Multi_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30550e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "from PIL import ImageGrab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9c0c255",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56a1b2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGBA size=853x482 at 0x1FB34303BB0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b03d1a7c",
   "metadata": {
    "scrolled": true
   },
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>162</td>\n",
       "      <td>23.203569</td>\n",
       "      <td>135</td>\n",
       "      <td>133.415985</td>\n",
       "      <td>44.013139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>162</td>\n",
       "      <td>23.203569</td>\n",
       "      <td>132</td>\n",
       "      <td>133.140074</td>\n",
       "      <td>43.353123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>140</td>\n",
       "      <td>19.086341</td>\n",
       "      <td>160</td>\n",
       "      <td>124.715241</td>\n",
       "      <td>52.997752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>140</td>\n",
       "      <td>19.086341</td>\n",
       "      <td>129</td>\n",
       "      <td>121.864163</td>\n",
       "      <td>42.618698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>175</td>\n",
       "      <td>18.762837</td>\n",
       "      <td>129</td>\n",
       "      <td>132.864163</td>\n",
       "      <td>42.778219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>50</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>263</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "71  162  23.203569  135  133.415985  44.013139\n",
       "72  162  23.203569  132  133.140074  43.353123\n",
       "73  140  19.086341  160  124.715241  52.997752\n",
       "74  140  19.086341  129  121.864163  42.618698\n",
       "75  175  18.762837  129  132.864163  42.778219\n",
       "76  322  36.900000   50  169.598513  16.132947\n",
       "77  238  19.197888  115  150.576579  37.923113\n",
       "78  263  34.000000   50  151.598513  15.769625\n",
       "79  295  19.833733  119  167.944460  39.423099\n",
       "80  236  12.101263  107  139.840817  34.948615"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science/Python/CSV files/Cars.csv\")\n",
    "cars.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5eba66",
   "metadata": {},
   "source": [
    "# Correlation Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cca35b1",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.725038</td>\n",
       "      <td>0.077459</td>\n",
       "      <td>0.973848</td>\n",
       "      <td>0.076513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPG</th>\n",
       "      <td>-0.725038</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.529057</td>\n",
       "      <td>-0.687125</td>\n",
       "      <td>-0.526759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL</th>\n",
       "      <td>0.077459</td>\n",
       "      <td>-0.529057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102170</td>\n",
       "      <td>0.999203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>0.973848</td>\n",
       "      <td>-0.687125</td>\n",
       "      <td>0.102170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT</th>\n",
       "      <td>0.076513</td>\n",
       "      <td>-0.526759</td>\n",
       "      <td>0.999203</td>\n",
       "      <td>0.102439</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HP       MPG       VOL        SP        WT\n",
       "HP   1.000000 -0.725038  0.077459  0.973848  0.076513\n",
       "MPG -0.725038  1.000000 -0.529057 -0.687125 -0.526759\n",
       "VOL  0.077459 -0.529057  1.000000  0.102170  0.999203\n",
       "SP   0.973848 -0.687125  0.102170  1.000000  0.102439\n",
       "WT   0.076513 -0.526759  0.999203  0.102439  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4368bfa8",
   "metadata": {},
   "source": [
    "# Plotting Correlation (Pair Plot) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "219ec807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x17bce42afd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style = 'darkgrid')\n",
    "sns.pairplot(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb61678",
   "metadata": {},
   "source": [
    "# Preparing a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c51f1c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   63.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 23 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>1.54e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:39:56</td>     <th>  Log-Likelihood:    </th> <td> -233.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   477.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    76</td>      <th>  BIC:               </th> <td>   489.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   30.6773</td> <td>   14.900</td> <td>    2.059</td> <td> 0.043</td> <td>    1.001</td> <td>   60.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2054</td> <td>    0.039</td> <td>   -5.239</td> <td> 0.000</td> <td>   -0.284</td> <td>   -0.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.3361</td> <td>    0.569</td> <td>   -0.591</td> <td> 0.556</td> <td>   -1.469</td> <td>    0.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.3956</td> <td>    0.158</td> <td>    2.500</td> <td> 0.015</td> <td>    0.080</td> <td>    0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WT</th>        <td>    0.4006</td> <td>    1.693</td> <td>    0.237</td> <td> 0.814</td> <td>   -2.972</td> <td>    3.773</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.780</td> <th>  Durbin-Watson:     </th> <td>   1.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  11.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.707</td> <th>  Prob(JB):          </th> <td> 0.00285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.215</td> <th>  Cond. No.          </th> <td>6.09e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.09e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.771\n",
       "Model:                            OLS   Adj. R-squared:                  0.758\n",
       "Method:                 Least Squares   F-statistic:                     63.80\n",
       "Date:                Wed, 23 Mar 2022   Prob (F-statistic):           1.54e-23\n",
       "Time:                        11:39:56   Log-Likelihood:                -233.96\n",
       "No. Observations:                  81   AIC:                             477.9\n",
       "Df Residuals:                      76   BIC:                             489.9\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     30.6773     14.900      2.059      0.043       1.001      60.354\n",
       "HP            -0.2054      0.039     -5.239      0.000      -0.284      -0.127\n",
       "VOL           -0.3361      0.569     -0.591      0.556      -1.469       0.796\n",
       "SP             0.3956      0.158      2.500      0.015       0.080       0.711\n",
       "WT             0.4006      1.693      0.237      0.814      -2.972       3.773\n",
       "==============================================================================\n",
       "Omnibus:                       10.780   Durbin-Watson:                   1.403\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               11.722\n",
       "Skew:                           0.707   Prob(JB):                      0.00285\n",
       "Kurtosis:                       4.215   Cond. No.                     6.09e+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, 6.09e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols(\"MPG~HP+VOL+SP+WT\", data = cars).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e904429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    30.677336\n",
       "HP           -0.205444\n",
       "VOL          -0.336051\n",
       "SP            0.395627\n",
       "WT            0.400574\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d45f8006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    2.058841\n",
       "HP          -5.238735\n",
       "VOL         -0.590970\n",
       "SP           2.499880\n",
       "WT           0.236541\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Intercept    0.042936\n",
       "HP           0.000001\n",
       "VOL          0.556294\n",
       "SP           0.014579\n",
       "WT           0.813649\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(model.tvalues, model.pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa7bfdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7705372737359844, 0.7584602881431415)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.rsquared,model.rsquared_adj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7333bf",
   "metadata": {},
   "source": [
    "# Simple Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "229b3c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   30.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 18 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>4.38e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:05:44</td>     <th>  Log-Likelihood:    </th> <td> -280.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   564.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    79</td>      <th>  BIC:               </th> <td>   569.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   55.2296</td> <td>    3.876</td> <td>   14.249</td> <td> 0.000</td> <td>   47.514</td> <td>   62.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WT</th>        <td>   -0.6420</td> <td>    0.117</td> <td>   -5.508</td> <td> 0.000</td> <td>   -0.874</td> <td>   -0.410</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.735</td> <th>  Durbin-Watson:     </th> <td>   0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.255</td> <th>  Jarque-Bera (JB):  </th> <td>   2.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.263</td> <th>  Prob(JB):          </th> <td>   0.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.573</td> <th>  Cond. No.          </th> <td>    149.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.277\n",
       "Model:                            OLS   Adj. R-squared:                  0.268\n",
       "Method:                 Least Squares   F-statistic:                     30.34\n",
       "Date:                Fri, 18 Feb 2022   Prob (F-statistic):           4.38e-07\n",
       "Time:                        19:05:44   Log-Likelihood:                -280.42\n",
       "No. Observations:                  81   AIC:                             564.8\n",
       "Df Residuals:                      79   BIC:                             569.6\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     55.2296      3.876     14.249      0.000      47.514      62.945\n",
       "WT            -0.6420      0.117     -5.508      0.000      -0.874      -0.410\n",
       "==============================================================================\n",
       "Omnibus:                        2.735   Durbin-Watson:                   0.555\n",
       "Prob(Omnibus):                  0.255   Jarque-Bera (JB):                2.045\n",
       "Skew:                          -0.263   Prob(JB):                        0.360\n",
       "Kurtosis:                       3.573   Cond. No.                         149.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_wt = smf.ols(\"MPG~WT\", data =cars).fit()\n",
    "model_wt.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13906519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   30.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 18 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>3.82e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:05:46</td>     <th>  Log-Likelihood:    </th> <td> -280.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   564.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    79</td>      <th>  BIC:               </th> <td>   569.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   55.8171</td> <td>    3.957</td> <td>   14.106</td> <td> 0.000</td> <td>   47.941</td> <td>   63.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.2166</td> <td>    0.039</td> <td>   -5.541</td> <td> 0.000</td> <td>   -0.294</td> <td>   -0.139</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.691</td> <th>  Durbin-Watson:     </th> <td>   0.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.260</td> <th>  Jarque-Bera (JB):  </th> <td>   1.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.263</td> <th>  Prob(JB):          </th> <td>   0.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.562</td> <th>  Cond. No.          </th> <td>    462.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.280\n",
       "Model:                            OLS   Adj. R-squared:                  0.271\n",
       "Method:                 Least Squares   F-statistic:                     30.71\n",
       "Date:                Fri, 18 Feb 2022   Prob (F-statistic):           3.82e-07\n",
       "Time:                        19:05:46   Log-Likelihood:                -280.28\n",
       "No. Observations:                  81   AIC:                             564.6\n",
       "Df Residuals:                      79   BIC:                             569.4\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     55.8171      3.957     14.106      0.000      47.941      63.693\n",
       "VOL           -0.2166      0.039     -5.541      0.000      -0.294      -0.139\n",
       "==============================================================================\n",
       "Omnibus:                        2.691   Durbin-Watson:                   0.566\n",
       "Prob(Omnibus):                  0.260   Jarque-Bera (JB):                1.997\n",
       "Skew:                          -0.263   Prob(JB):                        0.368\n",
       "Kurtosis:                       3.562   Cond. No.                         462.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_vol = smf.ols(\"MPG~VOL\", data =cars).fit()\n",
    "model_vol.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4d95de",
   "metadata": {},
   "source": [
    "# Calculating VIF (Variance Inflation factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "643379c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "r_sqr_vol = smf.ols('VOL~HP+WT+SP', data = cars).fit().rsquared\n",
    "vif_vol = 1/(1-r_sqr_vol)\n",
    "r_sqr_wt = smf.ols('WT~HP+VOL+SP', data = cars).fit().rsquared\n",
    "vif_wt = 1/(1-r_sqr_wt)\n",
    "r_sqr_sp = smf.ols('SP~WT+HP+VOL', data = cars).fit().rsquared\n",
    "vif_sp = 1/(1-r_sqr_sp)\n",
    "r_sqr_hp = smf.ols('HP~WT+VOL+SP', data = cars).fit().rsquared\n",
    "vif_hp = 1/(1-r_sqr_hp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7356d79",
   "metadata": {},
   "source": [
    "## Store VIF values in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "223204a1",
