotRpVNX6ngMlC+Hlv6VOIklaSxZPSVJu+m60s1lb6HZ06jSqDsVbQKdD4Y2bYd601GkkSWvB4ilJyk0fPgNT34RdzoKCeqnTqLrschaULYXRf02dRJK0FiyekqTcU14Owy+B5hvDtkekTqPq1GIT6PoLGHsbzJmSOo0kaQ1ZPCVJuWfSYzDt3cw5gPmFqdOouvU5M3No9cgrUyeRJK0hi6ckKbeUl8GIS6F4S+h4SOo0qgnrbATbHQNv3w2zPkudRpK0BgpSB5AkabW8+y+Y8SEcehfk5adOoxVoP/jJNd538pB9VvxA7zPg7Xtg5BVw4D/X+PUlSWk44ilJyh1lJfDiZbBBZ9hyv9RpVJOatoLuJ8A798OMj1KnkSStJounJCl3jLsXvp0Mu54Lef4Iq3N2/gMU1IcXh6ROIklaTf7UliTlhtIl8NIV0Lo7bN4/dRql0LgYep0EE4bCtPdSp5EkrQaLpyQpN7x1F8ydAv3OhRBSp1EqO/4O6jWBEZekTiJJWg0WT0lS9itdAqOvhrbbw8a7pk6jlBo2h+1PhvefgP++mzqNJKmSLJ6SpOw37l6YOxV2OcvRTsH2v4J6TTMz3EqScoLFU5KU3UqXwqi/QpsesEm/1GmUDRqsmznX873HPNdTknKExVOSlN3GPwBzvoRdzna0U/+z/clQ1NhRT0nKERZPSVL2KiuBkVfChl1h091Tp1E2adgceg6EiY/A9A9Sp5EkrYLFU5KUvd79F8z+3NFOrdgOp0Bhg8yXE5KkrGbxlCRlp7LSTKHYoBNsvlfqNMpGjVpCjxNgwsMw4+PUaSRJP2GVxTOE0DaEMCKEMCmEMDGEcGrF9gtCCFNDCOMqLgOqP64kqc6YMBRmfeJop37ajr+D/CIYdVXqJJKkn1CZEc9S4PQY41bA9sBvQghbVzx2dYyxS8XlqWpLKUmqW8rLMpPGrLcNbLFP6jTKZo3Xg+7Hw/gHYdanqdNIklZilcUzxvh1jPGtitvzgElA6+oOJkmqwyY+AjM/yqzbmedZIVqFHX8HeQWZZXckSVlptX6ahxDaA12B1ys2nRJCGB9CuC2EsO5K9hkUQhgbQhg7ffr0tUsrSar9ysszo53FW8JW+6dOo1zQtBVsdwy8cz98+3nqNJKkFah08QwhNAaGAr+PMc4Frgc2AboAXwMrPLkixnhTjLF7jLF7cXHx2ieWJNVukx6H6e9DnzMd7VTl7fR7CHkw+urUSSRJK1Cpn+ghhEIypfPeGOO/AWKM02KMZTHGcuBmoGf1xZQk1QnfjXa22Ay2OSh1GuWSZq2h6y/h7XtgzpTUaSRJP1CZWW0DcCswKcb41+W2t1ruaQcBE6o+niSpTvngKZg2oWK0Mz91GuWanf8ARBj9t9RJJEk/UJkRz52Ao4B+P1g65fIQwrshhPHArsAfqjOoJKmWixFe+gs03xg6HpI6jXLROhtBlyPhrTth7lep00iSllOwqifEGEcDK1pAzeVTJElV58Nn4L/j4cDrIX+VP56kFdv5NHj7Xnj577D3kNRpJEkVnLVBkpRejJlzO9dpB51+njqNclnzDrDt4fDm7TBvWuo0kqQKFk9JUnqfjoCpb2bO0csvTJ1Gua736VC2FF79R+okkqQKHsskSUpv5FXQZEPociTtBz+ZOo1yXYtNMrMij70t82VGw+apE0lSneeIpyQprc9fgc9Hw06/g4J6qdOotuh9OiydD6/fkDqJJAmLpyQptZFXQsOW0O2Y1ElUm6y/DWy5b6Z4Lp6bOo0k1XkWT0lSOlPfgk+GwY6nQFHD1GlU2/Q+HRbPgTG3pE4iSXWexVOSlM6oq6B+M+h+Quokqo1ad4NNdoNXr4OlC1OnkaQ6zeIpSUpj2kR4/wno9Wuo3zR1GtVWfc6EhTPgrTtTJ5GkOs3iKUlKY9RVUNQYep2UOolqs3Y7QLud4OVroHRJ6jSSVGdZPCVJNW/GxzDxEehxgktdqPr1OQPmfQ3j7kudRJLqLNfxlCTVvNFXQ34R7HBK6iSuG1oXbLwrbNgt87nrehTk++uPJNU0RzwlSTVr9hcw/gHY7lhovF7qNKoLQsic6zn7c5jwcOo0klQnWTwlSTXr5WuAADv+LnUS1SWb7wXrd8ycW1xeljqNJNU5Fk9JUs2Z+zW8dTd0ORKatU6dRnVJXh70Pg1mfAiTHk+dRpLqHIunJKnmvPoPKC+Fnf+QOonqoq0PhBabwsirIMbUaSSpTrF4SpJqxoKZMPY26PQzaN4hdRrVRXn50Pt0mPYufPhs6jSSVKdYPCVJNeO1f0LJItj5tNRJVJd1+jmssxGMvMJRT0mqQRZPSVL1WzQb3rgJttoP1tsydRrVZfmFsNPvYepY+PTF1Gkkqc6weEqSqt8bN8OSuZklLaTUuvwCmrTKzHArSaoRFk9JUvVaMj9zmO1m/aFV59RpJCisn1nOZ/Io+OK11GkkqU6weEqSqtebt8OiWdDnjNRJpP/Z7hho2BJGXpk6iSTVCRZPSVL1KVkEr1wLHXaBtj1Tp5H+p6gR7HAyfPw8fPV26jSSVOtZPCVJ1efte2D+NM/tVHbqMRDqN3PUU5JqgMVTklQ9SpfC6L9B2+2h/c6p00g/Vr8p9PoVvP8ETHsvdRpJqtUsnpKk6jH+AZg7JTPaGULqNNKK9foVFDV2hltJqmYWT0lS1SsrhdFXQ6susOluqdNIK9ewOfQ4ASb+G2Z8nDqNJNVaFk9JUtWb+AjM+jQzk62jncp2O5wC+UWZL0skSdXC4ilJqlrl5TDqSijeCrbYJ3UaadUarwfbHZs5PHz2F6nTSFKtZPGUJFWt95+A6e9nRjvz/DGjHLHj74AAL1+TOokk1Uqr/I0ghNA2hDAihDAphDAxhHBqxfbmIYTnQwgfVVyvW/1xJUlZLUYYeQU03wS2OSh1GqnymrWGrr+At+6GuV+nTiNJtU5lvoouBU6PMW4FbA/8JoSwNTAYGBZj3AwYVnFfklSXffQ8/Hc89D4N8vJTp5FWz06/h/JSePUfqZNIUq2zyuIZY/w6xvhWxe15wCSgNXAAcGfF0+4EDqymjJKkXPDdaGezttD5sNRppNXXvAN0PhTG3gYLZqROI0m1ymqdfBNCaA90BV4H1o8xfg2Zcgqst5J9BoUQxoYQxk6fPn0t40qSstbkUTDlDdjpVMgvTJ1GWjM7nwYli+C1f6ZOIkm1SqWLZwihMTAU+H2McW5l94sx3hRj7B5j7F5cXLwmGSVJuWDkFdB4feh6VOok0por3hy2PgBevwkWfZs6jSTVGpUqniGEQjKl894Y478rNk8LIbSqeLwV8E31RJQkZb3PX4XPRmZmBi2snzqNtHb6nAFL52XKpySpSlRmVtsA3ApMijH+dbmHHgeOqbh9DPBY1ceTJOWEl/4CjYqh+/Gpk0hrb4NOsMWAzOG2iyt9kJck6SdUZsRzJ+AooF8IYVzFZQAwBNgjhPARsEfFfUlSXfPlG/DpCNjxt1DUMHUaqWrschYsng1vOOopSVWhYFVPiDGOBsJKHt6tauNIknLOS5dDwxbQ/YTUSaSqs2FX2Kx/ZmmVXidBvSapE0lSTlutWW0lSfqeKW/Cx8/DDqdAvcap00hVq+/ZmQmG3rg5dRJJynkWT0nSmht5OTRYF3oOTJ1Eqnqtt4NN98iMei6ZnzqNJOU0i6ckac189TZ8+Azs8BsPQ1TttcvZsHAmjL01dRJJymkWT0nSmnnpCqjfDHoOSp1Eqj5te8Am/eDlv8PShanTSFLOsnhKklbf1+Phgydh+99kyqdUm+0yGBbOgLG3pU4iSTnL4ilJWn0jL4d6TTOzfUq13Ua9oMMu8PI1jnpK0hqyeEqSVs+0iTDpP9DrV9BgndRppJrRdzAs+AbeujN1EknKSRZPSdLqeelyKGoC2/86dRKp5rTbEdr3htF/g5LFqdNIUs6xeEqSKu+bSfDeY9BrEDRsnjqNVLN2ORvm/9dRT0laAwWpA0iScsjIK6GwIexwSuokqqXaD35yjfabPGSfKk6yAh16Q7udYPTV0O0YKKxf/e8pSbWEI56SpMqZ/iFMGAo9Bzraqbprl7Ng3tfw9t2pk0hSTrF4SpIqZ9SVUNgAdvxt6iRSOh12gbbbZ0Y9S5ekTiNJOcPiKUlatRkfwbv/gu7HQ6OWqdNI6YQAfc+GuVNh3L2p00hSzrB4SpJW7cXLoKAB7PT71Emk9DbeFdr0hJFXOeopSZVk8ZQk/bRpE2HCv6HXSdC4OHUaKb0QoN+5MHcKvOkMt5JUGRZPSdJPG3Ep1GviuZ3S8jrsAu12zpz7vHRh6jSSlPUsnpKklfvqbXj/CdjhN85kKy3vu1HP+dNg7K2p00hS1nMdT0nSyo24FOqvA9v/erV3XdP1GKWc0W5H2GS3zAy32x2bOTJAkrRCjnhKklbsyzfgo+dgp99B/Wap00jZaddzYeFMeP2G1EkkKatZPCVJKzbiEmjYEnqelDqJlL3abAdbDIBXroVFs1OnkaSsZfGUJP3Y5NHw6Yuw8x+gXuPUaaTstuv/weI58Op1qZNIUtayeEqSvi9GGH4JNN4AepyQOo2U/TboBNscBK/9ExbMTJ1GkrKSxVOS9H2fDIcvXoE+Z0Bhg9RppNzQ9xwoWQgv/y11EknKShZPSdL/xJg5t7NpG+h2dOo0Uu4o3gI6HQpv3AzzpqVOI0lZx+IpSfqfD5+BqW/CLmdBQb3UaaTc0vdsKFsKo/+aOokkZR2LpyQpo7w8M9q5bnvocmTqNFLuab4xdP0FjL0N5kxJnUaSsorFU5KU8f5/4L/vwi6DIb8wdRopN/U5K3M98sq0OSQpy1g8JUlQXgYjLoWWm0PnQ1OnkXLXOm1hu2Ph7bth1mep00hS1rB4SpJg/EMw/X3oOxjy8lOnkXJb79MhrwBe+kvqJJKUNVZZPEMIt4UQvgkhTFhu2wUhhKkhhHEVlwHVG1OSVG1KFmfO7WzVBbY+KHUaKfc12QB6DoTxD8K091KnkaSsUJkRzzuAvVaw/eoYY5eKy1NVG0uSVGPG3AxzvoQ9LoQ8D4SRqsTOp0G9JvDCBamTSFJWWOVvGDHGkcCsGsgiSappi77NTIKyyW6wcd/UaaTao2HzTPn86Fn4bFTqNJKUXMFa7HtKCOFoYCxweozx2xU9KYQwCBgEsNFGG63F20mS1lT7wU+ucPvggvsZlD+Hfd7bnUkrec7kIftUZzSpSqzsM74q1fr57nUSvHETPP8nGDgcQqi+95KkLLemx1RdD2wCdAG+Bq5a2RNjjDfFGLvHGLsXFxev4dtJkqpaK2ZyXP4zPFq+E5Niu9RxpNqnsAH0Ow++egsmPpI6jSQltUbFM8Y4LcZYFmMsB24GelZtLElSdftDwcNA5K+lP08dRaq9Oh8G620Dwy6C0qWp00hSMmtUPEMIrZa7exAwYWXPlSRln83DlxySP5K7yvZkSvRoFKna5OVnJu769jN4847UaSQpmcosp3I/8CqwRQhhSgjhBODyEMK7IYTxwK7AH6o5pySpCp1d8AALaMB1pQekjiLVfpvuDu17w0tDYPHc1GkkKYlVTi4UYzxiBZtvrYYskqQa0CtMYrf8txlScjizaZI6jlT7hQB7XAQ37wqv/D1z3qck1TEu2CZJdUrknML7+Co25/ayFS3RLKlatO4GHQ+BV6+DuV+nTiNJNc7iKUl1yIC81+mS9wlXl/6MJRSljiPVLf3+CGUl8OJlqZNIUo2zeEpSHVFAKWcWPMgH5W0YWtYndRyp7mneAXqcAG/fDdM/SJ1GkmqUxVOS6ojD80fQIW8afyk9nHL/+ZfS6HMmFDWGFy5MnUSSapS/eUhSHdCIRZxaMJTXy7dkeHnX1HGkuqtRS9jpVPjgSfj81dRpJKnGWDwlqQ74VcF/KA5zuazkSCCkjiPVbdufDE1awfN/hBhTp5GkGmHxlKTabtZnDMp/kkfLdmRc3DR1GklFDWHX/4MpY2DC0NRpJKlGWDwlqbZ77jzKyGNIyYqWZZaURJdfQKtt4bk/wtIFqdNIUrWzeEpSbfbpi/D+E/yj9AD+S4vUaSR9Jy8f9r4c5n0Fo/6aOo0kVTuLpyTVVmWl8PRgWLc9t5YNSJ1G0g9ttD10OhReuRZmfZY6jSRVK4unJNVWY2+F6ZNgz0tYQlHqNJJWZI8LIa8AnjsvdRJJqlYWT0mqjRbMhBGXwMZ9Yct9UqeRtDJNN4Q+Z8D7T8Anw1OnkaRqY/GUpNpoxJ9hyXzY6y8QXD5Fymo7/AbW7ZA5NL6sJHUaSaoWFk9Jqm3++y68eQf0HAjrbZk6jaRVKagHe10GMz6AN25OnUaSqoXFU5Jqkxjh6bOh/jrQd3DqNJIqa/O9YJPd4MUhMH966jSSVOUsnpJUm0x8BD5/GXb7IzRYN3UaSZUVAuw1BEoWwPCLUqeRpCpn8ZSk2mLpwsxi9Bt0gm7HpE4jaXUVbw69fgVv3Q1fvZ06jSRVKYunJNUWL18Dc6dkFqXPy0+dRtKa2OUsaNQSnjorc+i8JNUSFk9Jqg1mfwEv/w22ORja7Zg6jaQ1Vb8Z7H4BTHkDxj+UOo0kVRmLpyTVBs+cAwTY8+LUSSStrW2PhA27wfN/giXzUqeRpCph8ZSkXDfpP5nF5/sOhmZtUqeRtLby8mDAFTB/Ggz/c+o0klQlLJ6SlMsWz4GnzsxMKLTDKanTSKoqbbpDjxPg9RthytjUaSRprVk8JSmXvXBhZlRkv79DfkHqNJKq0m7nQ5NW8PjvoKwkdRpJWisWT0nKVV+8BmNvhV6/htbdUqeRVNXqN4V9roJvJmZmrZakHGbxlKRcVLoE/nMqNGsLu/5f6jSSqsuWA2DrA+Cly2HGx6nTSNIas3hKUi56+RqY/j7s81eo1zh1GknVae/LoaB+5sum8vLUaSRpjVg8JSnXzPgIRl4BHQ+BzfdMnUZSdWuyAex5EXw+Gt6+O3UaSVojFk9JyiXl5ZlRj8KGsNeQ1Gkk1ZSuR0O7neH5P8K8aanTSNJqs3hKUi55+274/GXY88/QeL3UaSTVlLw82O8aKFkMT5+VOo0krbZVFs8Qwm0hhG9CCBOW29Y8hPB8COGjiut1qzemJIl50zKjHe17Q9dfpk4jqaa13BR2ORPeexQ+eDp1GklaLZUZ8bwD2OsH2wYDw2KMmwHDKu5LkqrTM2dnRjv2/RuEkDqNpBR2PBXW2xqePB0Wz02dRpIqbZXFM8Y4Epj1g80HAHdW3L4TOLBqY0mSvueDZ2DiI5nRjpabpk4jKZWCItj/Wpj7FQy7KHUaSaq0gjXcb/0Y49cAMcavQwgrPdEohDAIGASw0UYbreHbSVIdtnAWPPF7KN4qM9pRw9oPfrLG31PST2jTHXqdBK/fCJ1+BhttnzqRJK1StU8uFGO8KcbYPcbYvbi4uLrfTpJqlxjhiT/Aghlw0A2Z0Q5J6ncerNMWHjkJlsxLnUaSVmlNi+e0EEIrgIrrb6oukiRpmfEPZiYS2fX/YMMuqdNIyhb1msBBN8HsL+AZp9qQlP3WtHg+DhxTcfsY4LGqiSNJWubbz+HJM2CjHWGnmj/EVlKWa7cD7HwavH0PvPd46jSS9JMqs5zK/cCrwBYhhCkhhBOAIcAeIYSPgD0q7kuSqkp5GTzyq8ztg26AvPy0eSRlp76DYcOu8J/fwdyvU6eRpJVa5eRCMcYjVvLQblWcRZL0nZevgS9egYNuhHXbpU4jKVvlF8LBt8CNveHRX8Mv/w151T6FhyStNv9lkqRs89U4GHEpbH0gdD4sdRpJ2a7lptD/Evh0BLxxY+o0krRCFk9JyiYli+Dfg6BRS9j3agghdSJJuWC742DzveH582HaxNRpJOlH1nQdT0lSdXj+fJjxARz1KDRs/qOHXVNT0gqFAPtfC9fvAEMHwsDhUFg/dSpJWsYRT0nKFh+9kDlMbvuTYZNdU6eRlGsaF8MB/4RvJsLwi1OnkaTvsXhKUjZYMBMeOxmKt4Ldzk+dRlKu2nxP6HEivPoP+PTF1GkkaRmLpySlFmNmKYRF38IhN3t4nKS1s8fF0HJzeOTXsHBW6jSSBFg8JSm9V/8B7z8Bu/0JNuiUOo2kXFfUEA6+GRZMh8d+A+XlqRNJksVTkpL6bFRmQqGt9ocdTkmdRlJtsWEX2PPP8MFTMPqq1GkkyeIpScnMmQr/OhZabAoH/tOlUyRVrV4nQadDYfglmcnLJCkhi6ckpVC6BB46KnN92D1Qr0nqRJJqmxBgv2tg/Y4w9ASY9VnqRJLqMIunJKXw9Nkw9c3MSGfx5qnTSKqtihrCYXdnbj/4S1i6MG0eSXWWxVOSatrb98Cbt8NOv4et90+dRlJt17wDHHIrTJuYmUE7xtSJJNVBFk9JqklfvQ1PnAYb94V+f0ydRlJdsdnu0O9cePdf8PoNqdNIqoMsnpJUUxbOggePhsbrwSG3QX5B6kSS6pKdT4ct94Vnz4XJo1OnkVTHWDwlqSaUl8HDx8P8aXDoXdCoRepEkuqavDw48PrMobf/OjYzs7Yk1RCLpyTVhBGXwKcjYJ8roXW31Gkk1VX1m8Lh90HJInjo6MzM2pJUAyyeklTd3nkARl0F3Y6BbkenTiOpriveIjPyOXUsPHmakw1JqhGeYCRJ1enjYfDYb6DDLjDgymWb2w9+MmEoSXXe1vtDnzNh5BXQrC30HZw6kaRazuIpSdXlq3GZQ9mKt4LD7oGCotSJJOl/dj03c57ni5dB4/Wh+3GpE0mqxSyeklQdZn0G9/4MGjSHXz6cOa9KkrJJCLD/32HB9Mwht43Xgy33SZ1KUi3lOZ6SVNUWzIB7DoHyUvjlUGiyQepEkrRi+YVw6J3Qqktm5u0vXkudSFItZfGUpKq0dAHcdyjMnQpHPAjFm6dOJEk/ragR/OJf0LQ13HcYfDMpdSJJtZDFU5KqSlkp/Os4+Opt+NntsFGv1IkkqXIatYSj/g0F9TJHbLjGp6QqZvGUpKoQIzxxKnz0LOxzFWw5IHUiSVo967aHXzwMi+dmyueib1MnklSLOLmQJFWFFy+Dt++BPmdB9+NTp5FUzdZ0SaTJQ7J88p5WneHwezPF8/4jM6OghQ1Sp5JUCzjiKUlr65Vr4aW/QNdfwq7/lzqNJK2djXeBg2+EL16Bh0+AspLUiSTVAhZPSVobI6+A586DbQ6Cff+WWZ5AknJdx0Ng78vhgycz6xGXLkmdSFKOs3hK0pqIEUZcCsP/DJ0Pg4NvySxLIEm1Ra+TYMCV8MFTcP8RsHRh6kSScthaneMZQpgMzAPKgNIYY/eqCCVJWS1GeOECePlvmcNr9/s75OWnTiVJVa/nwMxMt4//LrNU1BEPQL3GqVNJykFVMeK5a4yxi6VTUp0QIzz7f5nS2f0E2O9aS6ek2q3b0XDwTfD5K3DPwbB4TupEknKQh9pKUmWVl8NTZ8Br/4Rev84sm5LnP6OS6oDOh8LPb4epb8JdB8DCWakTScoxa/sbUwSeCyG8GUIYVBWBJCkrlZdn1ukccwvsdCrsdZkTCUmqW7Y+AA67B6ZNhDv3g/nTUyeSlEPWdh3PnWKMX4UQ1gOeDyG8H2McufwTKgrpIICNNtpoLd9OkqrHT63Jl0c5VxTeyCH5o7im9CCuHtYThj0F5MCafJKySs6v/7nF3pnzPB/4BdyxDxz9GDRtlTqVpBywViOeMcavKq6/AR4Beq7gOTfFGLvHGLsXFxevzdtJUo1rxCJuLPwrh+SP4sqSn3N16c8BRzol1WGb7ga/fBjmTIE7BsDMT1InkpQD1rh4hhAahRCafHcb2BOYUFXBJCm1NmE6Q4suYNe8cZxXchz/KDsodSRJyg7td4ajH4VFs+HmfvDJiNSJJGW5tRnxXB8YHUJ4B3gDeDLG+EzVxJKktLYLH/Bo0R9pFWZyTMnZ3FO2R+pIkpRd2vaEgcOhSSu45xB44+bUiSRlsTU+xzPG+CmwbRVmkaSscEjeSC4tvIWpsSUnlpzBp3HD1JEkKTs17wAnPAf/HpiZ9fub92DvyyG/MHUySVnGdQAkqUIe5QwuuJ+rim5gTPkWHLT0IkunJK1K/aZw+H2w0+9h7G1w90EutyLpRyyeksR3kwhdza8K/sPdpbtzbMnZzKFx6liSlBvy8mGPC+GgG+HL1+HmXeGb91OnkpRFQoyxxt6se/fucezYsTX2fpJUKd9+zqSr92Xz8CUXlh7NXWX9UyeSpO/JmuVUKuPLNzLLrZQsgp/dBpvvmTqRpBoUQngzxtj9h9sd8ZRUt737MNzQm9ZhBseWnG3plKS11bYnDBqROf/zvkNh2MVQVpI6laTELJ6S6qbFc2DoQBh6AhRvzr5LL2FUeefUqSSpdmjWBo5/BrocCaOuhFv3gBkfpU4lKSGLp6S65/NX4PqdYcJQ6HsOHPcMX8T1U6eSpNqlqBEc+E849C74djLc0BvG3AI1eJqXpOxh8ZRUd5SVwLCL4I59IC8Pjn8W+g6G/DVeWUqStCpbHwC/fhXa7QhPnp45/HbetNSpJNUwi6ekumHGR5lDvUZdBdseCb8aDW17pE4lSXVD01bwy6Gw9xXw2Ui4fgeY9ETqVJJqkMVTUu1WXgZv3Aw39skc6nXoXXDgdVCvSepkklS3hAC9BsGgl6DphvDgL+CxU2DJvNTJJNUAi6ek2uuLirXknjoD2vaCX7+SOeRLkpTOelvCicNh5z/A2/fAtdvBOw9AeXnqZJKqkSc2SYm0H/zkGu+bK+u5rc2fcU1NHrJP5tyhF86Hd+6HJhvCIbdCx0My37ZLktIrKILdL4At94WnzoRHToIxt8Lef4HW3VKnk1QNHPGUVGsUUAqv/CPz7fmEobDzaXDKGOj0M0unJGWjNt3hxGFwwHXw7Wdwcz94/Lcwf3rqZJKqmCOekmqFnfLe5YKCu+C5qbDZnrDXEGixSepYkqRVycuDrr+ErfaDly6H12+AiY9lZh3vORDyC1MnlFQFHPGUlNO2DF9wQ+HV3Ft0GYWUwhEPwi/+ZemUpFxTvxn0vySz9Eqb7vDsOXD9TvDhs679KdUCFk9JOalj+JSbCq/imXqD2SlvAleUHEr/pX+BLfZKHU2StDaKN88svXLEA1C2NLPu54194L3HnYBIymEeaispp3QLH/LbgkfYNf8d5sSGXF1yCLeX9WcujVNHkyRVlRBgi71h091h/IOZNZgfOgqKt4Tep8M2B0O+v8ZKucT/YyXlhF5hEr8t+Dc7509kZmzC5SWHcVfZHsynYepokqTqkl+YOf9z2yNg4iMw8kr490AYcSn0Pg06H56ZIVdS1rN4SspahZSyZ95Yji54jl557zM9NuPPJb/g3rLdWET91PEkSTUlLz8zQ/k2B8MHT8HIKzKz3774F9jhN7Dt4dCweeqUkn6CxfMH1nTdwVxZV1HKBe3D1xyeP4Kf5Y+kZZjLlNiS80uO4YGyXVnCT3+znWLtUEmqjWr639NK/S6Vlwdb7Qtb7gMfD8sU0GfPgRcugK0PgO2OgXY7uYRWBX+vVTaxeErKCkWUsGfeWI7MH8aO+e9RGvN4vnw77i/rx+jyTpQ7F5ok6TshwGa7Zy5fj4e37oTxD8G7D0GLzaDb0dDlSGjUMnVSSRUsnpISimwTJrN//iv8LH8kLcI8viwv5vKSQ/lX2S5MZ93UASVJ2a5VZ9jnKtjjIpj4aKaEPv9HGHZRZmS021HQYRfXA5USs3hKqlGBcrqFj9grfwx75Y2hbd50SmL+cqObHYmObkqSVldRI+j6i8zlm0nw1l3wzv3w3qNQf53MLLlb7gub9IMiJ6aTaprFU1K1K6CUXnmT2CtvDP3zx7JemM2SWMDo8k78veQgXijrxrc0TR1TklRbrLcV7HUZ7HY+fPwCvP8EfPB0pogWNoRNd4Mt94PN+0ODdVKnleoEi6ekqhcjzPqUI/KHsUPee/TOe5d1w3wWxnqMKN+WZ8t6Mry8i0uhSJKqV2H9zGREW+0LZSUweXSmhE56Aib9B/IKoH1v2LgvdOgNrbpkZtCVVOUsnpKqxuwvYfIo+Gxk5jJ3KpcVwn/jugwv78qzZd0ZWd6ZxdRLnVSSVBflF8Imu2Yue18BU9+ESY/Dh8/AC+dnnlOvKbTbMVNGO/SG9TtlZtKVtNYsnoll5VTlK7A2OWt6Sm6X06h6P/w7rcdStg6f0ynvUzrnfUb38AHt86YBMDM24dXyrXmtfE9eKd+GT2MrwGntJWlt5NLP4TVVo0t/5OXR/rpvgO2B7SnmW3bIm8T2pRPZ/v1xbPzhMwDMjo14vXwrxpVvyvjYgQnlHZhD4yS/T0m5zuIp6aeVLKZz+ITOeZ/SKXxGp7zP2Dx8SUEoB2BGbMrb5ZtyV0mmaH4Q2zg5kCQpp0xnXR4v35HHy3cEYANmsn3eJHbIe49eeZPoXzh22XO/KC+Ghx7MHJa7YZfMdcPmSXJLucTiKSlj4SyY8RHM+BBmfJC5Pf0DmP05j9fLlMxZsTHvlm/MsPKuvFvegXfLN+ZrmuOIpiSpNvkvLXi0fGceLd8ZgGbMp2PeZ3QKn9Ex7zM2+mocvPfY/3Zo0gpabgYtN6+4bJZZT7Rpaw/VlSpYPKW6omQRzJkCs7+AOV9mzsmc82Xm/syPYcH0/z03vx602DTzTW7nQ/n184sZX74xU2mJJVOSVNfMoTEvl3fiZTpBGez7+30yX9h+/Q58PS7zRe2MD2H8Q7Bk7v92LGyY+XnafGNYpy3H5M9lamxZcWnBXBrhz1XVFWtVPEMIewHXAPnALTHGIVWSSlLlxAhLF8CCb2D+NzB/WsV1xe0F02HefzMFc/liCRDyM9/ErtM2s7bZsm9pN4d1NvrerH5PP+s5KZIkfU/D5v+brOg7MWZ+Bs/4sOJScSTRf9+FD57mwsIl33uJebEBU2NL/hubMz02YwbNmB7XYUZsxnSaZbbFZsymMRZU5bo1Lp4hhHzgOmAPYAowJoTweIzxvaoKJ9V2+ZTRiMU0YjGNwyIas4hGYTGNWETj5batE+azDvNZJyygWZgP110Ei77NXMqWruCVAzQqhsbrZS4bdIRmG2VKZrO2mesmG0K+Bz1IklRlQoAm62cuHXp//7Hycrr/3wO0DtPZMMykdZix7LJBmMXmeV/SkjkUhbIfvWxJzGcOjZgTGy27nk3jivuZ6/nUZ35swAIaMD/Wz1xTnwUVt5dSgOVVKa3Nb509gY9jjJ8ChBAeAA4ALJ76aTEudx3/d3/Z7Z+6Lv/+tu9tzzy2ATPJI5IXIoFy8inP3F/B7cz9cgooJy+UU0BZxfayZY8XUkYBpRSGMgoppYAyCihbtr0olFJECfUoyVx/734p9cNS6pO5NGAJDcJS6rOE+ixd4Q+XFVkQ61X8gGnM7NgIWm6aWfC6wbrQoPn/Cmbj9aHRetCwhaVSkqRskpfHDDIjmO/ETVfypEgzFtAyzGG9MJuWzKE4zKFFmEMzFrBOWEBTFtAizGUTvqJZ3gKahYWVevvSmMci6rGYQhbHeiyiiMUUZbbFIpZSyBIKWEohPPY0FNTLnHpTUJS5zi/MXPK+uy5Y7n5B5jqvoOKS97/bIf9/20I+hLzMUVVh+ft5Ffd/cCEsdz9kLlRcL3s8LPe874r1D5677JrlnqOatja/mbYGvlzu/hSg19rFSa9NmM4zRWev/o6XLvdXuaxIrdrEeqWr/16s+vuqwEoy/Pm7wycrkXG5P8cH9cp/tM937xFWsO17ty+o5PtVkdfq19hbAVAWA0soYmnFP9ZLKWBJLFz2D/gSipgdG1f8417EovIiFlMvcz8WsZB6zKchC2L9739bWXF7Pg0p+cH/qpMPy42p8SVJ0uoIFSOYjfkktq7UHnmU04SFNGYRjcOiZUdRLX/diMU0CEtoUPFFeP2wpOIL8cylWViw7Av0Qkrh40+gdEnmqKrSJVBeUs1/7pR+WEiX+832J7fx48fW6PHV1LAF/GHC2r1GIiGuRkn63o4h/BzoH2M8seL+UUDPGONvf/C8QcCgirtbAB+sedwa1RKYkTqEspKfDa2InwutjJ8NrYyfDa2Mnw2tTC58NtrFGIt/uHFtRjynAG2Xu98G+OqHT4ox3gTctBbvk0QIYWyMsXvqHMo+fja0In4utDJ+NrQyfja0Mn42tDK5/NlYm4WFxgCbhRA6hBCKgMOBx6smliRJkiSptljjEc8YY2kI4RTgWTLLqdwWY5xYZckkSZIkSbXCWk17GWN8CniqirJkm5w7PFg1xs+GVsTPhVbGz4ZWxs+GVsbPhlYmZz8bazy5kCRJkiRJlbE253hKkiRJkrRKFs+VCCFcHEIYH0IYF0J4LoSwYepMyg4hhCtCCO9XfD4eCSGskzqTskMI4echhIkhhPIQQk7OOKeqFULYK4TwQQjh4xDC4NR5lB1CCLeFEL4JIeTmYnyqNiGEtiGEESGESRU/T05NnUnZIYRQP4TwRgjhnYrPxoWpM60uD7VdiRBC0xjj3IrbvwO2jjH+KnEsZYEQwp7A8IoJtv4CEGM8O3EsZYEQwlZAOXAjcEaMcWziSEoohJAPfAjsQWYJsjHAETHG95IGU3IhhD7AfOCuGGPH1HmUPUIIrYBWMca3QghNgDeBA/13QyGEADSKMc4PIRQCo4FTY4yvJY5WaY54rsR3pbNCI8CGLgBijM/FGEsr7r5GZg1biRjjpBjjB6lzKGv0BD6OMX4aY1wKPAAckDiTskCMcSQwK3UOZZ8Y49cxxrcqbs8DJgGt06ZSNogZ8yvuFlZccqqfWDx/QgjhkhDCl8AvgD+lzqOsdDzwdOoQkrJSa+DL5e5PwV8gJVVSCKE90BV4PXEUZYkQQn4IYRzwDfB8jDGnPht1uniGEF4IIUxYweUAgBjjuTHGtsC9wClp06omreqzUfGcc4FSMp8P1RGV+WxIFcIKtuXUt9OS0gghNAaGAr//wVF4qsNijGUxxi5kjrbrGULIqUP112odz1wXY9y9kk+9D3gSOL8a4yiLrOqzEUI4BtgX2C16onSdshr/bkhTgLbL3W8DfJUoi6QcUXH+3lDg3hjjv1PnUfaJMc4OIbwI7AXkzCRldXrE86eEEDZb7u7+wPupsii7hBD2As4G9o8xLkydR1LWGgNsFkLoEEIoAg4HHk+cSVIWq5hA5lZgUozxr6nzKHuEEIq/W0khhNAA2J0c6yfOarsSIYShwBZkZqj8HPhVjHFq2lTKBiGEj4F6wMyKTa8547EAQggHAdcCxcBsYFyMsX/SUEoqhDAA+BuQD9wWY7wkbSJlgxDC/UBfoCUwDTg/xnhr0lDKCiGEnYFRwLtkfgcF+L8Y41PpUikbhBA6A3eS+XmSBzwUY7wobarVY/GUJEmSJFUrD7WVJEmSJFUri6ckSZIkqVpZPCVJkiRJ1criKUmSJEmqVhZPSZIkSVK1snhKkiRJkqqVxVOSJEmSVK0snpIkSZKkavX/t8kdCy2dMQ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.145\n",
      "Model:                            OLS   Adj. R-squared:                  0.133\n",
      "Method:                 Least Squares   F-statistic:                     11.90\n",
      "Date:                Fri, 19 Nov 2021   Prob (F-statistic):           2.37e-12\n",
      "Time:                        15:59:52   Log-Likelihood:                -434.57\n",
      "No. Observations:                 428   AIC:                             883.1\n",
      "Df Residuals:                     421   BIC:                             911.6\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2662      0.200     -1.331      0.184      -0.660       0.127\n",
      "x1             0.0398      0.071      0.564      0.573      -0.099       0.179\n",
      "x2             0.1033      0.015      6.798      0.000       0.073       0.133\n",
      "x3             0.0003      0.000      2.568      0.011    8.01e-05       0.001\n",
      "x4             0.0043      0.003      1.286      0.199      -0.002       0.011\n",
      "x5            -0.0624      0.086     -0.730      0.466      -0.231       0.106\n",
      "x6            -0.0219      0.027     -0.813      0.417      -0.075       0.031\n",
      "==============================================================================\n",
      "Omnibus:                       83.417   Durbin-Watson:                   1.989\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              288.443\n",
      "Skew:                          -0.851   Prob(JB):                     2.32e-63\n",
      "Kurtosis:                       6.644   Cond. No.                     2.21e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.21e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "190.93732173097357\n"
     ]
    }
   ],
   "source": [
    "stack =np.column_stack((a, \n",
    "                        df['live in SMSA'], \n",
    "                        df['educ years of schooling'], \n",
    "                        df['expersq'], \n",
    "                        df['nwifeinc'], \n",
    "                        df['kids below 6'], \n",
    "                        df['kids between 6 and 18']))\n",
    "\n",
    "\n",
    "mod1 = sm.OLS(np.asarray(df['log(wage)']), stack)\n",
    "mod1 = mod1.fit()\n",
    "res1 = mod1.resid\n",
    "SSR1 = res1.T@res1\n",
    "\n",
    "\n",
    "plt.hist(res1,50, label='OLS on wage')\n",
    "\n",
    "x = np.linspace(-3, 3, 100)\n",
    "y = norm.pdf(x,0.15,0.6)*55  ## We mutliply to normalize the 2 curves\n",
    "plt.plot(x,y, label = 'gaussian curve')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(mod1.summary())\n",
    "print(SSR1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393cb7a1",
   "metadata": {},
   "source": [
    "Nous pouvons encore observer une gaussienne centrée en 0 (ou plutot juste au dessus de 0), mais cette fois ci les valeurs sont bien mieux distribuées et elles sont cohérentes: il y a beaucoup moins de valeurs seules loin de l'origine (grâce au log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adffa386",
   "metadata": {},
   "source": [
    "### Question 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1f968688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is:     p-value = 0.14340799213513744\n"
     ]
    }
   ],
   "source": [
    "SSR1 = res1.T@res1\n",
    "stack_troncated =np.column_stack((a, \n",
    "                                df['live in SMSA'], \n",
    "                                df['educ years of schooling'], \n",
    "                                df['expersq'], \n",
    "                                df['nwifeinc'], \n",
    "                                df['kids below 6'], \n",
    "                                df['kids between 6 and 18']))\n",
    "\n",
    "n,k =np.shape(stack1)\n",
    "\n",
    "mod2 =sm.OLS(df['log(wage)'],stack_troncated)\n",
    "mod2 =mod2.fit()\n",
    "res2 =mod2.resid\n",
    "SSR2 =res2.T@res2\n",
    "\n",
    "\n",
    "F2 =((SSR2-SSR1)/SSR1)*(n-k)/1\n",
    "fisher2 = f.sf(F2,1,n-k)\n",
    "print(\"The p-value is:     p-value =\",fisher2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f71a1f",
   "metadata": {},
   "source": [
    "On ne rejette donc PAS H0 pour aucun des seuils (14>10>5>1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e171e2",
   "metadata": {},
   "source": [
    "### Question  10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "535b2f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-val is :  0.12442539027343329\n"
     ]
    }
   ],
   "source": [
    "stack3 = np.column_stack((a,\n",
    "                       df['live in SMSA'], \n",
    "                       df['educ years of schooling'], \n",
    "                       df['expersq'], \n",
    "                       df['nwifeinc'], \n",
    "                       df['kids below 6'], \n",
    "                       df['kids between 6 and 18']))\n",
    "\n",
    "\n",
    "data3 = df['log(wage)']-0.01*df['nwifeinc']\n",
    "mod3 = sm.OLS(data3,stack3)\n",
    "mod3 = mod3.fit()\n",
    "res3 = mod3.resid\n",
    "SSR3 = res3.T@res\n",
    "n,k = np.shape(stack)\n",
    "f3 = ((SSR3 - SSR1)/SSR1) *(n-k)/1\n",
    "fisher3 = f.sf(f3,1,n-k)\n",
    "print(\"The p-val is : \", fisher3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db49669",
   "metadata": {},
   "source": [
    "On ne rejette donc pas l'hypothèse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d511bc2",
   "metadata": {},
   "source": [
    "### Question  11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "bc5f0be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-val is :  0.2636174065428352\n"
     ]
    }
   ],
   "source": [
    "stack4 = np.column_stack((a,\n",
    "                       df['educ years of schooling'], \n",
    "                       df['expersq'], \n",
    "                       df['kids below 6'], \n",
    "                       df['kids between 6 and 18']))\n",
    "\n",
    "data4 = df['log(wage)']-0.01*df['nwifeinc']-0.05*df['live in SMSA']\n",
    "mod4 = sm.OLS(data4,stack4)\n",
    "mod4 = mod4.fit()\n",
    "res4 = mod4.resid\n",
    "SSR4 = res4.T@res4\n",
    "n,k = np.shape(stack)\n",
    "f4 = ((SSR4 - SSR1)/SSR1) *(n-k)/2\n",
    "fisher4 = f.sf(f4,2,n-k)\n",