   "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>VIF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variables</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>19.926589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>20.007639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL</th>\n",
       "      <td>638.806084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WT</th>\n",
       "      <td>639.533818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VIF\n",
       "Variables            \n",
       "HP          19.926589\n",
       "SP          20.007639\n",
       "VOL        638.806084\n",
       "WT         639.533818"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_frame = pd.DataFrame({'Variables':['VOL','WT','SP','HP'], 'VIF':[vif_vol,vif_wt,vif_sp,vif_hp]}, index = None)\n",
    "vif_frame.set_index('Variables', inplace = True)\n",
    "vif_frame.sort_values(by = 'VIF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a16c508",
   "metadata": {},
   "source": [
    "## Another Meowthod to find VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "583c4698",
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP</td>\n",
       "      <td>11.191296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SP</td>\n",
       "      <td>50.541968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WT</td>\n",
       "      <td>12172.827328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VOL</td>\n",
       "      <td>12708.789369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features           VIF\n",
       "0       HP     11.191296\n",
       "1       SP     50.541968\n",
       "2       WT  12172.827328\n",
       "3      VOL  12708.789369"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "x = cars[['HP','SP','WT','VOL']]\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['Features'] = x.columns\n",
    "vif_data['VIF'] = [vif(x.values, i) for i in range(len(x.columns))]\n",
    "vif_data.sort_values(by = ['VIF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "33570a62",
   "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>HP</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "      <th>VOL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>238</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>263</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>295</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>236</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP          SP         WT  VOL\n",
       "0    49  104.185353  28.762059   89\n",
       "1    55  105.461264  30.466833   92\n",
       "2    55  105.461264  30.193597   92\n",
       "3    70  113.461264  30.632114   92\n",
       "4    53  104.461264  29.889149   92\n",
       "..  ...         ...        ...  ...\n",
       "76  322  169.598513  16.132947   50\n",
       "77  238  150.576579  37.923113  115\n",
       "78  263  151.598513  15.769625   50\n",
       "79  295  167.944460  39.423099  119\n",
       "80  236  139.840817  34.948615  107\n",
       "\n",
       "[81 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d34aa7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   85.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 23 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>1.81e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:39:01</td>     <th>  Log-Likelihood:    </th> <td> -234.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   476.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    77</td>      <th>  BIC:               </th> <td>   485.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   28.7848</td> <td>   14.490</td> <td>    1.986</td> <td> 0.051</td> <td>   -0.069</td> <td>   57.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WT</th>        <td>   -0.5994</td> <td>    0.067</td> <td>   -8.895</td> <td> 0.000</td> <td>   -0.734</td> <td>   -0.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.4078</td> <td>    0.156</td> <td>    2.609</td> <td> 0.011</td> <td>    0.097</td> <td>    0.719</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2085</td> <td>    0.039</td> <td>   -5.386</td> <td> 0.000</td> <td>   -0.286</td> <td>   -0.131</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.924</td> <th>  Durbin-Watson:     </th> <td>   1.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  12.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.705</td> <th>  Prob(JB):          </th> <td> 0.00242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.257</td> <th>  Cond. No.          </th> <td>5.23e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.23e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.769\n",
       "Model:                            OLS   Adj. R-squared:                  0.761\n",
       "Method:                 Least Squares   F-statistic:                     85.68\n",
       "Date:                Wed, 23 Mar 2022   Prob (F-statistic):           1.81e-24\n",
       "Time:                        11:39:01   Log-Likelihood:                -234.15\n",
       "No. Observations:                  81   AIC:                             476.3\n",
       "Df Residuals:                      77   BIC:                             485.9\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     28.7848     14.490      1.986      0.051      -0.069      57.639\n",
       "WT            -0.5994      0.067     -8.895      0.000      -0.734      -0.465\n",
       "SP             0.4078      0.156      2.609      0.011       0.097       0.719\n",
       "HP            -0.2085      0.039     -5.386      0.000      -0.286      -0.131\n",
       "==============================================================================\n",
       "Omnibus:                       10.924   Durbin-Watson:                   1.370\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               12.048\n",
       "Skew:                           0.705   Prob(JB):                      0.00242\n",
       "Kurtosis:                       4.257   Cond. No.                     5.23e+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, 5.23e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alternate_model = smf.ols('MPG~WT+SP+HP', data = cars).fit()\n",
    "alternate_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b023fbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept    1.986465\n",
      "WT          -8.894562\n",
      "SP           2.609482\n",
      "HP          -5.386453\n",
      "dtype: float64 \n",
      " Intercept    5.053995e-02\n",
      "WT           1.914892e-13\n",
      "SP           1.089096e-02\n",
      "HP           7.560276e-07\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#t and p_values\n",
    "print(alternate_model.tvalues, '\\n', alternate_model.pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bada6678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept    2.068548\n",
      "SP           2.570678\n",
      "VOL         -8.928340\n",
      "HP          -5.353199\n",
      "dtype: float64 \n",
      " Intercept    4.194554e-02\n",
      "SP           1.207855e-02\n",
      "VOL          1.648229e-13\n",
      "HP           8.644870e-07\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#t and p_values\n",
    "print(final_model.tvalues, '\\n', final_model.pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7aecaf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   86.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 23 Mar 2022</td> <th>  Prob (F-statistic):</th> <td>1.56e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:38:36</td>     <th>  Log-Likelihood:    </th> <td> -233.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   476.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    77</td>      <th>  BIC:               </th> <td>   485.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   29.9234</td> <td>   14.466</td> <td>    2.069</td> <td> 0.042</td> <td>    1.118</td> <td>   58.729</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.4007</td> <td>    0.156</td> <td>    2.571</td> <td> 0.012</td> <td>    0.090</td> <td>    0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.2017</td> <td>    0.023</td> <td>   -8.928</td> <td> 0.000</td> <td>   -0.247</td> <td>   -0.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2067</td> <td>    0.039</td> <td>   -5.353</td> <td> 0.000</td> <td>   -0.284</td> <td>   -0.130</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.810</td> <th>  Durbin-Watson:     </th> <td>   1.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  11.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.704</td> <th>  Prob(JB):          </th> <td> 0.00271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.233</td> <th>  Cond. No.          </th> <td>5.87e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.87e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.770\n",
       "Model:                            OLS   Adj. R-squared:                  0.761\n",
       "Method:                 Least Squares   F-statistic:                     86.11\n",
       "Date:                Wed, 23 Mar 2022   Prob (F-statistic):           1.56e-24\n",
       "Time:                        11:38:36   Log-Likelihood:                -233.99\n",
       "No. Observations:                  81   AIC:                             476.0\n",
       "Df Residuals:                      77   BIC:                             485.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     29.9234     14.466      2.069      0.042       1.118      58.729\n",
       "SP             0.4007      0.156      2.571      0.012       0.090       0.711\n",
       "VOL           -0.2017      0.023     -8.928      0.000      -0.247      -0.157\n",
       "HP            -0.2067      0.039     -5.353      0.000      -0.284      -0.130\n",
       "==============================================================================\n",
       "Omnibus:                       10.810   Durbin-Watson:                   1.389\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               11.822\n",
       "Skew:                           0.704   Prob(JB):                      0.00271\n",
       "Kurtosis:                       4.233   Cond. No.                     5.87e+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, 5.87e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = smf.ols('MPG~SP+VOL+HP', data = cars).fit()\n",
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8662559f",
   "metadata": {},
   "source": [
    "#  Residual Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5062c470",
   "metadata": {},
   "source": [
    "### Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0561c907",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Residuals values  = y - yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53acafae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.631961053868437"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.resid.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c11e283e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10.258747\n",
       "1      7.624608\n",
       "2      7.734060\n",
       "3      3.157963\n",
       "4      8.331584\n",
       "        ...    \n",
       "76    15.617904\n",
       "77     1.298838\n",
       "78     7.863547\n",
       "79     7.517122\n",
       "80    -3.458218\n",
       "Length: 81, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea2768ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitted Value is the Predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c6dce61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     43.441935\n",
       "1     42.388793\n",
       "2     42.279341\n",
       "3     42.538360\n",
       "4     42.172648\n",
       "        ...    \n",
       "76    21.282096\n",
       "77    17.899050\n",
       "78    26.136453\n",
       "79    12.316612\n",
       "80    15.559481\n",
       "Length: 81, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa6f2699",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(model.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5f0e350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0, 76], dtype=int64)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.where(model.resid>10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1296b63d",
   "metadata": {},
   "source": [
    "## Residual Plot for Homoscedasticity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "edb46a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0e3e5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model.fittedvalues), get_standardized_values(model.resid))\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('Standardized Fitted Values')\n",
    "plt.ylabel('Standardized Residual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8f76e9",
   "metadata": {},
   "source": [
    "#  Residual VS Regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "762e13a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "plot_SP = sm.graphics.plot_regress_exog(model, 'SP', fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b5ef8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "plot_HP = sm.graphics.plot_regress_exog(model, 'HP', fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44f9f2cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "plot_VOL = sm.graphics.plot_regress_exog(model, 'VOL', fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f65d11b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "plot_WT = sm.graphics.plot_regress_exog(model, 'WT', fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e6be7e",
   "metadata": {},
   "source": [
    "#  Model deletion Diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad9bc9f",
   "metadata": {},
   "source": [
    "## Detecting Influencers/Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f4111b9",
   "metadata": {},
   "source": [
    "##  Cook's Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d413f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_influence = model.get_influence()\n",
    "(c, _) = model_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47be5001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.04797739e-02, 2.80207825e-02, 1.84833331e-02, 7.27752720e-03,\n",
       "       2.96019879e-02, 4.81278004e-03, 2.07405847e-02, 2.55586700e-03,\n",
       "       4.05692942e-03, 7.54004910e-04, 2.97311924e-03, 8.72887201e-02,\n",
       "       3.32487183e-04, 3.06716174e-03, 5.27414633e-03, 5.29293219e-03,\n",
       "       1.16531605e-03, 7.63273362e-02, 6.47114749e-04, 5.79692771e-04,\n",
       "       1.13359020e-05, 1.03054091e-02, 1.53244850e-04, 3.10730928e-04,\n",
       "       1.49488694e-03, 5.85742198e-02, 1.00598054e-02, 6.55605611e-05,\n",
       "       3.30438001e-02, 1.81295962e-04, 1.33814932e-02, 9.97753151e-04,\n",
       "       4.02570308e-04, 1.13711936e-03, 2.85608656e-03, 4.41332141e-05,\n",
       "       3.27632348e-04, 4.70959014e-03, 1.21704644e-02, 8.96948188e-03,\n",
       "       2.35324656e-04, 9.75839299e-06, 4.49488700e-03, 4.48437031e-03,\n",
       "       1.02593360e-05, 4.75617324e-04, 2.22586830e-05, 4.66305687e-05,\n",
       "       1.27051048e-02, 5.33728062e-03, 2.98638028e-03, 5.00671925e-03,\n",
       "       1.29659561e-04, 1.14393086e-02, 8.61438068e-04, 2.46695414e-04,\n",
       "       1.39988281e-02, 5.43648632e-03, 1.26939574e-02, 3.75528829e-03,\n",
       "       2.73160527e-03, 1.13382659e-02, 1.34658848e-03, 3.70550296e-03,\n",
       "       2.72053841e-03, 2.36724088e-02, 5.79292874e-03, 1.71235466e-04,\n",
       "       6.23810482e-04, 1.82506221e-03, 2.41644005e-01, 6.81796865e-04,\n",
       "       9.32782333e-06, 2.11972740e-04, 2.04958742e-02, 4.06714404e-03,\n",
       "       1.08651940e+00, 1.42362225e-03, 1.81267746e-01, 1.23888350e-01,\n",
       "       3.73455378e-02])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4af935df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99749559, 0.999611  , 0.99986003, 0.99998609, 0.99955511,\n",
       "       0.99999503, 0.9998141 , 0.99999897, 0.99999675, 0.99999995,\n",
       "       0.9999985 , 0.99403955, 0.99999999, 0.99999838, 0.99999376,\n",
       "       0.9999937 , 0.99999986, 0.99565009, 0.99999997, 0.99999997,\n",
       "       1.        , 0.999967  , 1.        , 0.99999999, 0.99999973,\n",
       "       0.99767993, 0.99996892, 1.        , 0.9994181 , 1.        ,\n",
       "       0.99993697, 0.9999999 , 0.99999999, 0.99999986, 0.99999865,\n",
       "       1.        , 0.99999999, 0.99999529, 0.99995016, 0.99997662,\n",
       "       1.        , 1.        , 0.99999581, 0.99999583, 1.        ,\n",
       "       0.99999998, 1.        , 1.        , 0.99994456, 0.99999357,\n",
       "       0.99999849, 0.99999452, 1.        , 0.99995725, 0.99999993,\n",
       "       1.        , 0.99992953, 0.99999327, 0.99994468, 0.99999732,\n",
       "       0.99999879, 0.99995818, 0.99999979, 0.99999741, 0.9999988 ,\n",
       "       0.99974271, 0.99999212, 1.        , 0.99999997, 0.99999956,\n",
       "       0.94273321, 0.99999996, 1.        , 1.        , 0.99981945,\n",
       "       0.99999673, 0.37474197, 0.99999976, 0.96885989, 0.98663664,\n",
       "       0.99921622])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fbc83840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "x = [0,80]\n",
    "y = [1.0,1.0]\n",
    "plt.plot(x, y,color='red', linewidth=2)\n",
    "y1 = [0.5,0.5]\n",
    "plt.plot(x , y1, color = 'green', linewidth = 2)\n",
    "plt.stem(np.arange(len(cars)), np.round(c, 3))\n",
    "plt.xlabel('Row index')\n",
    "plt.ylabel(\"Cook's Distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead043d4",
   "metadata": {},
   "source": [
    "### Index and value of incluencer where c is more than .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fffac28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 1.086519399817991)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(c),np.max(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e67e373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.HP.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7894568",
   "metadata": {},
   "source": [
    "# High Influence Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6aa1dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "influence_plot(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fcfb53f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81, 5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "95f9cbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2222222222222222"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = cars.shape[1] # Numbers of columns\n",
    "n = cars.shape[0] # Numbers of rows\n",
    "leverage_cutoff = 3*((k + 1)/n)\n",
    "leverage_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cacb8802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ytm1bL0qm0WgOBERkmVJqTvP17c0UtgPbOrEkFaVUJbCQAdrV7ZNPPuHkk5te+sknn8z27dspKCjoJak0Gs2BTnuO5svYZ8/3ALcA1cACYC+QC5wPpAG/TIYwIjIYiCqlKkXEBxyPXbZ7wFFYWEhubm6TdfXvCwsLyc/P7w2xNBrNAU6bSkEp9UT9axH5A/AFcJZqZG8SkTuBfwOTkiRPHvAPEXFgz2IWKKVeS9KxDxjsJHKNRqNJPomGpF4IXKKaOSCUUkpEHsYumHdtV4VRSq3C7vA24MnLy6OoqKjJur179wK0mEFoNBpNskg0+igVGNzGtiFASnLE0dRz1FFH8fbbbzdZ99ZbbzFq1ChtOtJoNN1GokphIfArEZnbeKWIHIqdvLYwuWJprr32Wj7//HNuvvlmNmzYwJNPPsmDDz7IjTfe2NuiaTQdMnr0aESkxTJ58uSGMX/5y1+YNGkSfr+fvLw8vvOd7zTMhjW9iFKqwwW7dPYWwMSOSloc/2sCm4HRiRynO5bZs2erA5XXXntNTZs2TbndbjVy5Eh1//3397ZIGk1CFBcXq8LCwoZl8+bNyufzqTvvvFMppdSCBQuU0+lUjz32mNq2bZv66KOP1OTJk9WJJ57Yy5IPHIClqpV7aqKls7eJyETgEuwCeHnAGuAz4B9KqWgyFZXG5tRTT+XUU3UBWk3/Y/Dgptbmxx57jGg0yuWXXw7YIdfTpk3ju9/9LmDPLL73ve/xi1+0VmZN05Mk3KM5fuN/LL5oeoEdZXW8tqqQDYXVREyLLL+b4w8ZwrwJg/G6HL0tnkbTJo888ghf//rXGTZsGADz5s3j0UcfZeHChRxzzDHs3buXF154QT8E9QESVgqa3mNvdYjfvLmBNXuqAPA4DQwRdpUHWL6zkj++t5nvHDmKc2bl63BVTZ9j6dKlLFu2jLvvvrth3bnnnktlZSWnnHIKsViMWCzGaaedxuOPP96LkmqgHUeziGwVkenx19vi79tatvScyAOLgooA3//nF6wrrCLL7yI7xU2Kx4nP7SDd5yIrxYXLITz8wVYeWril3gek0fQZHnnkEcaMGcOJJ57YsO6jjz7i5ptv5re//S3Lli3jjTfeYNu2bVx22WW9KKkG2o8++gA7g7n+dXvLh90o44AlErP4+UurqQ3HyPS725wFuJ0GWX4XLy3fzdtrdfTGgUhH0TyxWIx7772Xgw8+GK/Xy4QJE/jzn//cy1JDdXU1zzzzDFdeeWWT7+/NN9/M2Wefzfe//32mTZvGKaecwkMPPcS//vUvNm/e3IsSa9rLaL600etLekQaTRMWbyujuDpMVoqrw7EOQ0hxO3jys+2cNHmoNiMdYCxZsgTTNBve19XVMXXqVC644AIAbrvtNh599FEeffRRpk+fzmeffcaVV16J2+3miiuu6C2xefrpp4lEIlx66aVN1tfV1WEYTZ9JHQ7bL6Znu71Ll3wKIpKjlCpLljCapixYWoCjE/5jr8ugrDbCyoIqZozI7Da5ND1PR9E8//jHP7j++us566yzABg7diyff/45d999d68qhUceeYQzzzyToUOHNll/5pln8utf/5pDDz2U+fPnU1BQwDXXXMO0adMYN25cL0mrgQST10TkChH5SaP3U0WkACiO9zXQdReSTChqsqGwmjRP4npbRDCVxRc7KrpRMk1foHk0TygUwuv1Nhnj8/nYsWMHO3bs6A0RWbRoEatWreJ73/tei2033XQTt9xyC7/61a+YNGkS3/jGN5g4cSKvvvpqixmEpmdJ9NP/IRBs9P53QCVwDZAB3JlUqTQEIyaGdL74nUOEyqBOGzmQqY/maXyzPeWUU3jggQdYvXo1SikWL17M3/72NwD27NnTK3IefvjhKKU47rjjWmxzOBzcdNNNbNy4kWAwyJ49e/jnP//JyJGdauOi6QYSVQojgQ0AIpIBHAP8VCn1IHAbcFL3iDdw8bocmKrz9lVLQapH5ywcyLQWzfPHP/6ROXPmMGPGDFwuF+edd16DacnRGRukZsCTqFJwAFb89TzsPgsL4+93YRfF0yQRr8tgVI6fQMTseHAjDEOYOjyze4TS9DptRfNkZ2ezYMECgsEg27dvZ/v27YwYMQKAMWPG9Ja4HWJaip1lAT7ZXMp76/fy0aYStpXWETOtjnfWdAuJGqw3AacC7wMXAJ8qpQLxbcOA8m6QbUAjInxjzgh++85GUjyJ7ROKmqR5nMwdndW9wml6jbaieepxu90NVXSfeeYZ5s+f38JJ3RfYWRbglZW7eXNNEaalEOwnzXo1JyIcf8gQzpg5nHGDU3tR0oFHokrht8BTIvIdIAs4r9G2rwCrki2YBo6eMJhHPtxKTShGmrf9f5VSitqwyffmj8Lp0I66A5W2onmWLFnC9u3bmTVrFsXFxdx///2sWLGCjz/+uJckbZ1wzOTpRTtYsKQAhSLN68TVyvc1Zlm8tbaIt9YW8fXpw/juvLH43NoM1hMkdPdQSv0L24/wa+ArSqmXGm3eCzzYDbINeHxuB78+eyoiUBNq23lsWoqyuihHTxjEObN0r4UDlfaiecLhMHfccQdTpkzh5JNPJhwO8+mnnzJ9+vRekLR1qoJRrn12Bc9+vos0n4PsFHerCgHAaRhkp7jJ8Ll4ZcUe/u+fyyirDfewxAMT6e+JInPmzFFLly7tbTG6lc3Ftdz2yhpKayKIKPxuJyIQMxXBqImIcPr0YXxv/lg9S9D0ScIxk2ufXcHmklqy/K5OR9VVBKLkZXj500WzSO1EmLambURkmVJqTvP1CX+6IpICXA7MB3KAK5VSm0TkAmCFUmpD0qTVNGH8kFSeuuwwlu+q5KUvCti0t4aopcj0uzl/8lBOmpzLoNQEHQ8aTS/w5Kc72FRcS3ZK5xUCQJbfxZ7KII98sIXrTzy4GyTU1JOQUhCREdjRRvnYoalTgLT45q8AxwPf7Qb5NHEMQ5g9KovZo7QTWdMUpRRbSmp5dWUhG4tqqA3H8LoM8jJ8fG1qHnNHZ/XqDHJ7aR0vLCsgYz9mCI3J9Ll4e20RJ0/JZfKwjCRKqGlMojOF+4EwMAHYA0QabfsAuD25Yh2Y1IZjLNxQzKdbygjHLIZnejl5Sh6H5KXpWkWa/eKTzaU8+dl2tpXWAXZ+i0OgJgS7K4J8vq2MNK+Lc2fnc9as4XicPe+s/c+K3SgUTqNr33HDEAThxWUFWil0I4kqhROwzUU7RaT5t2o3MDwZwsRnJE8Cudh5EY8qpf6YjGP3JkopXl25h4c/2ErMsnAagmEIqwsqeWvtXsYNTuHOM6YwOE2bgDSJoZTisY+28vzSAjzxKrnNHyzqo3VCUZO/fryVRVvLuPPMKaR7Oy6wmCyipsXb6/aS3kH0XKJk+Jx8srmM2nBM+xa6iUTnlG6gpo1tGUCy6irEgOuVUodgt/38vohMStKxe41/r9jNg+9vxuuyIyrSfS5SPU6yU91k+Z1sK63jx88up6Iu0vHBNBrgb59sY8HSXWT6XaR6ne3ONL0uBzkpbtYWVnPTS6sJRTuXENkVdpUH7L6/STJfGYZgGLCtpC4px9O0JNH/1CrgnDa2nQIsS4YwSqlCpdQX8dc1wHqSNAvpLSrqIjz6wVbSfU7czpYft4iQ6XdRXBPmqUW9U7hM079YvLWMZz/fRZbfjSNBk4yIkO13sbGohkc/7LmeWDvLAyQ7wNE0FTvLAx0P1OwXiSqF+4DLReQx7OgjgEkicgd2RNJ9yRZMREYDM4HFrWy7Ml6ddWlJSUmyT51U3l5bhKlUm/HY9WT6nLy9tohAJNZDkmn6K//6fCcuhySsEOoRETL8Tt5cU9Ru3ksyCUUtrCRrBVOpHp3tDDQSMsoppV4Skf8D7gHq++U9iW1S+oFS6q1kCiUiqcCLwDVKqerm25VSjwKPgp2nkMxzJ0o4ZrJ4azkbCqtBYPKwDGaOzGRVQRV7q0O4HAZTh2fw4aZSPK3MEBqjFNRFTPZWh7nuuRVMHp7BCZOGcvBQ7YDWNGVnWYD1hdVk+ffPL+A0DEwrxnvrizlzZvdPwj1OI+nfYUMEVwe/Kc3+k7CnRin1sIg8BRyBXQCvDLsGUlu+hv1CRFzYCuGfzTKn+wwfflnM7/67iWAkhqVAKYu6sEV1KEp2ihuPy0H9Q1xVMEpOirvNY9WEYuwoC2BaClNZrC+sZmtJHa+u3MOEIanc9vXJDEn3trm/ZmDxxppCoPMl1Rvjczl4YVlBjyiF4Vk+uhh01AKXwyA/y5fcg2oa6JS6VUrVKaXeVUr9Syn1tlKqRkTOEpGk+BTE/qY/DqxXSv0uGcdMNh9tKuGXr68HFFkpbnJS3ZgWVAYjWChKayO4HbZDOcvvIhQx2VZaRzjWcrpbE4qxtaQOhbLNAWKQ7nPFHdAutpTYDuhy7YDWxNlcXIu7i05br8ugqCqEZXX/JHtUjh+l7FIsycBSiphlMW6QLpLXXbT77RKRdBE5X0RuiN/8HY22nSMiq7Cf6pOVUXUU8C3gqyKyIr58LUnH7jJR0+J373xJituB17Uv3K+oOoTTEFyGgWHArgrbuSYiDMv0YSooqAg2OZZSthPOMOzGOKZlKwZ/PIyw3gFdWhvh759s6/Fr1fRN6sIxutqYTEQQA0KtPKgkG4/TwdETBlEdTI6vrCYUY+aITDL203ym6Zg2v17xUNDVwDPAvdg3/w9EJEdE/gssALKBHwBJyTtXSn2slBKl1DSl1Iz48kYyjp0MPt9WTiBiNigEgLJa+ym+fjrvECFmKmrD9o8g3efC7RCqg7Ems4WaUIyYqXCIoJTCVIohaZ4WZoEMn5N31+/tMcegpm/jczno6kO3Ugpl0eUZR6KcPSsfheqyw1kphWkpzp0zIkmSaVqjvW/FrwAf9pP7JOx+ChnA59ilLe4Exiul/qKUGhB3rC/31mCqps0/qkNRHM1u5Aq7UB2ACIwbnIoAxdWRhh9GdbxlpqUUUUuR5XeRk9rS91Af372hKKmuG00/ZUS2n0i0aw1oIjGLzBRXj5W+OCQvneMPGUplXdduExWBGIeNyWaOLvXSrbT3rTgK+EXcf7BBKfUmduTRGOAOpdQdSqlQj0jZR2jNYaYadwZpRONVPreDvEwfYwenUBmIURGIUheJNSiIYRleRmb723YeKvuHrNGcPCUXpPNtWhtTFzE5c8awJErVMVcfO44h6R4qA/unGKqDMTJ8Tq45/iAdkdfNtBd9lE3L5jkr43/f6x5x+jaH5GW0iA33ugxqQzEMh73e1hHSoiGIx2lww4kHk5vhZV1hNW+uLuKzLaUMy/R2+CVXQKa2oWqAiblp5Gf5KKkJk7IfZR7qncsnTc5LtmjtkuZ1cf/5M7huwQpKasJk+l0YCdzclVJUBmOke53cf94McnQ14G6nvZmCYJedaEy9UXxAzRDqmT0qiwyfu0mC2aBUD4p9T26WpXA7jSZ1WYIRk0y/i6nDMxia7uUrBw/h/44dl1AnqWDEJMvv5pDc9KRfj6b/ISJcMHckwai5X7OFymCMI8fl9EqdraHpXv5y0WyOnjCYykCUykC0TT+DUoqqYJTyuihzRmXx0MWzGZnj72GJByYdPWpcKSKnNXpf30r1ahEpbLReKaVuS7p0fQyHIfzs5Inc9PJqlIrhdztI8zrxu53URWIYKBBhZPa+L2/UtKiLmFx3wkEYjWYZowelMCM/kxUFVWSntD4LsJSiLhLj8nljmuyrGdgcf8hQFm0t56NNJZ3qT1AZiDI4zc01xx/UzRK2TYbfxS2nHsKKXXksWLKLL3ZVYoj9O1HKnmW7nHY03tThGZw/ZwSHjsnWJqMepM3OayLSGSO2Ukr1SgPV3ui8tnxnBb99eyOltRE76cxSlNaGGyKIMv0uLKUIRSwMQ/i/Y8dx+oyWiUKVgQjXPLeCgvIAGX5XQykMpWxHdSBscdLkodxw0sH6R6FpQjhmcvfr6/l0Sxnp3tbratVjWorKYJQhaR7uO3c6wzL7TuJXZSDClpJadpUHCUZNvC6D/Cw/YwelaFNRN9NW5zXdjnM/UUqxsqCK7fE69qMH+TFNxb9X7mFnWQC302De+EGcMjWXIWltZyRXh6L8a/FOXl9VSMyyEMBStlnqokNHcsrUXK0QNK1iWopnl+xkwZJdhKImTofgdzsxxP4ORWIWwaiJIcIxBw3m6mPHkelvO7teM7DQSqGPE4qafLm3hlDUItPvYsKQVK0MNAlRX4fr+WW72FZSRyhq4XIIg1I9nD5jGCdMGqqVgaYFXe7RrOlevC4H0/Ize1sMTT/E43Qw/6DBzD9oMGDPYvUDhWZ/0aUGNZoDDK0QNF1BKwVNv0Ap1aWELY1GkxhaKSSR8roIy3aUs2xHBVXBAVH5o1sJRU3eWVvElU8u5aQ/fMhJf/iQ7/5jCW+vLSIY6RtNVkpLS7n66qsZNmwYHo+HMWPG8PDDDzdsf+KJJ+wCdM2Wd999txel1mjaRvsUkkBFXYQH39/EJ1vKGkphKAUnTBrKVceM26/M04FOQUWAn76wirLaMC6n0ZDRvbc6xP3vbOSJT7Zz77nTGJHdewlNtbW1zJ8/n+HDh/PMM88watQoCgsLiUabPhA4HA4KCgqarMvOzu5JUTWahNF3qy5SFYjyo2eXU1QVItPvaiiDYVqKt9YUsbm4lt+dPyOh7GWNTUVdhOsWrKQmGCWrWYOiFI+TFI8d3379gpU8/K3ZZLfTxKg7ue+++wgEArz22mt4PHZM/ejRo1sdm5ub24OSaTT7T3uls9/vxDIgayGB3S+3sCpETmrTJuoOQ8hOcbFpby2vr97TixL2P/69YjcVdZF2a+Zn+F2UByK8vHx3D0rWlBdffJF58+Zx7bXXkpeXx8SJE/nJT35CINC0qbxpmowdO5a8vDyOPfZYXnvttV6SWKPpmPZ8CgZ2WYv6ZSJwLDAau6T26Pj7g2m1TuiBTzhm8vrqPWT4Wp9wiQgpXgfPLy3QTtIEicQs/r18N2nejiex6V4n/1mxu9Wudj3Bli1beOGFF6irq+PVV1/l3nvv5bnnnuOKK65oGHPwwQfzj3/8g5deeomXXnqJGTNm8PWvf53HH3+8V2TWaDqizV+eUurY+tcicibwR+BwpdTnjdYfBjwX3zbgKK+LYJoKl6dt3epzOSirjRCOWU2a82haZ3dlkIhpkeHuuCqs22kQDEbZVR5k/JCeb89oWRaDBg3i8ccfx+m0f0qRSITzzjuPBx98kOzsbI444giOOOKIhn2OOOIIysvL+c1vfsPll1/e4zJrNB2RqE/hl8CtjRUCgFJqsYjcDtwF/CfJsvV5XA4DMx4q2VZseH0VSGc3FrRTSrGlpI43VheyoyxAisfBVycO4YhxOXic/UsRRU2rU9NOAWJW7/SayMvLY/To0Q0KAWDy5MkA7Nixo01n8pFHHsmzzz7bIzJqNJ0lUaUwAShpY1sxMD454vQvclLcjMrxU1gValIquzHVwSiHjsnuti5XUdPivrc3snBjMWA/PZuWYvHWcrJSXNxz9jRGD0rplnN3B9kpbkwrsaxcpRQxS5HdSyUcjj76aBYuXIhpmjgctvLduHEj0LbDGWD58uWMGKFbSmr6JoneqbYB32tj2/eA7UmRpp8hIlx82CjCUQuzlca5UdPCUvCNud13A3jwvU28v6GYTL+L7BQ3qR4nGT4XWSkuqoMxbnh+JRV1kW47f7IZlOphan4G1aGOG71Xh2JMHpbBkPS2Cw52JzfccAPFxcX84Ac/YOPGjfzvf//jhhtu4Nvf/jZZWXbLyNtvv5033niDzZs3s3btWu644w7++te/ct111/WKzBpNRyQ6U7gD+KeIrAFeAPYCQ4FzsR3Q3+we8fo+8w8azMVlAf65eAcAqR4nCkVd2ERE+MFXxndbTaPi6hBvrS0iu40uVuk+J+V1Ed5cU8hFh43qFhm6gwvmjuDnL1ViWqpFp7t6TEsRMxUXHjqyh6Xbx/Tp03njjTe48cYbmT59Orm5uZx33nnccccdDWOqq6v5/ve/T1FRET6fj4kTJ7JgwQLOOeecXpNbo2mPhKukisjx2MphDuACosAS4DalVNJCUkXkb8BpQLFSakpH4/tKldT1hdW8vLyAVbuqEIFDx+Rw5szhjOlG080Lywp45IMt5KS2bT4JRU18LifPXHl4t8nRHfz9k238a/FO/G5HixwPu9eEyQVzR3D50WN7ScKWWJaiPGD32Mj0u/qdP0czsOhylVSl1LvAuyJiAIOAUqVUd3j4ngD+BDzZDcfuFsIxk6LqED6XkyPG5TB3TDaHju4+P0I9JTVhOqp95nYaVAT6j/monkuOHE1ehpcnPt1ORV0UUykEMETI9Lu4av44u4l9HyAYMXlj9R6eX1pARSCKiB1YcMrUPM6ZlU9uRu+YtzSa/WF/Mpr92HkKDiDpSkEp9aGIjE72cbuLTXtruOnl1VQFYwgKBbyxpojBqR5+ffbUbi3DkJ3iohVXRhOiMYt0b8fhnX0NEeHkKXmcOCmXlQWV7KoIApCf5WNGfmafaU9aFYzy0xdWsqWkjhS3g6x4a9WoaedbvLO2iHvPnc7BuWm9LKlGkxgJP8qKyGki8gVQBWwFpsbX/1VELuom+dqS5UoRWSoiS0tK2gqK6n7KasP85IVVBCIm2SkuslLcZKe4yfK74ttWdmvhtqMnDI532WpbM9SFTb42rW88Ue8PhiHMHJnF6dOHcfr0YcwamdVnFALAr99Yz7bSOnJSXE3MXC6HQU6qG9NS3PjiKqpDukCipn+QkFKIJ6/9BygFfkbTDOZtwHeSLlk7KKUeVUrNUUrNGTx4cE+euglvrikiEI61mn2b4XdRXhfhwy+Lu+38wzJ9zJ8wmIq6aKsZ07XhGD63g1OnDus2GQYy20rr+GJnBVl+V5vhs6leJ3WRGO+v777vgUaTTBKdKdwG/F0pdSLwh2bb1gAdOoQPRN7fsLfdQncuh/BuN98MbjjpYOaOzqYiEKUiECEYMakNxSivi+ByGPzm3GkMTtMN0LuD9zcUo1THTW28LoN/r+i9Gk0aTWdI1KdwCPDT+Ovmj6QVQE7SJOpHhGMWRjtq1RAh1M11ebwuB3efNYVVBVX8Z8VudpYHSfE4OOGQoRw7cUiTpDqlFGv3VPP5tnLCMYuDc1M5ctwgXX5jPymsDOJ0dGzK8jgdlNWGe0AijabrJKoUqrEjjlpjNG1nO3caEXkGu9DeIBEpwA557VPVw2KmxZLtFeytCrGzIojLEFK9ToakeUnx7LvBhmMWU4ZlJPXcpbVhlm4vJxS1GJ7lY9bILByGMH1EJtNHZLa73y/+vYbNJbVYFhiGbQP0uhz84uuTmD2q79T3rwpG+WxLGeV1EbL8Lg4fm9OihHZfINXjbDVpsTmmpbTi1fQbElUK/wV+LiJvAjXxdUpEPMAPgDeTJZBS6sJkHas7qA3HuPHFlSzaUk5lKIppKiJAIGJSXhdhUKqHkdl+YpaFIJw6LS8p542aFn/+32beXF2EpRRK2eW5M/0ubj1tElOGt618IjGLn76wit0VgRb272DE5JaX1/DAhTOZMLR3I2SUUjz52Q6e/XwnplKYlsIQwTCEs2YO54qjx7aZzNYbHDVhEG+uKepwXG04xpkzhveARBpN10nUp3AzkAtsBP6KbUK6EVgB5AO3d4NsfZLfv7ORpdsrqIvE8DkN3E7BkHhhNlNRVhtmW2kdNaEY3//qOPKzkhOS+sd3N/HaKrtMd06qm0FpbrJSXAQiMX724iq2lda12CdmWtSFY3y2pdRWCCnuFvZvn9uBpeDpeEZ2b/LEp9t5etEOUjwOslPcDE7zkJPqJt3r5IVlBfxl4ebeFrEJs0ZmkZ3ioqadkhx2gT/htGna2a/pHySkFJRS24FZwGvACYAJzAcWAYcppQZEF5nimhAfbiohEDEx4r12PU4HXpejIUyyvvzC3WdN5fTpyXk63FMZ5J11RWSnuFuEY6Z4nERNi38u2ndT31Ue4J4313Pagx9z5l8+4boFK6kMRts0dWT4nHy2pYxQtPf6HlfURXhuyS4y/K4WSX8OQ8jyu3h15R6KqkK9JGFLHIZw2+l2VdSqQMsIsGDEpDoY44r5YxiZ03ttQzWazpBwnoJSqkApdblSKl8p5VZK5SmlLlVK7epOAfsSm/bWorCf/hrfm52G4Hc7SPE4cDkMhmZ4yPAlL2Hs082lWIpW6xsBZPhcfLSplEjM4su9NfzfP7/g/Q3FpHmd5KTYsfIVgSibi2tbVQyGIQgQjvZOCWqAjzeXYFqqzRLjDkNQCt7bsLdH5SotLeXqq69m2LBheDwexowZw8MPP9ywffwgP5NLF7Lo3m/zwg+O5ZWbz2PlW89RURfF53Lws1Mmcu5sXRFV039IyKcgIt8GvlRKLWpl2yDga0qpflOWYn8xRJCGRnQtEREMA5SCZJm+wzGT9UXVBCMxIjEnbmdLPe4w7FzqUDTG3a+vx1JWk77FKR4H4ZhFMGqytzrEsExfk/0jMQuP09HESd7T7K0O0zKwrSmGQY/OFGpra5k/fz7Dhw/nmWeeYdSoURQWFhKN7ktEu+222/j7o4/yt0cfJXvEBF5/9wMeuOMnnHvYaO748Y/7VKKdRpMIiTqanwBiInKtUurPzbaNA/5OP6pV1BpKKTYV11JaEybT7+aQvLQW9vdJw9IxBNxOIWYqmkcjxkxFqseJz+VgVE7XCuEppXh+aQFPL9pBeSBCSU2Y8roo6T4nI7J8TUws4ZhFitvJ9rIARdUhMpu1B81J9VBeF8VhCGW1EXIzvE1mHdWhGBfMHdHttZraI8vvRnXQXsey7B4WPcV9991HIBDgtddew+Oxcz2a90n4xz/+wfXXX89ZZ50FwDFzphAu/JKnHvoDv/zZNT0mq0aTLDpzF/gP8ICI/FE6ytbpZ2zaW8Pl/1jKD5/5gl++vo7rFqzg4scXs3JXZZNxGT4XX58+HL/b0dBxrR671ITC53ZwwaEjcHXxBvvkZzt49KOtOB3CsAwvLoeBIYrqYIzNJXVNzEA1oShnzhzOnspQq81p/G7bcWtZYCrb5wG2/6OsNkJehpfzetnEcdT4Qe2W7LCvC445eEiPyfTiiy8yb948rr32WvLy8pg4cSI/+clPCAQCDWNCoRBeb9OCdz6fjx07drBjR+877zWaztKZO9d9wAXAd4FXRKT/tPNqh13lAa5/fiVFVUEyfS4yfC4yfE6qglFufGkVG4tqmoz/3jFjOW3aMNK9TsIxi1DUJBS1iJgWOakezp2d3+UbbGUgwjOf7yTTZ5uLDEMYke3HUoIIhKMmlYEIUdOitDbCmEGpnDM7H5/b0abfYUSWj9x0