    "print(\"The p-val is : \", fisher4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824c6def",
   "metadata": {},
   "source": [
    "On ne rejette donc pas l'hypothèse à 5%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9c8488",
   "metadata": {},
   "source": [
    "### Question  12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0024a3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (16, 7)\n",
    "\n",
    "plt.grid()\n",
    "plt.title('wage with education')\n",
    "both = df.groupby(['educ years of schooling']).mean()\n",
    "both = both.sort_values(by=['educ years of schooling'])\n",
    "plt.plot(both.index, both['wage estimated'])\n",
    "plt.xlabel('education')\n",
    "plt.ylabel('wage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c17ef0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6YAAAG5CAYAAACdnSmMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAACkPElEQVR4nOzdd3hb930v/vcXAEGQAME9JYoUlyRKtjUo25K1HNuxnWlnryZ20mbv3s77a5uu2za5TdLcxmm2kzTNthNneca0JEu2RYmSbYkSh8Qh7gUCIEDM7+8P4FCUzAGQAM45wPv1PHliiSTwFQ8HPuezhJQSRERERERERGoxqH0AIiIiIiIiymwMTImIiIiIiEhVDEyJiIiIiIhIVQxMiYiIiIiISFUMTImIiIiIiEhVDEyJiIiIiIhIVQxMiYiI4iCE+L0Q4n3LvP1BIcQ/pfJMiSCEOCuEOKT2OYiIKDOZ1D4AERGRnkgp71b+WwhxH4A/llLuU+9EiSGl3Kr2GYiIKHMxY0pERJTBhBC8SU1ERKpjYEpERCknhLhfCPHrBX/uFkL8dMGfB4QQ26P//R/RPzuFECeFEPsXvF+OEOJ7QohpIUSHEOLPhRCXF7y9SgjxCyHEuBDikhDik0ucZ6MQwiGEMET//C0hxNiCt/+3EOLT0f9uFUL8sRBiC4D/ArBHCOEWQjgWPGShEOK3QgiXEOJ5IUT9Mp+Lm4UQx6LPf0YppxVC7BVCTAghqqN/viH6Ppujf+4VQvyVEOJc9N//XSGEZcHjvk4IcTr6MceEENcveFuvEOIvhBAvApgVQpiif3d79O0GIcRfCiF6hBCTQoifCiGKom+rFUJIIcT7hBD90TP+7wWPbRRC/HX0Y13Ra6b8GzYLIZ4QQkwJIS4IId621OeFiIgyCwNTIiJSwzMA9kcDoEoAWQBuAQAhRB0AG4AXo+97AsB2AEUA/gfAzxYEYH8HoBZAHYA7ALxHeYJokPlrAGcArANwG4BPCyHuvPYwUspLAJwAdkT/aj8AdzT4BIAD0TMv/JgOAB8GcFxKaZNSFix48zsB/D2AQgDdAP55sU+CEGIdgN8C+Kfov+9/AfiFEKJUSnkMwNcBfE8IkQPgBwD+Pynl+QUP8W4AdwKoB9AE4P+LPu5OAN8B8CEAxdHHeUQIkX3NGV8LoEBKGbzmaJ8EcA+AgwCqAEwD+Oo177MPwCZEPq9/u+Bz9dnoY78GgB3A+wF4hBBWAE8gcg3Lou/zgBCCJcRERMTAlIiIUk9KeRGAC5GA8yCAxwAMRrOBBwEckVKGo+/731LKSSllUEr57wCyEQmIAOBtAP6PlHJaSnkZwFcWPM1uAKVSyn+QUvqjz/lNAO9Y4ljPADgohKiI/vnn0T9vRCTAOhPHP/EhKeUL0YDvh9F/52LeA+B3UsrfSSnDUsonALQhEtQBwOcA5AN4AcAQXhkc/qeUckBKOYVI8PvO6N//CYCvSymfl1KGpJTfA+ADcPOCj/1K9GO9i5zrQwD+t5TyspTSFz3HW64p+/17KaVXSnkGkc/NDdG//2NEAugLMuKMlHISwOsA9Eopvxu9lqcA/ALAW5b43BARUQZhXwkREanlGQCHADRE/9uBSFC6Bwuyk0KIP0Uk2KkCIBEJEkuib64CMLDgMRf+dw2AqmtKbI0AjixznjcAuAzgMIBWAH8EYA4LAuUYjSz4bw8iGeDF1AB4qxDi9Qv+LgvA0wAgpQwIIR5EJOD+rJRSXvPxC/+9fYh8PpTHfZ8Q4hML3m5e8PZrP3axcz0shFj4bw4BKF/w56X+jdUAepZ4zJuuuR4mRDLBRESU4RiYEhGRWp4B8HoAGwH8H0QC03cjEpj+JwBE+0n/ApFy0bNSyrAQYhqAiD7GMID1AM5F/1y94PEHAFySUjbGcZ4vIBKYPgPgKCI9pHO4pox3gWsDxXgNAPiBlPJPFntjtNT37wB8F8C/CyF2RzOYioX/3g2IZFWVx/1nKeWiJcRRy519AMD7pZTPLnKm2mU+TvnYegAvL/L3z0gp71jh44mIKAOxlJeIiNTyDIBbAeREy3CPALgLkZ7I9uj75AEIAhgHYBJC/C0iGVPFTwH8lRCiMBrEfXzB214A4IwO+cmJDuXZJoTYvdhhpJRdALyIlNcellI6AYwCeDOWDkxHAawXQpjj/cdH/TeA1wsh7oyezyKEOCSEWC+EEAAeBPBtAB9AJAj/x2s+/mPR9y0C8NcAfhL9+28C+LAQ4iYRYRVCvFYIkRfjuf4LwD8LIWoAQAhRKoR4Y4wf+y0A/yiEaIw+9/VCiGIAvwHQJIT4IyFEVvR/uxf0phIRUQZjYEpERKqQUnYCcCNaWhsNBC8CeFZKGYq+22MAfg+gE5FS1TlcXYL6D4hkOC8BeBKRvlBf9PFCiGRkt0ffPoFI0JS/zLGeATAppexf8GeBK4Hytf4A4CyAESHERAz/7KtIKQcAvBGRoHIckX/bnyHy+/mTiJTO/k20hPd+APeLBVOJERkk9Dgin7eLiAxRgpSyDZE+0/9EZHBRN4D74jjafwB4BMDjQggXgOcA3BTjx34RkRsGjyMyUOrbiNx8cAF4NSI9vkOIlAL/GyI9w0RElOHEK9tViIiI9EkI8REA75BSHlT7LMkmhOgF8MdSyifVPgsREdFaMWNKRES6JYSoFELcEl07swnAnwJ4WO1zERERUXw4/IiIiPTMjMiOzo2IDE/6MYAH1DwQERERxY+lvERERERERKQqlvISERERERGRqjRVyltSUiJra2vVPsaSZmdnYbVa1T4GrQGvob7x+ukbr5/+8RrqG6+f/vEa6huvH3Dy5MkJKWXpYm/TVGBaW1uLtrY2tY+xpNbWVhw6dEjtY9Aa8BrqG6+fvvH66R+vob7x+ukfr6G+8foBQoi+pd7GUl4iIiIiIiJSFQNTIiIiIiIiUhUDUyIiIiIiIlIVA1MiIiIiIiJSFQNTIiIiIiIiUhUDUyIiIiIiIlIVA1MiIiIiIiJSFQNTIiIiIiIiUhUDUyIiIiIiIlIVA1MiIiIiIiJSFQNTIiIiIiIiUhUDUyIiIiIiIlIVA1MiIiIiIiJSFQNTIiIiIiIiUhUDUyIiIiIiIp07PeCALxhS+xirxsCUiIiIiIhIx6Zm/XjPt57H5x45p/ZRVo2BKRERERERkY795x+64fEH8YF9tWofZdUYmBIREREREenUwJQHP3iuF29rqUZDWZ7ax1k1BqZEREREREQ69cUnOmEQAp++vUnto6wJA1MiIiIiIiIdOjfkxC9PD+L+WzaiIt+i9nHWhIEpERERERGRDn3+sfOwW7LwkYP1ah9lzRiYEhERERER6cyxngm0XhjHx26tR35ultrHWTMGpkRERERERDoipcS/PXoBlfkWvHdPrdrHSQgGpkRERERERDry6MsjODPgwGfuaIIly6j2cRIiaYGpEGKTEOL0gv85hRCfTtbzERERERERpbtAKIwvPHYBTeU2vHnnerWPkzCmZD2wlPICgO0AIIQwAhgE8HCyno+IiIiIiCjd/bRtABcnZvGt97bAaBBqHydhUlXKexuAHillX4qej4iIiIiIKK14/EF8+ckutNQU4rYtZWofJ6FSFZi+A8CPUvRcREREREREaec7Ry9h3OXDX969GUKkT7YUAISUMrlPIIQZwBCArVLK0UXe/kEAHwSA8vLyXT/+8Y+Tep61cLvdsNlsah+D1oDXUN94/fSN10//eA31jddP/3gN9W2t18/ll/jzwx5sLjLiUzstCTxZ6tx6660npZQti70taT2mC9wN4NRiQSkASCm/AeAbANDS0iIPHTqUgiOtTmtrK7R8PloZr6G+8frpG6+f/vEa6huvn/7xGurbWq/fP/7mHHyhS/j8u29BY3le4g6mEako5X0nWMZLRERERES0KgNTHvzgeB/esmt9WgalQJIDUyFELoA7ADyUzOchIiIiIiJKV196ohNCAJ++vUntoyRNUkt5pZQeAMXJfA4iIiIiIqJ01THsxMOnB/HBA3WoKshR+zhJk6qpvERERERERBSnzz96HnnZJnz0YIPaR0kqBqZEREREREQadLxnEk9fGMdHb21Afm6W2sdJKgamREREREREGiOlxL8+eh4Vdgvu21ur9nGSjoEpERERERGRxjx2dgRnBhz47B1NsGQZ1T5O0jEwJSIiIiIi0pBgKIzPP3oBjWU2vGnnOrWPkxIMTImIiIiIiDTkp22XcXFiFn9+12aYjJkRsmXGv5KIiIiIiEgHvP4QvvxkJ1pqCnH7ljK1j5MyDEyJiIiIiIg04jvPXsKYy4e/uHszhBBqHydlGJgSERERERFpwPSsH//V2oPbt5Rjd22R2sdJKQamREREREREGvDVp7sx6w/iz+/apPZRUo6BKRERERERkcouT3vw/eN9ePPO9Wgqz1P7OCnHwJSIiIiIiEhlX3yiExDAZ+5oUvsoqmBgSkREREREpKLzI0483D6I+/fWoqogR+3jqIKBKRERERERkYo+/+gF5GWb8JFD9WofRTUMTImIiIiIiFTy3MVJ/OH8GD5yqAEFuWa1j6MaBqZEREREREQqkFLiX39/HhV2C+6/pVbt46iKgSkREREREZEKHjs7itMDDnzmjkZYsoxqH0dVDEyJiIiIiIhSLBgK4/OPnUdDmQ1v3rle7eOojoEpERERERFRiv3s5GVcHJ/Fn925CSYjwzJ+BoiIiIiIiFLI6w/hS090YueGAry6uVzt42gCA1MiIiIiIqIU+u6xSxhz+fCXd2+BEELt42gCA1MiIiIiIqIUmZ7142utPbh9Sxlu3Fik9nE0g4EpERERERFRijzQ2g23L4g/u3Oz2kfRFAamREREREREKTDo8OJ7x/rw5p3rsakiT+3jaAoDUyIiIiIiohT40hOdgAA+c0eT2kfRHAamRERERERESTbgCuMXpy7jvr21WFeQo/ZxNIeBKRERERERUZL9vNMPW7YJHz1Ur/ZRNImBKRERERERURI9f3ESZ8ZD+MihehTkmtU+jiYxMCUiIiIiIkoSKSX+9dHzKMgWuH/vRrWPo1kMTImIiIiIiJLk8XOjaO934N6GLOSYjWofR7NMah+AiIiIiIgoHQVDYXz+0fOoL7Vi3zqp9nE0jRlTIiIiIiKiJPj5ycvoGZ/Fn925GUaDUPs4msbAlIiIiIiIKMG8/hC+9GQndmwowJ1by9U+juYxMCUiIiIiIkqwB4/1YtTpw1/etRlCMFu6EgamRERERERECeTw+PFAazdetbkMN9UVq30cXWBgSkRERERElEAPtPbA7Qviz+/apPZRdIOBKRERERERUYIMOrx48Fgv3rRjPTZX2NU+jm4wMCUiIiIiIkqQLz/RCQD47KubVD6JvjAwJSIiIiIiSoALIy784tRlvPfmGqwryFH7OLrCwJSIiIiIiCgBvvDYeVjNJnzs1ga1j6I7DEyJiIiIiIjW6IVLU3iyYwwfPlSPQqtZ7ePoDgNTIiIiIiKiNZBS4l9/34GyvGy8/5aNah9HlxiYEhERERERrcET50Zxqt+Bz9zRhByzUe3j6BIDUyIiIiIiolUKhsL4/GMXUFdqxVt3rVf7OLrFwJSIiIiIiGiVfnHqMrrH3PjzOzfBZGR4tVr8zBEREREREa3CXCCELz3Rhe3VBbhza4Xax9E1BqZERERERESr8OCxXow45/CXd2+GEELt4+haUgNTIUSBEOLnQojzQogOIcSeZD4fERERERFRKjg8fjzwdDdu3VSKm+uK1T6O7pmS/Pj/AeBRKeVbhBBmALlJfj4iIiIiIqKk+1prD1y+IP78rs1qHyUtJC0wFULYARwAcB8ASCn9APzJej4iIiIiIqJUGHJ48d1jvbh3xzpsqbSrfZy0IKSUyXlgIbYD+AaAcwBuAHASwKeklLPXvN8HAXwQAMrLy3f9+Mc/Tsp5EsHtdsNms6l9DFoDXkN94/XTN14//eM11DdeP/3jNdSOb7/kw/GhIP71QA5KcmLrjuT1A2699daTUsqWxd6WzMC0BcBzAG6RUj4vhPgPAE4p5d8s9TEtLS2yra0tKedJhNbWVhw6dEjtY9Aa8BrqG6+fvvH66R+vob7x+ukfr6E2dI66cNeXD+P+Wzbib17XHPPH8foBQoglA9NkDj+6DOCylPL56J9/DmBnEp+PiIiIiIgoqT7/6AVYzSZ87NYGtY+SVpIWmEopRwAMCCE2Rf/qNkTKeomIiEhDnuoYxaWJ2ZXfkYgow53oncKTHaP48KF6FFnNah8nrSR7Ku8nAPwwOpH3IoD7k/x8REREFIe5QAgf+e9T2FKZh19+7Bbu4SMiWoKUEv/2+/Moy8vG/bfUqn2ctJPUPaZSytNSyhYp5fVSynuklNPJfD4iIiKKz5kBB/yhMM5cnsEfzo+pfRwiIs16smMMbX3T+NTtjcg1Jzu/l3mSGpgSERGRtrX1Re4ZV+Zb8MUnOpGsoYhERHoWDIXx+UfPo67Eire1VKt9nLTEwJSIiCiDneidQmOZDX/66k04O+TE4+dG1T4SEZHmPHRqEF1jbvzZnZuQZWQIlQz8rBIREWWoUFjiZN80WmqLcM/2KmwsseJLT3QiHGbWlIhIMRcI4YtPdOKG6gLcta1C7eOkLQamREREGapz1AXXXBC7awthMhrwqdsacX7EhUfPjqh9NCIizfjesV6MOOfwl3dt5oC4JGJgqkHhsMRnf3IaZwYcah+FiIjSWFvvFABgd20RAOD1N1ShocyGLz3RiRCzpkREmPUF8dWnu3FoUyn21BerfZy0xsBUgxzeAB5qH8STHezzISKi5DnRO40KuwXrC3MAAEaDwKdvb0TXmBu/eXFI5dMREanveM8knHNB/Mn+OrWPkvYYmGqQxx8EAIzMzKl8EiIiSmdtvVNoqS28qjTtNdsqsak8D//xZBeCobCKpyMiUt+RrnHkZBnRUluo9lHSHgNTDfL6QwCAEScDUyIiSo7L0x4MzczNl/EqDAaBz9zRiIsTs3jkDLOmRJTZjnRP4Ka6ImSbjGofJe0xMNUgTzQwHWVgSkRESdLWG9lfulgW4NXNFWiutOM/nmLWlIgy1+VpDy6Oz2J/Y6naR8kIDEw1SAlMWcpLRETJcqJ3CrZsEzZX2F/xtkjWtAl9kx481D6owumIiNR3tGsCALC/sUTlk2QGBqYaNBeIBKbOueB8WW86+Obhi/izn51R+xhERIRIxnRnTSGMhsVXH9y+pQzXr8/HV57qQoBZUyLKQEe6J1Buz0ZjmU3to2QEBqYa5FkQjKZTn+lT50fxyJkhriAgIlLZjCeAC6Mu7K5ZepiHEJGs6eVpL35+8nIKT0dEieTxByElX3vFKxSWeLZ7AvsaSrm7NEUYmGqQMpUXSK9y3iHHHHzBMPqnPGofhYgoo53sj+wvbblm8NG1DjWVYseGAvznH7rhC6ZPBQ9Rppj1BXHT/3kKPz4xoPZRdOfs0AwcngAONLGMN1UYmGqQN3Dll3+6DEAKhyWGZ7wAgAsjLpVPQ0SU2U70TiPLKLC9umDZ9xNC4LN3NGHQ4cVP+cKWSHc6hp1wzQXxu5eG1T6K7hyJ9pfe0sDANFUYmGrQwlLe4TTJmI65fAiEImUkXaMMTImI1NTWO4Vt6/KRY155/cG+hhLsri3Efz7dDX+I5YBEetIx7AQAvHBpan6GCcXmcOc4mivtKLFlq32UjMHAVIOUwNRqNqZNxnTQ4Z3/7wsMTImIVDMXCOHMwMwr9pcuRek1HXX68MxAcOUPICLNODccec3lC4bx3MVJlU+jH7O+IE71T2M/y3hTioGpBs0FQrBkGVCRb0mbHlMlMK0uykHXqFvl0xARZa6XBmfgD4XRsszgo2vtrS/BzXVF+M2lQFpNiydKdx3DTmyvLoDZZMDhzgm1j6Mbz1+aRCAksb+B+0tTiYGpBnn8QeSaTZHANF0yptORwPTWTWXoGXfDH+TqASIiNZzojQw+2hVHYAoAn7m9CTM+iR8+35eMYxFRgoXCEhdGXNixoQA3bSzC4a5xtY+kG0e6JpBtMqClNr6fk7Q2DEw1yOMPISfLiHK7JW1KeYccXuTnZGHnhkIEwxK9k7NqH4mIKCO19U6jvtSK4jj7pm6qK8bWYgO+1tpz1fR4ItKmvslZeAMhbKm042BTKbrH3Fe1VtHSjnRN4MaNRbBkrdyHT4nDwFSDvP4Qcs1GVNgtGHP50mLv56DDi6qCHDSWRxYUd7LPlIgo5cJhibbeqZj7S691b4MZk7N+fP84s6ZEWtcR7S9trrTjQFOkJPVwJ7OmKxme8aJ7zI0DjSzjTTUGphrk8YeQYzaiIt+CUFhi0u1T+0hrNjjtxbqCHNSX2mAQQCdXxhARpVzXmBvOueCK+0uX0lBoxKFNpfj6Mz1w+5g1JdKyjmEnjAaBhjIbGstsqLBbGJjGQFkTw8FHqcfAVIO8gUgpb4XdAgBp0Wc65PBifWEOLFlG1JZYOZmXiEgFSn/pjasMTIFIr+m0J4AHn72UqGMRxeyxsyN4xzeOI5wG1WTJ1jHsRH2pFZYsI4QQONhUiqPdEwiGOOdjOUe6JlCal41N5XlqHyXjMDDVoPlS3vxoYKrzybwz3gBcviCqCiL/nqayPE7mJSJSQVvvFMryslFdlLPqx7ihugC3bynDNw5fhHMukMDTEa3sqY5RPHdxCn1THrWPonkdw05sqbTP//lAUylcc0GcHnCodyiNC4clnu2ewP6GEggh1D5OxmFgqkHzU3mjGVO9D0BSJvKuK8gFADRV5KF3cpaLnomIUuxE7zR21xat+QXXp29vgnMuiO8cZdaUUqtnPDI88dyQU+WTaJvD48fQzNxVgem+hhIYBPtMl3Nu2ImpWT/2NbKMVw0MTDXIG+0xLbZlw2gQui/lHYpOgFtXGLlD31RuQ1gCPePMmhIRpcqgw4tBhzch6w+2rcvHnVvL8e0jlzDjYdaUUkNKie6xyGuHjmEGpstRBh8tDEzzc7OwvboAz3Rxn+lSlP7SfQ0MTNXAwFSDPNEeU6NBoCwvGyMz+h5+pIwmV0p5lZp9TuYlIkqdtmh/6Won8l7r07c3weUL4ltHLybk8YhWMjXrx4w3ciPkHAPTZSmB+5bKq/skDzSV4sXLDkzN+tU4luYd6RrH5oo8lEWrFim1GJhqkCfaYwogLXaZDjq8MJsMKLFGdubVlliRZRS4MMKMKRFRqrT1TsNqNmJzRWIGemyptOO111XiO0cvYZovcikFlDLeEls2S3lX0DHsRInNjLK8qwOsA02lkBI42s2s6bW8/hDaeqexn2W8qmFgqjGhsIQ/GEZONDCtsFt0X8o76IisijEYIj1NWUYD6kps6GLGlIgoZU70TmFnTSFMxsT96v/07Y3wBEL4xhFmTSn5lBag111fiRHnHLN+y+gYuXrwkeKG9QXIz8lin+kinr80CX8ojP3cX6oaBqYa440OBFIyphX5FozqfCrv4LR3voxX0VSRx5UxREQpMuMN4MKoK2FlvIrG8jy84YYqfO9YLybSYOc2aVv3mBuWLANu21IGgH2mSwmGwugcdS8amBoNAvsaS3C4cxxScuXOQke6JmA2GXDjxsT+nKTYMTDVGI8/srA8x2wCECnldfmCmNXxIvOhaMZ0oU3lNlye9ur630VEpBen+qchJRIy+Ohan7ytEXOBEL5xmFlTSq6ecTfqSmzYWpUPgJN5l3JxYhb+YPgV/aWKg42lGHP5cH6ECYKFjnZN4MbaIliyjGofJWMxMNUYrz+aMc1SMqaRvky9lvP6giGMuXzzq2IUjdEBSF1j7DMlIkq2E5emYDIIbK8uSPhj15facM+Odfj+8V6MufT5u4r0oWfcjfoyG4qsZlTYLRyAtIQrg49emTEFIn2mANfGLDTqnMOFURfXxKiMganGeKKBac6C4UcAdFvOO+yInPvaUt75yby8W0dElHRtvdPYui4fudFqnET75KsaEQhJfK21JymPTzQXCOHytBf1pVYAQHOVnRnTJZwbdsJsNKC+1Lbo2yvyLdhUnodnGJjOOxpdE8PBR+piYKoxSo/pwuFHgH4zptfuMFVUF+Ui22TgyhgioiTzBUM4fdmB3TWJL+NV1JZY8ead6/DD5/sxotMbqaRtlyZmISXmg63mSjt6xt2Yi75uois6hl1oKLMha5lBZweaStDWOz3fQpbpjnSNo8RmxpaKxbPMlBoMTDXmlaW8+g5MLyuB6TU9pkaDQGO5jQOQiIiS7OXBGfiDYbQkePDRtT7xqkaEwxIPtHYn9XkoMykTeRvKooFplR3BsEQ3W4Je4dzQ4hN5FzrYVAZ/KIznLk6m6FTaFQ5LHO2ewC0NJfMbJEgdDEw1RinlVcqtcs0m5FlMui3lHZz2QgigMj/nFW9rKs9jxpSIKMlO9E4DSM7go4Wqi3Lx1pZq/PiFAQxGb0oSJUr3mBtCABtLIqW8SuDFct6rjbt8mHD7lhx8pGipLYQly4BnLrCc9/yICxNuP9fEaAADU425MpX3ykQwPe8yHXJ4UZaXDbPplV9qTeV5GHX6MOMJqHAyIqLM0NY7hboSK0ps2Ul/ro+/qgEA8NWnmTWlxOoZn8X6wpz5iak1RbnINRs5AOkayuCj5hUyppYsI26uK8bhaG9lJjvSFQnO9zWwv1RtDEw1xnvN8CMgUs474tTnfrhBhxdVBa/MlgILBiCNMWtKRJQM4bBEW990wveXLmVdQQ7ecWM1fnpiAANTnpQ8J2WGnjH3VcN8DAaBLZV2BqbXWGki70IHGktxaWIW/ZOZ/b16tHsCTeW2+fY5Ug8DU41Rhh/lLtihVG636LeUd5EdporG8sgvGJbzEhElR8+4Gw5PIOllvAt99FADDAaB//wDs6aUGOGwxMUJ9yumzDZX2tEx5ISUUqWTaU/HsBMVdgsKreYV3/fgpkjp6jNdmVvOOxcI4flLU9jXwDJeLWBgqjHXrosBIqW8424fQmF9/eANhyWGHXOvmMirWFeQA6vZyJUxRERJ8kLvFACkLGMKRKp83n3TBvz81GX0Tsym7HkpfQ3NeDEXCL8iMN1SaYfLF8TlafY0KzqGXSv2lyrqSqxYV5CT0ftMT/ROwR8MY38Ty3i1gIGpxnj9IRgEkL2gJ7M834JQWGLCra9y3gm3D/5QeMmMqRACTRV5nMxLRJQkbb3TKLFlo6Y4N6XP+5FD9cgyCnzlD10pfV5KTz3jkRscyg5TRXNVpFz1LAcgAYishuoZd8dUxgtEXocdaCrF8Z5JBELhJJ9Om450TcBsNOCmjam7eUdLY2CqMR5/CLlmE4S4Mq56fpepzsp5l1oVs1BTWR66RjnqnYgoGU70TmF3beFVv1NSoSzPgj+6uQa/bB+cX/NBtFo9Y1evilFsKs+DQVzpq8x0XaNuBMMy5sAUAA42lcLtC+JU33QST6ZdhzvHsaumcH4bBqmLganGeAPBq8p4gQWBqc4m8w4pgekSpbwA0FSRh8lZv+6ywUREWjc848XlaW/S95cu5UMH65FtMuIrTzFrSmvTPe5GQW4Wiq7pm8wxG1FXauMApKh4Bh8p9jYUw2gQeCYDy3nHXHM4P+JiGa+GMDDVGI8/hJysqwPT8vzIiP9RnQWmg9Gej6Wm8gILJvOyz5SIKKHaovtLd6dw8NFCJbZsvG9vLR45M4QutmzQGigTeRfL/G+ptHOXaVTHsAuWLMP8rtdY2C1Z2LmhAIczcADSs92RVTn7OfhIMxiYaozXH0LuNRnTEms2TAahu1LeQYcXeRYT7JasJd+niZN5085cIIRZX1DtYxBlvLbeKeSajSvuM0ymDx2oQ26WEV9m1pTWoGd89hX9pYrmSjsGHV7uREckY7qpwg6jIb7S/YNNpXh50Jlx1WtHuiZQmJuFrVXq/YykqzEw1RhvIPSKUl6DQaAsL1uXpbzL9ZcCQGleNgpys3CBfaa6J6XEI2eGsO/fnsYffft5tY9DlPFO9E5j54ZCmIzq/aovtJrx/n0b8dsXh3F+hFktit+MJ4AJt+8VE3kVygCkjgz/+pJSomPEieYYJ/IudKApkjE8kkFZUykljnZNYF9jKQxxBvKUPAxMNcazSMYUiEzm1Vsp7+XplQNTIUR0ABIzpnrWNzmL937nBXzyR+3w+oNoH3Bgxsu710Rqcc4FcH7EmdL9pUv54311yMs24ctPMGtK8euZiNy4XjIwjVYEZHo574hzDg5PIK7+UsW2qnwUWc043DmRhJNpU+eoG2MuH/Y3sL9USxiYakykx/SVk8Eq7BZdlvIuN/hI0VRhw4VRFxdk65A/GMZXn+7Gq790GO39Dnzu9c342nt2QUqgvT8zJ/wRacGpvmmEZWr3ly4lPzcLH9i/EY+eHcHLgzNqH4d0ZqmJvIrSvGyU2LIzfgDSagYfKQwGgf2NJTjSNY5wODNeiynZ4X2NDEy1JKmBqRCiVwjxkhDitBCiLZnPlS68/ldO5QWAcrsFo0791P475wJwzQVXzJgCkQFIrrmg7kqVM90Ll6bw2q8cwRceu4BXbS7Dk589iPtu2YhdNYUwGgROZujoeSItaOudhtEgsL26QO2jAADev28j7BYTvvxkp9pHIZ3pHnfDbDRg/TI3upurOACpYzhSeba5Iv5SXgA40FiKCbc/YwL8w10TqC+1Ljugk1IvFRnTW6WU26WULSl4Lt3zBkLIzXplYFqRb4HbF4RbJ0NllFUxsXzDNyqTedlnqgsOjx9/8fMX8bavH4fHH8J37mvB196zCxX5kbVG1mwTtlTmzU8EJaLUO9E7ha1VdliztbGbz27JwgcP1OHJjjGcGXCofRzSkZ6xWdSW5C7bK91caUf3mBv+YDiFJ9OWc8NOVBflIG+ZgZPLUVamZMLamLlACC9cmsT+Rk7j1RqW8mqMx//K4UfAgl2mOinnjWWHqaKJK2N0QUqJh05dxm3//gx+fuoyPnSwDk989gBetbn8Fe/bUlOE9oFpBEKZ+yKBSC3+YBinBxxoqVG/jHeh+27ZiILcLHyJWVOKw8Vx95L9pYrmKjv8oTB6xjP3BnfHsBNbKlY/XbYsz4LmSjsOZ0BgerJvGnOBMPazjFdzkn0rVQJ4XAghAXxdSvmNa99BCPFBAB8EgPLycrS2tib5SKvndruTfj6PL4iJkUG0tl79g2F4MgQAePzI87hc/MrAVWue7o8Mvuk/1w7nxZXvf9jNAs+c6UJjuD+p50rFNUxHI7NhfO+sDx1TYdTnG/DpPRZU54zihWOji75/zmwQc4EwfvCbp1GXn7ivV14/feP1S41uRwi+YBg5s0NobR1L6GOv9RresR742YVxfOvhp9BQqP3fZelGb9+DwbBE76QHW+3+Zc/tdkdugj70h+dxy7rVZQz1YrFr6AtJXBr34LoVPk8rqbX48VhvAL9/8mnkmNJ3Uu1PL/hhFEBg8BxaRztS+tx6+x5MtWQHprdIKYeEEGUAnhBCnJdSHl74DtFg9RsA0NLSIg8dOpTkI61ea2srknk+fzCM0KO/x+aGjTh0qPGqt9VMzOLfTrSivHYTDu1an7QzJMrx33fAbOzFG159a0xjuK/rfg6uuSAOHdqX1HMl+xqmG18whK+19uCBYz3IzjLgn+9txjt3b1jxmm6a8eJrZ/4AWVyHQ/s2Juw8vH76xuuXGp2HewCcx32v3Y/SvOyEPvZar+GNe4P4w789jWembPjje29K3MEoJnr7HuwecyH8+GG8anczDu1Y+rVPKCzxD88/inD+Ohw61JzCE6beYtfw9IAD8olncfee63FoW8WqH9tcPYHfffN5GCu34NDW1T+O1n3hxSNoqc3DXbfvSflz6+17MNWSWsorpRyK/v8YgIcB3JjM59M7rz+SFc0xLz6VF4BuBgQNOeZQWWCJeTdUU3keOkfdGTMNTg+O9Uzg7i8fwZef7MJd2yrw1J8exLtvqonpmlbm52BdQQ5O9k2l4KREtNCJ3mlsLLEmPChNhFyzCR85VI8jXRN44RJ/PtDyusdmASy9KkZhNAhsqrDPT6bNNMq/u3kVE3kXaqkpQq7ZiMNpvM90wu3D2SEny3g1KmmBqRDCKoTIU/4bwKsBvJys50sHnkBksFHOIsOPcsxG2C0m3ewyHZz2xDSRV9FUngdvIITBaG8qqWfS7cNnf3oa7/rm8wiGJb7//hvxlXfuQFmeJa7HaaktRFvvNNcAEaWQlBJtvVNoqVF/f+lS3n1TDUrzsvGlJ9hrSstTekZXCkyBSFB2btiZkb9zOoadsGWblp1cHAuzyYC99cV4pnM8bT+Pz3ZHdrVy8JE2JTNjWg7gqBDiDIAXAPxWSvloEp9P95SMae4iw4+AyGRevQw/GnR44xrBrQxAusABSKoJhyV+cqIft33xGfz6zBA+fmsDHv/MARxoWt0P75aaQoy5fLg8zZsNRKnSM+7GtCegif2lS8kxG/HRQ/U4fnESx3om1D4OaVjPmBuV+ZaYpks3V+bB4QlgWCevkxKpY9iJzRV5MVepLedAUykGprzonfQk4GTac6RrAvk5Wdi2Ll/to9AikhaYSikvSilviP5vq5Tyn5P1XOnCM1/Ku3hgGtllqv0fuP5gGGMuX1wZ08byyN3QC6MMTNXQNerCO77xHP7iFy+hqSwPv/vkfvyvOzfBskj2Pla7ohNB21jOS5QyJ6JrmlpqtZsxBYB33rgB5fZI1jRdMzO0dj0xTORVNFdFylgzbZ+plBLnh13YssYyXsWBaCYxHafzSilxtGsC+xpKYExAEE+Jx3UxGuINrJAxtVt00WM6MjMHKRFXYGq3ZKEq34IuBqYpI6XEkMOL//vYBbzmK0fQOebC5998PX78wZvnd8uuxaaKPORlm7jPlCiFTvROodhqxsYSq9pHWZYly4iP39qAE73TONrNrCm9kpQSPeOzqC+N7Wt5U4UdQiDj+kwvT3vh8gUTFpjWllhRU5ybloFp95gbI8459pdqmDY2bxOAKxnT5Up5x10+BEPhZRdNq+2yI1L+EcsO04WaKvJwYTRzd5Alk2sugM5RF86PuHB+2IULIy6cH3HCORfpa37zzvX469dsRrEtccNSjAaB7RsKcLKPgSlRqrT1TqOlthBCaD8b8Lbd1fhaaw+++EQn9jWU6OLMlDpjLh/cviDqy2LLmNqyTagttuJchgWmyr93S+XabygrDjSW4ucnL8MXDCHblD5rnY50RW6C7WNgqlkMTDXE648ECUuVT1bkWxCWwLjbh8r8tTW4J9OQI5LVjSdjCkT6TI/1TGo+8NayYCiM3snZ+QD0fDQAXdjnmZdtwqaKPLxhexU2Vdixc0MBtlYlp9eipaYIX36qEzPeAPJz0nu3HJHaRp1z6J/y4L17atQ+SkyyTUZ84rZG/NVDL6G1cxy3bipT+0ikIT1jsQ8+UmypzMPZDCvl7Rh2QohIlVKiHGgqxQ+e68PJ3mnsbUifIO5I1zjqSqxYX5ir9lFoCQxMNeRKKe/il2V+ZczMnKYD08FoEFSRH98U16byPPiDYfRNeeL6RZSpxl0+nB9xXhWAdo254Q9GFo0bDQJ1JVbs2FCId964AZsr8rCpIg/rCnJSlploqS2ElMCp/mm+6CRKMqVsXsuDj671ll3r8UBrN770RCcONZUya0rz4pnIq2iutON3L43ANRdAniUzboZ2DDuxsdi65GvH1dhTX4wso8AzXeNpE5j6giE8d3EKb21Zeh8uqY+BqYasVMpbHg1MtT4AadDhQWledtyDczZF+xo7R1wMTJcw4w3gz39+Bm2905ic9c//fVleNjZV5OG+vbXYVJ6HzZV5qC+1rWl4USJsry6A0SBwspeBKVGyneidQk6WcX4IjB5kGQ34xKsa8ec/fxFPdozhjuZytY9EGtEzPgtbtgnl9thbTJSv/QsjLrTo6AbNWnQMu3BdgifM2rJN2FVTiGcujOOv7t6S0MdWy6k+B7yBENfEaBwDUw3xrjCVV8lAan1lzJBjLu4yXgBoKLNBCKBz1I27r0vCwdLAvz16Hk92jOHNO9dhc4UdmyvzsLnCjiKrWe2jLcqabUJzpZ2TeYlSoK1vCjs2FCBLZ60Qb9qxDv/3sQt45MwQA1Oa1z3mRn2pNa4senNlJEA7N+zMiMDUNRdA/5QHb0tCFvBAUyk+/+gFjDnnUGaPrwJOi450jcNoELi5Lv2/LvRMX7+90tx8xnSJLFdRrhlZRoERpy+Vx4rboMO7qsA0x2zEhqJcdHIy76JO9k3jf57vx/17a/H5t9yA9+/biL31JZoNShW7agpxesCBQCis9lGI0pZrLoBzQ/p8MW4yGrC1yj7fU0gExLcqRlFuz0ZhblbGrIxRdr8naiLvQgejO8wPd6XH1Oyj3RPYuaEgY0q89YqBqYZ4/CGYjYYlB/8YDAJledreZRoOy0hgGudEXkVjWR53mS4iEArjrx96CVX5Fnzmjia1jxOXltpCzAXCGfNCgUgN7f0OhCWwW+P7S5dSX2rDxQk3wmHuNCXA7QtieGYu5om8CiEEmqvsGTOZt2N+Im/iA9MtFXaU2LLxTBqsjZme9eOlwRmW8eoAA1MN8fqDsGQtf0kq8i2aLuWdnPXDHwyvKmMKAJsqbOidmIUvGErwyfTt20cv4cKoC3//xm2wZuurAr+lJpLBaePaGKKkaeudgkEAOzboNDAts2EuEMbQjHfld6a0d2l8FgBi3mG6UHOlHRdGXAhmQJXOuWEX8nOyUBnnsMlYGAwCBxpLcLRrHCGd3zB6tmcCUnJNjB4wMNUQbyC04lS1Cru2M6aDjsiLiqpVBqZN5XkIhiUuTcwm8li6NjDlwZef7MSdW8t12X9VkW/BuoIcnGSfKVHSnOidRnOVHTad3bhSKCWb3SznJaxuIq+iucoOXzCcEa8jOoad2FKZl7Rp1gc3lWLaE8DLgzNJefxUOdI5AbvFhOsTPCSKEo+BqYZ4/KElJ/Iqyu0WjDjnIKU2714pq2JWmzFtUibzjvLFCQBIKfE3v3oZRiHwuTdsVfs4q9ZSW4i23mnNft0S6VkgFEb7wPR8dYIeNURLNnvG0z+YoJX1jLthNAjUFMefMVXKWtO9nDcUlrgw4kpKGa9iX0MJhICuy3mllDjSNY699SVLtsqRdvAKaYjXH1pyIq+iIj8bHn8ILl8wRaeKz1A0Y7raHtO6UiuMBoHOEfaZAsDvXhpB64Vx/OmrN2l6d+1KWmoKMebyYWCKZXpEiXZ2yIm5QBg3btRvYFpkNaMwN2s+U0aZrXvMjZqiXJhN8b9MrS+1wWw0pP1cg77JWXgDoaQGpsW2bGyrysdhHQemFydmMTQzh/1NLOPVAwamGhJrxhQARjXaZzro8MKWbYLdsrpysmyTEbXFuRyABMA5F8Dnfn0W163Lx/v21qp9nDXZNd9nynJeokRr6418X7XU6LO/VFFfauNkXgIQyZjWrXKfeZbRgKYKW9pnTJV/X3MSA1MgMp23fcCBGW8gqc+TLEeiQfUBDj7SBQamceoadeHeB56FOwkZS08gBMsSq2IUFdHAdESjfabKqpi19DtsqshDFwNTfOHRC5h0+/B/7r0ORkNy+kdSZVNFHvKyTRyARJQEJ3qnUFOcq/tdg/WlNpbyEoKhMHonPKgvi7+MV9Fcace5IWdat490DDthNIj5MvhkOdBUilBY4li3PtfGHO2eQE1xLqqLctU+CsWAgWmczg450d7vQN9k4n95zsWQMa2ITl7T6mTewenVr4pRNJXnoW/KA68/cyfztvdP47+f78P79tbiuvX6b9Y3GgR21BTiZC8DU6JEklKirVff/aWK+jIrJtw+zHj0mZmhxLg87YU/FF7V4CPFlko7Jmf9GHdpe+/7WnQMu1Bfal0xobFWOzYUwJZtwuEu/ZXz+oNhHO+ZxH5O49UNBqZxCkfvvnmSEDR5AsEVp/LOl/JqOGNaVbC2u/ZN5XmQMnOnMwZCYfzVQy+hPM+CP331JrWPkzAtNYXoHHPpthyISIsuTsxictav2/2lCymZn272mWa0tUzkVSjlrWfTuJw3MpE3uWW8QKQ0em99MQ53TuguA93eP41Zfwj7GljGqxcMTOOk7HJKRilvLMOPLFlGFORmxVzK6w+mbo+X2xfEjDeAdQVrK5e4Mpk3M8t5v/vsJZwfceFzb9iq29UPi2mpKYSUwKl+Zk2JEmW+v7Q2DTKmpcpkXgammUy5/g1ryZhWRQK2jjQNTB0eP4Zn5lISmAKRtTGDDq/uSu2Pdk/AaBDYU1+s9lEoRgxM4zSfMfUlIWPqDyE3hpKMCrsFIzMrl6ec7JvGzn98Av/9XF8ijreitU7kVdQW58JsNGRkYHp52oMvPdGF27eU486t+ttZupztGwpgNAiW8xIl0IneaRTmZqG+dPX9eFqxvjDys5+BaWbrGZtFiS0b+blZq34MuyUL1UU5aTuZVxl8lKrAVBkcpLe1MYe7JnDD+nzk56z+a4lSi4FpnKIJU8wmOGMqpYQ3sHKPKRAp512plLd/0oMPfr8Nbl8QX3mqC3OB5PdrXtlhurZSXpPRgLpSa8YFplJK/N2vzkII4O/fuDVpC7PVkms2obnSrsnJvDPeAKZm/Wofgyhubb1TaKktSoufF0aDwMYSK3rG9JWVocTqHncn5EbLlgp72k7m7RiOvD7aUpmXkuerLspFXYlVV2tjHB4/XrrswH5O49UVBqZxUkp5Z/2JDUznAmFICVhiCEwr7JZlS3lnPAHc/+ALCIYl/umebRhz+fDzk5cTedxFDSoZ0zWW8gKRKa6do5l11/yxsyN46vwYPntHE9YV6Hdn6XJ21RTi9IADgVDqSsxj8aEftOHA55/Gb14cUvsoRDEbc82hd9KDG9OgjFdRX2ZlxjSDSSnRPeZGfQImzTZX2XFpYhaeBL9e04KOYSdKbGaU5aVuEveBplI8f2kyJYmORDjWM4mwBA5wf6muMDCNk1LKm+iMqTf6jR5LKW95vgUTbt+iL+79wTA+8sOT6J/y4Ot/tAvvvmkDdmwowH8905P0YGDQ4YXJIFCal73mx2oqz8OgwwvXXGYMynHNBfB3j5xFc6Ud9+l8Z+lyWmoLMRcI46yGyqv6Jz147uIUjAaBj/9PO/7mly/r5hcvZTalLL4lDQYfKRpKbeif8sAX5PdgJpqa9WPGG1jT4CNFc6UdUgIXRtKv+ipVg48WOthUirlAGC9c0l7V02KOdE0gL9uEG9YXqH0UigMD0ziF54cfJfaXpnJHb6WpvEAkYyolXjEGXUqJ/++XL+FYzyT+9U3X4+a6Yggh8PFbG3B52otHTic3GzQ47UVlgSUhOzeVAUhdGTKZ998f78SYy4f/86brYDKm77elstJCGdiiBb86PQgAeOTjt+CDB+rwg+f68Jb/OpaUlVBEiXSidxqWLAO2Vul/pZSivsyGUFiif9Kj9lFIBcpwnUSU8jZHByClWzlvMCzRNeqenzycKjfVFcFsNOiinFdKicOd49hTX5zWr6nSEa9WnELRHtNEl4YoOztXmsoLABX5kYzkteW8D7T24Kdtl/HJVzXgzbvWz//9qzaXYXNFHh5o7Z4PrJNhyOFNWAnqJmUybxre6bzWmQEHvne8F390cw22VxeofZykqsi3YH1hDk72aWMAkpQSD7cP4qaNRagptuKvX7MF33pvCwamvHjdV47i9y8Nq33EmM0FQnj6whiCGiuTpuQ50TuF7dUFMJvS51c5J/NmtkSsilGsK8iB3WJKuwFII7MS/lA45RnTXLMJN24s0sU+095JDwYdXu4v1aH0+W2WIuEkrYtR9qLGOvwIAEZnrgSmv3lxCF947ALeuL0Kn7mj6ar3F0LgY7c2oGd8Fo+dHUngqa8W2WGamMB0fWEOcrKMuJDmA5CCoTD++uGXUGrLxv+6M312li6npaYQbX3TmtiH9uLlGVycmMWbdq6b/7vbm8vx20/uQ12ZDR/54Sl87pGzmi4rDIclftk+iFf931bc/90T+OxPz8z3wlP6cvuCODs0g91p1F8KABtLIpkyva2loMToGXPDkmVIyE1uIQS2VKbfAKR+V+TmY6oDUyDSr9k56p7fwqBVR6PBMwcf6Q8D0zgtty7G6w9h0r3yGpfFKIFpTozrYoArGdOTfdP47E/PoKWmEP/25usXnc74musqUVdixX8+3Z2UgCAQCmPUOYf1CQpMDQaBxnIbutJ8ANKDx3pxdsiJz71hK+yWzBhnvqu2COMuHwam1P/F9nD7IMwmA+7aVnnV368vzMXPPrQHH9i3EQ8e68Vb/+s4Bqa0V1p4sm8K937tGD79k9Mosplx395aPHJmCH/2Mwan6e50vwNhmR77SxeyZptQlW9Bd4a0cdDVusfdqCuxwZCAliAgUs57YcSVVj8PB1xhmKPbC1LtQFMk0Dui8azp4a4JVBfloKZ47cM4KbUYmMYpJJeeyvvFJy7g7d94blWPqwxbiaWUt8hqhtlowIhzDv2THvzJ99tQmW/BN97bAssSga3RIPDhQ/U4O+REaxL6A0Zm5hCWa99hulBjWV5aZ0yHHF588YlO3LqpFHdvq1D7OCnTUhMZ1KL22phAKIxfnxnC7VvKFt1xZjYZ8Deva8bX/2gXeidm8ZqvHMGjLyev4iAeA1MefOx/TuHNXzuOkRkv/u9bb8AjH9uHz71hK/70jiY81D6Iv/zFi0kt3Sd1neidgkEAOzcUqH2UhKsvs7GUN0P1jCdmIq+iudIOjz+UVjMDBpxhNJbbkKVC7+Sm8jyU27NxuHMi5c8dq0AojOd6JrGvoTQt1mhlGgamcVJe6C02lffShAcjM8vvF13KlVLelYcfCSFQZs9G54gL9z/4AkJhie/etxtFVvOyH3fvjnVYV5CDr/4h8VlTZVVMokp5AWBThQ3jLh+m03S/5N89chZhKfEPb9yWUT88m8rzkJdtQpvKfaZHuycwOevHPdvXLft+d26twG8/uR91JVZ8+L9P4h9+fQ7+oDp9nK65AP7t0fO47YvP4KmOUXzytkY8/b8O4S271s9nGD5xWyM+eVsjfnbyMv73L19mcJqm2vqmsLnCjrw0rLSoL7WhZ8ytiXJ/Sp25QAiXp70JGXykUMpd06mct9+V+v5ShRACBxpLcaRrXLPzDM4MOODyBXGA/aW6xMA0TsprvNlFSnknZ33w+IOr+mV6ZSrvyhlTIFLO+/SF8fm1MHUxDArIMhrwwQN1aOubTvi478FpZYdp4gJTZTJvZxpmTR87O4Inzo3i07c3oboos0pNjAaBHTWF86su1PLwqUEU5Gbh0KayFd+3uigXP/vwXtx/Sy2+8+wlvPXrqS3tDYUlfvRCP279v634WmsPXntdJf7wp4fw2TuaFr2Z9ZnbG/HRQ/X40Qv9+LtHzvIFfpoJhMJo73fgxo3pVcarqC+zYdYfwqhzda0xpE+XJmYhZWIGHykay20wGUTaDEAad/ng9EvVAlMgUs7rnAvizOUZ1c6wnCNdEzAIYG89A1M9YmAaJ6VPYbFS3km3H2EJ+FaRTfHGUcoLRKabAphfCxOrt++uRoktG//5dHfcZ1zOUBIypukamLp9QXzukbPYXJGHD+zbqPZxVNFSU4jOMRdmPOrsqXX7gnj83Ahed31lzBNNzSYD/u71W/Ff79mJi+NuvPYrR/B4EoeJKY52TeC1XzmCv3roJdQWW/Grj92CL719+7Lfa0II/Nmdm+bX3/zDb84xOE0jHcNOePyhtNpfupCSMWM5b2ZJ5EReRbbJiIYyGzrSJGOq/Du2VOapdoZ9DSUwCGh2bcyRrnFcv74A+bnpV02SCRiYxkl5cbdYKa8y+Gixt60knqm8APDRQw34z3ftuGotTCwsWUb88f6NONI1gTMDjniPuaRBhxclNvOSPa6rUZlvQV62CZ1pNgDpi493YsQ5h3++9zpVekS0oKWmEFICp/rVyZo+9vII5gJh3Ltj+TLexdy1rRK//cR+1BRb8cEfnMQ//eYcAkkoaeoZd+MDD57Ae779PNy+IL76rp342Yf34IYYVwoJIfBXd2/G/bfU4rvP9uJffn+ewWmaOBGtNlD2AqebBq6MyUg9Y7MQAgkf6tOcRpN5lcA01TtMFyq0mnH9+gI8o8HAdMYbwOkBB9fE6Fhmvipeg/nhR9eU8nr9IcxGg0slyIyH8jEWU2yBXXOVHa+7viru5wGA99xcA7vFhK8mMGs6mMAdpgohIpN502kA0suDM3jw2CW868YN2FWTntmOWGzfUACjQag2AOmXpwdRXZSDnRtWdw02FOfi5x/Zg/ftqcG3jl7C275+fL7Peq0cHj8+98hZ3Pmlw3j+0hT+4q7NePKzB/Ha6yvj7kUWQuBvX9eM9+6pwTcOX8TnH7vA4DQNnLg0heqinPnKmXRTmpeNvGwTJ/NmmJ5xN9YX5iT0BjcQeb006vStemuClnQMO1FkESjIXX6mSLIdaCrFi5cdcHi0NQPkeM8kwpJrYvSMgWmclMSIPxS+agDK5OyVH3irCUznAiFYsgwJG5G+HFu2CffdshGPnxtNWJnsoMOb0Im8ik0VeegcdaXFi+lQWOKvHnoJRdZs/Pldm9U+jqpyzSZsrbKjTYU+01HnHJ7tnsC929etaehUtsmIv3/jNnz1XTvRNerGa/7jCJ7qGF314/mDYXz76CUc/EIrvn+8F2/bXY3WPzuEjxyqX9MLNSEE/v4NW/Gumzbga609+NITnat+LFKflBJtfVPYnabZUiDyNcvJvJmne8yd0DJehZJd7BjW/03ujmEXqvPUf+l+sKkUYRkZIqglR7rGYTUbsSMNp5VnCvW/unVmYYDkWdBnOum+ctdosf7TlXj8wZgm8ibK/XtrkWs24oEEZE2llBhyeFGVn/jAtKk8Dw5PAONpcKfz+8d78dLgDP729c2LrifJNLtqCnHmsiMpZbDL+fWZIYQlcM8qyngX89rrK/GbT+zDuoIcfOB7bfiX33XE9W+SUuKJc6O488uH8Y+/OYfr1+fjd5/aj/9z73UosWUn5IxCCPzTG7fh7S3V+MofuvGVp7oS8riUer2THky4/Wm3v/Rakcm86bPig5YXDktcnEhOYHplMq82h/XEyhcMoWfcrYnA9Ib1+bBbTHjmgrbKeY92T2BPfXHGtkmlg5ivnBAiRwixKZmH0YOFS5pnF2RGF2ZMvass5c1JcPnKcgqtZrzn5ho8cmZozfu9pmb9mAuEk5IxnR+ANKLvO+fDM178++OdONBUitdfX6n2cTShpaYIc4EwzqZ4WuJDpwZxQ3VBTJOsY1VbYsVDH92L99y8AV8/fBHv+MZz8wPBlnNuyIl3f+t5/Mn322AQwHfv243vv/9GbK5IfP+QwSDwL2+6Dm/auQ5ffKITD7QmdgAapcaJ3kj5++40HXykqC+zYsQ5B/cqZjaQ/gzNeDEXCCclMC20mlGZb9H9ZN6uUTeCYYkNGghMTUYD9jWW4HDXuGYq2vomZ9E36WEZr87F9NUthHg9gNMAHo3+ebsQ4pEknkuzQgu+ARcOOZpYmDFdxS9Srz8U8+CjRPnjfRthMhrwX8/0rOlxlN66RPeYAvqczOsPhtEx7MTD7ZfxL7/rwHu/8wJe+5WjCITC+KcM21m6HGWiaFtv6vpML4y4cG7YiXu3r64/ezmWLCP+6Z7r8P/euQMXRlx47VeO4OkLY4u+75hrDn/x8xfx2v93BOeGnfj7N2zFo58+gFs3lyX168NgEPjCW27AG7dX4fOPXsA3D19M2nNRcrT1TqEwNwsNZYl/Aa8lSoBykeW8GaFnPHKDPJE7TBdKhwFIyuAjLWRMAeBAYylGnT7NDKg80hUpK97HwUe6Fmvt6OcA3AigFQCklKeFELXJOZK2LbwxtDAAXVjKu9rhR6kOTMvsFrytZT1+cmIAn7ytEZWrLMVVdpgmclWMosRmRpHVrMnAVEqJcZcPHSMunB92omPYifMjLnSPRe5qAoDZaEBjuQ23birDG7dXYUNxZu0sXU653YL1hTk42TeNP96fmuf85elBGA0Cr7sh8YGp4vU3VGHbunx89IencP93T+Ajh+rxp3c0wWQ0YC4QwrePXsIDT3fDHwrj/bdsxCdf1ZjSsfZGg8C/v/UGBMMS//y7DhgNAu/P0LVFetTWO41dNUVpf4NLCUy7x9y4fn2BuoehpOuJDrqqT9INl+YqO1o7x6PzPFL7WitROoZdsGQZUG7Vxvf+gaZIZvJw5zg2Vai3vkZxtGsC6wpyUFeSnJsblBqxBqZBKeVMuv8ijMVVpbwLJvMunPa2msDUq9IPyw8dqMePXhjANw9fwt++vnlVj6FkTNcnoZRXCIHGMvUn884FQugec6Nj2ImOYRfOj0SC0KnZKzckKvMt2FyRh1dtLsPmSju2VOShtsTKXodltNQU4tmeSUgpk/5COxyW+FX7IA40liSsd3MpG0usePije/H3vz6Hr7X2oK13Cm/ZtR5feaobgw4v7mgux1+/Zgs2qvQL1GQ04Mtv345QSOIffnMOWUaBP9pTq8pZKHYTbh8uTszi7bur1T5K0tUU58JkEByAlCF6xt0oyM1CsTU502abK+0IhSW6Rt24bn1+Up4j2TqGndhUYYdBqLP/+1pVBTloLLPhmc5x/MmBOlXPEgyF8WzPBF57XfzT60lbYg1MXxZCvAuAUQjRCOCTAI4l71jadVUp78LhR7N+FORmweEJXDUUKVZefwglttSP/64uysU929fhf17ow8durUfxKl6wDzq8sJqNSRvos21dPv77ub6UD4gCANdcAB/4XhtO9k3P35SwZBmwqcKOVzeXY3NFHjZX2rG5Ik/18e16tKu2CL88PYT+KQ9qipMbpL3QO4WhmTn8xd2pmYhsyTLiX950HW6uK8JfPfQSTvS+hOZKO77w1uuxt179UqMsowFfeecOfPSHJ/E3vzoLo8GAd920Qe1j0TKUKdbpPvgIiHx91hTncgBShugZjww+SlZQsXAAkh4DUyklOkacuHtbBQB11qwt5kBTKX7wXB+8/hByUlz1t9CLgzNwzQVZxpsGYn2V/wkA/xuAD8CPADwG4B+TdSgtk0v2mPpQXZgLh2fmFTtOYxEJutQp8/zIoXo81H4Z33n2Ev7szvhftA9Oe1FVkJO0Xyi3bSnDt49ewjMXxnH3dakdHPT7l0fwwqUpfGDfRuyqKcTmijzUFFthTMFan0ywe77PdDrpgenDpwZhNRvx6uaKpD7Ptd64fR1uWF+A8yNO3NFcoamvHbPJgK++eyc+/IOT+OuHX4LJKPC2lvTPxunVid4pZJsM2LYu8cOxtKi+lCtjMkX32CxetTl5Q2s2FOXCajbqdgDSiHMODk8gEmD7tBOYHmwqxbePXsJzlyZx66Yy1c5xpHMCQgC3aOCmL61NTDWGUkqPlPJ/Syl3Sylbov89l+zDaVEoLJFljLywvLbHtNyeDbPJsOqMqVp3mxrKbLh7WwW+f6wPM574S0SGZpKzw1RxY20RCnOz8NjZkaQ9x1J+fWYIG4py8f+9dgtec10l6kptmgos9K6pLA95FhPa+pK7z3QuEMLvXhrGXdsqVfk+qy2x4q5tlZr82sk2GfG19+zC/sYS/MUvXsRDpy6rfSRaQlvvFG6oLkC2SZ89cvGqL7Ohd3IWwRSvlKLUmvEEMOH2JWUir8JgENhSadftLlNl8JGS+dWKGzcWIdtkUH1tzNHucVy3Lh+FSSoFp9SJdSrvr4UQj1zzvx8IIT4lhLAk+5BaEgoDeZZIyeq162KKrdmwmo2rG34USP3wo4U+fmsjXL4gvnU0/imdSsY0WUxGA27fUo6nzo/BH0zdC5QJtw/Heibx+hvYs5AsBoPAzg2FONmX3DvAfzg/BpcviHsTtLs03ViyjPjme1uwp64Y/+tnZ/Cr04NqH4mu4fEH8fKQM+3XxCxUX2pDICTRP+VR+yiURD0T0cFHSQxMgcgApHPDToTD2lhvEg8loN6sgSFDC1myjLiprhiHu9QLTF1zAZzqd2A/y3jTQqxTWS4CcAP4ZvR/TgCjAJqif84YUkrkmo0wiCsZUyklJt1+FNvMyDWbruo9jVWq95heq7nKjtdeX4nvHL101SCnlXj8QUx7AklZFbPQnVsr4JoL4vjFyaQ+z0K/f2kYobDEG25gMJNMLTWF6Bx1rypbH6uH2wdRlpeNPfXFSXsOvbNkGfGt97WgpbYIn/3pGfz2xWG1j0QLnO53IBSWGdFfqlBW4iirRCg9JXsir2JLpR1uXxCXp1feMa0154adqC7KmU+MaMnBplJcHJ/FgEo3kI73TCIUltjXwP2l6SDWwHSHlPJdUspfR//3HgA3Sik/BmBnEs+nOSEpYTQIWM2m+V5SpzeIYFii2JYNa7YRnjh7TENhCX8wrGrjOAB85vZGeAMhfD2O3YZDSZzIu9C+xhLkmo0pLed95MwQmsptmhiDns52RTNAp/qTU847PetH64UxvHF7lSZLabUk12zCd+/bjR3VBfjkj9vx6MupL5+nxZ3onYYQwK6azMmY1kV3WrLPNL31jM/CbDSgOsmvI5oXDEDSm45hJ7ZUaKuMV3GwKZKpVCtrerR7ArlmI3bWFKjy/JRYsQampUKI+XGN0f9Wcub+xT8kPYXCEkYhkJttnM+YTsxGMowlNjNyzCZ4AvEFpt7o+6tZygsADWV5uGfHOnzvWC/GnLG1EF9O4g7ThSxZRty6qQyPnx29amVPsgw5vDjRO403JHHfJUVsry6A0SDQlqRy3t+8NIxASOIelvHGxJptwnfv343r1+fjEz86hSfPjap9JALQ1jeFzRV22DWYMUkWuyULZXnZ8xk1Sk89427UluTClOTVapsq8mAQwDmd9Zl6/SH0Tsxqrr9UUV9qQ1W+BYc71QlMj3RN4Oa64ozpvU93sf4U+FMAR4UQTwshWgEcAfBnQggrgO8l63BaJCUgROTFm1KyO+mOxOYltmiPqS++Ul5lWFJOilehLOZTtzUiFJb46tPdMb2/ssM02aW8APDqreWYcPvQnqTM2kJKGePrrmdgmmy5ZhO2VtnnV2Ek2i/bB7GpPG/+bjmtLM+She+9/0ZsqbTjoz88hacvjKl9pIwWDIVxqm86o/pLFZzMm/56xtxJ7y8FIje460ttupvMe2HUhbDU3uAjhRACBzeV4lj3JAIpHlQ2MOXBpYlZ7Gtgf2m6iHUq7+8ANAL4dPR/m6SUv5VSzkopv5y002lQKLywlFcJTCMZ0ys9pnFmTKPvn6tij6miptiKt7ZU439e6Mfl6ZX7BYYcXhgNAuX25M/AunVzGbKMIiXlvI+cGcIN6/NRW5LcFSYUsaumEGcuOxI+3Kp/0oOTfdO4Z8c6DrCKk92ShR+8/yY0ltvwoR+cVO1uOEUGn8z6QxnVX6qoL7Oie8x91ao2Sh/+YBh9U56UBKYAopN59RWYKufV8s3VA42lcPmCaO93pPR5j3ZPRJ6/iYFpuoinbqIRwCYA1wN4mxDivbF8kBDCKIRoF0L8ZjUH1JqQlDAIAWu2cT4AnVACU2s2cs1GeOMcfqRM8VW7lFfxiVc1QEDg/z21ctZ0cNqLCrslJb17dksWbmkowWNnR5P6IuXSxCxeGpzB61nGmzK7a4swFwjj7FBie39+GZ0u+8btvJarkZ+bhf/+wE2oK7HiT77fhmPRFwGUWid6I2XumZgxbSi1wTkXxIQ7o7qGMkb/1CxCYYn6stTcBG6usmPQ4YXDo5+vp45hJ2zZpqTP8liLvQ0lMBpEym9gHu2aQIXdkrIbG5R8sa6L+TsA/y/6v1sBfB7AG2J8jk8B6FjV6TRIKoHpgozphNsPIYDC3KyrAtZYKT2mFo0EplUFOXjXTRvw81OXcWli+WmIg47k7jC91p1bK9A/5cH5keT1iPz6zBCEYBlvKrVEB7qcTOA+Uyklftk+iJvripLeA53OCq1m/PCPb0JNcS4+8L02PJ/CydgU0dY3hXUFOajMz7yv4/r5ybws501H3WOR1xipCiyUrKOe9pmeG3Jic0UeDBoe3pefk4Ud1QUpHYAUCksc7Z7A/sYSVkSlkVgzpm8BcBuAESnl/QBuAJC90gcJIdYDeC2Ab636hBozX8qbvaCUd9aHwlwzTEYDcs2muHtMtVTKq/jorfXIMgr8x5Ody77fkGMO61P4ov/2LeUQAkmbFiqlxCNnhrC7tggV+Rm1oldVZXYLqotyEtpn+uLlGVycmOXu0gQotmXjh398M6oKLLj/wRNo603u3lm6QkqJE72Z2V8KXAlYGJimJ+W61qWwlBeIrF/Rg3BY4vyIS7P9pQsdaCrFS4Mzca0cXIuXBmcw4w1gfxPXxKSTWAPTOSllGEBQCGEHMAagLoaP+zKAPweQ2m7oJApJwGC4upR30u1HsdUMIFKO6wmE4io1vVLKq/7wI0VZngXv21uLX50ZQufo4ncWg6EwRpxzKc1GleZlo6WmMGl9pudHXOgec7OMVwUtNUVo65tOWJn2w+2DMJsMuPu6yoQ8XqYrzcvGj/7kZlTYLbjvuyfmy0spufqnPBh3+TKyvxQAKuwW5JqN6BnjLtN01DPuRmW+Bbbs1Lz+Kc3LRmletm4GIF2e9sLtC+omMJXySt9nsh2NZmdv4X7ytBLrT4ITQogCAN8EcBKAG8ALy32AEOJ1AMaklCeFEIeWeb8PAvggAJSXl6O1tTXGI6We2+3G5KQXc0FgyuiG0xNEa2srega9MAqgtbUVI5f9kBJ4/A+tyDbGVlpwciiSYX3p9ElMdid3XHo8rjNKZBuAv/6fZ/HxHa/MHk54wwiFJdxj/WhtHU7ZuRosAfyo14+f/u4PKMuN7/PldruX/Rr7eacfBgHkOy+itfXSGk9K8cjzBTDh9uNnv396yeu60vVTBMMSv2jz4IYSI04992yCT5rZPrEtjH99IYS3f/04Xl+XhdfXZ8EUY4lZrNePrjg6GAAAiPEeTfxMUuMallokXrjQj9Y8TodeK619D57u8aLIhJSeqSI7iBe6htDamvwJ/2t1cjTy+tA73IXW1siOea1dQ0VYSlizgJ8efhn5jq6kP9+vT3hRYzfgpbbjSX+uRNLq9dOKWAPTPABvBdAK4FEAdinliyt8zC0A3iCEeA0ACwC7EOK/pZTvWfhOUspvAPgGALS0tMhDhw7FfvoUa21tRX6BBTnBMDbVl+Cx3i4cOHAQ/3DyGWyptOPQoZ3oz+7FzzrPYtdNe1FiW7HaGQAw8kI/8OJLOLRvj+Z64bpEJ/7jqS6UNO7AtnX5V73thUtTwDPH8aqbtuNACksp6qc8+NH5p+Gw1eBtB+rj+tjW1lYs9TUmpcTfvPA09jUW4A2vvjEBJ6V4VIw48f1zR2Asb8KhXesXfZ/lrt9CT58fg8t/Ah989Q4cai5P8EnpVQcD+NwjZ/Fw+yAu+XLxxbdtR0PZyqV4sV4/uuLRX7yI/JwRvPO1t2qix0yNa7hjpB0neqf5tZMAWvoelFJi7OnH8ead63Do0LaUPe/zc+fxrSMXsXffAZhN2kkGLKb9iU4I0YV3vubgfFWdlq7htV410o7jPZM4cOBgUn9euX1BXHzicXxgXx0OHdqctOdJBi1fPy2I9TvyuwAqERl+9BSAvxNCfGq5D5BS/pWUcr2UshbAOwD84dqgVI9CYQmDQcCWHekH9QRCmHT7URIt5c2J9ol64xiApJTy5miox1Txgf0bkZ+ThS8+8cpe00FHZJ1MqoPp6qJcNFfa8djZ0YQ+7ukBBwamvHgDy3hV0VSWhzyLCW0JGID0cPsgCnKzcJC9J0mRn5OFL719Ox549070T3nw2q8cwXeOXkI4zJUeiXaidwotNYWaCErVUl9qw6DDG9fvVdK+MZcPbl9wfsBVqjRX2hEISXSPab9vuWPYiY3FVk21ei3nQGMJJtw+dIwkt1T6+YuTCIQkDjRyTUy6iXWP6R8A/DOAv0FkkFELgI8k8VyaJSVgEFf6QR0eP2a8ARRHs6PWaJ/EbBwrY5SpvDkamcq7kN2ShQ8drMMfzo+9YmLqkGMOALBOhSzvXdsqcKp/GmOuuYQ95iNnhmA2GfDqrcywqcFgENi5oRAn+9bWu+j2BfH4uRG87vpKzd8N17vXXFeJxz99AHvri/EPvzmHP/rO8xhyeNU+VtqYdPvQMz6bsf2lCiVwuTih/UCCYtcTDQxTvepDTwOQOkacuugvVSjVc4c7k9tneqRrApYsA3Zl6FC4dBbrupinADwL4O0ALgDYLaWMOXcupWyVUr5udUfUlpCMTOVVGvUHpiIvwoptV4YfAcCsL/Y7u15/CAYBZGv0RfR9e2tRYjPj3x+/cNXfX572othqViWgvnNrBaQEnjiXmKxpKCzx2xeHceumUtgtWQl5TIrf7tpCdI66MeMJrPoxHn15BHOBMO7dsXg5MCVWmd2C79y3G//ypuvQ3u/AnV8+jIdOXU7qruFMoVQPZOpEXsWVybwcgJROlIm8qQ5MN5ZYYckyaH4AkmsugIEpL7ZU5ql9lJiV2y3YXJGX9H2mR7rGcdPGYmSbtJfQobWJNRJ6EYAfwDYA1wPYJoTQVjNkioTCkT2mSgA6MBUpZy22Xp0xjbeUN9ds0uweplyzCR851IBjPZM4tmDa2qDDq1pPbFO5DbXFuQlbG/PCpSmMuXycxquyXTWRzNDJ/tVnTX/ZPogNRbnYuaEgQaeilQgh8M4bN+D3n9qPTeV5+OxPz+CjPzyFqVn9LLHXorbeKZhNBly3Pn/ld05jtSW5MAjoovSSYtczPgtbtgnl9tjmcSSK0SCwucKODo1nTJV97XrKmALAwaZStPVNza9UTLQhhxc947PYzzLetBRrKe9npJQHANwLYBKRnlNHEs+lWVJGAlMlY9o3FbmDW2K7usc0vlLeoCbLeBd6900bUGG34N+f6JzPhAw5vKqU8QKRF8J3bq3A8Z5JzHhXn11TPHJmCLlmI27bzDJeNW2vLoDJIFa9z3TUOYdneyZwz451mr3Rk85qiq34yYf24C/u2ownO0bx6i8dxlMdie0FzyQneqdxw/r8jM8KZJuM2FCUy12maaZn3I36UqsqP6ubq+w4N+zUdGWHEjg3V+krMD3QVIpASOJ4z2RSHv9oVyRBsr+RMyTSUaylvB8XQvwEwGkA9wD4DoC7k3cs7VJKeXOVwHQymjG9psfUE0dgGsmYavuFhyXLiI+/qgEn+6bR2jkOKSUGp71YV6he4vzObRUIhiWePr+2FQKBUBi/f3kYdzSXa/4GQbrLMRuxtcq+6gFIj5wegpTAPduZ+VaL0SDwkUP1+NXH9qHEZsYHvteGv/zFi3An6e55uvL6Q3h5cCbj+0sV9aW2+Z5ESg/dY+6Ul/EqtlTaMeMNYGgmcXMqEq1j2ImC3CxU2F+5rk/LWmoLkZNlxOGu5JTzHu4aR1leNprK1fnaoeSKtZQ3B8AXAWyWUt4mpfz76ECkjBMKI5oxvaaUN5oxta6yx1SLE3mv9baWaqwvzMG/P34BU7N+eAMhVdfbbF9fgLK87DWX8x7tmoDDE+A0Xo3YVVOEMwMO+IPhuD/24fZB3FBdgDqVXuzQFc1Vdvzq47fgwwfr8ZO2Adz9H4dxYYpTVWN1esCBYFjiRgamACIDkC5NzCKUAZOfPf4gTvSubQic1rl9QQzPzKV8Iq+iWRmApOE+03PDLmypsOuu+ifbZMSe+mI8k4Q+03BY4tnuCexrLNHd54ViE2sp7xeklM9LKTP+lneklPfKVN6+KQ/MRgPyoplSJeMWT4+pNxDSRabObDLg07c34eVBJx481gtAnYm8CoNB4NVby/FM5zjmAqt/wfvrM0PIz8liWYhGtNQWwhcM4+zQTFwfd2HEhXPDTrxpx7oknYzilW0y4i/v3oyffmgPAOBfX5jDv/y+A74gA9SVtPVOQQhg54bMHnykqC+1whcMZ8TU5+8+24u3ff04BtP433opOsiqvtSqyvNvrsiDENBsn2koLHFBZxN5FzrQWIK+SQ/6JhM7sOzskBPTngAO8PVa2tLmGFgNC4UjpbxKya7DE0CxzTx/50YJWOPpMdVDKa/inu1VqCu14mutPQDUDUyByHRebyC06glwc4EQHjs7gru3VXC1iEa01EReiF+7nmglD7cPwmgQeN31lck4Fq3B7toi/P5TB3BgvQlff+Yi3vifz2o6U6EFJ/qmsak8D/m5nBIOXJncmgkDkNp6pyAlktajpwVqTeRVWLNN2Fhs1ezPod7JWcwFwrqayLvQwU1lAJDw6bxKefAtDRx8lK74SjxOYSlhMIj5kl3gShkvEOmvsmQZ4IlzKm9Olj6WJ5uMBnzm9iYEo+VUavaYAsDNdcWwW0x47OzqBqw8fX4Ms/4Qp/FqSJndguqinLgGIIXDEr86PYiDTaXz/d6kLbZsE+7flo3v3NeCCbcfb/zqUTzQ2p0RpZnxCoUlTvVNoyXD18QsdGVlTHoHplJKnB5wAEj/wNRoENhQnKvaGbZU2jW7y1TJ5Oo1Y1pbnIvqopyEl/Me7ZrAlko7SvP4ez5dMTCNU1hGekxNRsP83lFlVYwi12yKa/iR1x/UTcYUAF57XSU2V+Qh12xEocp387OMBty+pRxPdowiEIq/J/GRM0MosWXj5rriJJyOVmt3TRHa+qZjnpj4/KUpDM/M4R6W8WreqzaX4/HPHMDtW8rx+Ucv4O1fP57wci+96xh2wu0LYjf7S+cVWs0otprTPjDtm/Rg2hOA2WjAcxcnNT01di16xt3YUJSr6sTp5io7+qc8cM2tfbJ/onUMO2EyCDTqdMCPEAIHGktxvGdyVfMiFuPxB9HWN4UDXBOT1hiYxikUljBG+62Vct6FGVMAyDUb4Ylj+JFHJ8OPFAaDwFfeuQNffNt2TTSfv3prBWa8AbxwKb5hEa65AP5wfgyvu74SRoP6/w66YldtISbcPvRHh4ut5Jftg7Blm3DHFq770YMiqxkPvHsnvvT2G3Bh1IW7/+MI/uf5/rR9ER6vtujgG07kvVpkMm9638RoH4hUirylZT0GHV4MTKVnn2nP2KxqZbwKZQCSsi9USzqGXagvtel6VdTBplLM+kNxt+Us5flLUwiEJPYxME1rDEzjpJTyAoA1Opm35JrSQavZFOceU30MP1qoqTwPd22rUPsYACI//CxZBjx2Nr7pvE+cG4UvGMbrb2BPota01ERekJ+IoZx3LhDC714axl3bKnT3fZTJhBC4d8d6PPbpA9ixoQB//fBLeP+DJzDm1O76hlQ50TeNqnyL6j38WlNfZk37jGl7vwNWsxHv3VMDADh+cULlEyVeMBTGpYlZ1JepM/hIoewH1WKfacewU7f9pYo99cUwGUTCynmPdE4g22RgJUmaY2Aap3BYwhDNElqjg45KrsmY5piNcfWYenU0/EiLcsxGHGwqxeNnRxGOo1/t12eGsK4gh1MvNaixzAa7xYSTfStnwZ/qGIPLF8S9LOPVpaqCHPzg/Tfh717fjGM9k7jzy4fxu5eG1T6WaqSUaOudwu6NfPF1rfpSGyZn/Zie9at9lKRp73fg+vUF2FSehxJbdlr2mV6e9sIfCqueMS3Ly0aR1ay5wNTh8WN4Zk63/aWKPEsWdtYUJmwA0tHucdy4sQgWHVUYUvwYmMYpJCWMSmCqlPJe02NqzY49MPUHwwiGJQPTNbpzawVGnHM4c9kR0/tPz/pxpGsCr7uhUhPlyHQ1g0FgZ01hTAOQHm4fRLmdfcJ6ZjAI3H/LRvz2k/tRXZSLj/7wFD7943bMeLTX+5Vsl6e9GHX6WMa7iHQfgOT1h9Ax7MSODQUQQuDmuiIc60m/PlO1J/IqhBBo1uAApHM6H3y00MGmUpwbdmLc5VvT44zMzKFz1I39LONNewxM4xSWWFDKu1SPqQmzvthKeZV9pzlmfUzl1arbNpfDZBAxT+f9/csjCIYl3sBpvJrVUlOIrjE3HJ6lsyNTs360XhjDG7evY59wGmgos+EXH9mLT9/eiF+/OIw7v3wYR7vSr5RxOSei/aW7OZH3FRrK0jswfXloBsGwxI5oFc+e+mKMuXy4OJFefbVXAlN1S3mBSDnvhVEXgqsYnpgsHcORntd0CUwB4EjX2rKmysdz33z6Y2Aap0gpb+S/lZUx1/aY5pqN8AZiy5h6AsH5j6HVy8/Nwp76Yjx2diSmu8u/PjOEulLr/PAD0p5d0T7TU/1LZ01/+9IwgmGJe7azjDddZBkN+PTtTXjoI3uRm23Ee779PD73yNmMWStzoncaeRYTmsr03V+WDFUFOcg2GdAznl6BmuJ0vwMAsL26AACwJ1oFkm7lvD1jsyixmVGQa175nZOsudIOfzCsqeC/Y9iJElt2WqxEaa60o9hqXnOf6dHuCZTYsrG5gj8X0x0D0ziFpJzPzCyfMY0tMJ3PmLJmfs1evbUClyZm0bXCAvZR5xyeuzSJN9xQxTJeDdteXQCTQSxbzvvL9kFsrsibH2JB6eOG6gL87pP78d49NXjwWC9+8+KQ2kdKiRO9U2ipKZyvzKErjAaBjSVW9KzwM16v2gemUV2UMx+QbCyxotyejeMX0ywwHXerXsarULKSWuozTYfBRwqDQWB/YwmOdE3ENQNkoXBY4mjXBPY3lvA1WwZgYBqnq4cfRYLJIuvVganVbIx5j6lnvpSXgelavbo5sirksZeXn8772xeHISXwepbxalqO2Yit6/LRtsSo+b7JWZzsm+bu0jRmyTLic6/fio0lVjx4rFft4yTd1Kwf3WNu9pcuo77MlralvO39DuyovlLCLYTAnrpiPJ9m+0x7xt2oL9NGYFpXaoXZZNBMn2kgFEbXqDutqrkObirF1KwfZ1cZ/HeMODE568e+BvaXZgIGpnEKS8wHpi21RXjV5rJX7JnKzTbBGwjFdHdIKfllKe/aldst2LGhAI+dWz4wfeTMELZW2TVzx5aW1lJTiDMDjkUXdP+yfQhCgH3Cac5gEHjvnhq09ztwZsCh9nGSStn3x3UIS6svtaF/yoO5GNtl9GJ4xovhmTns2FBw1d/vqS/GhNu/YiWQXky6fZj2BDTz+zfLaMCm8jx0aCQwvTg+C38onBb9pQqlL/SZzrFVffyR6JwBDj7KDAxM4xQKSxijn7XX31CF79y3+xXvk2s2QkpgLrjyL04lY8rANDHu2lqBlweduDztWfTt454wTg84mC3ViZaaQviCYZwdmrnq76WU+OXpQdy8sRhV3PWY9t6yaz2sZiO+l+ZZ07beKZiNBly/Pl/to2hWQ5kNYQn0TS7+M16vru0vVeypi7wYT5c+U6U/WAuDjxTNlXacG3JqIivdkUYTeRUltmxsrbLjcOfqBtkd6RrH5oo8lNktCT4ZaRED0ziFpVyx90cp8Y2lz9QbLfnNyeJU3kS4c2sFACw5nff54cjn+3XXV6bsTLR6u6KTSa/tMz1zeQaXJma5uzRD5Fmy8OZd6/GbF4fXvHZAy45fnMT16/O5p28ZSkCTbuW87QMOmI2GV/TLVxflYF1BThoFptpYFbPQlso8TM76MaaBny0dw06YjQbUaShwT4SDTaU41T8N51x8K8C8/hBO9E6zjDeDMDCNU1he6TFdSm509Ys3hl2m7DFNrNoSKzaV5+Gxs4uX8z4/EsKumkKsL8xN8cloNcryLNhQlIu2vqmr/v6X7YPINhlw13UVKp2MUu29e2rhD4Xxoxf61T5KUpzsm8aLl2dw1zZ+TS+nriS6MiZNSlsV7f3T2LrO/orWICEE9tQX47lLk6seHqMlPWNuZJsMWKehSpfmqkiFghYGIJ0bdqKx3IYsY3q9PD/QVIpgWOJYd3w3WF7onYI/GMb+Jq6JyRTp9ZWfAqGwhHHFwDSaMY1hABJ7TBPvzm0VaOudwoT76rufXaMuDLjC7EnUmZaaQpzsm54vswqEwvj1mSHc3lwOuyVL5dNRqjSU2bC/sQQ/fL4PAQ3tHEyUr7V2ozA3C++8cYPaR9G0HLMR6wpy0ipjGgiF8dLgzFWDjxbaU1cMhyeA8yOuFJ8s8XrG3agrtWlq6vTm6ARcLQxA6hh2pVUZr2LnhkJYzUYcjnOf6dGucZiNBtzIvvuMwcA0TmGJFX+g5kbXyMQymdfLjGnC3bm1HGEJPHnu6nLeX58ZggBwN7NsurKrthATbv98T9mRrnFMzvpxL3eXZpz7b6nFqNOHR1eYvK03HcNOPNkxhvtv2Ti/hoyWVl9mQ3caBaYXRlyYC4RfMfhIsac+us80DdbG9IzPokEjE3kVdksWNhTlqh6Yjrt8mHD70jIwNZsM2NtQgsOd43H18h7pmsDujYV8jZxBGJjGIRz9ZlrpRl88Pabzw4/YU5QwzZV2rC/MuaqcV0qJR84MYUuxAWV5bKDXk5aayJ1SZW3Mw+1DKMzNwgGW9mScQ01lqCnOTbvVMV9r7YHVbMT79tSqfRRdaCi1oWdsNi1KW4FIGS+AJQPTqoIc1BTn6r7PdC4QwsC0R1ODjxRbKvPQoXIp75XBR+mxw/RaB5pKcXnai4sTszG9/5hzDudHXNjXwN/1mYSBaRyU34ErlfIqd3Y8MfaYmo0GmNKsn0BNQgjcubUCz3ZPwhVttH950IneSQ9uqmQ2Qm8ay2ywW0w42TcFb1Di8bMjeN31VTCb+D2TaQwGgT+6uQYn+6bx0uWZlT9AB3onZvGbF4fwnj01yM9laXos6sus8AZCGHHOqX2UhGjvd6A0L3vZvss9dcV4/tIkQjoOxi9NzEJKbQ0+UjRX5uPS5GzMO+iTQQlM02mH6UIHo2tjDnfGVs57tJtrYjIRX9nFQfl1sPJU3nhKeYMsUUiCu7ZVwB8Ko/VC5Afgr18cQpZRoKWcganeGAwCu2oK0dY7jZOjQfiCYdy7k2W8meqtLdXINRvTJmv69cM9MBkN+MC+jWofRTeUwCZd+kzbBxzYXl0AscxN7z31xXDNBTUxoGe1tDiRV9FcZYeUULWPt2PYicp8CwpyzaqdIZk2FOdiY4k15sD0SNcEiq3mtA3UaXEMTOOg3KhccSpvtjL8KIZ1MYEQcljGm3A7NxSixGbGo2dHEA5L/PrMEA42lcKapZ2BCxS7ltoidI258VRfEDXFudhxza4/yhz5OVl40851+PWLQ5h0q7/eYS1GZubw85OX8faWarYYxGE+ME2DybzTs35cmphdsoxXsadO6TNd3S5ILegZm4UQwMYS7ZXyKmt61Az803Xw0UIHGktw/OIk5gLLvz6WUuJI1wRuaSjR1KAsSj4GpnGYL+Vd4bN2ZV3MyhlTjz/EibxJYDQI3NFcjtbzYzjWM4nhmTm8ntN4dWtXTWRa5SVnGPdsX7dsZoHS3/v21MIfDOPHJwbUPsqafPPIRYQl8MEDdWofRVdKbGbYLaa0GIB0esABAEtO5FWU2S2oK7Xqus+0Z9yNdQU5mqwSq8q3wG4xqTYAaS4QQs+4O237SxUHmkoxFwi/Yjf5tc6PuDDh9rGMNwMxMI2DjDFjqmRAYxl+5PWHNPlDOh3cubUCs/4Q/vaRl2HJMuD2LeVqH4lW6Yb1BTBF75res4NlvJmusTwP+xpK8IPj+l0dMzXrx/883483bq9CdRH3KsdDCIGGssgAJL1rH3DAIIDr1+ev+L576opxoncaQZ1+zfeMuzU3kVchhEBzlV21jGn3mBvBsEz7jOnNdcUwGw0rro052qX0l3LwUaZhYBqHWEt5jQaBnCxjTD2mzJgmz976EuRlm3BxfBa3bSnnGgYdyzEbsbOmEE2FBk2WgVHqvW9vLUacc3j87OjK76xBDz57CXPBED56qF7to+hSfaktLXpM2/unsanCHtPvpz31xXD7gnhpUH+Dv8JhiYvjs5rsL1U0V+bjwohLlQFT5+Yn8qZ3YGrNNqGltnDFPtPDXeNoLLOhIp8tDpmGgWkclHuUxhjq3a3Zxtim8gZCyDEzYEoGs8mAWzeXAQDewDJe3fv6e3bhkzv4S4oiXrW5DNVFOfieDocgueYCePBYL+5srkBDWXqX7iVLfZkNYy4fnNHJ63oUDkucHnCs2F+quLlOv/tMh2a88AZC2g5Mq+zwBkLonUx9Jr5j2AlLlgG1xel/4/VAUynOj7gwMrP4VO25QAgvXJpitjRDMTCNw5VS3pXfN8ccW2Dq9QeRk8XLkCz33VKLO5rLcZA7L3Wv0GqGzczeUoowGgTee3MtXuidwtkhfWWQfvh8P5xzQXz0VmZLV0sJcC6O67ec9+KEG665YMzD3Eps2Wgqt+myz7Qnep20uMNUofR3qlHO2zHsxKYKe0yJD71TXo8tVc7b1jsNXzDM/tIMxYgoDuFoZBrLhDCr2YRZXwzrYgKh+WFJlHg7NxTim+9tgYWTj4nSzttaqpGTZdRV1nQuEMK3jlzC/sYSXL++QO3j6JYS4HTreDLvqX4HAMScMQUifaZtvdPwB/XVZ6pMUK7XaI8pADSW5SHLKFI+AElKiY5hF5rTfPCRYnNFHsryspcs5z3SNY4so8BNdUUpPhlpAQPTOMxP5Y1hImiu2QjvCuOwAQ4/IiJarfzcLNy7cx1+eXoIU7N+tY8Tk5+1DWDC7cPHbm1Q+yi6tqEoF1lGoes+0/Z+B/IsJtSVxB6s7akvhjcQwouXHck7WBL0jLuRn5OFYqt2d3SaTQY0lOWhI8WB6fDMHGa8gbTvL1UIIbC/sRRHuiYW7ec90jWBXTWFTNpkKAamcVC+fVYafgREVsbEkjH1+EPIZTaPiGhVrqyO6Vf7KCsKhML4r2cuYldNIW7ayGzAWpiMkX48Pe8ybe+fxvbqgrj2NN60sRhCQHflvD3jbtSXWjW/6qu5MvWTeTsyZPDRQgeaSjDjDbziBsu4y4dzw072l2YwBqZxmJ/KG8MvkdwYekyllNFSXgamRESrsakiD3vri/Hfx/s0v0bjkdNDGHR48bFb6zX/Al0P9DyZd9YXROeoCzs2LL+/9FqFVjM2V9h1NwCpZ3xWs6tiFtpSmYcxlw/jLl/KnlMJTDdXZEYpLxBZAyMEcLhz4qq/P9ajrIlhf2mmYmAah/lS3hg+a9Zs04qB6VwgDCnBqbxERGvwvr21GJqZwxPntLs6JhyWeKC1G5sr8nDrpjK1j5MW6sus6Jv06HKX7YuXZxCW8fWXKvbUFeNk3zR8wZXbhbRgxhvAuMun6Ym8iuaqSNYyleW8HcMubCjKRZ4lK2XPqbYiqxnXr8vHM51jV/394c4JFOZmYWvVynt9KT0xMI2DjHGPKaBM5V2+lFfpQeVUXiKi1bt9SznWFeTgQQ0PQXr83Ah6xmfxsVsbmC1NkPpSG4Jhib5Jj9pHiVv7wDQAYPsqBmDtqS+GLxhGe3R4ktYpWW1dBKaVagSmzvmJwJnkQFMpTg84MOOJrHySUuJo9zhuaSjJiOnEtDhGRHFQ7snGEphazUbM+pa/m6kErmzwJiJaPaNB4L17avD8pamUDy6JhZQSX326B7XFuXjNdZVqHydtKIGOHst52/sdqCuxonAVw4Bu3FgEg476TPUwkVdRkGvGuoKclE3m9fiDuDQ5m1H9pYqDTaUIS+DZaPlu15gbo04fy3gzHAPTOFwp5Y1t+JE3EEJ4kYljCm+01JdTeYmI1ubtu6thyTJocnXMka4JvDQ4g48cqmcmIIGUQEdvgamUEu39DmyPcX/ptfJzIqWOeukz7RmfRZZRoLowR+2jxGRLZV7KBiBdGHFByswafKTYXl2APIsJz1yIrI1R1sfs4+CjjMbANA5S2WMaw+sKZaDRcitjlB5UDj8iIlqbglwz7t2xDr88PYhpja2O+erT3ajMt+DeHevVPkpasWWbUGG3oGdsVu2jxOXytBcTbt+q+ksVe+qLcbrfgbkY1tKprWfcjdpiK0yxDOjQgOZKO3rG3Sn53HYMu+afM9OYjAbcUl+Cw13j0TLeCdSVWrGuQB83MCg59PFTQiPCcfSY5mZHynNnl+kz9TBjSkSUMO/bW4u5QBg/aRtQ+yjz2nqn8PylKfzJ/jqYTfyVm2j1ZVbdZUzbBxwAEPdE3oX21BXDHwrjZN90gk6VPD3jbl1M5FU0V9kRlkDnqCvpz9Ux7ERetgnrdZJNTrSDm0oxPDOHs0NOPHdxEgeYLc14/C0ZB6UoN5ZSLGs02PQs02fqDbDHlIgoUTZX2HHTxiL84Hjfoovb1fBAaw+KrGa848ZqtY+SlupLbegZc89XNOlBe/80LFkGbFrDepDdG4tgNAjN95kGQmH0T3p0MfhI0VwZmQibinLejmEnNlfmZexAtANNkUD0S090Yi4QZn8pMTCNR1wZUyUwXWZljNcfGaeUk8WMKRFRItx/Sy0GHV482aH+6pizQzP4w/kx3L+3ljcgk6S+1AaXL5jSvZNrdXrAgevXFSBrDaWttmwTrlun/T7TvslZBMMS9WVWtY8Ss/WFObBlm5I+ACkcljg/4srI/lLFuoIc1Jda8dT5MZgMAjfVFat9JFIZA9M4zAemMQ4/ArDsypgrU3kZmBIRJcLtW8pRlW/Bg8/2qn0UfK21B7ZsE967p1bto6QtpUS0WyflvL5gCGcHnWvqL1XsqS/GmQEHZn3Lr6ZTU3e0/1dPGVODQaRkANLlaS/cvmBGB6YAcLApstd5Z00hbNm8gZfpGJjGYb6UN5Z1MdmRYHN2uYxpgD2mRESJZDIa8J49NTh+cRIXRpLfI7aUi+Nu/PalYbzn5hrk52apdo50d2VljD4GIJ0bcsIfCicmMK0rRjAscaJ3au0HSxKl/7dOR4EpEBlGdH7EtexmhbVSMrKZHpgeaIqU7+5vYBkvMTCNy5VS3pXfV8mYemMYfsSMKRFR4rxj9wZkmwz43vFe1c7w9Wcuwmw04AP7Nqp2hkxQbs+G1Wyc35Wpde39DgDA9urVDz5StNQWIssoNF3O2zPuRoXdortMWHOVHW5fEAPTnqQ9R8ewEwYBbCpffa9xOriloQSfvK0R77hxg9pHIQ1gYBqH+Ep5oxnTZYYfKYGpxcTAlIgoUYqsZrxxexUePjWIGU8g5c8/5PDiofbLePvuapTmZaf8+TOJEAL1ZTbdTOZtH3CgMt+CinzLmh8r12zCDesL8JyGByD1jM/qqr9UoWQxk1nO2zHsRG2JNeOr5rKMBnz2jib+rCQASQxMhRAWIcQLQogzQoizQoi/T9ZzpYoy9C+Wqbyx9JjOBUKwZBliCnSJiCh279tbC28ghJ+qsDrmm0cuQkrggwfqUv7cmUiZzKsH7f3TCSnjVeytL8ZLgzNwzqX+BsxKpJS4OOZGg87KeAGgqTwPRoNI6gCkjhFnxpfxEl0rmRlTH4BXSSlvALAdwF1CiJuT+HxJF45GprHEkUqP6XJTeT3+ICc1EhElwdaqfNxYW4TvHe9N6eqYSbcPP3qhH2/cvg7rC3NT9ryZrKHMhqGZOU0PAQKAcZcPl6e92JGAMl7FzfXFCEvgxCXt9ZmOu3xw+YKo19EOU4Uly4j6Uis6khSYuuYCGJjyopmBKdFVkhaYygjlFmZW9H/6WTS2iHD0/2NZF6OU5y43/MjjD3FVDBFRkrxvby0uT3vxh/NjKXvO7z7bC18wjI8cYrY0VepLI6Wilya0PQDp9IADABKaMd25oRBmk0GT+0y7o1lsPU3kXai50p60Ut7z0cFsWyozu7+U6FpJTdcJIYwATgJoAPBVKeXzi7zPBwF8EADKy8vR2tqazCOticc7B0DgdPspzFxcOaDMNgIXei6hNWto0bf3D85BBsOa/jenG7fbzc+3jvH66Vuqr192WKLIIvDl355C1lhO0p/PE5D49hEPdpUZcfncSVw+l/SnTDktfg9OuiO3jX/9zAlMVGm3CulXnX4YBTDVcwatvYlr4amzA0+82Id9tpVvwKTy+j3VHykvHut+Ea2X9TfSxOz1Y2gmgN88/jRs5sS2XD3ZF/ncOHrPonWkI66P1eL3IMWO1295Sf0JLqUMAdguhCgA8LAQYpuU8uVr3ucbAL4BAC0tLfLQoUPJPNKanPrJkwB82N3Sgm3r8ld8/7yjT6K4vByHDl236Nu/e/EFlGb5cejQvgSflJbS2toKLX+N0fJ4/fRNjev3AdGNLzx2Aeu27EJjkqdfPtDaDW/wAv7urXtw3fqVf0fokRa/B33BEP722GMwl1Tj0KFNah9nSV/vfA5b1wXx6tsS+zv/TLALX36qE9tv3IuCXPOy75vK69f6yFlYzQO4585bIWKoNNMa47px/PTCCyiquw57E7zK5LGHXkRB7gjuXcXnRovfgxQ7Xr/lpeQWlpTSAaAVwF2peL5kubIuJrYfIrlmIzzL9Lx4/aGMn8ZGRJRM79hdDXMKVsd4/SF8+8glHGgqTdugVKuyTUZsKMrV9GTeUFjizGUHdlQXJPyx99QXQ0rgeY31mfaMu1FfZtNlUAosmMybhD7Tc8MubKmw6/ZzQ5QsyZzKWxrNlEIIkQPgdgDnk/V8qaA0yMYylReIBKbL9Zh6A+wxJSJKpmJbNt5wQxUeOjWIGW/yJpf+tG0Ak7N+fOxQfdKeg5YWmcyr3R7TzlEXPP4Qtiewv1RxQ3U+LFna6zPtGXPrtr8UAEps2Si3Zyc8MA2FJS5wIi/RopKZMa0E8LQQ4kUAJwA8IaX8TRKfL+muZExje39rtgleTuUlIlLVfXtr4fGH8LMkrY4JhML4xuGLaKkpxI0bi5LyHLS8+jIrLk3MpnQCczza+x0AkNCJvIpskxEtNUV47qJ2AtNZXxBDM3No0OFE3oW2JGEAUu/kLOYCYQ4+IlpEMqfyviil3CGlvF5KuU1K+Q/Jeq5UmQ9M48qYspSXiEhN29blo6WmEN8/3peUwOVXp4cw6PDiY7c2sDRPJfWlNvhDYVye9qh9lEW190+jMDcLNcXJWSG0p74Y50dcmHT7kvL48bo4HsleKxOT9aq50o7uMTd8waWTDPFSVtAwY0r0Svobk6ai+VLeuHpMl8mYBkLIZWBKRJR079tbi/4pD1ovJHZ1TCgs8