Dyg7RLI6GKM6FOPYgwfzwAUzyfD3btG83AwvJ0zKpaIu2kIxKKUor4tyxLhB3VqKvDlbtmzhhRdeoK6ujldffZV7772X5557jiuuuKJhzCmnnMIDDzzA6tWrUUqxePFi/va3vwGwZ8+AiL/QHGB0qkqqUup5EdmGPWv4WERO6x6xeo7nluwiFDHJbtSTQERI9TipDER44pNt/PqcaQ3bXA6Dm0+dxEWHjeLVVbtZuasSA7to22nThiUlymTR1nIspZqYczL9LpxGCoVVIWpDMYqqwnhdTs6dnc/Fh48i1eNk1shMROxQ1BamIAGv28FJU3K56phxxExFfpavTyWF/fi4CURNi4Ub7fIRTocQs2yj0uFjs/n5KRN7VB7Lshg0aBCPP/54Qx/mSCTCeeedx4MPPkh2djZ//OMfueqqq5gxYwYiwrBhw7j88su55557Glp0ajT9iU6XzlZKLRWRw4BXsJvs/CrpUvUQSine21BMur/1jyHd52LZzgqCEbNFjaMxg1L40VcP6vQ5LUuxsqCSRVvLMS3FzJGZHDomu4m5qS4cazVKKNXrZII3lbpwDL/byZOXH9qkkUua18V5s0fwr8U7yUpxNUn0CkVNLAsunzeGQ/LSOy13T+B2Gtz0tUO48NCRvLtuL0XVIQaneTj+kKGMG5zSYY2hZJOXl8fo0aMbFALA5Ml2COqOHTvIzs4mOzubBQsWEIlEKC4uZtiwYQ3RSWPGjOlReTWaZLA//RRQShWIyDzgn8ADdBQ20oeJxCzSva0/0dWbYiKmhY99YwKRGEu3V1AbjjEs00duhof31xezszzA4DQvJxwytNUZQ2Ugwk0vr2ZLcR2WshARXl21m0GpHn5zzrSGRLe8TG9DTZ1Q1CIQiSHYpTRcDiFiWszITWu1s9clR44mZlm89MVuTEvZMw7DwOsyuO3rk7qtNWgyGTMohSvm935HtaOPPpqFCxdimmbDU//GjRuBlg5nt9tNfn4+AM888wzz58+nNyv4ajT7S2d6NBc0XqGUCgBniciNwMHJFqwnEBHGD0lhd0WQ1FbKXwejJll+N2mNisq9vmoPf1m4hZilUJaiNhyjMhhlcJoHn8uBpRTPLdnJWTPzueqYsQ1Pt0op7nh1HZuLa1uUmiirjfCzF1fx90sOxe00mDs6G6/TwcbCakKxRvpWINvvwu92ctbM1hPjDEO4cv44zp09gkVby6gNRcnN8HHY2OxOt4esDcf4fFsZNaEYueleZo/K6tUIpZ7mhhtuYMGCBfzgBz/gmmuuYc+ePdxwww18+9vfJisrC4AlS5awfft2Zs2aRXFxMffffz8rVqzg448/7mXpNZr9IyGloJS6o51t9yRPnJ7nwrkjueuN9fiaNYm3lKIuZHLpV0c3xJp/sLGYP7y7iTSvkzSnQV04xq4KO2a9tDbMwUPTcDsNTEvx4he7GJXt52vx2kebimtZt6eq1dr7mX4XpTVhFm0tY/5BgxsK1dVFLRzx1o4ApoLimjCzR/qZ2U7xO4DsFDdfm7r/dZde+qKAv360DdNSmMpOKkvzOvnFaZOZmp/cIn99lenTp/PGG29w4403Mn36dIbm5nLm2efwq1/e2TAmHA5zxx13sGXLFtxuN/Pnz+fTTz9l6tSpvSi5RrP/tKkURGQ+8IVSqjb+ul2UUh8mVbIe4piDB7O+qJp/L9+NUuBxGURiFgo47pAhfD1eqkIpxV8/3obPbTQkkBVXhxv68UZNi7LaMHmZPhyGkOJ28vTiHZwyNRcRYd2eattp2o5dfOn2cuYfNJgl2yuoCEQ4JDeV4poIFfFm8G6HQV6Gl7K6MGXx4nvdwbvr9/LQwi2k+5wtfB03vrSKhy+e3a1tRvsSxx13HAveXMg/Pt3Op1tKWafg0qdXcf6cfM6cMZx58+axZs2a3hZTo0ka7c0UFgKHA5/HX7flN5D4tn4ZaiEiXHXMOI49eAivrdrDrvIAueleTp02jGn5GYgINaEoLywrYOWuSlwOg0y/kyy/m9pwrGF24RChIhglryFbWLG5uJZ73tzA1OEZhGNmuwpBsc+H8cGXxfHZghOnEQVlH99SipLaCAbw7rq9XHDoyKR/Hpal+NvH2/C7HS1yLVI8Tirq7M/i2hM672Tvj6wvrOaG51cSMxWZPheGIYSiJg9/sJWVu6q4/fTJfapyq0bTVdpTCl8B1sVff5V+7EzuCBHhkLz0VqNydpYFuP75FZTWRgjHLExLsacyxt7qMJYCUSD1lS+UPaPYXRGkrM5+un9vw17+t7EYBYQiMZRqvXWjAIePs3sVRWIWhiHsrQ5RWhvG5TQacn0Vdp7C3z7exukzhuF371esQJvsrgxSXhchs428hVSvgw++LOlWpbC7MsjrK/ewfFclXpeDEycN5diDh7Tb5a47UEpx79sbAUVWyr7Pw+ty4HEaLNpaxiebS5l/kHYoaw4c2ryjKKU+aPR6YY9I08ewLMUv/rOG2lCMIWkeSmrs+jwuwyBmKUzLQilp8CNkpLgoqQ1TWhdBgFSvq8HEE4yalNaE2VsdZmi6p4kDujIQIz/Lz5xRtvNy1sgsPtpUSnFNGIdhNC3+oGzfR8S0+PDLEk6ekpx+DfXELNVu3SZDhLDZfdVUP9lcwl2vr483pjEwLVi7p4p/fb6T350/o0dbi24qrqWwMkiGr+XPRERwOYWXl+/WSkFzQJFQKImIbBWR6W1smyIiW5MrVt9gZUElhVVBPC6DYNRkUJob07Kf1p2GYIg0VE0VEXJS3RRXh+M3VSEvY1/5A5/LQU6Km3Sfi4pAjJKaMCXVYSqDUcYOTuE3505riOw5duIQRIg3mWkqU8xS+N1O/G4HH35ZmtB17K4M8r+NxXy0qYSqYLTdscMyvTgdtl+lNWpCUSYMTeWLnRVsLq5pUS66K5TUhLnr9fV4nAbZKW78bidpXifZKfbnes+b65N2ro4wLcXuygC0UjakHq/TQVFVsMdk0mh6gkRtD6OBth7RvMCopEjTC5TWhnlrTSErd1Xhczs4YdJQDh+bg8th8N76vewsDzS5KTgdQsy0AMFUdgG8cMwi2++iJmQSNRVOhzAy209aszDXVK+TTJ+bX3x9El/srMCyFJOGZTB5WHqTc6R6nHzvmLHc/PIaoqZC4pY7heBxORg9yE8oanbYCrImFOU3b23g823lDf4KAc6Znc9lR41ptYKnx+ng3Nn5PPnpDrJTXU3KZgTCMfZUhghETDYW1WAqGJru4boTDmZGB9FQifDOuqI2W1dm+p2s3l3FrvJAtzq5S2vD/GvxTt5eW0RNKEZxTYjcmMmQNG+Lzyscs8gfIA53zcChMwbptu5Ac4DKrovS8yzdXs5tr6wlalq4HAaWUizaWsa4wamcM2s4L32xG0uB2yEN3vSYad+00n0uqoNRTp2ax/UnHsy2sjo2Flbzl4VbyM3wtup8tJTC5TQYOziVsYNT25XthEm5PPLBVrt7WrypT6bfRZrXScy0C+ONyvFjNgulrUcpxc0vr2FDYTVZKfv8GDFL8eySXYgIl89rPeP2okNHsqs8wAdflkC83EQ4ZrGnMkSmz0VehhcRidckinDji6u4//zpTO5iP+p1e6obkvaaIyI4DGFHWfcphdLaMD/41xeU1UbI8DlJ83qpqItQWBWmNmwydnBKg5JUShGJWW3mi2g0/ZU2zUcicq2I7BSRndj3w1fr3zdaSoA/A2/1lMDJoqIuwu2vrMVpCNkpbtK8TjJ8LrL8LjYX13Lrf9aS6Xfa5pu4iUSwb5ChqIXf5WBImocffHUCWSluZo3M4oJDRzJ2cEqbHcxCUYuvJljlM93r4vTpwzEMg9E5fkbl+ElxO9hZFmDtnmpKa8O8vGI3Fz66iA+/LG6x/4pdlWwsqmmiEMA2e2X67PaWNaHWTUlOh11u4o8XzOTUaXnMGZ3N5GEZDE33MGqQv+F49TWiRODxj7YldF3tkep1Ypptz36UAp+7+5LnnvhkO2W1EXJS3Q2mvFGD/BgC1cEo5bVhwA4EKKuNMn1EJvPGD+o2eTSa3qC9mcJW4L346+8AS4GSZmPC2BFKf02+aN3Lf9fvJWJaZHubFoQTEbwugx1lYfIyPAxN97K3OoTDqG+yAwpFYVWI7x49ltxGfgMR4Yqjx3LHq+twOsyGDGKl7Cd7j9PuyrZpbzWhqEVxTZh0n4sZIzJbLbV95fyxVAYifLjJ7kpWVBUialq4nQbjBqficzsIRkx++dp6bj9dOGr8PofnZ1vKsNqwhzsdBkqZrN5dxZHjWr+pNY/IuuyJz0lvo5tcutfJmj1VVAWjXeo4d8IhQ1m4oaTV8t/hmIXHaTBlePckzoVjJu9t2NuiWmyqx8mEoWnsKKll1at/o3bt/whXlZCZlcPB551D5Gv34Eo5IAoGazRA+9FH/8Guhlr/A71TKdX1x8EOEJGTgT9i5z38tbsyptfurmphdrGUbULYWxUiYirW7alhSJqtGEprw3Yfgvgu44ek8H/Hjmtx3HkTBnPDSQfz5/c3UxmJ2o7omEVtOEaK28ndr62jMJ70NjjVg9flwO92cMOJB3Nks6dOt9PgltMmsa20jic+2cbrqwsZkeIjw++m/p7pcztQKB5auJUjxw1quJnazu/2P4NYO0/lzQlFLdqqcCFiO93bck4nysyRWUzNz2Dlrkoy/fsK+oWiJnVhk2uOn9DpUh2JUhe2Cwa21g7U73ZgrX6VikUv8shfH+foIw5j6+ZNXHrppcSiER555JFukUmj6Q0SLXNxaXcLAiAiDmxz1AnYtZaWiMgrSql17e/ZedK8riaOWqVgW0kdteFow4xABEpqw7idBgcNTSUWHx8Im5w5M7/NVosnTc7lmIMGs3xnJXsqgzz20VayHS58bgdfxvs8K0tRXBPmoKFp1IZj/N8/v2BwmocMv4vjJg7h7Fn5DE23ZyFjBqVgWsre3sqTuM/loLQ2zI6yAKPj/QZmjMzijdWFrcpnKbtQ3sTctIQ/rynD01m4saTVm3IoapLqcZLVxZ4MDkP45RlTePiDLbyzrgjBbjOa6nFyw4njOCnJ4beNSfXYxQbr/UvNKdm8ilHTjuCyiy8EYMK4sVx44YW8//773SaTRtMbJOxoFpGxwPnASOyIo8YopdTlSZDnUGCzUmpr/JzPAmewL4muy9SGY7y7rogvdlawuyJIIBxjUJqHQMSkNhzD6TCwLIXLYTeKdzkNwjGL4poI+Vk+QlETj8vByVNy2z2P1+XgiHE5PPzBFkylyElxs6cyiKXA5bCz3aKmYntZHeGohakUZXVh0rxOFizZxYKlu7j62PGcNCmXDL+LQNRseHKOmraDtzoYRQQy/W4cYucu1HP42GyyUzxUBiJNTCJKKSrqohxz0GCGpDf/N7bNObNG8MHGUiIx23wVjJi249xhUBuKccX8sUkpludzO7j2hIO4bN4YdpYF4qaylG4vxOd2Gnxtah4vLS9oUT5EKUX6qCmULnqRVatWMW3aNLZu3cobb7zBOeec061yaTQ9TUJKQUTOAJ7HdkwXY/sSGpOsYPXhwK5G7wuAw1qR50rgSoCRIxMv9VBcE+La51ZQUhPG4zDwuAz21th1hAyxLUOWZT9Jj8rxU14XJRAxEaC0JozXaeB2OvjF1yclXHfo3fV7G6qsVgaiTUwwDgOqAlFSPE4cSlETirGxqJpwzG4sc/t/1jRkLk8bnsGagirqMNlaWotl0ZDDUBsKxGct+/4NHqeDe8+dxs9eXEVZbQSFsvcxhBkjMrnuxM5lJB+cm8aPjxvPb97aSEltGNNS9tkUjB+SylcnDu3U8Toiw+fq8cJ73zpiFMt3VrKttI4UrwO3w85PCUctLr7yBzA3j1mzZiEixGIxrrjiCn75y1/2qIwaTXeT6EzhLuz6R99USjV3NieT1uwxLRSOUupR4FGAOXPmJKyQ7n1rI6U1EbLj3cbGe9LYUxmktDZMIGLhcQpuh8GITB+ZfjfZKR5qQlHK6yIEIibnzx3BmTOHMyQt8SfsSNTC7ZRWL6TeHKVQhKIWprLt/PHcN8yY3Qf6hWUFTBicSlldhOpgBIfDwOWwM52VUlhi273ve3sjj35rToNfYUS2n39cdiiLt5azsqASj9Pg8LE5LfIiEiU/24/DIWR4nZjxWUKm30UkZnLD8yv5yzdnkeJJbtmNniTN6+IPF8zg9dWF/Gf5biqDUfKzfJw3O5+y1R9y7cMP8/e//50ZM2awceNGrr32Wm655Rbuvvvu3hZdo0kaif6CxwLXd7NCAHtm0LjBcT6QlEa3BRUBVhVUkdWoy5ohkJ/lIy/Dy9o91aR6HIwZlNIo5NLuvuZ1OYhZisvnje108bPJwzNYvrOCTL9BmsdJRSCCEY/FN+OtJsNRi3r3hlHvzMAus7GrIoigKCgPkOpxErUgZlm2GcdhIGKXtB6V42dHWZD1hTVMGravhpPLYTBvwiDmTeh66OTDH2zB5zIYktYy2qawKsh764s5fcawLp+nN0nxODl/zgjOn9O0z/bIk2/gxz/+Md/61rcAmDp1KsFgkMsuu4xbb70VrzfxBwWNpi+TqKF2A5DTnYLEWQJMEJExIuIGLsBu+9ll9lTaYaWtPSE7DCEnxU0k1nLSoeJmndOnD9uvapjfmDsCSymipmXX7RHBqje9YJtzTGW/t2cITc8RjJjELHA4DByG4HUKbqftCDcMYdyQFMYMSsFhGIBiZ3mg0zImQnFNiM3FtaS2MRPwOA3eWNO6Y/tAoK6uDsNo+nNxOBwopZJa6kOj6W0SnSn8FPiDiCyudwJ3B0qpmIj8AHgbOyT1b0qptck4dorHQWu/XUtBVSBCTcjOI9hVHiQ7xY3P7SASs6iLxBg3OJULO1GmOhQ1+WRzKf/bWEwkZjFzRCZf7KwEYFCKi+KaCMq0SPO4qApG7Kxhwy6NXY8dq29HRQkKlMSrsQoeh4HbaZuaXIbRoOgMEfzdVEk0HLVwiLRpdnIYQiAS65ZzdwWlFBFz36xqfznzzDP57W9/y/jx45k5cyYbN27klltu4ZRTTsHn83V8AI2mn5CoUrgde6awXkQ2AeXNtiul1DHJEEgp9QbwRjKO1ZiJuelk+FwEIrGGctPhqMXmklqiMdt8k+F3Eo1ZFFaFGJTqYVCah28dMYpTpw5LuGxzaW2Y6xespLAqiCNeNC8QMQlGTEbn+En3pXD2rFSyUzyEohb/XVfEyl1VOBzSMFOpf/J0GBLPjbBvZoNSvdSEapskdwWiJh6XQdS0cBjC7Hil1WQzJN2D02G0GbIZiJgc0UYiXG9QXB3i2SW7eHttEeGYRZbfxTmz8jlz5vBWayt1xAMPPEB2djbXX389e/bsYciQIZx22mncdddd3SC9RtN7SCJTXxFZSAcRRkqpryRJpk4xZ84ctXTp0oTGLtpaxi/+sxaXA1LcTtYX1di9CwSy/G5GZPsQESrqoowfksKfLprV6afLHz+7nA2FdnkJpaCgwu5PUD8LGJnlJ83n5K4zpzJpWDq7ygNc8Ohn1IZj1IRiKGVHJTkMg5hpO5/dDiHT72Z0TgpbS2upCkZxGIIgjMrx43EZ1IVNrj5mLOfMHtGBhPvPXz/ayrNLdpHTrHRG1LSoDcX400WzmDA08dyH7mJ3ZZAfPbOc6mCUDJ8Tp8MgFDWpDZlMGpbOvedO2y/FALbC3lRcy2dbSglGLQ4emsYR43L2+3gaTW8hIsuUUnOar080ee3YpEvUCxw+Nod7zp7Cna+tY2VBVUMGrt/tILvRjS7T72RTcS0b99YwMbdl45222FZax/rCmgZndlF1iPK6CE6HfQOPmgoltn/h5y+t4vFL5jIi28/Xpw/jvfXFjMwSviyuJRKL92qIH9dS4HUZrN1TZSfcKXuWA3Yl1Ayfn6vmj+swd6KrfOuIUWwtqWPpjnJEbCd2JGYhCD88bkKfUAgAf/jvl9SGY+Sk7ithUt8YZ11hNa+s3NPCkZwIdeEYv3xtHct3VthBAiIYAj63kztOn8z0JFSK1Wh6m+7NCOqDfLa1nNpQjEy/C7cDUtwGSim2lNTZ9n2IVwCFzcW1nTr21pJaROybhWXZ/QHsJ3obQVEXNknxOAlGTd6KO2avO+FgvjY1j5K6iJ0w5xCcDiE9XqRPBHaVB1Eo3E4Dr8vA7RQGp7pxORz8/JRDOGVqXpds5ongcTq468wp/OqsqcwbP4iDhqZx9qx8Hvv2HE6b1jeijvZWh1hZUElmG41xUj0OXlhWsF/H/tUb61m2o4JMv4tBaR5yUt1kpbixlMVNL69mVzc5+QcqlmVx5513Mn78eHw+HyNHjuRHP/oRdXV1TcYtXryYI488Eq/XS15eHj//+c8xu7ER1IFOZzKahwPXA/Ox/QtfV0qtEZFrgM+UUou7R8TksaWklv+s2E1WihtTQXUwhmEIBnZ4aEF5iPRh9oyh/km4M7idBvWpFsF4pdTGAUsKGpLXvC6DjzeVcfHho3E7DX7w1fG8v6E4Xm7BIMXtxOkQTEuxencVMVMRjSlwWCBCbrqP3AwP1aEYTy/ewYyRmV3+fBLBMIQ5o7OZMzq7R87XWfZWh3AabTvEPU4jXsfK6lSW9LbSOpZsL28yo6zH73ZSXhfh5eW7+dFxE7okv2Yf999/P/fddx9PPPEEs2fPZuPGjVx66aWEw+GGelO7du3ihBNO4JxzzuGxxx5j06ZNXHbZZSiluOeebimbdsCTaEbzZOAjwAQ+A2YC9XPzUdjlKS7qDgGTybvr9oKyHbgZPidFVTSEgtpOXYu6sInP7UCk807bmSOzMMS2sTfH9t3YvgH7fdPo0y0ltcQsq9VMaUHiJaNtH0KKx9lQuC3d62TlrkrCMbPbisX1J+yaVjRxxjcmZil8Lkenw4uXbS9vMBm1RqrHyf82FGulkEQ++eQTTjzxxIZSIqNHj25Rb+qhhx4iPT2dxx9/HMMwmDx5Mrt37+anP/0pt956Kym6gm2nSfRR6X5gPTAGOJummcefAocnWa5uobgmjCOeOOZ1OcjwuYiZ+3IGFEI4ZlIRiHL69GEJl7KoJ9Xj5OLDRlEVjMX9CLY/QClFzFKkeBykxKOYwjGLYxr19q0NxagNxSiuCVNeF2ko1lcvm4jgNIQMn6vVSp4ddWFrTjBi8vbaIq59bgVXPLmU+97awMaimk4doy8yOsfP8CwfteHWzQdVwRinThvWaVNbuIMKsA5DWn0Y0Ow/8+bN45NPPmHVqlUADfWmTj311IYx9YqjcQ7JySefTCAQYPny5T0u84FAouajecCFSqnaeCXTxuwFutfDmSRG5fj5aNO+m+fIHD97KoKUByJ2uQgFpgUXHTaCS49qvStZR1x02EicDuHpRTtJ89pmBYdhkJXiZnimD0vB3qr6wngGVcEoy3dUcN87GymuCcerswoFEmRYppdBqR7cToNQNEZGK0oqEDEZme3H14nol5KaMNcvWEFRdQiXw8BpCDvK6vjv+mK+edhIvn3EqG73T3QXIsKPjpvAz15YRU0oRqrHnvVZSlEVjJLld3HenPxOH3fC0LQ2u8KBXWhxShc7z2macv311xMMBtutN1VYWMhRRx3VZL/c3NyGbZrOk6hSaO8RaBDQL7qXnzgpl38t3tkQa28I5Gf7yM3wUlITJifVzT8uO5Q07/6XgBYRvjF3JGfMGM66PdU8v2wXS7ZVxJ3FASoCUQyBQakeHvlwCw++v4lg1GRYhpch6R5Ka+xoJaUUuyuCOETITnGxuyLWULOpHtNSBKMW3zxsZMI3caUUd762jr3V4SbH87kdmJbi6UU7OCgeZtlfmTEik3vPncaD729iV3kQhwExC+aMyuZHx03o9AwQYNbITLL9HqpDkRbfD9NSxEzF+XO7Lxx4IPLCCy/w0EMPdbreVOPOgJrOk6hS+By4FHi1lW3nA58kTaJuJDfDy/8dO44//W8zIpDmcWIpqIlHI9177vQuKYTGeF0OZo3KYtaoLIqrQyxYWsBTi7YzIstPTuq+Jjkbi2qoC5tk+FzkZfiIxCyqg3ZmsFKwqyLIyGwfZ8wYzsqCSspjEZyGHd4qAhfMHcFXJibW4hNs38XGoupWex84DMHlEJ75fGe/VgoA00dk8ti357CrPEh1KMrQdK9dZmQ/cToMfnnmFG54fiXltRH8HtsvUReOYSk4f84I5nRT4uBA5frrr++w3lReXh5FRUVN9qt/Xz9j0HSORJXCL4F3ReQd4F/Ypu7jReTHwFnYEUn9gtNnDGfMoFSeW7qTVbuqcDsNzpo5nLNn5TdprZlMhqR72VpSS5bfjlwpqLBDF/1uB6GoidMBRVUhsv1uxgxKoS5sUloTpjoUJWYp8jJ8fHXiEC45ajQfbCyhsCrE8CwfJ03OZcygpo60oqoQhVVB/G4nE4aktmgEtKGoJu7kbv0pKs3rZO2eaixLtdlEqL8gIozM8SfteOOHpPLX78zhtVV7+O+6YsJRk9mjsjl3dj4zRmTqJ9Mkk0i9qaOOOoqnnnoKy7Iaxr711lv4/X5mzpzZ4zIfCCSavPaBiJwJ/AH4W3z1PcB24Mz+EI7amKn5GUzNn9pj5wtEYnyxq4KK2ggRU1HvPi6rVURMhc9tV2ENxSy8LruGf2UwCtgRSrsqAvz+3U3kpLj47fkzGJ7ZstbOrvIAf3xvE6sKKuN1lOyeBFfMH8vxh+zrdSBIu206FXYYbV+7v4VjJp9uLuPVVXsor4swONXD6TOGcfjYnE6HDneFQakeLjlyDJccuX8+J03iJFJv6uqrr+ZPf/oTV1xxBddddx1btmzh1ltv5Yc//KGOPNpPEs5TUEq9DrwuIuOBIUCZUmpjt0l2ABGImBRXhVGo+A0s3l9BKSKmSTBsd3NTyk5u210RjIdMKpQSsv1uDEOoCES56aXV/P2SuU2e4vfEyzoEImbDbATsCKN73txAIBzj9BnDAZgWb1zTVshmdTDG3NHZfeqpt7g6xE9fWMWeqiAuh+ByGBTHk9RGD0rhN2dPI6uZv0XT/0mk3tSIESN45513uO6665g9ezaZmZlceeWVuiZVF0io9lFfpjO1j7qDnWUB1hVWISJMGZ7R5Cm+rDbMsh0VrNxVyeMfb8PhEBzNbraRmEkopjCAnFQ3wahFzLJwGUIs3pd5WKNjVtRFufusKU2Sx+56bR0fbipp4Yi2j28RilosuOqIhrLXt/x7NYu3tkzEisQsasMx7j9vRo93PWsL01Jc+dRSdlcEyWzmB1FKURGIMTE3jT9eMKNPKTKNpq/T6dpHIvLtzpxAKfXk/gjWF7HLXtSyqqAKpWDSsHQm5qY1uelUBiL86vX1rCiobLLv4WNzuPaEg3jik+28tbYIlKK4Jmw3xjHtKJ96xaCw+y3Xv64OxQjHLAQwDSHT72rh5zCVxcqCqgalUBuO8fHmUjJ8rTvI3U6D2nCMTzeXcuJk2/F24ymHcMvLq1lXWAMoHIZgmrYP4drjD+ozCgHgi50VFJQHyUppeX0iQpbfycaiGjYU1XBIXuJ1qjT9j5hpsWR7BSsLKglGTAaneTjmoMGMyE6e30jTvvnoiWbvG/KoWlkHcEAohapglDtfXcvq3VUNCWEOQ5gwJJU7zpjCoFQPkZjFT15YxY6yQBNzjVKKz7aU8dGXn1AVihIzFaZSRKIWlgVKIBC2S107DGnouOYQu9+Dz+WktDYc75sAmX4XRitPv43XVAYiDRnZbWFaipKafW21Uz1Ofv+NGazYVcn7G4qpCcU4aGgqJ07O3a9wze7kvfV7aa9Ab30OwgdflmilcADz3vq9PLRwCzWhKJay/V6mgic/287U4Rn85KSJ3RYoMtBoTyk09qTlY0cdvQ48i52wNhS4EDgl/rffY1mKm15azZfFNWQ3u9lvLq7jJ8+v5JFvzWHR1jJ2lNU1UQhQH9Gj2F4WwO0QLGiSMS12gVO7OY7DQGHPHJRSZKd6yE33IiJUBe1chuIaO5eg8TkchtGkGmea14WlFJZSrSoQsJvvNLe5iwgzR2Yxc2TfDqOsDsXaTRoDu55UddwxrznweOmLAh5auIUUj6PF91gpxZo9VfzwmS944MKZ5GXohkddpc2wDaXUjvoF+AnwrFLq/5RSHyqlNsb/Xg08h92Zrd+zoqCSzc0UAsTNFCku9lSGWLytjLfXFtnVT5vdhGOWYkd50L7xK4VpKQzZVxSvPrLHUnZvZrfDaMhgzo7XRBqSvu9JPRKzGsxLAJWBKMMzfcxspBQyfC5mj8qmqo2bYsyym+8c2U/zDkZl+5t8Bq1hWjAiS5sQDkQ2F9fyyAdbSfc5W+1ZYZsQ3dSEYtz56jrdGjUJJBrLdxzw3za2/Te+vd/z8aZSu0Bea4XUTEVNKMqD721iQ2E1rT27VtTZ5TIE+0ZV33O5vu4+1NdCssNUY6bdM2FUji9eYdXOXRiR7UPFj1EXjlEdjFJRFyUn1c3dZ01pkT9w2bwxOA2DmlAU01JUBqLsqQxRUBGkqCrMBXNHNBTi62/U94iw2vix1yve4yYlnsCn6T/8e/nuRlF7bZPhc7K1pI5NnSx3r2lJoiGpYWAO8G4r2+YCkaRJ1IuEoiatmeaLa8IUVYWImYracAynw6A2FGV4lo+h6fvsmDWhWNyEE7+BNZttiLJNSU7D7pfgchiMG5yCp9kTUHaKG7fDoCIQ4eDcNFI8To6bOIR5Ewa3+rQ0fkgq9507nZ+/tIrVBVX7bqAiZPlc/G9DMV+dODSpiVw9xaicFE6anMubawrJ8rub+E5ipkVlMMb5c/IZkqbtyQca4ZjJ+xuKSW+lN0ZzRASF4q3VRRzUR5o99VcSVQoLgNtFxASeZ59P4XzgNuDx7hGvZ5k6PCPu2NxHeV2Ewkq7Rr8yFDmpbjJ8LjYUxSisDOIwDAbFO3yJ7Jt6tTaTELHXjxmcglIKr8tBKGq1UApKKUIxk6uOGce3jhjdpryWpVhRUMnuiiAFFQGqQ1FG5vgR7L4HqR4nDkMorglz7YIVPPKt2X3OkZwIPz5uAn63g/+s2IMVrzjrNASHIVx8+Ei+ffjo3hZR0w3UhGJYSuE0EjNouBwGBZW60VFXSVQpXA+kAb/GzmSuR2E7oK9Psly9wvyDBvPwB1sIRGL43U6UgsKqEIaxL9krO8WNy2EwLNMbN9HEI5CwZwCmgrwML0XVoSYJYnZqvm0eyvS5qAxGuemUQ7j/v19SVhshxePA5bCzmcNRi9kjs/jG3JFtyrpsRzn3vb2RirooplIUVQWJxBRZKS5GZPublNfO8Lsoq43wnxV7uHxe/8vEdToMrj52PBceOpJFW8uoDkbJ9Ls5YlxO0mpVafoe9Zn5iaJQuHswu/1AJdEyF0HgWyLyS+AwIA8oBBYrpb5MhiAich5wO3AIcKhSqscz0lI8Tn555hRuenk15XURDOyGOfWlIUZl+xpsm0PSvPhcDgrKg+ytDuN1GcwbP5jVuytB7OlsvWKox+WwQ1urglGmDs/giPGDeDQvnddW7eGNVYXUhmPkZ/k5d/Zwjj14SJudwb7YWcFNL63B7bQd4DWhKOGY1RB6Wl4XYVROSjwT2t4nzevklRW7ueyo0V1K8qpvXL9pby2WUozK8TN1eEaPJI5l+t2cPCWv28+j6Rtk+Fxk+10EI3bjq46ImUr3yU4CCZe5AIgrgKQogVZYg93A55FuOn5CTMvP5G/fmctrqwp5c00hDsOeHQxKdbfobJbmdTE8S/jlmZOZNTILEWFLSS0/e2EVtYaJz2kQipkItg9haLqHQNRkeKaPm0+dBNj+g28fMZpvt2MmaoxSit//90vcTiHF46QmFGVzcR0xUzX4Q2KWYmdZgNKaMOOGpOJyCG6nQWlthIhp7XeHto1FNdz/zkZ2lNVhKRoipwalufnRVydw2Nj+GeGk6ZuICOfNyefhD7Z2qBTsgAPhhElD2x2n6ZhE23G2bceIo5Ta2RVBlFLr4+fqymGSwpB0L5fNG8PZs4ZzwaOLSPM6W00Os5TCtCxGZqc0yG2I4HM77BmGAW6HAwuLFI+Tg4amc87sfOZPGJzQk09rrN1TTXFNmEyfk3DMYltpoKGAXX3klCg7ac7eXsdBQ1OJxXtI7O/0en1hNTc8vxKlFJnNcjiqg1Fu/c8afnHaJOZNGNzBkTSaxDlhci4LlhZQHYy16XC2lKIiEOWcWcP7bZRdXyLRmcJ22ksrtemxBsEiciVwJcDIkR3qq/0m0+/m6AmDWPhlCTmt1BWqCkQ5dExOQ53+PZVBrnluOZGYxchsHyJ2tI9SispAjFDU5Kjxg5oohJpQlPWFNcRMi/wsf4cRQoVVIYiXvi6rDaGUbXuNiDSYqiT+2ukQglGTurBJOGZyypS8/VK6lqW46/V1AKQ3K6chYs9YHIbJb97ayOxR2fut8DSa5qR7Xdx37nRueH4lZbURUr1OPPHwbaXsaMBwTPHViUO44uixvSztgUGiSuEyWiqFHOBUYCx2v4UOEZF3ab11581Kqf8kKAtKqUeBR8EuiJfofvvD978ynk17a9ldGSTV48DtNIiYFrUhkyHpHn58/L5G7f9avJNAxGyhQOqT3wqrQryztoizZ+UTjJg88uEW3l5b3yBEsCzFwblp/Oi4CYwfktqqPB6n0RDparf6tI/vNoSwqZo4twVAQUlNiJwUD+fM6nwbSoCVBZWU1IRbbcxTj9floKIuygdfFmu7vyapjMzx88i3ZvPKyj28vHw3VcEogmBaivFDUvnG3BEcPWFQn7AyHAgk6mh+oo1NvxORp7AVQyLHOT5BufoMmX43D140k1dW7uHfy3dTWhsl0+/iW0fkc8aM4Q2F6OpjqjPaian2ux38e/luTp2Wx89eXMW6wmoy/c6GkDulFF/ureGa55bzh2/MYPyQlvHW9Y4001INdZMAXE4Dhd2fwbLsIncxyzYjxSzF3WdP3e88hZUFlVhW66W2G2MY8NmWMq0UNEknK8XNd44czYWHjmR3ZZBIzCLD17JgpKbrdMrR3AZPA38HbknCsfokaV4X3zxsFN88bFSbfQjqwiam1X5Mdb2z9601RawrrCYnpWU5jUy/i6pglPve3sjDF89uca4Mn4uTJufyxupCnA67umm9YnA7HRiGIhKzSPM6cToMHAKXzhvNjC5EZUSiVkJPYYZAtDMxhBpNJ3E7jRbdBjXJJRlBvUOALqtrETlLRAqAI7Cb+bzdZcm6gbZujn63AxEaKqu2RtS0yPA5eX5pASluR5vHSvc62VZax5aS1lP2rz52HDNGZOJzOohadpa0pfaV4R47OIWDc9MYM8hPus/FqVOHde4imzEqx59QJ7aIqRg3uHWzl0aj6R8kGn3UWg9mNzAF+DnwUVcFUUq9DLzc1eP0Fl6Xg6MnDOK9DXuxLKgIRDAthcdpMCjNQ4bPRV3Y5OyZ+Ty1aAfZrfQHqEfi9ZJ2lAVaNSF5XQ5+ffZU/rt+L7e8vIbacAyXwyA7xc3gNDdelwNLKcrropxwyBDyu1gsbt6Ewfzx/c1E4xFMrVFf5O+UKbpZukbTn0nUfLSQlo7m+mfHD4CrkyVQf2b2yCyeWrQDpcBl2DUtglGLnWUBnIYwITeNk6fk8uSiHQkdr3nhu8Y4HQanTMljxohMbnppNXsqQ7ZPIWYRiJgAHH/IEK494eAuX1eKx8nFh43kb59sa1F/CGhQQCdPzm3SJU6j0fQ/ElUKX6WlUggBO5RSRa2MH3CU1IT588LNjMjyU1RtF88jHiKqlN0QJD/Tx5B0L5Py0tlcUkN6GyUaLGVHEU1OoGlMXoaPv35nLit2VfD+hmJqQzGGZ/k4eXJeUgvgXXjoSIJRkwVLdqEQvC677Hcwaiug4yYOaRKJpdH0NUaPHs2OHS0fyCZNmsTatWsBWLx4Mddeey1ffPEFWVlZXHLJJdx11104HAMnzDrR6KOF3SxHv+eN1YWEoxY5qW6yU9xUh6IEwiYidqezFI+DDUU17CwL8I25I7j9lbVYlmp1NlAZiHH42ByGpCfmqnEYwuxR2cweld3x4P1ERLh83lhOmpzLa6sK+WJHBZaCQ/LSOGPGMMYNTtUhgZo+zZIlSzBNs+F9XV0dU6dO5YILLgBg165dnHDCCZxzzjk89thjbNq0icsuuwylFPfcc09bhz3gSNSnYAJHKKU+b2XbbOBzpdTAUaWt8M66vfg99kcgYkcJNe+bbCnFZ1tLOX/OCE6fMYz/rNiD12mQ4rGdzuGYRU0oxvBMH9cef1BvXEaH5Gf5ueqYcb0thkbTaQYPbppt/9hjjxGNRrn88ssBeOihh0hPT+fxxx/HMAwmT57M7t27+elPf8qtt95KSsrAiHpKNPqovUdABx1nOx/wBMKxdvskg/0h1oZNRIQffGU8t542ieFZPioCUSoCUVBw8eEjefCimS3aDmo0muTyyCOP8PWvf51hw+zovE8++YQTTzwRo1FY+cknn0wgEGD58uW9JWaP065SEBFDROpnAEb8feMlBbtHc2m3S9rHyc3wEo5a7Y4REYbFk21EhGMOGszDF8/m+auO5F/fPYznrzqCS44c06av4UDmww8/5IwzzmDUqFGICHfddVeT7WvXruW8885jwoQJGIbBd7/73V6SVHMgsHTpUpYtW8b3vve9hnWFhYXk5jaNnqt/X1hY2KPy9SZtKgURuQ2IYndVU8An8feNl2rgF9iNdwY058waTjhmtdkjNmbavZKPmjCoyXoRIcPnIifV02ap7L5ORzf0Y489tiHMtvHSeDpeW1vLpEmTuPfee1v8MAECgQAjR47kF7/4BdOnT+/2a9Ic2DzyyCOMGTOGE088sd1xDSVjBpC/rD2fwsL4X8G+8T8OFDQbEwbWAa8lXbJ+xtEHDeb5ZQVsK62zm+40+hLVt4287KjRB+QsoP6GftFFF3HNNde02P7SSy8Riezr2KqU4tBDD23yg/za177G1772NQB+9rOftTjG3LlzmTt3LgCPP35ANPrT9BLV1dU888wz3HLLLU1+p3l5eRQVNQ2mrH/f2oPKgUqbSkEp9QF2DgIiooDHlFJ7ekqw/obH6eDec6dz92vrWFFQiWXta7/pcAiXHmXXbTkQ6eiGnp3dNCrqv//9LwUFBVx11VU9Ip9G05inn36aSCTCpZde2mT9UUcdxVNPPYVlWQ1+hbfeegu/38/MmTN7Q9ReIdGQ1DsavxeRDGACUKSUaj57GLBk+Fzce950tpbU8tmWMurCds7AvAmDW0QiDWQefvhhZs6c2fDkr9H0JI888ghnnnkmQ4c2bchz9dVX86c//YkrrriC6667ji1btnDrrbfywx/+cMBEHkE7SkFETgK+opS6sdn6m7HNSc74++eAbyulYt0paH9i7OBUxuoaQK1SVFTEK6+8wp/+9KfeFkUzAFm0aBGrVq3id7/7XYttI0aM4J133uG6665j9uzZZGZmcuWVV7bwkR3otDdTuIpmoaYicgJ274TVwF+x+yl/D1gG3N9NMmoOIB5//HG8Xi8XXXRRb4uiGYAcfvjhbQaD1G//9NNPe1Civkd7SmEmLZvnXIpd3uKk+vIWcUfNRWiloOkAy7J47LHH+OY3v0laWstCfxqNpvdpTykMAbY0W3cC8HGzekevA99KtmCaA4+33nqLHTt2NIkNr6e2tpbNmzcDEIlEKCoqYsWKFaSmpjJ+/HgikQjr1q1rGFteXs6KFStwu91MmjSpR69DozmQaU8p1AAN3hURmYDdgnNRs3HV9GB/Zk3fo6Mbej2PPPIIc+fObTWSY+nSpXzlK19peP/nP/+ZP//5zxxzzDEsXLiQPXv2NNlv2bJlvPzyy4waNYrt27d338VpBixVgShbS2spqQljKYXH5WBElp+R2X7czv6ZU5QI7SmFDcAZ2DMB4q8V8E6zcWOAvckXTdPb7CwLsL6omlDUxOdyMC0/s9X2hx3d0AF2797N66+/ziOPPNLquY499th2bb2jR49ud7tGkwwCkRgfbCzhuaW72FMZxGnYvaAVYIggApYFR43P4exZ+Uweln7AJbZJWz80ETkTeCm+7AUuATYBM1WjnUTk5fhxzuxmWVtlzpw5aunSpb1x6gOWz7eV8/SiHWwoqgZo0gt65sgsLj58FFOGZ/SILEop1uyuZvnOCioCETwuB/lZPo7WYb6aJKKU4uPNpdz/zpcEIjE8TiPeTbHlDd+0FNXBGKCYmp/JT046mKEJVjTuS4jIMqXUnBbr23v6EpEfAdcD2cDnwFVKqU2NtucDa4CfKqUeTbrUCaCVQvJQSvHUoh089dkOXA4hzets8qOwlP1jsJTi2uMP4pSpeR0ec3tpnd1o3bRIcTuZmJeWUFZ3zLR4e20Rzy3Zxd7qMKalcBi2ghIBhwjHHDyYiw4dldS+EZqBRyRm8dt3NvK/DcX43Q587sSs4UopKgMxXA7h5lMP4YhxgzreqQ+xX0qhP6CVQvJ4ZcVuHnx/Mxl+F852Kr5G4iW+7zxjcqs/hJhp8cmWMp5bspMtxXUYAhZQf8jjJg7lzJnDGT+k9VyOUNTkl6+tY/HWcvweA5+r5RObaSmqglFcDoM7z5jcrb0kNAcuMdPijlfXsWhrGVkpLoz9MAWFoiaBiMUvTjuEeRMGd7xDH6EtpXDgeks0nSIUNXnso22keZ3tKgQAt9PA5zZ48P3NLez8teEYP39pNXe9to4dZXVk+p1k+F1k+e3+EikeJ++sK+L7//qCV1bsbnHsmGlx1+vr+XxbOTmpLvxuZ6tTeIchZKe4cRrCLS+vYc3uqq59AJoByd8+2cZnW8vI3k+FAHbPdL/b4O431rOjrC7JEvY8WiloAPhoUwmRmJVwVIXP5aCsNsKKXZUN68Ixk1teXs2KXZVkp7hI87pa3NCd8Zt5qsfBg+9v5vVVTctp/W9jMYviP9JEHHg+twOHQ7jr9XWYVv+e9Wp6lvWF1bywbDeZ/sS+a+3hdTlQCn795gZiZvsl9Ps6fUYpiMh9IrJBRFaJyMsiktnbMg0k3lxThLMTgcUigkLx7vp9gWcvLitgzZ6qhG7oLodBmtfJg+9vZm91CLBttM8t2WX3f+7EjzTV46SiLsoXOysSvwDNgOdP72/GYdDhzDhRMnxOthTX8uGmkqQcr7foM0oB+C8wRSk1DfgS+HkvyzOgKKsN4+pkPweXw6CkJgzYZp8Xv9hNmqd1c09ruJ0GllK8udpuYPLl3lp2lgdISdDR1xhD4Pmluzq9n2Zgsrm4lk3FNaR7E6oJmhAigsdp8Oznu/p1+HSfUQpKqXcaFdVbBOT3pjwDDadhdLqnqkI1KJIl2yuoDcfwuDp3Q0/1OHl5+W4iMYu1e6riJcc7/+SW5nOyYldlv/4xanqO99fvBZX85jkpHgc7yuooqAgm9bg9SZ9RCs24DHizrY0icqWILBWRpSUl/Xuq1lcYPySVYMTs1D4xUzEhHkG0dk8VptV5W6rbaRCJWRRWBakORUH276ZuiGApCMf6tz1X0zMs31WJx5X82199V8HNJbVJP3ZP0aNKQUTeFZE1rSxnNBpzMxAD/tnWcZRSjyql5iil5gwe3H9CwPoyp88YBkjCT9qWUghw0hS7I1V1KIZjP5+6RCAQsbOm9/dBXykFCtz9tKWppudQSrGttA5vJ2e1iWJaFpuKarrl2D1B8gxqCaCUOr697SLyHeA04Dil7QA9yqS8dEZk+SisCpHu6/hrURWIMWtUFnkZPgBS3Q66EvzjcznIy/DhNPbvph6KWgxJ92AkyWmoOXAJx6x4MmT3fFcchkFpXaTjgX2UPvNYJSInAz8DTldKBXpbnoGGiHDraZNwOoSaULTdsZWBKOk+J9edcHDDuol56ezPbyxqWhiGkJvh5dAx2Q3mpM4SjJqcM2t45wXQDDhE6LT/rLPs76y5L9BnlALwJyAN+K+IrBCRh3tboIHG6EEp/O78GfjdTirqItSGYw3mJKUU1aEoFXVRhqR5eODCmQxO8zTse9jYbLwuR6dv6DUhk9OmDcPrcuB1OTh9Rh7Voc418YtZFg4Rjp80cJqra/Yft8PA6zSI7YcPLBFipmJIuqfjgX2UHjUftYdSanzHozTdzfghqTx1+WF8tKmE55bsYntZAIcIplIckpfON+aM4LCx2S3CVz1OB2fOHM4/F+0gJ9WdUFRHzLQQ4NRGNZTOnJHPm6uLqAnFSEsgXNCK15/55qEjdYE8TUKICBOGprJpby2p3uQ/FzsdwkFD+28TqT6jFDR9B6/LwQmTcjn+kKGEopZdOtvt6NAx9425I/h0Sxk7yurI6iBLNGZZVAZjXHbUaEZk7ytoNzjNwz3nTOMnz6+kKm6maus4MdM+xvGHDOE7R47er2vVDExmj8pizZ5qkt1J3VIKpRQThvbfHu19yXyk6WOICD63g6wUd0KRGn63k3vPmcbYwamU10UJRswW0Uz2k32EqkCMbx0+igsPHdniOAcNTePBC2cxIttPZSBGeV2EqGmhlMK0FHVhe10gavHtI0bxs5MnagezplMcf0guBvb3MZnUhGJMy89kSFr/K6Vdj54paJJKVoqbP3xjBm+vLWLBkl2U1kZQ2D8+hwFKCXNHZ3PenBHMGJHZ5nFG5vh55Fuz+XJvLS8vL2DR1nJqQlGchkFuhpezZw7n2IlDSPXor7Cm8+RmeJkzOpulO8rJ8ruTckylFDFTcf7cEUk5Xm+hS2drug2lFKt3V7GnMkg4ZvdTmJqf0S8bkmgOPPZUBvnuP5bidRlJaa9ZEYhy6Ogs7jxjSr/oxtZW6Wz9mKXpNkSEafmZTMvP7G1RNJoWDMv0cdUxY3ng/c1k+V1dyluoDcXwuRxcc/xB/UIhtIf2KWg0mgHL16cP47zZ+VQEovtd8ro6GMUwhHvPnUZOav8NRa1HKwWNRjNgERGunD+W784bQ03IpDoYS7jUi2kpyusiZKW4+d35M/p1GGpj+r1PQURKgB29LUcPMggo7W0hepGBfv2gP4OBfv2QnM9glFKqRfG4fq8UBhoisrQ159BAYaBfP+jPYKBfP3TvZ6DNRxqNRqNpQCsFjUaj0TSglUL/49HeFqCXGejXD/ozGOjXD934GWifgkaj0Wga0DMFjUaj0TSglYJGo9FoGtBKoY8gIieLyEYR2SwiN7ayXUTkgfj2VSIyq9G27SKyOt6cqN8WgkrgM5goIp+JSFhEbujMvv2BLl7/QPkOfDP+/V8lIp+KyPRE9+0PdPH6k/MdUPH633rpvQVwAFuAsYAbWAlMajbma8CbgACHA4sbbdsODOrt6+iBz2AIMBe4G7ihM/v29aUr1z/AvgNHAlnx16fU/w4G0Heg1etP5ndAzxT6BocCm5VSW5VSEeBZ4IxmY84AnlQ2i4BMEclrfqB+TIefgVKqWCm1BGjeRDqRz6+v05XrP1BI5DP4VClVEX+7CMhPdN9+QFeuP2lopdA3GA7savS+IL4u0TEKeEdElonIld0mZfeSyGfQHfv2Fbp6DQPxO3A59ux5f/bti3Tl+iFJ3wFdOrtv0Fqt3eaxwu2NOUoptUdEhgD/FZENSqkPkyph95PIZ9Ad+/YVunoNA+o7ICJfwb4pzuvsvn2Yrlw/JOk7oGcKfYMCoHG7pnxgT6JjlFL1f4uBl7Gnof2NRD6D7ti3r9ClaxhI3wERmQb8FThDKVXWmX37OF25/qR9B7RS6BssASaIyBgRcQMXAK80G/MK8O14FNLhQJVSqlBEUkQkDUBEUoATgTU9KXySSOQz6I59+wr7fQ0D6TsgIiOBl4BvKaW+7My+/YD9vv5kfge0+agPoJSKicgPgLexIxD+ppRaKyJXxbc/DLyBHYG0GQgAl8Z3Hwq8LHa3JyfwL6XUWz18CV0mkc9ARHKBpUA6YInINdjRGdWt7dsrF7KfdOX6scsoD4jvAPALIAf4S/x6Y0qpOW3t2ysXsp905fpJ4n1Al7nQaDQaTQPafKTRaDSaBrRS0Gg0Gk0DWiloNBqNpgGtFDQajUbTgFYKGo1Go2lAKwVNv0BELhERJSLjW9nmjG+7vYNjjI6P+263CarR9HO0UtBoNBpNA1opaDR9EBHx9LYMmoGJVgoaTTNE5BgReU9EakSkTkTeFpEpjbb/RUT2ioiz2X4eEakQkT80WjdIRB4Skd1iN8fZ0LyCZSPT2HwReV5EKoHF8W1zReQFESkQkWC8AcuvRMTX7BgOEblLRApFJCAi74vdlKeFWU1EpovIK3FZgyLyiYgcnazPT9O/0UpB099wxH0IDQt2SYCkICKnAu8BtcDFwEVAGvCRiNQXK3sSu+HNic12Pw3IBJ6KHysd+AQ4Fbg9/vdV4CER+WErp/8nsA04F6jvujUSWAFcBZwM/BG4DPh7s33vAG6Ky3YGdqmEFrV/xO7Y9ymQDVwBnAOUAe+KyOzWPxXNgKK3uw3pRS+JLMAl2GWE21tu7+AYo+PjvtvOmM3Ae83WpQOlwB8arfsSeKbZuH8D6xq9vxUIAROajXssfjxns2v7fQfyC3Zdm4sBC8iJr8/CVmJ/aTb+uuafC7bCWw+4G61zxNf9u7f/z3rp/UXPFDT9jbOwW1I2Xg5PxoFFZAIwDvhns5lIAPgMmN9o+NPAGY0qU2Zjt0d8stGYk7HNQNuaHe9t7KJmk5qJ8HIrMqWLyG9EZAsQxu669hS2gpgQHzYVSAGeb7b7C82O5QOOiY+zGskjwLvNrk8zQNFVUjX9jTVKqc2NVzS37XeBIfG/j8eX5uxs9PopbJPQudimnAsAF7YJqPHxxtN2+8ycZu8LWxnzd+B47OqYK4A67Dr5fwa88TH1bVmLm+27t9n7bOxZwa3xpQUiYiilrDbk1QwAtFLQaPZR37Dk59hPzs2J1L9QSm0TkU+wTTl/j/9dqJRq3E6xDPtG/eM2zrex2fsmJYtFxIvtH7hdKfXHRuunNtuvXpkMARqXix7abFwlttnpzzSd0ewTQCuEAY9WChrNPjYC24HJSql7Ehj/FLbT+FjgCPb1uKjnLeCHwE5ld8PqLB7sJ/vmM41Lmr1fjT2DOA/4X6P15zUepJSqE5GPgOnAF1oBaFpDKwXNQGR2POyzOa8A3wf+E+98tQDbITwUOBL75v67RuMXAA9g+xeCwIvNjvd74BvYkUu/x1Y6KcBE4Gil1BntCamUqhKRRcD1IlIYl+UymjVzV0rVh8HeJCI12LOcWdg9fMGeHdRzHfAh8LaIPI49yxgUH+9QSt2IZkCjlYJmIHJVfGnOYKXUGyIyH7gZuw+uDygCFgHPNR6slKoUkVex/QrPKKVqmm2vEpEjsf0BP8O+mVdiK4fmCqQtLgQewjb5BLEV0Y+B15qNuw3bYXw58CNsB/cl2CGxVY1k+kJE5sbHPwBkACXAF8DDCcqkOYDRndc0mgMUETkPW4nMV0p91NvyaPoHWiloNAcAInIYdnLcYuzciNnYCXAbgSOV/qFrEkSbjzSaA4Na7DyD72Mn2xVjzxJ+rhWCpjPomYJGo9FoGtAZzRqNRqNpQCsFjUaj0TSglYJGo9FoGtBKQaPRaDQNaKWg0Wg0mgb+H7RqQHOwK+dyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [0.22,0.22]\n",
    "y = [-2,4]\n",
    "plt.plot(x, y, color ='red', linewidth = 2)\n",
    "influence_plot(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6577456c",
   "metadata": {},
   "source": [
    "### From the above plot, it is evident that point 70 and 76 are the influencers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "80527b35",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>280</td>\n",
       "      <td>19.678507</td>\n",
       "      <td>50</td>\n",
       "      <td>164.598513</td>\n",
       "      <td>15.823060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>50</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "70  280  19.678507   50  164.598513  15.823060\n",
       "76  322  36.900000   50  169.598513  16.132947"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[cars.index.isin([70,76])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d092b8dc",
   "metadata": {},
   "outputs": [
    {
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       "   HP        MPG  VOL          SP         WT\n",
       "0  49  53.700681   89  104.185353  28.762059\n",
       "1  55  50.013401   92  105.461264  30.466833\n",
       "2  55  50.013401   92  105.461264  30.193597\n",
       "3  70  45.696322   92  113.461264  30.632114\n",
       "4  53  50.504232   92  104.461264  29.889149"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5f7ae7",
   "metadata": {},
   "source": [
    "## Improving the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c19bd6d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>175</td>\n",
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       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
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       "      <th>76</th>\n",
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       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
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       "      <th>77</th>\n",
       "      <td>79</td>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>80</td>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
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       "</table>\n",
       "<p>79 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index   HP        MPG  VOL          SP         WT\n",
       "0       0   49  53.700681   89  104.185353  28.762059\n",
       "1       1   55  50.013401   92  105.461264  30.466833\n",
       "2       2   55  50.013401   92  105.461264  30.193597\n",
       "3       3   70  45.696322   92  113.461264  30.632114\n",
       "4       4   53  50.504232   92  104.461264  29.889149\n",
       "..    ...  ...        ...  ...         ...        ...\n",
       "74     75  175  18.762837  129  132.864163  42.778219\n",
       "75     77  238  19.197888  115  150.576579  37.923113\n",
       "76     78  263  34.000000   50  151.598513  15.769625\n",
       "77     79  295  19.833733  119  167.944460  39.423099\n",
       "78     80  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[79 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1 = cars.drop(cars.index[[70,76]],axis = 0).reset_index()\n",
    "cars1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfca14fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars1.drop(['index'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7b6f650",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>104.461264</td>\n",
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       "      <th>...</th>\n",
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       "      <td>129</td>\n",
       "      <td>132.864163</td>\n",
       "      <td>42.778219</td>\n",
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       "      <th>75</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>263</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "0    49  53.700681   89  104.185353  28.762059\n",
       "1    55  50.013401   92  105.461264  30.466833\n",
       "2    55  50.013401   92  105.461264  30.193597\n",
       "3    70  45.696322   92  113.461264  30.632114\n",
       "4    53  50.504232   92  104.461264  29.889149\n",
       "..  ...        ...  ...         ...        ...\n",
       "74  175  18.762837  129  132.864163  42.778219\n",
       "75  238  19.197888  115  150.576579  37.923113\n",
       "76  263  34.000000   50  151.598513  15.769625\n",
       "77  295  19.833733  119  167.944460  39.423099\n",
       "78  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[79 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecfafb9",
   "metadata": {},
   "source": [
    "# Rebuild Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4aba9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   111.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 20 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>1.65e-27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:26:53</td>     <th>  Log-Likelihood:    </th> <td> -219.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    79</td>      <th>  AIC:               </th> <td>   446.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    75</td>      <th>  BIC:               </th> <td>   455.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   25.5275</td> <td>   13.051</td> <td>    1.956</td> <td> 0.054</td> <td>   -0.471</td> <td>   51.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.1825</td> <td>    0.023</td> <td>   -8.012</td> <td> 0.000</td> <td>   -0.228</td> <td>   -0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.4415</td> <td>    0.141</td> <td>    3.124</td> <td> 0.003</td> <td>    0.160</td> <td>    0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2291</td> <td>    0.035</td> <td>   -6.592</td> <td> 0.000</td> <td>   -0.298</td> <td>   -0.160</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.541</td> <th>  Durbin-Watson:     </th> <td>   1.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.038</td> <th>  Jarque-Bera (JB):  </th> <td>   5.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.620</td> <th>  Prob(JB):          </th> <td>  0.0541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.485</td> <th>  Cond. No.          </th> <td>5.76e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.76e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.816\n",
       "Model:                            OLS   Adj. R-squared:                  0.809\n",
       "Method:                 Least Squares   F-statistic:                     111.0\n",
       "Date:                Sun, 20 Feb 2022   Prob (F-statistic):           1.65e-27\n",
       "Time:                        16:26:53   Log-Likelihood:                -219.06\n",
       "No. Observations:                  79   AIC:                             446.1\n",
       "Df Residuals:                      75   BIC:                             455.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     25.5275     13.051      1.956      0.054      -0.471      51.526\n",
       "VOL           -0.1825      0.023     -8.012      0.000      -0.228      -0.137\n",
       "SP             0.4415      0.141      3.124      0.003       0.160       0.723\n",
       "HP            -0.2291      0.035     -6.592      0.000      -0.298      -0.160\n",
       "==============================================================================\n",
       "Omnibus:                        6.541   Durbin-Watson:                   1.130\n",
       "Prob(Omnibus):                  0.038   Jarque-Bera (JB):                5.833\n",
       "Skew:                           0.620   Prob(JB):                       0.0541\n",
       "Kurtosis:                       3.485   Cond. No.                     5.76e+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, 5.76e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rebuild_model = smf.ols(\"MPG~VOL+SP+HP\", data = cars1).fit()\n",
    "rebuild_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54515c68",
   "metadata": {},
   "source": [
    "## Again Calculate Cook's Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f30db4ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_influence_again = rebuild_model.get_influence()\n",
    "(ck_d, __) = model_influence_again.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "604396eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "x = [0,80]\n",
    "y = [1.0,1.0]\n",
    "plt.plot(x, y,color='red', linewidth=2)\n",
    "y1 = [0.5,0.5]\n",
    "plt.plot(x , y1, color = 'green', linewidth = 2)\n",
    "plt.stem(np.arange(len(cars1)), np.round(ck_d,3))\n",
    "plt.xlabel('Row Index')\n",
    "plt.ylabel('Cooks Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ad7405de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 1.1629387469135095)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(ck_d),np.max(ck_d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8d4aa072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(ck_d > 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ebe30843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False,  True,  True, False])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ck_d>0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "933e27e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.16293875)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(ck_d[np.argmax(ck_d > 0.4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b955079c",
   "metadata": {},
   "source": [
    "### Index of the data points where Cooks Distance is more than .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f7bda55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([76, 77], dtype=int64),)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(ck_d > 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cb25d55f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ziNoOq7ZVd1jTUR0RwWMJP2yrZloXmNXb0VJ8Hi46aDgnTRvAW99s4tkF6ymtjuCxIC3gadNiPbaj1ISjRGxI8VmcNmMwP56YT0F2alzvb+1fhkX8nbyJujUuAB4VEU/s/E+r6isJOnZC7bfffrz55ptcc8019dveeOMNhgwZwsCBA5MYmWG0bG1pLQLtOuKoJRFbWbm1imlDWr9T7cn6Zqbws70LOW3GYOatKuGFhev5bmMFEcdGgKit+L2C1CUn1O15qLyWoIAlwpj8LI6d0p99hvci4G3j7PSWXlDVg3bv8tpOVRfjzoPo9C677DL23Xdfrr76as4880zmzZvH//3f/3HHHXckOzTDaNHGslpIUvOm1xKWb65Myrm7Gp/HYr8RvdlvRG9UlU0VQVZtrWbZ5kqKtlUTjDpEbAefxyLFazEoL43R+ZkM65NBQVbKbhX6JiHeLpoxYwYvvPACV111Fbfddhv5+fnccMMNXHjhhckOzTBaFLKdpI2Ntyw3C6vRNiJCQXYqBdmp7Duid7ufr02FgojkAiOBlKavqepHiQqqqzjqqKM46qijGm1TVUJRB48l+DpwdIdhxCOpqZsVkzm1C4irUBCRFOBh4FRa7j9o31kwnVzRtmpe+moDb36ziXDUvRvLz07l1OmD+NHYvrudaMwwEiHV5yFZ38u2qlmIpwuI95vqf4GDgLOBfwMXA0HgHNzO4UvaIbYuwXaU+z/6gRcWrkdVyUz1kpniRVWpCEa4+/3lPPjJD1x/7PidDgUzjPY2pFcadpJyBKi6awsYnVu87RsnAX8Cnow9n6uq/1TVA4Gv6IJzCRJBVbnngxU8++U6slK95GX465uMRIQ0vye2qIZy1XNL+Hp9eXIDNnq8vpkB/B4rKSknPJYwrE96h5/XaJt4C4XBwDeqagMRoOEn+zBwWqID6wqWrC+vzzLZWltpmt+L1yP86ZVvu9QC3kb3IyJMHJBNdQd3+EYdBYXCXqZQ6OziLRSKcbOhAqwFJjd4rTcQ36yIbua5L9djIXF1nqUHvJTXRFiw2uR/MZLr2Cn9cTr45qSyNsIhY/u1ewI+Y/fF26fwOe78gdeBZ4E/i0gmbsrsy4FP2ie8zqsiGOG/K4vJTo2/A1kEXly0nj2H5rVjZIbRuulDcslM9RGM2G1ednNX1A2BPW5K/3Y/l7H74q0p3Awsjf3+F+A93D6Gm3HTZl+U+NA6t5KqMB6rbTNDU3wW68tq2zEqw9g5r8fip3sNpjpkd8ichfLaKOP6ZzOib8bOdzaSLt4sqfNV9bnY75WqehJuc1JOLEvqmvYMsjPa1fHeJlee0RkcM6k/I/tmUF7bvimbw1EHEbjiiNEmUWQXscuzq1Q1pKoViQymK8nL8OMo9R3Hm5fO5+kL9+fVP57S4ntqIzaFeaajzUg+r8fiyiPHIiKEIu3T6ezEhmVfOHM4A3J6ZLdjlxTv5LWzdraPqv5r98PpOjICXg4a3Yf3lm4hza5i7iN/od/YGVRtbX6BHVVFVTjWtKsancTgXmlcOWs0f3n1OxBJaDptR5XS6giHju3HMZPNv/muJN5e0kda2N6wQbJHFQoAx08dwLvfbuazB69j5EEnYkfCLRYKlaEofTMDTBmU07FBGkYrDhzdl4it3PLmUmzHIs2/+zPvo7ZDWW2EQ8b044rDRyclI6ux6+K9NRjazGM6cD2wHNirXaLr5MbkZ5H+7QvYjjLikDNa3K8yGMUS4bpjx5t2VaPTOXRcP246cSJ+j4eS6jDOLnY+qyoVtVEqgzZn71PIH2aN6dDV3YzEiOu2QFVXN7N5NfCluN9y/wO0/K3YTb3//vvMfe0p/vTQS7z0fS21YRtVt+qsCjXhKFEbslK93HDCRDP6wui0pg3J4+Gfz+C+D1by7ndbUJSsVG9ci7uoKpXBKFEHBuelcuWRY82/9S4sEVnaPsYtFHqUbdu28bOf/YyHH36YI4+cwakHhLjg6+d4eyGU1UTwWhbD+mRwyvRB7D0sr80LXRhGR8tK8fH7WWM4cY+BvLhoPe98twVVG9tRAl4Lv9eqX9gl6ijBiB17Lkzon8XJ0weyZ2GeqR10cbK745RF5CrgYlUdkJiQ2mb69Ok6f/78Dj/vBx98wMEHH4zHs/3L3nHc7Kgej4d//etfnHFGj6s8Gd1IZTDCknXlfL+5kq/WlbOxrJaIo3gEstP8TOyfzdj+WYwtyGRgblqywzXaSEQWqOr0ptvjHX10TTOb/cAE4Cjg7t0Lr+uZMWMGS5YsabTtnnvu4ZVXXuG1115j0KBBSYrMMBIjM8XHviN6d8jCLkbnEW/z0XXNbAvh9ivcANyUqIC6ivT0dCZMmNBoW9++ffH7/TtsNwzD6Cri7Wg2jYSGYRg9wG73KSRbsvoUWqKq2I6azjbDMDq1NvcpiMjgtpygJ+Y/quM4ypdrSpmzYB1frinFdpSMgJejJhZw1OT+Zoq/YRhdRmvNR0U0nrG8Mz1yzGUwYvOnV75lflEJlgg5aT4EiNjKM1+u4/mF67nk0FHMmpCf7FANwzB2qrVC4Vy2FwoB4I9ABfA0sBnIB04FMoE/t2OMnZaqcsOr3/LFqhLy0n2NZiv7vUIvr59w1OFvby0jK8VrRnEYhtHptdjwraqPqOqjqvooMA74Epioqn9S1dmqej3ukNSFsdd7nG82VDB3VekOBUJDfq9Fqt/i7vdXdPhqV0bH2LZtGxdddBH9+/cnEAgwdOhQ7rvvvvrXH3nkEURkh8c777yTxKgNo3nxDkn9CXCONumVVlUVkftwE+ZdtrvBiMgg3MR6+YAD3K+qd+7ucdvLCwvXgzpEbMVxHAJeC2km+Vea30txVZiv1pUxdXBuEiI12ktVVRUzZ85kwIABPPHEEwwZMoSNGzcSiUQa7efxeFi3rnGyxLw8swKf0fnEWyhkAH1aeK0vkKhFAqLA5ar6ZWy5zwUi8raqfpug4ydMcVWIOQvWUl4bbdTxkuazKOydTlaqr9H+Ucfh2w0VplDoZm699VZqamp45ZVXCAQCABQWFja7b36+6VcyOr94x01+ANwoIjMabhSRPXEnr32QiGBUdaOqfhn7vRL4DkhK+ozW/LC1iv3/+h5lTQoEgJqIw7cbK9nQZNlNEQjbTscFaXSIZ599lv3335/LLruMgoICxowZw+9+9ztqamoa7WfbNsOGDaOgoICDDjqIV155JUkRG0br4i0Ufo07g/lzESkSkbkiUgT8FwjGXk8oESkEpgJzE33s3WHbNsfc/Qm10da/4NeU1FJeu70JQRD6ZaW0d3hGB1u5ciVz5syhurqal19+mVtuuYWnnnqKX/7yl/X7jB49mkcffZTnnnuO5557jilTpnDMMcfw0EMPJTFyw2he3JPXRMQHnAPsDRQAG3ELhUdVNdLKW9selEgG8CFwQ93a0E1ePx84H2Dw4MHTVq9uLrN3+3jk01Vc9/K3bmkq0FrfcarPYvKgHGxHqQpGefKCfchu0qxkdG2BQIDevXuzevVqvF63NXbOnDmccsopFBcXt9hvcNZZZ/H555/z/fffd2S4hlGvpclrcU+7VdWIqj6gquep6o9jPx9shwLBBzwLPN5cgRCL5X5Vna6q0/v0aamro33M/nAlAGIJO1supzbiEI3alNVEOHRcP1MgdEMFBQWMHDmyvkAAGD9+PACt3azsu+++FBUVtXd4htFmnSoXQ2zBnoeA71T19mTH05zS2sj2wkCEna00uLkyzKh+mfzqoBHtHZqRBAcccAArV67Etu36bcuWLQNa7nAGWLhwocmka3RKraW5+AE4QVW/EpFVtD67WVV1eALi2Q84E1giIoti265S1dcScOyEaFo/EBEs3JXWmvsDHT62H1fMGkOqv0dO+O72rrjiCp5++ml+/etfc+mll7JhwwauuOIKzjrrLHJz3ZFm1113HXvuuSejRo0iFAoxZ84cHnzwQe66664kR28YO2ptSOqHuDOY635v95lXqvoJ7LRVJqmG9k7j242Vjba5k5EA1fo/kqPgtYSrjhrTaCEeo3uZPHkyr732GldeeSWTJ08mPz+fU045heuvv75+n4qKCi6++GI2bdpEamoqY8aM4emnn+akk05KYuSG0TyTJbWNPlm+lZ89NA8Lmp2oBqCO4gAHjerNI+fu1WGxGYZhxGu3Vl5r5aC9VLV4d47R1ew/sg+j+mXw/eYqLEd3KBjqCgS/R7jhhEnJCdLoNEqrw2yrChGKOqT5PRRkp5qmRKNTi3c5zl8COap6a+z5ROB1oEBEFgJHq+qm9guzc3n9t/tz5F2f8P3mKnC0vr2rrs6V6rN4+oJ9GJBrUmb3RKrKorVlPPvlOr4oKsUj2/9tWCL8eGIBx0zqz+BeZl1jo/OJq/lIRBbj5iG6O/b8bdy5CrOB3wLvq+r57RloS5K5yM77Szdz8+tLWVVcg6qSlerjrL0LufjgYaYfoYfaWhni6heWsHpbDYiSnerDapAsMWI7VAajABw6th+XHTYKn1mQyUiC3W0+GgwsjR0oGzgQOF5VXxORYnrgGs0AB4/px8Fj+iU7DKOT2FIR5NdPLKSiNkJuevNzUnwei7x0P44qb3+7meLqMH85foIpGIxOI95/iR7crKUA++PWhj+IPV+LmxTPMHqsYMTm988upqI2Qk7azicpWiLkpfv4cnUpd7+3ogMiNIz4xFsoLAeOiv1+OvCZqtZl/OoPlCQ6MMPoSj5evpUNZbVxFQh1RITcdB9vfL2JzRXBdozOMOIXb6FwG3CpiGwDzgD+r8FrBwOLEx2YYXQVqspTX6zF7217E5AlgqK8tmRjO0RmGG0X179iVf0Pbj/CTcDBTXISbaZxIWEYPcryLVWsLaklfReHmmYEvLywcD0Rk1rd6ATinqcQm238STPbr01oRIbRxawtqQGhxSVZd8bvtSiriVBaHaavSa9uJFnc9V0RSReR34rIHBF5X0RGxrafLiJj2i9Ew+jcasL2bq+/bQnURuyd72gY7SzeyWuDcEcbDcQdmjoByIy9fDBwKPCLdojPMDq9gNfaabbcnVGFFJ+Z22IkX7zNR3/DXXltJLABCDd47UPgusSGZXQFxVUhlm2uJGIrWSleJg7IxtsDx9v3z0kFcTucd6UJKWI7WJa0aeSSYbSXeAuFw4DzVXWNiDS9nVlPJ1xH2Wg/q7ZV86//FvHZymL3DjnWcpLq93DiHgM4edqgHnXXO75/Fr0zAlQGo6TtQmdzZTDKsZP7E/D2nL+Z0XnFe1vnBypbeC0bSOjqa0bntXBNKb9+/Es+W1FMdoqX7FQf2WnuQ1Ee+bSIy5/+iqpQNNmhdhgR4bTpgwnuQp9AXZqZYyeb+yqjc4i3UFgMtJT8/UhgQWLC6dxqwzZVoShdPd34rtpYXsv/vvg1Hg/kpvuwmjSkB7weemX4Wb65kr++/l2SokyOH43tS3aqj4ra+O+PVJXSmgh7D+tlkuMZnUa8zUe3AnNi7aX/iW0bJyLHAecBx7ZDbJ2CqvLJim38Z+4alm+pQoB+WSmcNn0QR07M71Ft6C8v2kAo6tAr3d/iPnWzdL9YVcrq4mqG9ErvwAiTJyPg5eaTJnHpU4uorI2Smdr6/1p1BUJh73SuPNIM3jM6j3gnrz0H/Ao4BXgntvlfwKXAr1X1jXaJLslUldkfruRPL3/LmpIaeqX7yEv3URGMcOd733PNi9/0mAlHoajNy4s3kpWy8/sIic3SfWVxz5qlO6xPBneePpWMFC+l1eFma5WOKmU1YUprIkwemMPtp04hzb9by5oYRkK1ZfLafSLyb2Af3AR4xbg5kFrqa+jyvlxTynML15OT5sPToKkkze8h1Wcxr6iEFxet5+Rp3X8B9q2VIaK2Q3ogvhEyKT6LbzdU7HzHbmZo73QePXdPPl6+lae+WMvaklqs2HoKImA7yozCPE6aNpApA3N2aIIzjGRr0y2KqlazvaYAgIicAPxRVaclMrDO4Jn56/CINCoQ6ogIGQEPT89fxwlTBza7T3eiShtXzxbs3ZzQ1VWl+DwcNi6fQ8f2Y+XWarZWhghGbdL9Xgp7pZlZy0an1mqhICJZwCzc9RRWAi+pqh177STgWtyJbEXtG2ZyLF5XTkYrzSUpPg+lNRGKq0Ld/n/03HQ/qu6dbjwFYCjiMCivZ688JyKM6JvBiL4ZyQ7FMOLWYp+CiIwDlgBPALcAzwIfikiv2MprTwN5wK+B0R0Qa+fU5jvorikj4OWAkb2pqN35UFO3HV05elL/9g/MMIyEaq2j+UYgFTgTGIe7nkI2MA83tcWfgBGqeo+qdst5ClMH51AZbPnSasM2eRl+eqcHOjCq5Dlxj4Eg7LRzvaI2yoDcNCYNzO6gyAzDSJTWCoX9gGtU9T+qulRVXwfOBYYC16vq9ararVcGOWX6IJxYk0lTqkp12Oa06QN7TGfh2IIsfrH/UMpro81O1FJVSqvDpPo9/Om48bucNdQwjORprU8hjx0Xz/kq9vPd9gmnc5kyKIef7jWEx+auxmtJ/XDM6rBNKOJwwMjeHNPDZqKeMn0Quel+Zn+4ktKaMI7j5vtRdUfXTBqYw+WHj6Igu2f3JxhGV9VaTUGApg3IdbeH3bqG0NDZ+xZyw/ETGJyXxootVSxeV07RthpsVXwei5Vbq5IdYoc7dGw/nvjl3vzp2AmcMn0QR08q4Of7FfLwOTO47ZTJPa5A2LZtGxdddBH9+/cnEAgwdOhQ7rvvvvrXo9Eot9xyC6NHjyYlJYWRI0fyj3/8I4kRG0bLdjYk9XwRObrB87r0ZxeJSMOZSZqoxXZE5GHgaGCLqk5IxDF3x5bKII99voZFa8uoDLplpKpDeXWE977bwofLtnLpoSM5cmJBkiPtWF6PxV7DerHXsF7JDiWpqqqqmDlzJgMGDOCJJ55gyJAhbNy4kUhke1/Utddey/3338/999/P5MmT+e9//8v555+P3+/nl7/8ZRKjN4wdSUt5fESkLVN1VVUTkuJRRGYCVcC/4ikUpk+frvPnz0/EqXdQHYpywb8XsK60hs0VQTyWxNbUhaitBHwWhb3SCUZs7v3ZNIb27hkpHYztrr32Wh599FGWLVtGIND8gIOBAwfy61//miuvvLJ+2yWXXMKLL75IUVFRB0VqGI2JyAJVnd50e4vNR6pqteGRsJy/qvoRUJKo4+2Od77bzJbKIKGoWz5asY5TAXweIRRxCEZsHFVeWrQ+iZEayfLss8+y//77c9lll1FQUMCYMWP43e9+R01NTf0+wWCQlJTG81hSU1NZvXo1q1ev7uiQDaNVXTKbm4icLyLzRWT+1q1b2+08ryzeSMBrURmMtDCr2V1oJs3vYf7q0naLw+i8Vq5cyZw5c6iurubll1/mlltu4amnnmrULHTkkUdy1113sWTJElSVuXPn8vDDDwOwYcOGZIVuGM3qkoWCqt6vqtNVdXqfPn3a7TwVtRF8HqvFoZUiEIkNV+0ho1KNJhzHoVevXjz00ENMnz6dY489lttvv53//Oc/lJS4Fd4777yT6dOnM2XKFHw+H6eccgrnnXceAB6PWVjH6Fy6ZKHQUQbmphKM2GSleJudq+AopHg9VIdt9hneOwkRGslWUFDAyJEj8Xq3j9kYP348QH3TUF5eHk8//TS1tbUUFRVRVFTEoEFuEsWhQ4d2fNCG0QpTKLTiyPH51ISj9ErzgQhOg055jf0nK8WL1xKOmWxSOvREBxxwACtXrsS2t0/mW7ZsGQCFhYWN9vX7/QwcOBDLsnjiiSeYOXMm7VnTNYxd0ekSuYvIE8BBQG8RWQdcq6oPdWQMry/ZyLUvfc2WynD9Nr8FeCxsFEGwVUn1e/B6LP541FgG5DQem7+6uJol68txFMbkZzKyb4aZ4dsNXXHFFTz99NP8+te/5tJLL2XDhg1cccUVnHXWWeTm5gLwxRdfUFRUxB577MGWLVv429/+xqJFi/jkk0+SHL1h7KjTFQqq+pNknv+Jeau56rmvUdxqlAjYCmEHcBx6pfuI2Mqg7DRO3KM/x04Z0GiyVnlthBtf/Y6Fa0tRdWsUHoERfTO49pjx3Tab6pbKIK8v2cR7S7cQitoU9krnhKkDmF6Y163Tik+ePJnXXnuNK6+8ksmTJ5Ofn88pp5zC9ddfX79PKBTi+uuvZ+XKlfj9fmbOnMlnn33GxIkTkxi5YTSvxXkKXUUi5ynYts3Ya94kbO+YHtpxFAUmDMjkuYv2x+/dseUtajv89omFrNhSRW66r75moKqU10bpleFn9pnTyQh0urJ4t8xbVcz1L39LJOqQ6vfgsYTaiI1tK9MKc7n2mPGk+HpGh+rmiiBfrS0jYit9swJMHZTTo5ZsNbqOluYptPjtJCLvteH4qqqH7FJknciTX6wjbGuzHS2WJTiO8t2GymYLBIB5RSWs2Nq4QAA3r35Omo+tlSE+WLqFo7tR/8O60hquf/lbvJaQmbF97Wa/10JVmV9Uyt3vreCKI7p3dvXiqhC3v/09XxSVIiiOgscS0gNefrH/UGZNyDfNh0aX0NotjIU7T6vuMQa3rb8QN6V2Yez5aLrJigLfbih3L7aF5g7BbUqqqg03+/pb32xCgJqwzabyIJvKg1QGI/Xr9Aa8Fq8s6V7rFr+wcD0R260hNFVXGL7z3WZKqpv/m3UHpdVhfvvkQuYXlZCT5iU33U+vDD85aT5sx+H2t7/n6flrkx2mYcSlxZqCqh5U97uIHA/cCeytqvMabN8LeCr2WpfXJ7P1dRHq1tNp7gsQYEtliI3lQSK2omh9Sen3ehjWOw2vJVS1sj5DV/TOt1vIbGV1Oo/ljtr6oqiEI8bnd2BkHeehT1axpSJErwY1pTopPg9eS3j4kyIOGNmH/jk9K1mg0fXE29j5Z+B/GxYIAKo6F7gO+EuC40qKX+w/FBG3/6A5CgzpldbshCPbUb7fVEkwYuO1wO+x8MUekajNiq3VVIeiFHaz/Eg1kehOO5IddRck6o4qghHeXbqZnDRfi/t4PRaK8uoSM3vZ6PziLRRGAi3lk9gCjEhMOMmVkernhKn9URoXDOootqNYAjed0PyIkQWrS7FV3fxITdqOvR6LqO1QGYxywtTutf5C38wUQpHWcyd6LdlpLayrWrHFTZ2+s4Ix1edh3iqTCsXo/OItFFYBF7Tw2gVAUUKi6QT+dupUTt5jAIJbMNiO4gBpfg/3/HQa+4xofubyJyu24fNY5GenELUd7NhoJQVsVWwHslP97DE4d5dj21IR5NXFG3l2wTrmrSohupNlMTvCCVP7U9NKLSAUdfB7LaYX7vp1d2aOo3F1qIlA1En+52UYOxPv2MjrgcdF5GtgDrAZ6AecjNsB/dP2Ca/jqCqfrNjGY5+vZuXWaob1SSc94GV43wwOH5fPrAmtt4eHIjaWCPnZKaT4PGyqCNbfQfs8Ql6Wn5H9dm0CW9R2+L/3VvD61xtRBUcVr0fISfVz3bHjGVuQtUvXnAhHTCjghUUb2FoZIjvV2+j6IrZDVTDKJYeOJODtnkNSB+SmEnUUVW31s60J2+w1NLMDIzOMXRNXoaCqT4rINtzC4f8BPiACfAEcoapdfnnOx+eu4ZHPikjxWuSkeimrjVC0rZqVW6vxWxYTBmQxMDetxfdPHJDN+8u2AJCT5iMnzUfUdpugvB6hpDrM1F2sJdz9/gpeXbKRvDRfo/Wgq4JRfj/nK+47c/oOM6o7SkbAy+2nTuG6l75mxZYqbAesWP3T67G4+OARHNWNFyAqyE5l0sAcvl5f3mK/Qt3os+OmdK+mQ6N7insWlaq+A7wjIhbQG9imqt2iPryutIZ//3c1OWleVGH5lmrCUTu29rDy/KL1LFxbypVHjuXgMX2bPcZBY/py/0c/UBOOkuZ3/6xej/sFHoraeCzhx7vw5bi1MsRrzRQIABkpXoqrwjy7YB2/PWRkm4+dKH0yA9x9xh4s21zJ3B+KqY04DMlL44BRfbrdRL3mXDBzGJc8uYjqUJT0JtfrqFJaHWG/Eb0ZW2BqCkbntytTLdNw5yl0m/aAt7/djIPitSzWFNcQjtr4PBZeS/B5LCwRbEe5+Y2lbCirbfYYGQEvfzp+ArYDJVVhghGbYMSmpDpMbdjhD0eMIT+77SkuFqwuQZUdCoQ6Wale3vluc5uPm2giwpj8LM7edygXHjicIycW9IgCAWBkv0xuPmkSPo9FaXWEspoIFbURiqvClNVEOHhMX/7fj8eYyWtGlxD3/7WxtZr/BEyObZoBfCkiDwLvqep/2iG+DrG+tBavJQQjNlWhKN7mvoDFTYL3yuINnD9zeLPHmTIoh4fOnsErizfw8fKtOA78aEw/jpvSnyG9dm0oaijq4HZZN89jCVXB7jncsyuZODCbJ87fm89/KOHj5VsJRmyG5KUza0I+g/JabnY0jM4mrkIhNnntWeBd4A/ALQ1eXgWcDXTZQqEgJ5Worag6iEizd3RurUH4en15q8fKz07hFwcM4xcHDEtIbIPz0vBIyxW66pDN8L7da+5DVxXwejhwVB8OHGXSYRtdV7zNR9cC/1TVw4G/N3nta2BCIoPqaIeP6xebWhBLa9qAo+78hKxUH7bttg/P/nAlLy5aTzTa/l0qkwfm0CfTT0UzM6EdVUJRh1OnD2r3OAzD6BnibT4aC/w+9nvTtoxSoFfCIkqCQXlpnLHnYP71+WoUxVbq+xFU3VnM5TVhVmypQrZUMq+oBFT5f88tYWS/DMJRh6itDOmVxp5D8zhifP4uNxc1ZVnCdceO5/Knv6K4OkxmwIvHEmrCUUJR5fBx/cydqWEYCRNvoVCBO+KoOYW0PNu5yzh730KG9ErjljeWsWJrFR4RMlK85GenuNlRN1WBQJrXwmNZhKIONWGbr9aWE/BaCLC6uIZPlm/jyXlrmTUhn/85bFRC0iaP6JvJ/WdN5/mF63nrm01Uh2yG9cng1OmDOGBkb9OBaRhGwsS1noKIPA5MBGYClbhzFKYB3wIfA4tU9fx2jLNFiVxPAdyF2J/6Yh2Pz13trsssULStmlDEIT3gQWI1iNqI27kb26U+KVzEUTL8HrLT/Jw+Y1DC+hYMwzASqaX1FOK9jb0ayAeWAQ/iNiFdCSwCBuImxesWog4cPbmAZy7Yh7+cMJFrjh6PqpsZte6OPBKblFZXnipu4QBuh3RVyCbdZ/H8wvXUhKNJuAqjOYWFhfUDCRo+xo8fD8A///lPDj74YPr06UNmZibTpk3j8ccfT3LUhtGx4p3RXCQie+DOaD4CsHFrDW8A16hql0//uKGslkc/K+KD77eiqmSl+Dh52kBOmjbQTZndoIXGiZUGjetYbmJtie3rIDjqsHJLNRMHZnfYdRgt++KLL7Dt7cN3q6urmThxIqeffjoA7777Lsceeyy33HILubm5PP/885x55pl4vV5OO+20ZIVtGB3KLMeJO6P5t08spCoYJTvNh6PK5oogpdURctL9VNVGqI3Y9UtK1kZsN1Fegz9dZooXwS0aorYyJj+TYNTm1pMnM2FA2wuFus/F9Be0nwceeIBf/epXrF69mv79m18N75hjjsHv9/Pss892cHSG0b7avBxnkzefBXyvqp8381pv4Meq+q/dDzM57v1gJVWhKHkZfmrDNiu2VrnZL0XYVhkixWfF1m126tdIsJ3td5yWQDjq4PVIg6Ym8FkWI/tlxB2HqvLflcU8PncNSzdV4vMIM0f14ad7DU7YaCZju9mzZ3PMMce0WCAAlJeXM3bs2A6MyjCSK94+hUeAj0Tk4mZeGw78M2ERdbDymgjzV5eSk+ZDVVm1rRpV6tNceCzB7/GQneIlGHGoDUfrh6rWUXUzgtaEbMJRh/ysABXBKD/Za3CbsoM+9vkarn3pG4qKq+id4SMzxcsHS7fwq8e/5LuNFe1w9T3X/PnzWbBgARdc0FJGeHjsscf4/PPPW93HMLqbtoyXfBG4S0TulG7UplFeG8ESd15CVShKxHYapbmwxP3CH9s/iwG5qYwpyKIgK4W0gIfcNB+pPgsRt2Coe9uWyhCnTh/I6TPin1S2tqSGxz4vIjvNS2aKDxG3QMrL8APKX19fSldv6mvJkCFDmu0Abs879NmzZzN06FAOP/zwZl9/8cUX+eUvf8lDDz3EHnvs0W5xGEZn05ZC4VbgdOAXwEsi0qXbM6K2w2crtvHIZ6vYVB5ka0WQ2rCzw8w8WyHF5/6ZAl6Ln+w5hP85fDQDclIZnZ/JpIE5TBmUw+j8TAp7pzOkVxq5aT5OmzF4p/0BqsqWiiBrimt4fclGFPBaO34kGQEvmyuCLNtcmaCr71zOOecc0tPTeeCBB5g3bx4PP/wwABkZ8Te9tUVFRQVPPPEE559/frOf0ZNPPslpp53G/fffz5lnntkuMRhGZ9WmNJaq+oyIrMKtNXwSS5LX5ZTVhPn9s4sp2laDiJvGYm1pEInNZhYcPFYsB5JC7wx3KUlLhMwUL18UldQvvygCfq+F37v9y7yiNsqqbdVMGZTTYgxfrS3jng9WULStBktga1UIS4ScNJ+7pGcD7p2zm321O/rqq6844ogj+MUvfgHAokWLEBFqa5vPSLu7HnvsMcLhMD//+c93eO2BBx7gN7/5DY8++qgZcWT0SG2ebquq84G9cAfafIGbLbVL+evrSynaVk1umpfcNL/biSsQspWoowSjDtUhm9qwTa90HxkpXqKOYlnCPsN74fdY2+coqFJeG2F9aQ0bymqpDEaJ2jbFVSFKqpv/El+wupQ/PLuYdaU15KR5yU7zkeLzUFYTpmhbzQ7NRKpuH0avjO65zvH+++/Pp59+yuLFiwG46667SE9P5/jjj2+X882ePZvjjz+efv36Ndp+xx13cNFFF3HnnXdy4IEHsmnTJjZt2kRJSUm7xGEYndEuJbxX1XUisj/wOHAXO+ZD2mUiMgu4E3e9hgdV9a+JOja4bfdfriklN81X33SwoawWSyDgFcJRNyme288A0djs5eqQzZl7D6F3RoADR/fho+VbCUcdVm6tJhx1IJbgekNZEEfdPgARGN8/m4sOGs6ofu4CK6rKXe9+j9cjjdYb6JMZYFtliIpghKqQXT9DGqAqFCU/K4VRbRjJ1JVcfvnl1NbW1rfd27bNkUceyZ///OeEn+vzzz9n8eLF/PWWW1lbUkOq31NfE7zzzjuxbZsLL7yQCy+8sP49Bx54IB988EHCYzGMzijemsL1wLqGG1S1RlVPAK4CEjIcVUQ8wD+AI4FxwE9EZFwijl1n5dYqrAbpsUMRm/LaCF5L8Hs9pPgsemX4SY8lniupDoPCZYeO5Kx9hgCw19BeDMxNZemmytiCPILXYxGN1TQ8IpTVhslN8/Hdxgoue2oR38f6A4qKa9hUHiLd33hUUsBrUZCTiuPApvJaVJWo41BSHUZEuLIbL9IyZ84c7r33Xv75z39y3HHH0bdvX5YsWcIf//jHhJ9ryrQZ3PP+cu5elsr5/57PGQ98zm+fWMh3GysoKiqK1coaP0yBYPQkcRUKqnp9S7OWVfWvqrpj4+yu2RNYoao/qGoYeBI4LkHHBmg0RFRV2VgerE9uVxWKEo46+D0W4/pnMbYgi/45qZyz31COmtS//kvZ77U4c+9CfJYFCFFHCUcdbEdJ8Vqk+CyqglFCUSe2VrPDbW8upbQ6THUour2/ool+WQH656SQHvBRUh2hJuRw2Nh+3PvTaYzJz0rkn6FTufzyy7nkkks47rjjePPNN7nsssu48cYbueWWWwgGgwk7T9R2uOq5JTz75XpSfBbZqT5y03ws31LJ5U9/xTcbWl8rwzB6ghYLBRGZKSIZDX5v9ZGgeAYAaxs8Xxfb1jS280VkvojM37q1bQlaJw/KxmMJ4ajN+rJaiqtCOOrmLlJ1RxttqghSXBXG77ViHco7to6tLamhT6af0fkZ5KX5Edw1mX1eq/4LvzIYZW1JDWtLavjg+62cfN9nPPDRSmojNo7TfIub32tx7n6FvHXZTF675AB+N2tMt1+5q7q6GsuyGnUAezye+jv1RPn8hxKWrC+nV7oPXyx7rYiQneoDlH+8vyJh5+ppdpZXCuCee+5h3LhxpKWlUVBQwNlnn83mzclfStZorLU+hQ+AvYF5sd9b+r+z7lszEWs2N9c+ssN5VfV+4H5w01y05QRpfi9n7TOEu95dwabyIHaTd9ddzA9bq0n1WXgti7EFO96lez1CMOqwekOFu2pbbHvEjuL3CJYImyuC2I668x5UyAx4Wbq5kspgBFWlX1agUY0hFHVHPR05saDbNhU15/jjj+e2224jNTWVww47jEWLFvHHP/6RI488ktTU1ISd59UlG/B6mq+lZaZ4WbGlmo3ltRRkJ+6cPcXO8ko988wzXHLJJdx7770ceuihrFu3jgsvvJCzzjqLN998M1lhG81orVA4GDc1NsCPSGBncivWAQ1nfA0EEp5sLzvVR0VtZIcCAdwhpnXpsX/YVs0hY/sxOtZJ3NCgvDQ2lNXi1E1aa7BoW9hWLBSw8HmEuqLG7/WQ4vdg2xCM2pTWRPB53ElqwYiDJcLvZ42hX1ZKoi+5U7vrrrsIBoP85z//YcOGDSxY+BU/Puoobrv5poSep7w2Evs8diQieC2hOmTWu94Vffo0XujpgQceIBKJcN555wHw6aefMmnSpPphx4WFhVxwwQVcc801HR6r0boWCwVV/bDB7x90SDTuENeRIjIUWI87We6MRJ7g2w0V3P729wR8FjXh7V/kda057voIiuBmQ/39EaObvbN85av1jd7TlIO7ahoiRG2lV4afunlpeRk+SqqVXx88gg+/30p1yGbiwGyOmdSfwb26d1NRc9LT0zn/mjvYMOUXrC+rRUT4FLjg6aXcftoUBuUm5m8yql8mK7dUk+bf8TU3waFbezN2X9O8Uvvvvz/3338/H3zwAQceeCCbN29mzpw5HHXUUUmO1Ggq3oR4PwAnqOpXzbw2AXhJVXd7NRlVjYrIr4E3cZujHlbVb3b3uA09PX8tFkJ6wOuO7AEQwUMs66mAzwLBIj8rhYCv+VaxN7/ZeVuo7TioWqT5PfTP3n73b4ngtSz2HtaLY6fs0GWScMVVIV5ZvJG3v91MOGozaWAOJ00b2GyzWDJ8sGwLFz22AFvdPhVLBEeVRWvLOP7uT3jtkpkJqT0dM7k/ry/ZRMR26vsUwB1wUFYT4Yjx+WSm+Hb7PD1dXV6pG264oX7bySefTFlZGUceeSTRaJRoNMrRRx/NQw89lMRIjebEOyS1EGjpFioFGJKQaABVfU1VR6nqcFW9YefvaJtFa8tIT/GQl+7HEtneJibuWggoqAp+r0VOmp+c1B2/JFSV6rDbzNBay3/Udu88R/TNcGsNMeGoU3/89ramuIZf/ms+j32+mqpQhKijfLx8G5c+tYiXvlrf7uePxzUvfo2jkBIrEMAtOFN9HiqCUW57c1lCzjO8TwYXHTScymCUkuowtRE79nuE4X0zuPCg4Qk5T0/XXF6pjz/+mKuvvprbbruNBQsW8Nprr7Fq1SrOPffcJEZqNKctk9da6lOYDpTtfigdw+sRHEfdeQHZKawrrQXcGcP1ncWOQtT9wthYHtxh9M/aktr60rS1jhaP5XY2p/k9hKIOxVVhwraNiHDMpP6NEu+1B1Xlz69+687MztheAPm9FhHb4R/vrWSPwbkMTFDzzK74fnMlm8pD+L3N/y28lvDWt5u4lckJOd/xUwcwvn8WLyxaz7JNlWQEfBw1KZ+Zo/q0KaOt0by6vFJ//OMfGzW7Xn311Zx44olcfLGbaHnSpElkZGQwc+ZMrr/+ekaMGJGskI0mWhuSepmIrBGRNbjffS/XPW/w2Io72eyNjgp4d/1odF8qg+4SmQNyUxmQm9qoQAD37j/gtbBQLn/mqx2W1CyrDe20193vEfrHJqMt21TJ2pIaglEbx3GT3n1RVMJNr3/X4tDURFi+pYo1xdVkpe5Y9vs8Fory2pKN7Xb+eGyuCCKxLLXN8VhCKOok9Jwj+2XyuyPG8ODZM/j76VM4bFy+KRASpKW8UnXDjhvyeNy/eXfN/ttVtVZT+AF4N/b72cB8oOmkgBDuCKUHEx9a+zhhjwG8+c0mKmojZKZ4SW3SZ+C13C/MiO1QFoziAB8u28qREwvq91m1tQYRIeBt+QsrJ81PfnYKNaEo26rDBHweAl6L3hl+slJ9CPDBsq3sO7w3B4/p2y7Xur60tn68eHN8HosVW6ra5dzxGtHHTd3hqDZbMERtJauZJjyjc2opr9Txxx/PTTfdxJ577snMmTNZt24dl156KZMmTWL4cNNs15m0NvroRdxsqHVfKn9S1VUdFFe7KchO5W+nTuYvr37HhrJaVpfUAOARCPg89U06qkpFbZQ0v4ePl29rVChsrAiSmeKlOmSTEfBQHbJjKzRTv7ZCqt+Do1AVtgl4LQp7pZEeaPzn9nstnlmwtt0KhabnaypqO+R2QL9GawpyUhmTn8l3Gyvqlzut46g7IuiUae3fGW/svrq8UrfffvsOr1111VV4PB5uvPFGLrzwQnJzczn44IO56aabdqhBGMkVV59CAtNYdArpAS8nTh3A5z8Us60yRLUq/gYzkaGuIHSoDEXRJo1FvdL95KT5sSRCZdBNW2E7St3b/V6LVJ+HqO3EqsZSn1pbVakJ2wQjbhK9VVvb70598qBsAl6LUNTeoXlE1b2qw8fnt9v54/X306Zw4r2fUR2K4o1N/Ivaiq3KiD4Z/OZHI5MdohGHvffeG1VlQ1ktc+avpTwYpW9mgP1H9CY33c9VV13FVVddlewwjZ2Iu6NZRIYBpwKDcUccNaSqel4iA2sPUdvhzneX89Y3m9y7+FCUqlDUTWKn0LSvU4Bo1GG/4b0bbZ85qg8PfryKwXlpVAQjFFeGKAtG8VrgjaXVzkzxorjj33PS3LQKoahD0bZqghF35JKjbpv5P95fwQUzh+H1tH7HpKos31JF0bZqUnwepgzOIauVIZQBr4dfHTSc2976HtuvpPo8iAgR26GiNsqMwjymtrLmQ0cZ2ieDV367P7e8sZT3lrrZZzNTvJw8bSD/c9joRmtVGJ1XbdjmtjeX8vGKbbG5O4rg/vs+aY+BnLf/0Eaj8IzOKd55CscBz+B2TG/B7UtoqEv0FM3+6Ade/3oTeenuQjYZAS+l1WEsgWDEJs3vadSuHXWgT5qPg5o07/TNDDB5YDYvLXYnWwtu85PtuF/c/XNSCEVtasMOeRkB0v0ebEdZuaWqfrlP98tZ6ZsV4PmF67EELjqo5REY60pr+Mur37Fqa7V7TndeHCdPG8TP9y1s8X+2IyYUkOLz8MDHq9hWFUJEsAROmjaQn+/X8vs62oCcNO483Sx72VXZjnLNi1+zaG0ZuemNF4qyHeWp+WuJOk6r/8aNziHemsJfcPMf/VRV25aBrpMor43wylcbyG2wslnAZ5ER6xuI2A7hqOKx3JURFHf46p2nTW207gHAvz9fzYI1pfTLDFBcHY6tpwAeUQblpdE7M0Ca38tRE/MZ1z+LK59dwrqSGsK2m4FVgYjtDovtE8vl/+KiDfxkz8HNzl0orQ5z2VOLqAxGyUnz1jdzRR2HJ+auwXGUX85see7ggaP7MnNUH9aW1BK2bQbkpJHqN6NtjMT5ck0pX60rIy/dt8PABo8l5Kb5eGHhek7cY2CPS+PS1cRbLx8G3NZVCwRw01sA9cto1hmSl4Yv1o6dFrDok+EnK8VHfmYK9/50D6YMzqU6FGVbVYio7bCtKsTjc9eQleolbCu2o3gsN3+Rx+OhMhTllpMm8fA5Mzhp2iDGFmTz99On4vNaqOMu2mM7Sl66nxF9M+rfq8DCtWXNxv7qko2U10bISWv8P5zXsshN9/Hsl+soq2l9qU4RYXCvNEb0zTQFgpFwLyxc32idkqbq/o2/9c2mjg3MaLN4awpLgV7tGUh70x26i10+r8Xo/EzWl9Xi91iM7JfJ1EE5HDO5P2Hb4ernl/BFUQkiQorXYmjvdCJRm9WVQWrDdn1TELijZUqrw9z8xlL+8dNp9ecY2judMflZZKa45/DGkuA1ik+VSAvDW9/4elOLX+QeS1CF+UWlHDquX7P7GEZ7W1tSQ4qv9XtMjyX1o/2MziveQuH3wN9FZK6q/tCeAbWXsbFFauru7BvyWG7/wuWHjeKICe7Q02WbKvmfpxcRtd2FciwRwlGHj77fSkUwgu24bfphEXyW1Nc2LEv4/IcSKoKRRp3AkwflUFRc3WiZzTp1k3eG9Wl+uc3aiN3q7GdH3SVDDSNZ0gJeSndSW7Ud3aEp1uh84m0+ug63pvCdiHwtIh81eXy4k/cnXW66n8PH9aOsJrLDDMqK2ig5aX5mjnI7lFWVv721DFUlN5YjCdzaRk3EJups71lXdVddC0bd4aeCW0B8sarxYu/HTu6PJc1PdiuvjTImP4sRfZsvFEb0yaAm3PKXviXSaRbiqQlHqQju+Dc2urfDxvbd6cxzAQ4c1afVfYzki7fYtoHEZCVLoot/NIKqUJRPVxRjOwqieCx3lvFNJ0yqb6JZXVzD6uIactIa/3nWltQ2ei4NCgvbVsLi1kLSAt4dvsQH5aXxuyNGc9uby6gORUnxWdiOErGVflkp/PHosS3GfdK0gXy5phTH0R1GC1UEI/TJDDBpQPYu/112l6ry3x+K+c/cNSzbVImIO6P75GkDOXZy/x0mpRndz6Hj8vn352uoDkWbnTRZVhNhYG4akwfmdHxwRpvEO3ntoHaOo0MEvB6uOWY8P2yt4vMfiglGHMYWZDGjMLfRHIHSmjAei0adZqGoQ3Uois8Sopa7LrOq1qeRcFSJ2A5DemUgIgzI3XH1rkPG9mN0fiavLt7I4nXlpPgsDh/Xj5mj+rba+TujMJfjpw7ghYXr8XnctN+2o1SG3BnX1x07PqlDS//5WRFPzF2D3yv1o0+CEZv7P/qBD5dt5bZTJpvO7W4uO9XHDSdM4KrnllBSFSYt4InNzbEJhh36ZAW48cSJnWYItNGyHtnAN6xPRovt9wC90gP1cw7qCoZw1K4vAHwetxCom8kM1BcilcEoGQEveS2kjxiYm8YFB7Yt14uI8KuDhrPH4Fyemb+W7zdXkuLzcPIeAzl+6oCkDvFbtLaMJ+etISfN16ivJiWW6+n7zZU8/OkqLj7YjE/v7sb3z+bBs2fw2pKNvP71RqqCUXpnBjh+vwEcOq6f6U/oItoyo3kAcDkwE7d/4RhV/VpELgX+q6pz2yfEjuU4ytYqd23lFVuq6JURIDs19oVXn01V6JMZoKI2itfjtpXWhm1AKakO47WE8/89n32G9+bKWWMScpcsIuwzvBf7DO9cg8DmLFiLyI5DfcGNOSvVy+tLNvLz/QpJ85svhe6uT2aAs/ct5Ox9C5MdirGL4upoFpHxwBLgTNw1kwcDdbfCQ4BL2iW6DlZWE+bi/3zJVc99TdRxqA7ZFG2r4ZsN5TgOWJZbY8gIeCnslc7wvumk+z31aSuyU72M75/FgNxUctN8fLZiGze+/l2Sr6p9LVxT1mqqDZ/HwlFYta26A6MyDGNXxXvr9jfgO+AIIAg0HHv2GXBzguNqV6rKyq3VfPj9VqqCEYb3yeDA0X24/uVvWbm1itw0HyI+ctJ8bCoPUloTYfmWKvrnpFAdsumbFQDc4XWpPg9eyyLgsxjeN6N+mUcRt3197g/FrNpWzdDe6cm96HYSzyAjjS1iZBhG5xdvobA/8BNVrRKRpm0hm4Hkp9qMUzjqcPMb3/Hx8m2ogtvqIfz93e8JRRz656TU9yOk+DwU9k5nsCrFlWF+ts8QxuRn8n/vraAkljNpa1WIzBQPQ3qlN1r3F9yCwXHgvyu3ddtCYcKALJasKyc7rfnaQtR2EITCbnr9htHdxFsotDYAuTdQ28rrncrsD1fy0ffbdsjRsr6sls0VQdIDHoq2VhO0t9/aBjwwKDeN/64s5uf7DWXvYb1YvqWK6lCUPzy7eHufQ3NEqY0kduWwzuSU6YNYuKas2eGy4M7B+PHEAtPJaBhdRLyT1+YBLa2pcCrwaWLCaV/ltRFeW7JxhxxCAH6PRdRWlm6qalQgAIRsWLGthuKqIODWAEb1y2Tq4FzGFmRR3WS5zoY8YjG8T/e9S54+JJdjJvenpCZCdShaP2ktHHXYVhlmSK80fnHA0CRHaRhGvOItFP4MHCMib+F2NitwqIg8CpwA3NBO8SXU0o3NJ8UDSA94Wq0OASxeV7HDtlOnDyISdVcIa6om7M4j6GwjhhJJRPjNj0Zw5azR9M4IUFoTobwmgu0oP9lrMH8/fSqZrXREG4bRucQ7ee1DETke+DvwcGzzX4Ei4PjuMBxVnJ33hEYcZe7KYvZq8CW/7/BeHDkhn9e/3oTf22BiWTCKz7L484nju/2i8CLCoePyOWRsP4qrw0RtNwusWRzHMLqeuBt6VfVV4FURGQH0BYpVtUulvhidn1m/GlrT2kJpsOUmoIYen7ua17/ZxMI1pVgi7DUsjxOnDmB6YR5PfbGW5VsqCXg9HDE+n1OmDWJwr86Rk6gjiAi9Y+tDGIbRNbW5909VVwAr2iGWdpeT5ueI8fm8umQDvdL9jddkjnPM5Kcri+mTESA9xb37f3/pFt5fupXLDhvJP35qVg4zDKNra7FQEJGz2nIgVf3X7ofT/i46aDjF1SG+WFVan6qiNmKzsTwY1/uH5KXib5DgLTfdTzjq8Pd3ljOuILtH1QwMw+h+WqspPNLked2ttDSzDWC3CgUROQU3RfdYYE9Vnb87x2tJis/Dn4+bwHcbK3l/2RZWbqni1cUbUNwLa62+kOq1GhUIdfxei6pQlJe+Ws+vfzRyl2NTVdaV1lIRjJCd6mNATmqLK1kZhmG0h9YKhYbjCAcC/wFeBZ7EnbDWD/gJcGTs5+76GjgRmJ2AY7VKRBjXP4tx/bM49PYPUSA1lpenooW+hRSfxbj+WS0eM83vYf7q0l2O6bMV23j401WsLamNJdpzKOydznn7D2PPoXm7fFzDMIy2aLFQUNXVdb+LyJ3Ak6r6hwa7LAM+EpGbcVdmO2F3AlHV72Ln2p3DtMn3mytZU1LdaJRMVoqXaDRK0HZTOChw90+m8PCnRa0ea/vs6LZ77st13PvBSgI+i5w0LyJSX2u4+vklXHroSA4d14/PfyhhbUkNPo8wYUA24wqyTE3CMIyEirej+RDg7hZeexu4KDHhxEdEzgfOBxg8ePAuH+fbDRUIUr+yWh2v10tG7C8TijoEIw57Dc3j3aVbyEtvPiV2bcRmvxFtX1VqbUkNsz/8gaxUb6M0GSJCZoqXcNThhte+4+73V+A4EHXcZd88HouBuan88aixraYBNwzDaIt4B5KHgOktvDaDxgnyWiQi78SW82z6OC7OOABQ1ftVdbqqTu/TZ9eX98uJ5etpbuJZ/bmArFQfJ00bWL9Oc1PBiI3PsjhqYkGzx4jaDl+vL+eLohJ+2FrVaKnKVxZvRGGHvEl1KoJRtlaGqKiNkJvuo09mgD5ZAXLTvGwsr+Wypxaxunj3MpAGIzYfL9/Ki4vW88bXG1lrFlc3jB4r3prC08B1ImIDz7C9T+FU4FrgoXgOoqqH7kqQ7WXmyN6k+DyEozaWZ8dmmIjtkOK1+NHovni9Fv9z2Chuf/t7qkJRd40EdWsIPsvif48ZR35248VuVJU5C9bx5BdrqQ5FsQRshUG5qVx44HCmF+Yxb1Uxqf7mC4Soo2woq8VrWbH1GrYTEbJTfZRVR/jHeyu45ZTJbb7+iO3wr8+KeGHRBsJRB1sVAURgwoBsfvOjkd02kZ9hGM2Lt1C4HMgEbsKdyVxHcTugL09wXB3CsiwumDmUO95ZjoiD19r+5Rx1FNtRLjpwKN5Yn8Ph4/MZW5DFy19t4IvVJXhE2Gd4b46aWNBsgfD3d5bz6pKNZAY89bUSVWVTeZCrnl/CVT8eW/9F3JzS6nB9X0VLdZmsNC+L1pWxoayW/jk7LgHakqjtcO2L3zCvqITsVC/pge2pKFSVbzaU89snvuRvp05hVL/MuI9rGEbXFm+ai1rgTBH5M7AXUABsBOaq6veJCERETgD+D+iDO3N6kaoekYhjt+ZXB4+kOmTz8KdFBCN2/dBUj2Vxzr6F/PbQUY32H5SXxq/iWFpy4doyXv96I3lpvkbZQ0WEjBQvoYjNrW8uY59hvfho+dZmF7evDtmIuM1b6S2s3maJ4BFh1bbqNhUKz365nnlFJfRK3zE5oIiQm+anMhjlf1/4msd/sVejNawNw+i+2jSjOVYAJKQQaObYzwPPt8exd+Z3s8Zw/sxhPD5vDZsrQvTNDPDTvQaT08I6y/F47st1iLhNMRW1EUprIkQdJeAR8jL8pPo8VIcj9M1000K0lHra7X8QerWWPqKNA5CitsMz89eSEfC0OnopM8VLaXWYeUUl7Du8d9tOYhhGlxRXoSAiOx3io6prdj+c5MlO8/OrgxK3uPySdeX4PRZLN1USjiqgiAhVqhRXh8kIeMlN87O5Isgxk/vz4qINZKV4Gw2PTfEJJdVKfnagxXWeHXWbuYa1IT330k2VVIWi9U1arbFEeOPrTaZQMIweIt6aQhGtT/YF6N6pQNvIdpSi4mocR2Mji+ruyN05CFWhKKGogwj8+uAR9M4I8MS8NdTUROqPkZnio1eGQ68WhsEClNdE2WNwLgXZ8TcdVdRG4p7f4PUIJVVxDS4zDKMbiLdQOJcdC4VewFHAMNz1FowG0gPuHIPm+gpEBK8FwYhDTpqbmO8new7mxD0GsGhNGRXBKNmpPiYPyubtbzZz57vLAQg0OJaqUlEbJcXv4eI4+jgaSvF74ltcGbBVSQuY8t4weop4O5ofaeGl20Xk37gFgxGjqpTVhN2ZybFttqNEbQdH3X4GjyWIuMuA1gl4Pew1rPGCPEdP7o/PI9z34Q+UVkeIOu6axx4LhvRK56ofj2VQXtuS8I3Nz8LjESK20+L8iDpRWzlkbL82Hd8wjK4rEQvnPgb8E/hjAo7VLYSi7pd/n8wA2ypDRBxtNGFN1f2y9XstVm6p2unxjphQwMFj+vFFUQnrS2vweiwmDMhmZN+MXUpzker3cNTE/jy7YC29M1vuwA5GbAJeDzNH7voEQcMwupZEFAp9gZSd7tWDeGOjiPplBSiuCqPaeBa0R4SAz8J2lGWbK1lfVsuAnQwn9Xst9huRuM7en+09mHmritlQFqzPt9RQbdimNmJzzdHjWuzkNgyj+4l39NHMZjb7gQnA/wM+TmRQXZ3XYzGjMI/3l20G3P4FJ5ZdT0TqE+c5qgS8Fk/MXcMVR4zu0BgzU3z8/bSp3PT6d3y5pgyNrS3hxCbTpQe8XH/sePYxo44Mo0eJt6bwATt2NNfdWn5IByfE6wpOmT6Q5xeuwxLcPgCRRvMJbMcdoto/J4V3l27mooOGkx5IRMUtftlpPv560iTWltTw7tLNbCwLkurzsOfQPPYcmmcmrBlGDxTvt9CP2LFQCAKrVXVTYkPqHsYWZJEe8FIdiqKO4hHqU2JHHUUQhvROJcXnJRyNsLUy1OGFQp1BeWmcs+/Qne9oGEa3F9etoKp+oKofNnnMNQVCyzyWkJPmp7BXGqk+i6jj5lOKOkpWqo8R/TLITnXnHzjq7p9Mt956K/vssw+5ubnk5OSw//7788YbbzTa55FHHkFEdni88847SYraMIxEi7dPwQb2UdV5zbw2DZinqqY3sgGfx2J0v0yKiqsY2S+TiO3gOIrHY9V3RAOEIjbpAS8F2cntq3/vvfc499xzmTFjBqmpqTzwwAMcffTRfPjhh+y33371+3k8HtatW9fovXl5ZmU4w+gu4m2vaO021sPOZzv3SKfNGMSfX/kW1dis5ibFpqpSGYzy8/2HJr39/vXXX2/0/LbbbuPNN9/kueeea1QoAOTn53dkaIZhdKBWv4lExBKRuq8yK/a84SMdd43mbe0eaRe034je7DO8F8XVEaJ242GptqOUVkcY2S+TE6cOTFKELXMch8rKSnr3bjz6yLZthg0bRkFBAQcddBCvvPJKkiI0DKM9tFgoiMi1QAR3VTUFPo09b/ioAK7BXXjHaMJjCf979DhO2mMANWGH0uoI2yrDlFSHqQhGOWh0X249ZXKnnAdw4403UlZWxplnnlm/bfTo0Tz66KM899xzPPfcc0yZMoVjjjmGM844g4KCArxed77DoYc2XktpxowZzfZFpKebBXwMo7Nprfnog9hPwf3ifwhY12SfEPAtYG4XW+DzWFx00AjO3KeQeauKKa+NkOrzMqMwt/V02El0zz33cOONN/LSSy8xcOD2Wsw+++zDPvvs0+h5SUkJr776KqNGjeLUU0/l7rt3XMr7pptuYs6cOYwfP57Zs2czadIkPv74Yw4//PAOuR7DMNpAVXf6wF1ys388+3b0Y9q0aWokzq233qppaWn69ttvx7X/vffeqz6fr/65x+PRQw45pMX9DzzwQJ01a5YCOm/evN2O1zCMXQPM12a+U+NNiHd9w+cikg2MBDapatPag9FFXXPNNdxxxx289tprHHjggXG9Z+HChQwaNKhN51m6dClTp05lxowZuxKmYRjtqMVCQUSOAA5W1SubbL8atznJG3v+FHCWqkbbM1CjfV166aXMnj2bJ554gtGjR7NpkzsFJTU1lezsbACuu+469txzT0aNGkUoFGLOnDk8+OCD3HXXXXGfJxwOs3r1aq688sqd72wYRodrraZwIU2GmorIYbhrJywBHgTGAhcAC4C/tVOMRge48847ATjhhBMabT/77LN55JFHAKioqODiiy9m06ZNpKamMmbMGJ5++mlOOumkuM+zceNGvF4vZ5xxRsJiNwwjcVorFKay4+I5P8dNb3GExmYzx7JrnoEpFLo0jWPRndtvv53bb799l8/hOA4bN25k+PDhZGZm7vJxDMNoP63NU+gLrGyy7TDgE22c3uJVYFSiAzO6nzfeeINQKMTo0R2bEdYwjPi1VlOoBOoHkovISNwlOD9vsl8FZn3mbst2lC/XlPLm15vYUhlEgT4ZAQ4bl8+Mwly8HotNmzbx4YcfAm6NY8uWLTz11FP07t2bQw45hHA4zLfffsstt9xCWloalmWxaNEi/H4/48aNS+4FGobRiLTUbCAiHwLLVPX82PMrgJuBA1X1kwb7nQVcr6pJSbM5ffp0nT9/fjJO3a2pKi9/tYHH5q6hvCYCovhjqTgitoMiZAS8nD5jEGs/msPll//PDsfIzs6mrKyMoqIihg7d8Z/HkCFDKCoqau9LMQyjGSKyQFWnN93eWk3hDuA5EckDNgPn4HYwf9pkvxOArxIUp9EJOI5y+9vf8+Y3m0gPeMhN9zW7XyhiM/ujH5g58nAiUbvF/E2FhYVx9VkYhpF8LRYKqvqCiFwKXA7k4TYbXagN/u8WkYHAwcDv2zlOowPd//EPvPHNRvLS/VitrAEd8Hnwey0+Wr6NjHeXc9lho5pdMzoYjrJobTkl1WE8lpCfHWDigGwsyyziYxidTauT11T1LqDFQeixiWs5CY7JSKK1JTU89+U6ctNaLxDqiAh56T7e+GYTx0zuz8h+20cVrdxaxT/eW85b324mbGt9ql0FMgNeTpk+kPMPGE5ehr99LsYwjDbrNLdqInKriCwVkcUi8ryI5CQ7pp7olcUb0TYu+lNXeLy4aAPgDj296LEFHHnnx7yyeCOOKilei0Ds4fcI1aEoD3+yiv1ufo973l/eLtdiGEbbdZpCAXgbmKCqk4Dvgf+X5Hh6nGDE5tUlG8hKbfuyoNmpPt5dupnymhCn3Pdf3vluM36PkOLz4G3STGSJ4PdapPg8iCh/f2c51770daIuwzCM3dBpCgVVfatBqozPgc63yEA3t6UihO3EFgRqI4/lpsP+1eNfsmR9OQGvFVfzk9ey8Hosnpi7loc+bjotxjCMjtZpCoUmzgVeb+lFETlfROaLyPytW7d2YFjdWzBqI60uste6UMRmblEp/jgLhDpeS/BYcOd7K4hGnZ2/wTCMdtOhhYKIvCMiXzfzOK7BPlcDUeDxlo6jqver6nRVnd6nT5+OCL1HSPV50N1YWXVrZQhU21Qg1PF5LIJhh6cXmKS7hpFMbW883g2qemhrr4vI2cDRwCFqBrZ3uL5ZAbyWRTjq4Pe27X4hHHWoDEZJ8e16TUMEHv50FWfsNXiXj2EYxu7pNM1HIjIL+ANwrKrWJDuenijg9XDM5AIqQ23Pgr6lMoQl4PPsesYTn0dYU1yN45gmJMNIlk5TKAB3A5nA2yKySETuS3ZAPdHRk/ojuDmP4mU7StRWrDYMY22OJYICZTVmaQ7DSJZOUyio6ghVHaSqU2KPC5MdU0/UPyeVn+w5mNKaCE4cBYOjSmlNmD2G5OxSX0JzfN7EHMcwjLbrNIWC0Xmcs28hx07uT0lNhJqw3WLeotqITUlVhMPG5vOzPYcAbiGxq5xYJ3W63yTdNYxk6dCOZqNrEBF+86MRjM7P5N//Xe2OKgK8HvcOPmo7gJCb7uO8/YZyzOT+qCqpPg+hqI3l2bU7/bCtjO+fZXIiGUYSmULBaJaIcMT4fA4f148l68t5+9vNbK4Iogp9MgMcMrYfUwfl1PcjiAgnTB3A43PX4NuFG31HFRQuOnB4gq/EMIy2MIWC0SoRYdLAHCYNzNnpvhceNJwn5q0h6ijeNnY6R2wlO9XHoWP77mKkhmEkgqmnGwnTLyuF8/YfSsR24uqkrhOx3SGoN504wTQdGUaSmf8DjYT63awxHD9lACHbqf+yb4mjSijqoArXHDOOQ8fld1CUhmG0xDQfGQl36ymTGZyXyuyPfiAYcRBxJ6bVDVl1VAlHFVCyUn3ceMJEDh9vCgTD6AxMoWC0i98cMopfHjCM/8xbyyOfrWJjeTCWak9AYGxBJhfMHM6sCf1Mk5FhdCKmUDDaTYrfy7n7D+Xc/YcSjjoUV4XweYW8NL8pCAyjk5KunndORLYC1cC2ZMeSYL0x19QVmGvq/Lrb9UBirmmIqu6QZrrLFwoAIjJfVacnO45EMtfUNZhr6vy62/VA+16TqcMbhmEY9UyhYBiGYdTrLoXC/ckOoB2Ya+oazDV1ft3teqAdr6lb9CkYhmEYidFdagqGYRhGAphCwTAMw6jXqQsFEZklIstEZIWIXNnM6yIid8VeXywie8T73mTZzWsqEpElseVK53ds5C2L45rGiMh/RSQkIle05b3JspvX1FU/p5/G/s0tFpHPRGRyvO9Nlt28pq76OR0Xu55FIjJfRPaP971xUdVO+QA8wEpgGOAHvgLGNdnnx8DrgAB7A3PjfW9Xu6bYa0VA72Rfxy5cU19gBnADcEVb3tvVrqmLf077Armx34/sJv8/NXtNXfxzymB7f/AkYGkiP6fOXFPYE1ihqj+oahh4EjiuyT7HAf9S1+dAjogUxPneZNida+qsdnpNqrpFVb8AIm19b5LszjV1VvFc02eqWhp7+jkwMN73JsnuXFNnFc81VWmsFADSAY33vfHozIXCAGBtg+frYtvi2See9ybD7lwTuB/+WyKyQETOb7co22Z3/tZd+XNqTXf4nM7DrbHuyns7yu5cE3Thz0lEThCRpcCrwLltee/OdOaEeM0t3dV0/GxL+8Tz3mTYnWsC2E9VN4hIX+BtEVmqqh8lNMK2252/dVf+nFrTpT8nETkY9wu0rq26y39OzVwTdOHPSVWfB54XkZnAn4FD433vznTmmsI6YFCD5wOBDXHuE897k2F3rglVrfu5BXget7qYbLvzt+7Kn1OLuvLnJCKTgAeB41S1uC3vTYLduaYu/TnViRViw0Wkd1vf26Jkd6y00uHiBX4AhrK902R8k32OonGn7Lx439sFrykdyGzw+2fArK5wTQ32vY7GHc1d9nNq5Zq67OcEDAZWAPvu6t+jC11TV/6cRrC9o3kPYH3s+yIhn1NS/wBx/IF+DHyP26N+dWzbhcCFsd8F+Efs9SXA9Nbe2xkeu3pNuCMKvoo9vuli15SPexdTAZTFfs/q4p9Ts9fUxT+nB4FSYFHsMb+193aGx65eUxf/nP4Qi3kR8F9g/0R+TibNhWEYhlGvM/cpGIZhGB3MFAqGYRhGPVMoGIZhGPVMoWAYhmHUM4WCYRiGUc8UCkaXICLniIiKyIhmXvPGXrtuJ8cojO33i3YL1DC6OFMoGIZhGPVMoWAYnZCIBJIdg9EzmULBMJoQkQNF5F0RqRSRahF5U0QmNHj9HhHZLCLeJu8LiEipiPy9wbbeInKviKyPLciztGlGzgZNYzNF5BkRKQPmxl6bISJzRGSdiNTGFlC5UURSmxzDIyJ/EZGNIlIjIu/FFgLaoVlNRCaLyEuxWGtF5FMROSBRfz+jazOFgtHVeGJ9CPUP3MVFEkJEjgLeBaqAnwFnAJnAxyJSl2zsX7iL7Bze5O1HAznAv2PHygI+xc1ndV3s58vAvSLym2ZO/ziwCjgZqFs1azBuOoMLgVnAnbipkv/Z5L3XA1fFYjsOeBN4qZnr2wM3z08e8EvgJKAYeEdEpjX/VzF6lGTn+TAP84jnAZyDmwa4tcd1OzlGYWy/X7Syzwrg3SbbsoBtwN8bbPseeKLJfi8A3zZ4/r9AEBjZZL8HYsfzNrm2O3YSf13Ss58BDtArtj0XtxC7p8n+/9P074Jb4H0H+Bts88S2vZDsz9k8kv8wNQWjqzkBdxnMho+9E3FgERkJDAceb1ITqcFNPDazwe6PAceJSGbsvXm4yz3+q8E+s3CbgVY1Od6bQC9gXJMQnm8mpiwRuVlEVgIh3JXe/o1bQIyM7TYRN9PnM03ePqfJsVKBA2P7OQ3iEeCdJtdn9FCdeZEdw2jO16q6ouGGpm37u6Fv7OdDsUdTaxr8/m/cJqGTcZtyTgd8uE1ADY83gpaX7OzV5PnGZvb5J+4CKtfgNiNV4+b9/weQEtunbrnWLU3eu7nJ8zzcWsH/xh47EBFLVZ0W4jV6AFMoGMZ2dQuw/D/cO+emwnW/qOoqEfkUtynnn7GfH6hqw+UQi3G/qC9p4XzLmjxvlLJYRFJw+weuU9U7G2yf2OR9dYVJX9yUynX6NdmvDLfZ6R80rtFsD8AUCD2eKRQMY7tlQBHuwiR/jWP/f+N2Gh8E7AP8vMnrbwC/Adaou7pXWwVw7+yb1jTOafJ8CW4N4hTg/QbbT2m4k6pWi8jHwGTgS1MAGM0xhYLRE02LDfts6iXgYuBFEfEDT+N2CPcD9sX9cr+9wf5PA3fh9i/UAs82Od4dwGm4I5fuwC100oExwAGqelxrQapquYh8DlwuIhtjsZxLk8XYVbVuGOxVIlKJW8vZA3dNYnBrB3X+B/gIeFNEHsKtZfSO7e9R1SsxejRTKBg90YWxR1N9VPW12GLoV+Ou2pUKbAI+B55quLOqlonIy7j9Ck+oamWT18tFZF/c/oA/4H6Zl+EWDk0LkJb8BLgXt8mnFrcgugR4pcl+1+J2GJ8H/Ba3g/sc3CGx5Q1i+lJEZsT2vwvIBrYCXwL3xRmT0Y2ZldcMo5sSkVNwC5GZqvpxsuMxugZTKBhGNyAie+FOjpuLOzdiGu4EuGW4i9ab/9GNuJjmI8PoHqpw5xlcjDvZbgtuLeH/mQLBaAtTUzAMwzDqmRnNhmEYRj1TKBiGYRj1TKFgGIZh1DOFgmEYhlHPFAqGYRhGvf8PLK6QuKfMo60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "influence_plot(rebuild_model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dd07b8",
   "metadata": {},
   "source": [
    "### Drop The High Leverage Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7dd3ba2e",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>53.700681</td>\n",
       "      <td>89</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>45.696322</td>\n",
       "      <td>92</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>50.504232</td>\n",
       "      <td>92</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>140</td>\n",
       "      <td>19.086341</td>\n",
       "      <td>160</td>\n",
       "      <td>124.715241</td>\n",
       "      <td>52.997752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>140</td>\n",
       "      <td>19.086341</td>\n",
       "      <td>129</td>\n",
       "      <td>121.864163</td>\n",
       "      <td>42.618698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>175</td>\n",
       "      <td>18.762837</td>\n",
       "      <td>129</td>\n",
       "      <td>132.864163</td>\n",
       "      <td>42.778219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "0    49  53.700681   89  104.185353  28.762059\n",
       "1    55  50.013401   92  105.461264  30.466833\n",
       "2    55  50.013401   92  105.461264  30.193597\n",
       "3    70  45.696322   92  113.461264  30.632114\n",
       "4    53  50.504232   92  104.461264  29.889149\n",
       "..  ...        ...  ...         ...        ...\n",
       "72  140  19.086341  160  124.715241  52.997752\n",
       "73  140  19.086341  129  121.864163  42.618698\n",
       "74  175  18.762837  129  132.864163  42.778219\n",
       "75  238  19.197888  115  150.576579  37.923113\n",
       "76  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[77 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars2 = cars1.drop(cars1.index[[76,77]], axis = 0).reset_index()\n",
    "cars2.drop(['index'], axis = 1, inplace = True)\n",
    "cars2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1368e7",
   "metadata": {},
   "source": [
    "# Build Model Again "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3475c113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.861</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   158.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 21 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>6.81e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:22:44</td>     <th>  Log-Likelihood:    </th> <td> -200.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    77</td>      <th>  AIC:               </th> <td>   409.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    73</td>      <th>  BIC:               </th> <td>   418.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   25.2974</td> <td>   11.336</td> <td>    2.232</td> <td> 0.029</td> <td>    2.706</td> <td>   47.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.4335</td> <td>    0.122</td> <td>    3.560</td> <td> 0.001</td> <td>    0.191</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.1362</td> <td>    0.021</td> <td>   -6.366</td> <td> 0.000</td> <td>   -0.179</td> <td>   -0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2635</td> <td>    0.031</td> <td>   -8.634</td> <td> 0.000</td> <td>   -0.324</td> <td>   -0.203</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.478</td> <th>  Durbin-Watson:     </th> <td>   1.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>   9.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.770</td> <th>  Prob(JB):          </th> <td>  0.0101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.703</td> <th>  Cond. No.          </th> <td>5.72e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.72e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.867\n",
       "Model:                            OLS   Adj. R-squared:                  0.861\n",
       "Method:                 Least Squares   F-statistic:                     158.6\n",
       "Date:                Mon, 21 Feb 2022   Prob (F-statistic):           6.81e-32\n",
       "Time:                        10:22:44   Log-Likelihood:                -200.71\n",
       "No. Observations:                  77   AIC:                             409.4\n",
       "Df Residuals:                      73   BIC:                             418.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     25.2974     11.336      2.232      0.029       2.706      47.889\n",
       "SP             0.4335      0.122      3.560      0.001       0.191       0.676\n",
       "VOL           -0.1362      0.021     -6.366      0.000      -0.179      -0.094\n",
       "HP            -0.2635      0.031     -8.634      0.000      -0.324      -0.203\n",
       "==============================================================================\n",
       "Omnibus:                        9.478   Durbin-Watson:                   1.195\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.184\n",
       "Skew:                           0.770   Prob(JB):                       0.0101\n",
       "Kurtosis:                       3.703   Cond. No.                     5.72e+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, 5.72e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = smf.ols(\"MPG~SP+VOL+HP\", data = cars2).fit()\n",
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc38ebc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again Check for Influencers\n",
    "model_influence = final_model.get_influence()\n",
    "(c, p) = model_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8252744b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16,9))\n",
    "x = [0,77]\n",
    "y = [1.0,1.0]\n",
    "y1 = [0.5,0.5]\n",
    "plt.plot(x,y , color = 'red', linewidth = 2)\n",
    "plt.plot(x,y1, color = 'green', linewidth = 2)\n",
    "plt.stem(np.arange(len(cars2)), np.round(c,3))\n",
    "plt.xlabel('Row Index')\n",
    "plt.ylabel(\"Cook's Distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca3d2223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([65], dtype=int64),)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(c > 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2458f4",
   "metadata": {},
   "source": [
    "###  Since the cook's Distance is <1 now , we can stop the diagnostic and process and finalize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a6d8cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>MPG</td>       <th>  R-squared:         </th> <td>   0.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.861</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   158.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 20 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>6.81e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:39:32</td>     <th>  Log-Likelihood:    </th> <td> -200.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    77</td>      <th>  AIC:               </th> <td>   409.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    73</td>      <th>  BIC:               </th> <td>   418.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   25.2974</td> <td>   11.336</td> <td>    2.232</td> <td> 0.029</td> <td>    2.706</td> <td>   47.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.4335</td> <td>    0.122</td> <td>    3.560</td> <td> 0.001</td> <td>    0.191</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>VOL</th>       <td>   -0.1362</td> <td>    0.021</td> <td>   -6.366</td> <td> 0.000</td> <td>   -0.179</td> <td>   -0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HP</th>        <td>   -0.2635</td> <td>    0.031</td> <td>   -8.634</td> <td> 0.000</td> <td>   -0.324</td> <td>   -0.203</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.478</td> <th>  Durbin-Watson:     </th> <td>   1.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>   9.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.770</td> <th>  Prob(JB):          </th> <td>  0.0101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.703</td> <th>  Cond. No.          </th> <td>5.72e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.72e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    MPG   R-squared:                       0.867\n",
       "Model:                            OLS   Adj. R-squared:                  0.861\n",
       "Method:                 Least Squares   F-statistic:                     158.6\n",
       "Date:                Sun, 20 Feb 2022   Prob (F-statistic):           6.81e-32\n",
       "Time:                        16:39:32   Log-Likelihood:                -200.71\n",
       "No. Observations:                  77   AIC:                             409.4\n",
       "Df Residuals:                      73   BIC:                             418.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     25.2974     11.336      2.232      0.029       2.706      47.889\n",
       "SP             0.4335      0.122      3.560      0.001       0.191       0.676\n",
       "VOL           -0.1362      0.021     -6.366      0.000      -0.179      -0.094\n",
       "HP            -0.2635      0.031     -8.634      0.000      -0.324      -0.203\n",
       "==============================================================================\n",
       "Omnibus:                        9.478   Durbin-Watson:                   1.195\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.184\n",
       "Skew:                           0.770   Prob(JB):                       0.0101\n",
       "Kurtosis:                       3.703   Cond. No.                     5.72e+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, 5.72e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f5c646",
   "metadata": {},
   "source": [
    "##  Predicting for New Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "161423a6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>HP</th>\n",
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       "      <th>1</th>\n",
       "      <td>40</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   HP  VOL   SP\n",
       "1  40   95  102"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_predict = pd.DataFrame({'HP':40,'VOL':95,'SP':102}, index = [1])\n",
    "mpg_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afb54e4f",
   "metadata": {},
   "outputs": [
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       "      <th>HP</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>MPG</th>\n",
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       "      <th>1</th>\n",
       "      <td>40</td>\n",
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       "      <td>102</td>\n",
       "      <td>46.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   HP  VOL   SP   MPG\n",
       "1  40   95  102  46.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_predict['MPG'] = np.round(final_model.predict(mpg_predict),)\n",
    "mpg_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc2387e6",
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   HP  VOL          SP        MPG\n",
       "0  49   89  104.185353  53.700681\n",
       "1  55   92  105.461264  50.013401\n",
       "2  55   92  105.461264  50.013401\n",
       "3  70   92  113.461264  45.696322\n",
       "4  53   92  104.461264  50.504232"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars2.pop('WT')\n",
    "last_column = cars2.pop('MPG')\n",
    "cars2.insert(3, 'MPG', last_column)\n",
    "cars2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09c7c80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45.428872\n",
       "1    43.992392\n",
       "2    43.992392\n",
       "3    43.508150\n",
       "4    44.085858\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.predict(cars2.iloc[0:5,0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2c7a3dd7",
   "metadata": {},
   "outputs": [
    {
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       "   HP  VOL          SP        MPG\n",
       "0  49   89  104.185353  53.700681\n",
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       "2  55   92  105.461264  50.013401\n",
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      ]
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad32047a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42756172",
   "metadata": {},
   "outputs": [],
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