UBrN7ZU2nFoU2lCH5tip5SMKitKtOb0gAM7NhQm7cbFzXXFALTTZ6r0++q5lBcAmqvsCIZlQr+uOoadMBkEGsv1/bkhSgYGpnEIy0hoGuvwI6vZBE9g6YyphxlTIqKUuGtbBcrt2XjwWG9CH/fxsyO4OD6Lj91az2ypihqiAZAWByDNeAPoGnMnZfCR4vr1+cg1GzXTZ9oz7obRILAhSRniVGlWBiAlsJy3Y9iF+lIbsk18/Ud0LQamcbhSyhvb++cskzENhSX8wTCHHxERpUCW0YD33FSDI10TCct+SCnx1dZubCyx4u5tlQl5TFqd/NwslNiyNTkA6cXLDgDAjg2J7y9VZBkN2F1bhOMa6TPtGXdjQ1Gu7oOvmmIrcrKMCR2A1DHsZH8p0RIYmMYhmjCNeSqvNdu0ZI+pNxAJWFnKS0SUGu+8aQPMRgO+n6DVMYe7JvDyoBMfOVgf8+8FSp76UqsmM6bt/Q4IAVxfndw1Qnvqi9E95saYay6pzxOLnrFZ3feXApHXe5sr8xKWMXV4/BiemWN/KdESGJjGYTV7TOcC4UWHbXiiAWsOp/ISEaVEiS0br7uhEr84eRmuubWvjvnq092ozLfgnh3rEnA6Wqv6MptGA9NpNJTaYLdkJfV59kT7TJ+7qG6faSgscWliFvU6n8iraK60o2PYCSnXPjjtHAcfES2LgWkcwtH/j6fHFLiSHV1IWSOTy1JeIqKUuW9vLWb9Ifz85OU1Pc6J3im8cGkKHzxQB7OJv0q1oL7UhmlPAFOzfrWPMk9KifYBB3YkYX/ptbZW2ZGXbVK9z/TytAf+UFj3g48UzVV2OOeCGHR41/xYHcMuAAxMiZbC36ZxiLeUVxls5PG9spzX42cpLxFRql2/vgA7NhTge8d6EV7D6pgHnu5GkdWMd+zekMDT0VoopaNamszbO+mBwxNIan+pwmQ04MaN6u8zVT7/6RKYbkngAKRzQ06U2LJRmpe95sciSkcMTONwpZQ3tve3ZkeCzln/KzOmSmDKqbxERKl1395a9E568EzX+Ko+/uzQDJ6+MI4P7NvIn+Ea0lCmvcm8pwemASAlGVMg0md6aWIWIzPq9ZleWRWj/x5TANhckQchkJABSBx8RLQ8BqZxuDKVN9Ye00gpr2eRAUhz0fJeTuUlIkqtu7dVojQvGw8+27uqj3+gtQd52Sa85+aaxB6M1qQqPweWLAN6NJQxbe93wGo2orEsNcGIss/0+MWJlDzfYnrGZlFiM6Mg16zaGRIp12zCxhIrOtYYmAZCYXSPuedX0BDRKzEwjYNS9BXP8CPgSnZ0oSulvBx+RESUSmZTZHXMM53juBhndu3iuBu/e2kYf7SnBvk5yR1mQ/ExGATqSrQ1AKm934EbqgtSNrW5udKO/JwsVftMe8bdqEuTMl5Fc6V9zRnTnnE3/KEw+0uJlsHANA7haJOpMebANBJ0zi7SY+r0RiZCsgyMiCj13nlTNbKMAt8/3hfXx/3XMz0wGw14/76NSToZrUVkMq82dpl6/SF0DDtTVsYLRILzmzaqu8+0Z9ydNv2lii2VdgxMeTHjXf007w5O5CVaEQPTOFwp5Y3t/ZUeU+8iGdPfvjSMEls2NhTlJup4REQUo7I8C157XSV+fvIy3IvcPFzMoMOLh04N4p03bkCJjcNLtKi+1IqBac98u4yaXh6aQTAssb06+YOPFtpTX4yBKS8uT3tS+rwAMDXrx7QnMN/vmy6aqyLB5Pk1ZE07hl0wGw2oS5PeW6JkYGAah7hLebOiGdNrAtO+yVk8fWEM77ppA9cMEBGp5L5bNsLtC+IXMa6O+ebhiwCAPzlQl8xj0Ro0lNkgJXBpQv2saXt/ZPDR9uqClD7vnvpon6kK5bzpNvhIsTWa5VxLn2nHsBON5TZkGfm6j2gp/O6Ig5IxjbmUN1vpMb36bvz3j/fBKATefRPXDBARqWV7dQFuqI5tdcyE24cfn+jHvTvWYV1BTopOSPFSSki10Gfa3u9AdVFOyleDNJXlochqVqWcN91WxShK87JRYjOvqc80MpGXZbxEy2FgGod4p/Ja56fyXsmYzvqC+GnbAO6+rhLldkvCz0hERLG7f28tLk7M4kj38lNMv/vsJfiCYXz4UH2KTkarsbHECiEik2HVdnrAgR0pLuMFIq9Rbq4rwnM9k5By9bt6V6NnzI1skyHtbt4IIbBlDQOQxlxzmHD7OZGXaAUMTOMgZew7TAHAkmWAEIBnQf/Sw+2DcM0Fcd9erhkgIlLba66rRIktG9871rvk+zjnAvj+sT68Zltl2mWC0o0ly4j1hTmqZ0yHZ7wYnplL6eCjhfbUFWNoZg79U6ntM1Um8sZ6A19Pmivt6BxxIxAKx/2xHcMuABx8RLQSBqZxCEvENfJdCIHcLON8j6mUEt8/3ott6+zYuSH1d1GJiOhqZpMB77ppA56+MIbeJfoSf3C8Dy5fEB9htlQX6ktt8yWlajnd7wAA7FDpd71afaY947Np11+qaK6ywx8K4+Iqpj4rvanMmBItj4FpHCRiH3ykyM02zZfyHu+ZROeoG+/bUwsR5+MQEVFyvOemDTCKxVfHeP0hfOfoJRzaVIpt6/JVOB3Fq77UhosT7hX7hpOpfcABs8mgWiBSX2pDaV52SvtM5wIhDEx70m4ir0K5lueGZ+L+2I5hJ6ryLcjP5e5jouUwMI1DWMq4A1Or2Tg//OjBY70ozM3C62+oSsbxiIhoFcrsFrzmukr8rG3gFXunf3KiH5Ozfnzs1gaVTkfxaiizYS4QxtCMV7UztPdPY2uVXbXJ+0II3FxXjOMp7DPtnZyFlOk3+EixscQKs8mAc0Px95ly8BFRbBiYxiHeUl4AyDGbMOsL4fK0B092jOIdN26AJcuYpBMSEdFq3HdLLVy+IB46dWV1jD8YxjcOX8SNtUXYXVuk4ukoHlcm86ozACkQCuPFyzOqDD5aaE9dMcZcPlxM0eqcdJ3IqzAZDdhckRf3AKS5QAg947MMTIliwMA0Rl98ohOP9wXjGn4ERDKm3kAQP3guUiL2nps59IiISGt2VBfg+vX5ePBY73yG6ZenBzE0M4eP3MreUj1Rehx7VOozPT/sgi8YVm3wkULpMz2Woj7TnrFZCBHJLKar5ko7OoZdcWWhu8fcCIUlA1OiGDAwjdGk2wcg9lUxitxsEybdfvzkxABe3VyRdiPUiYjSgRAC79tTi57xWRztnkAoLPFfrT1orrTjUFOp2sejOBRZzSjIzUK3SpN5Tw9MA4DqgWltcS4q8y14LlWB6bgb6wpykGNO36qw5io7pmb9GHX6Yv4YJcO6pTIvWcciShsMTGNkigakxniHH2UZcX7EBYcngPftrU3CyYiIKBFed0MlSmxmfO9YLx59eQQXJ2bxsVsbOKxOZ4QQqC+1qZYxbe93oDQvW/Ub0UII7KkrxnMXU9Nn2jPuTtsyXsWWVQxA6hh2IifLiJri9M0kEyUKA9MYKZnS+DOmkTuHm8rzcHMde5SIiLQq22TEO2/cgKfOj+Hzj51HXYkVd22rUPtYtAoNpTbVekzbBxzYUV2giRsaN9cXY3LWj87R5Abp4bDExfHZtA9MN1dEsp7xDEDqGHZiU0Ve3DNKiDIRA9MYKZnS+HtMTQCA9+3lihgiIq179001MAqBvkkPPnyoni8mdaq+zIoJtw8znkBKn3d61o9LE7Oq7S+91p46ZZ/pRFKfZ9g5B28glLarYhR5lizUFOeiY9gV0/tLKdEx7GJ/KVGMGJjGyLjKUt7qohyU27Nxzw6uiCEi0rqKfAvecEMVaopzcc/2dWofh1ZpfjLvRGrLeU8POAAA26sLUvq8S6kuysX6wpyk7zPtmZ/Im/7lqs2V9pgn8w7PzGHGG0Az+0uJYmJS+wB6YVxlKe+f7K/De/fUckUMEZFO/Oubr0cgFFZtByWtnRKYdo+5sTOF2cv2/mkYBHD9+vyUPedK9tQV44mOUYTDyesznV8Vk+YZUyDSZ/ro2RG4fUHYspd/Gd0xP/iIGVOiWPC3bozmA9M4M6ZCCAalREQ6YjYZYF3hBSdp2/rCHJiNBvSkeDJv+4ADmyrsmvr62VNfDIcngI6R+PZvxqNn3I38nCwUW81Jew6taK60Q0rgQgyfTyUw3czAlCgmDExjZFhljykRERGllslowMYSK3rGUjcAKRyWON3vUH1NzLWUfabHk7g2JjKR15oRszSaq5TJvCv3mXYMu7ChKHfFzCoRRTAwjREHYBAREelHfZkVF1OYMb044YbLF8QOjfSXKirzc1BbnIvnkthn2pMBE3kVlfkWFORmxTSZt2PYyf2lRHFgYBojBqZERET6UV9qQ9+UB/5gOCXPd6rfAQCamci70J76Yjx/aQrhJOwznfEGMO7ypf1EXoUQAlsqVh6A5PEHcWlylv2lRHFgYBojJTBN/opqIiIiWqv6UhtCYYm+ydSU87b3O2C3mFBXor3JtDfXFcM1F0SfM/FBupKVzpSMKRAp570w4kRomYFSF0ZckJKDj4jiwcA0RsqamCTcbCQiIqIEm18Zk6Jy3vb+adxQXRD39P5UUPaZdkyFEv7YmTSRV9FcacdcIIxLE0vf9FB2nTYzMCWKGQPTGGnxFw0REREtri66U7NnPPkZU7cviM5RlybLeAGgzG5BfakV5ycTnzHtGZ9FllGgujAn4Y+tVVcGIC1dztsx7ERetgnrM+jzQrRWDExjZGRcSkREpBvWbBOq8i3oGUt+xvTFyw6EJTQ3kXehPfXF6JwOIRBKbHDaM+5GbbEVJmPmvKSsL7UhyyiWHYDUMezE5sq8jJhUTJQomfNTZI2M0R+4kl2mREREulBfZktJKW97dPDR9vUFSX+u1dpTV4K5EPDS4ExCHzeyKiZzyniByK7jxrK8JTOm4bDE+REX+0uJ4sTANEbsMSUiItKX+lIbesZnIZP8y/v0gAN1JVYUWs1JfZ61uLmuCEBi95kGQmH0T3pQX6a9gU/J1lxlR8cSgenlaS/cviADU6I4MTCNUQZVqBAREaWF+lIr3L4gRp2+pD2HlBLt/Q5s13AZLwAU27Kx3iYSus+0b9KDYFhmzKqYhZor7Rh3+TDmmnvF25RMKgNTovgw3IqRgRlTIiIiXVEmxSaznPfytBcTbp9mBx8ttLnIiLbe6YTtdu3JwFUxCiXoVKbvLtQx7IRBAJvK81J9LCJdY2AaIyOn8hIREelKQwpWxrQPOAAAO6oLkvYcibKl2AhvIIQzlx0JeTxlVUxdBgamyhqYxQYgdQw7UVtiRY7ZmOpjEekaA9MYMTAlIiLSl9K8bORlm5I6mbe9fxqWLAM2VWg/O7ap0AghEtdn2jPuRoXdAlu2KSGPpyf5uVlYV5CzaJ9px4iTZbxEq8DANEZKYJrsAQpERESUGEII1JXZ0J3MjGm/A9evK0CWDoZR2MwCWyrsCQxMZzNy8JGiucr+ism8zrkABqa88xlVIoqd9n+KaoSRe6iIiIh0p77Uip6x2aQ8ti8Ywrkhp6b3l15rT30xTvZPYy4QWtPjSClxcSzzVsUstKXSjovjbnj9Vz6X56M9p1sqtZ9BJ9IaBqYxMrCUl4iISHcaymwYcc7B7Qsm/LHPDTnhD4X1FZjWFcMfDM/vXl2tcZcPLl8wIyfyKpor7QhL4MLolQFIHZzIS7RqDExjZFJKeVU+BxEREcVOyehdTEI5rxLc6WEir+LGuiIYBHB8jWtjujN4Iq9ia5UymfdKOW/HsBMFuVmosFvUOhaRbjEwjZHBwHUxREREelOfxMm87QMOVOVbUK6jIMRuycK2dfl4bo19pj3jkfLoTA5M1xfmIC/bdNVk3o5hJ7ZU2CHYAkYUNwamMVJ6TCVzpkRERLpRU5wLk0HMrzZJpPb+aV1lSxV76orRPjB9VW9kvHrG3LCajSi3ZyfwZPoihMCWyisDkEJhiQujLpbxEq1S0gJTIUS1EOJpIUSHEOKsEOJTyXquVOC6GCIiIv3JMhqwoTg34QOQxlxzuDztxXYd7C+91s31xQiEJE72Ta/6MXrG3agvs2V8ZrC5yo6OYSfCYYlLE7OYC4Q5+IholZKZMQ0C+FMp5RYANwP4mBCiOYnPl1QGwVJeIiIiPaovtSW8lPf0fH9pQUIfNxV21xbBaBA4fnFi1Y/Rk+ETeRXNlXZ4/CH0T3nme02bq5gxJVqNpAWmUsphKeWp6H+7AHQAWJes50s2k5HDj4iIiPSoocyG3slZBEPhhD1m+4ADJoPAtnX5CXvMVLFlm3D9+vxV7zOd9QUxNDOH+tLM3WGqUILQc8NOdAw7YTKIjJ5UTLQWplQ8iRCiFsAOAM8v8rYPAvggAJSXl6O1tTUVR4pb93SkD8Pv92v2jLQyt9vN66djvH76xuunf3q9hoHJAAIhiZ8/2ooKa2Luybe+6EW1TeC5Z48k5PFSYeH1W2fy4/eXAnj0yadhMcVXjts7E3lNNDfWh9bWwUQfU1f8IQmDAH5//CX0ucKoyAWOH03e14RevwcpgtdveUkPTIUQNgC/APBpKaXz2rdLKb8B4BsA0NLSIg8dOpTsI61KwYADeP5ZmM1maPWMtLLW1lZePx3j9dM3Xj/90+s1zO+fxrdeOoaSjVtxqLl8zY8XCkt89A+P4a271uPQoW0JOGFqLLx+pnX/f3v3HhznVd5x/Pdodddaki1LTmzLsbU2ASckFnHBIh3GIb2EQpOWKVMyQNOWGaYdWminDJBy6ZR/SmkHaDoNJUNo6JAmpTRcJi00GSDN0NoJkDUmF0gl40hKnEi2tZYl2bo+/WPfdTaKLivJ77776v1+ZjzePftejvSMVvvonPOck7r/2COq77xCBy/vWNF1vnHkWenQEd103eu0ZwvrKfccfVjjtQ0amhxVz542HTy4L7R7xfVnEHnEb2mhVuU1sxrlk9K73f2+MO8VthRrTAEAiKWuYC1k70VaZ/r0C2c1MTUby4q8BddctlE1KVvVfqa9Q2NKVZl2tDWG0LP42bu1WT88flrPj56n8BGwBmFW5TVJd0p6yt0/HdZ9yuXFqrxkpgAAxElLQ43aN9Sp7yJtGZONceGjgobalLo7N65qnWnf8Jh2bGpUXXUqhJ7Fz95LmzV6fkaS2CoGWIMwR0yvlfQuSW80syPBv18L8X6hYrsYAADia/dFrMyb7R/RpqZa7dgU7xHDA5k2Pf7sGY2en17ReX1D4xQ+KlKcjJKYAqsXZlXe77u7uftV7r4v+PefYd0vbKngO8VUXgAA4ifT0aS+4XH5RfhFnh3IaV9na+z38OzpatOcS48eO13yObPBfp1sFfOiwvTd9g112pyui7g3QHyFusZ0PamK+S8fAACSLNOe1plz0zo1PrWm65w5N63eoTF1d7ZenI5FqHtHq2qrq1a0znRwZEJTs3MkpkXa0nW6tKVeexktBdakLNvFrAfVVfkcngFTAADip5BI9Q6NrWlU6+hgTpJiXfiooL4mpWt2rGydaWE6dIa9Ol/i72/uVktDTdTdAGKNEdMSVV2YyktqCgBA3BQSqbWuM83252QmXdXZcjG6FbmeTJueen5UuYnSRpL7hsYliTWm8+zfuYmtc4A1IjEtEcWPAACIr0ub69VYm7qQWK1Wtn9EezrSaq5fH6NjPZk2uUuHS1xnmh9xrlVrY23IPQOQNCSmJUqxxhQAgNiqqjJ1tTetacTU3ZUdyKm7M/7TeAuu3t6qhpqUDpe4zrRveOzCvrAAcDGRmJaoKhgxZSIvAADxlFnjljHHT00oNzGtfTHev3S+2uoq7d9Z+jrTvuExCh8BCAWJaYkKI6YsMQUAIJ4y7Wk9mzunc1Ozqzo/2z8iKV/Ndj050NWmn71wVqfGJpc87vT4lEYmpllfCiAUJKYlYiYvAADxlmlPy106dnJ1o6bZ/pyaalPa07G+itz0ZNokLb/OtDDavJuKvABCQGK6QlTlBQAgnnZfqMy7ugJIRwZyurqzdd0VRHz1thY11aZ06NjJJY/rGwq2imEqL4AQkJiWqL4mJUn6ze5tEfcEAACsxmVtjaqyFxOslTg3NaunToyuu2m8klSTqtIv7Nq07DrT3qEx1VVXaVtrQ5l6BiBJSExLVF+T0u3XN+rjv35F1F0BAACrUF+TUuemxlUVQHr8uTOamfN1VZG3WE9Xm/qGxzU0en7RYwoVeavW2YgxgMpAYroCjTW27qbvAACQJJn2tHpXMWJaKHy0niryFiusMz20xLYxfcPjFD4CEBoSUwAAkBiZ9ib9/OS4ZudWVjMi259T56YGbU7XhdSzaF2xtUUb6qsX3c/0/PSsBkYmWF8KIDQkpgAAIDF2d6Q1OTOn53LnVnRetj+3bqfxSlKqyvS6JdaZHj81LncpQ0VeACEhMQUAAIlRGPHrXcE60xNnzun50fPrsvBRsQNdbTp+akInzrw8ae8bylcy3s2IKYCQkJgCAIDEKCSmK6nMe6Q/J0nq3rF+R0ylonWmC4ya9g2PyUzatZk1pgDCQWIKAAASY2NTrTY11a6oMm92IKfa6irtvbQ5xJ5F71WXNKu1sWbBxLR3aEzbWhvUUJuKoGcAkoDEFAAAJEqmvenC1NRSZPtHdOXWZtVWr++PTVWFdaYLFEDqGx6j8BGAUK3vd1gAAIB5dnekSx4xnZ6d09HBM+t+Gm9BT1ebBkfOaeD0xIW2uTnXseFxElMAoSIxBQAAiZJpT+vU+JRGxqeWPfanJ85qcmZO+zpbw+9YBejJbJb00v1MT4ye17npWWU6WF8KIDwkpgAAIFEKI3/HTi4/apodGJGkdV+Rt+AVW9Jqa6rV4aJ1poVCUVTkBRAmElMAAJAoF7aMKaEyb7Y/p/YNddrW2hB2tyqCmelAV5sOHTsld5ekC9Oe2cMUQJhITAEAQKJs29ig2uoq9Q0vXwAp2z+i7s5WmVkZelYZDmTadOLMeT1zKr/OtG94TC0NNWprqo24ZwDWMxJTAACQKKkqU9fmpmX3Mh0Zn9LxUxOJKXxU8PrCfqbBOtPeoTFl2psSlZwDKD8SUwAAkDiZEirzHhnISUrO+tKCrs1N6thQd2E/0z4q8gIoAxJTAACQOJn2tPpPT2hyZnbRY7L9I6oy6artLWXsWfTMTD2Z/DrTM+emNXx2kvWlAEJHYgoAABIn096kOZeOn5xY9JjsQE6XX9KsxtrqMvasMvR0tWn47KQefPIFSWLEFEDoSEwBAEDiFBKtxabzzs25jvTnEjeNt6AnWGf65cPPSJJ2M2IKIGQkpgAAIHG62pskadECSH3DYzo7OaPuztYy9qpy7NjUqK0t9ToykFNNytS5MRnb5QCIDokpAABInMbaam1rbVh0xDTbn5OkxFXkLTAzHQhGTXe2Nak6xUdGAOHiXQYAACRSvjLvwnuZZgdyaq6vVtfmpjL3qnL0dOUTU9aXAigHElMAAJBImfYm9Q2PaW7OX/Zatn9E+3ZsVFVVcvfuLKwzzXQkNzkHUD4kpgAAIJEy7WlNTM3q+dHzL2kfm5zR0y+cTez60oLtGxt1283duqVnZ9RdAZAAJKYAACCRFqvMe3QwpzlXYivyFrvx6q3qaK6PuhsAEoDEFAAAJFJhC5T5lXkLhY/2JXzEFADKicQUAAAk0uZ0rZrrq19WACnbn1PX5ia1NtZG1DMASB4SUwAAkEhmpkxHWr1FI6buriMDI9rHNF4AKCsSUwAAkFiZ9vRL1pgOjpzTybGpxO5fCgBRITEFAACJlWlPa+jspEbPT0vK718qKfEVeQGg3EhMAQBAYhUKIB0L1plm+0dUX1OlV16yIcpuAUDikJgCAIDEyrQ3SXqxMm+2P6ertreqOsVHJAAoJ951AQBAYnVualRNytQ3PKbJmVk9+dwo03gBIAIkpgAAILFqUlW6rK1JvUNjeuK5UU3NzqmbirwAUHYkpgAAINEy7U3qGx5Ttj8nSVTkBYAIkJgCAIBE292R1jOnJvTD46e1taVeW5rro+4SACQOiSkAAEi0THtaM3Ou7/50iNFSAIhIaImpmX3RzIbM7PGw7gEAALBWmfb8ljGTM6wvBYCohDliepekG0K8PgAAwJp1BVvGSCIxBYCIhJaYuvvDkk6HdX0AAICLYUN9jbY016m6ynTF1paouwMAiVQddQcAAACi9uptLTpzblr1NamouwIAiWTuHt7FzXZKut/dr1zimPdIeo8kbdmy5Zp77703tP6s1djYmNLpdNTdwBoQw3gjfvFG/OJvPcdwfNrlLqVrLequhGY9xy8piGG8ET/puuuu+5G771/otchHTN39Dkl3SNL+/fv94MGD0XZoCQ899JAquX9YHjGMN+IXb8Qv/ohhvBG/+COG8Ub8lsZ2MQAAAACASIW5Xcw9kg5JutzMBs3s3WHdCwAAAAAQX6FN5XX3m8O6NgAAAABg/WAqLwAAAAAgUiSmAAAAAIBIkZgCAAAAACJFYgoAAAAAiBSJKQAAAAAgUiSmAAAAAIBIkZgCAAAAACJFYgoAAAAAiBSJKQAAAAAgUiSmAAAAAIBIkZgCAAAAACJFYgoAAAAAiJS5e9R9uMDMhiU9E3U/lrBZ0smoO4E1IYbxRvzijfjFHzGMN+IXf8Qw3oifdJm7ty/0QkUlppXOzH7o7vuj7gdWjxjGG/GLN+IXf8Qw3ohf/BHDeCN+S2MqLwAAAAAgUiSmAAAAAIBIkZiuzB1RdwBrRgzjjfjFG/GLP2IYb8Qv/ohhvBG/JbDGFAAAAAAQKUZMAQAAAACRIjEFAAAAAESKxLREZnaDmf3MzHrN7MNR9wcvZ2adZvY9M3vKzJ4ws/cH7ZvM7EEz+7/g/41F59waxPRnZvar0fUeBWaWMrOsmd0fPCd+MWJmrWb2VTP7afCz2EMM48PM/jR4/3zczO4xs3riV9nM7ItmNmRmjxe1rThmZnaNmf0keO02M7Nyfy1JtEj8/iZ4Dz1qZl8zs9ai14hfBVkofkWvfcDM3Mw2F7URvyWQmJbAzFKS/kHSmyTtlXSzme2NtldYwIykP3P3V0k6IOm9QZw+LOk77r5H0neC5wpee7ukKyTdIOn2INaI1vslPVX0nPjFy99J+ra7v1LS1crHkhjGgJltk/Q+Sfvd/UpJKeXjQ/wq213Kf/+LrSZmn5P0Hkl7gn/zr4lw3KWXf68flHSlu18l6WlJt0rEr0LdpQW+12bWKemXJfUXtRG/ZZCYlua1knrd/Zi7T0m6V9JNEfcJ87j7CXd/LHh8VvkPxNuUj9WXgsO+JOk3gsc3SbrX3Sfd/eeSepWPNSJiZtslvVnSF4qaiV9MmFmzpDdIulOS3H3K3XMihnFSLanBzKolNUp6TsSvorn7w5JOz2teUczM7FJJze5+yPNVMf+56ByEaKH4ufsD7j4TPD0saXvwmPhVmEV+/iTpM5I+KKm4yizxWwaJaWm2SRooej4YtKFCmdlOSd2SHpG0xd1PSPnkVVJHcBhxrTyfVf6NfK6ojfjFR5ekYUn/FEzH/oKZNYkYxoK7Pyvpb5X/C/8JSWfc/QERvzhaacy2BY/ntyN6vy/pW8Fj4hcDZnajpGfd/cfzXiJ+yyAxLc1C87zZZ6dCmVla0r9L+hN3H13q0AXaiGtEzOwtkobc/UelnrJAG/GLVrWk10j6nLt3SxpXMIVwEcSwggTrEG+StEvSVklNZvbOpU5ZoI34VbbFYkYsK5CZfUT5ZUp3F5oWOIz4VRAza5T0EUkfX+jlBdqIXxES09IMSuoser5d+elNqDBmVqN8Unq3u98XNL8QTJNQ8P9Q0E5cK8u1km40s+PKT5d/o5l9WcQvTgYlDbr7I8HzryqfqBLDePglST9392F3n5Z0n6TXi/jF0UpjNqgXp4sWtyMiZnaLpLdIekcwvVMifnGQUf6Pez8OPs9sl/SYmV0i4rcsEtPS/EDSHjPbZWa1yi9c/mbEfcI8QQWzOyU95e6fLnrpm5JuCR7fIukbRe1vN7M6M9ul/GLzR8vVX7yUu9/q7tvdfafyP2Pfdfd3ivjFhrs/L2nAzC4Pmq6X9KSIYVz0SzpgZo3B++n1yq/VJ37xs6KYBdN9z5rZgSD2v1N0DsrMzG6Q9CFJN7r7RNFLxK/CuftP3L3D3XcGn2cGJb0m+P1I/JZRHXUH4sDdZ8zsjyT9l/JVCr/o7k9E3C283LWS3iXpJ2Z2JGj7c0mflPQVM3u38h+83iZJ7v6EmX1F+Q/OM5Le6+6zZe81lkP84uWPJd0d/BHvmKTfU/6PoMSwwrn7I2b2VUmPKR+PrKQ7JKVF/CqWmd0j6aCkzWY2KOkvtLr3zT9UvsJog/JrGr8lhG6R+N0qqU7Sg8GuIYfd/Q+IX+VZKH7ufudCxxK/5dmLswMAAAAAACg/pvICAAAAACJFYgoAAAAAiBSJKQAAAAAgUiSmAAAAAIBIkZgCAAAAACJFYgoAAAAAiBSJKQAAFcjMUlH3AQCAciExBQBgBczsnWb2qJkdMbPPm9nrzOyomdWbWZOZPWFmV5rZQTN72My+ZmZPmtk/mllVcI1fMbNDZvaYmf2bmaWD9uNm9nEz+76kt5nZ+4Jzj5rZvcExbWb2gJllg/s/Y2abI/yWAACwZubuUfcBAIBYMLNXSfqUpLe6+7SZ3S7psKRXSKqX1CBp0N3/yswOSvq2pL2Sngkef17SQ5Luk/Qmdx83sw9JqnP3T5jZcUm3u/ungvs9J2mXu0+aWau758zsNkkng+PfLOl+Se3ufrJM3wYAAC666qg7AABAjFwv6RpJPzAzKZ+IDkn6hKQfSDov6X1Fxz/q7sckyczukfSLwTF7Jf1PcI1aSYeKzvnXosdHJd1tZl+X9PWg7Q2S3ipJ7v4fZjZy0b46AAAiQmIKAEDpTNKX3P3WlzSaXSIpLalG+ZHT8eCl+dOSPLjGg+5+8yL3GC96/GblE9EbJX3MzK5Y5LoAAMQaa0wBACjddyT9lpl1SJKZbTKzyyTdIeljku6W9NdFx7/WzHYFa0t/W9L3lZ/6e62Z7Q6u0Whmr5h/o+CcTnf/nqQPSmpVPvl9WNI7gmPeJGljGF8oAADlxIgpAAAlcvcnzeyjkh4IEsdpSd+QNOPu/xJU0v1fM3ujpDnlp+h+UtKrlU8ov+buc2b2u5LuMbO64NIflfT0vNulJH3ZzFqUH2X9TLDG9C+Dcx+T9N+S+sP8mgEAKAeKHwEAEIKg+NEH3P0tId/nuKT9FD8CAMQZU3kBAAAAAJFixBQAAAAAEClGTAEAAAAAkSIxBQAAAABEisQUAAAAABApElMAAAAAQKRITAEAAAAAkfp/qEbfhfsGONYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.title('wage with experience')\n",
    "both = df.groupby(['expersq']).mean()\n",
    "both = both.sort_values(by=['expersq'])\n",
    "plt.plot(both.index, both['wage estimated'])\n",
    "plt.xlabel('expersq')\n",
    "plt.ylabel('wage')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503aa056",
   "metadata": {},
   "source": [
    "On remarque que le salaire dépend clairement plus de l'éducation que de l'experience (puisque la courbe selon l'experience oscille autour d'une valeur centrale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0580310",
   "metadata": {},
   "source": [
    "### Question  13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c4c276ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              log(wage)   R-squared:                       0.142\n",
      "Model:                            OLS   Adj. R-squared:                  0.132\n",
      "Method:                 Least Squares   F-statistic:                     13.93\n",
      "Date:                Fri, 19 Nov 2021   Prob (F-statistic):           1.30e-12\n",
      "Time:                        15:00:18   Log-Likelihood:                -435.41\n",
      "No. Observations:                 428   AIC:                             882.8\n",
      "Df Residuals:                     422   BIC:                             907.2\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2564      0.200     -1.282      0.201      -0.650       0.137\n",
      "x1             0.0595      0.069      0.862      0.389      -0.076       0.195\n",
      "x2             0.1085      0.015      7.400      0.000       0.080       0.137\n",
      "x3             0.0003      0.000      2.394      0.017    5.63e-05       0.001\n",
      "x4            -0.0522      0.091     -0.576      0.565      -0.230       0.126\n",
      "x5            -0.0210      0.027     -0.779      0.436      -0.074       0.032\n",
      "==============================================================================\n",
      "Omnibus:                       84.273   Durbin-Watson:                   1.989\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              293.606\n",
      "Skew:                          -0.857   Prob(JB):                     1.76e-64\n",
      "Kurtosis:                       6.677   Cond. No.                     2.21e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.21e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "tot_kids = df['kids below 6'].add(df['kids between 6 and 18'])\n",
    "\n",
    "stack_kids =np.column_stack((a, \n",
    "                        np.asarray(df['live in SMSA']), \n",
    "                        np.asarray(df['educ years of schooling']), \n",
    "                        np.asarray(df['expersq']),  \n",
    "                        np.asarray(df['kids below 6']), \n",
    "                        np.asarray(tot_kids)))\n",
    "\n",
    "mod5 = sm.OLS(df['log(wage)'], stack_kids)\n",
    "mod5 = mod5.fit()\n",
    "res5= mod5.resid\n",
    "\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5090d17a",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fbfcfa",
   "metadata": {},
   "source": [
    "On remarque que pour la varibale x4 (kids bellow 6):     P>|t| = 0,56"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14dad034",
   "metadata": {},
   "source": [
    "La p-value est donc 56% ce qui est plus grand que 5%, donc on rejette l'hyptohese d'égalité des coefficients 'kidsbt6' et 'kidsgt6'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7c3707",
   "metadata": {},
   "source": [
    "### Question  14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "7f3434a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statistique de fischer =  1.585263938652012\n",
      "valeur critique=  3.0171506509713275\n",
      "p-value =  0.147253803625398\n"
     ]
    }
   ],
   "source": [
    "stack6 = np.column_stack((a, \n",
    "                        np.asarray(df['educ years of schooling']), \n",
    "                        np.asarray(df['expersq'])))\n",
    "\n",
    "mod6 = sm.OLS(df['wage estimated'],stack6)\n",
    "mod6 = mod6.fit()\n",
    "res6 = mod6.resid\n",
    "\n",
    "\n",
    "mod66 = res6**2 \n",
    "mod66 = sm.OLS(mod66, stack6)\n",
    "mod66 = mod66.fit()\n",
    "res66 = mod66.resid\n",
    "SSR66 = res66.T @ res66\n",
    "R6 = mod66.rsquared\n",
    "\n",
    "nbis,kbis = np.shape(stack6)\n",
    "fbis = (R6/kbis) / ((1-R6) / (nbis-kbis-1))\n",
    "p_values = mod66.f_pvalue\n",
    "\n",
    "print(\"statistique de fischer = \", mod66.fvalue )\n",
    "print(\"valeur critique= \",f.isf(0.05,2,n-k))\n",
    "print('p-value = ', p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121cef19",
   "metadata": {},
   "source": [
    "14>10>5>1 Donc on rejette l'hypothèse pour tous les seuils."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48494f80",
   "metadata": {},
   "source": [
    "### Question  15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b4330762",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SSR(plusOrMoins):\n",
    "    \n",
    "    if plusOrMoins == 'moins':\n",
    "        filtre = df['age women']<=45\n",
    "    elif plusOrMoins == 'plus':\n",
    "        filtre = df['age women']>45\n",
    "\n",
    "    data7 = df['log(wage)'][filtre]\n",
    "    stack7 = np.column_stack((a[filtre], \n",
    "                            np.asarray(df['live in SMSA'][filtre]), \n",
    "                            np.asarray(df['educ years of schooling'][filtre]), \n",
    "                            np.asarray(df['expersq'][filtre]),  \n",
    "                            np.asarray(df['nwifeinc'][filtre])))\n",
    "\n",
    "    mod7 = sm.OLS(data7,stack7)\n",
    "    mod7 = mod7.fit()\n",
    "    p_value = mod7.pvalues\n",
    "    res7 = mod7.resid\n",
    "    SSR7 = res7.T @ res7\n",
    "\n",
    "    return SSR7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "2fd22679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSR_Moins43: 105.36297749294738 SSR_plus43: 80.03719784649275 SSR_tot: 187.16273662839203\n",
      "Ficher chow: 1.162737927566472 \n",
      " P_value chow:  0.31373828930284803\n"
     ]
    }
   ],
   "source": [
    "SSR_Moins43 = SSR('moins')\n",
    "SSR_plus43 = SSR('plus')\n",
    "SSR_tot = SSR('tot')\n",
    "\n",
    "SSR_sum = SSR_Moins43 + SSR_Plus43\n",
    "print('SSR_Moins43:', SSR_Moins43,\n",
    "      'SSR_plus43:', SSR_plus43,\n",
    "      'SSR_tot:', SSR_tot)\n",
    "\n",
    "f7 = ((SSR_Moins43 - SSR_sum)/SSR_sum)*((n-2*7)/7)\n",
    "p_value7 = f.sf(f7,7,n-7)\n",
    "print('Ficher chow:',f7,'P_value chow: ',p_value7)\n",
    "print('P_value chow: ',p_value7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570d8232",
   "metadata": {},
   "source": [
    "### Question  16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "5c0ea296",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SSR_bis(plusOrMoins):\n",
    "    if plusOrMoins == 'moins':\n",
    "        filtre = df['age women']<=45\n",
    "    elif plusOrMoins == 'plus':\n",
    "        filtre = df['age women']>45\n",
    "\n",
    "    data8 = df['log(wage)'][filtre]\n",
    "    stack8 = np.column_stack((a[filtre], \n",
    "                            np.asarray(df['educ years of schooling'][filtre]), \n",
    "                            np.asarray(df['expersq'][filtre])))\n",
    "\n",
    "    mod8 = sm.OLS(data8,stack8)\n",
    "    mod8 = mod8.fit()\n",
    "    p_value = result.pvalues\n",
    "    res8 = mod8.resid\n",
    "    SSR8 = res8.T @ res8\n",
    "\n",
    "    return SSR8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "d476daeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSR_Moins43_bis: 105.50372924028493 SSR_Plus43_bis: 81.99284013840149\n",
      "Ficher chow: 0.8527829272729232 P_value chow:  0.5318200188810824\n"
     ]
    }
   ],
   "source": [
    "SSR_Moins43_bis = SSR_bis('moins')\n",
    "SSR_Plus43_bis = SSR_bis('plus')\n",
    "\n",
    "\n",
    "SSR_sum_bis = SSR_Moins43_bis + SSR_Plus43_bis\n",
    "print('SSR_Moins43_bis:', SSR_Moins43_bis,\n",
    "      'SSR_Plus43_bis:', SSR_Plus43_bis)\n",
    "\n",
    "f8 = ((SSR_Moins43_bis - SSR_sum_bis)/SSR_sum_bis)*((n-2*7)/7)\n",
    "p_value8 = f.sf(f8,7,n-7)\n",
    "print('Ficher chow:',f8,'P_value chow: ',p_value8)\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Aucun(e)",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}