{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn import metrics\n",
    "\n",
    "sns.set()\n",
    "sns.set(style=\"whitegrid\")\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "plt.rcParams[\"figure.figsize\"] = (8,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def precision_stats(model, y, X):\n",
    "    pred_y = model.predict(X)\n",
    "    coefficients = model.coef_\n",
    "    intercept = model.intercept_\n",
    "    MSE = metrics.mean_squared_error(y,pred_y)\n",
    "    # these 3 are all the saem\n",
    "    score = model.score(X,y)\n",
    "    explained_var = metrics.explained_variance_score(y, pred_y)\n",
    "    R2 = metrics.r2_score(y, pred_y)\n",
    "    # residuals\n",
    "    res = y - pred_y \n",
    "    print(\"Residuals info\", res.describe())\n",
    "    \n",
    "    print(\"coefficients:\", coefficients)\n",
    "    print(\"intercept\", intercept)\n",
    "    \n",
    "    print(\"MSE\", MSE)\n",
    "    print(\"explained variance\", explained_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     crim    zn  indus  chas    nox     rm   age     dis  rad  \\\n",
       "0           1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1   \n",
       "1           2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2   \n",
       "2           3  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2   \n",
       "3           4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3   \n",
       "4           5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3   \n",
       "\n",
       "   tax  ptratio   black  lstat  medv  \n",
       "0  296     15.3  396.90   4.98  24.0  \n",
       "1  242     17.8  396.90   9.14  21.6  \n",
       "2  242     17.8  392.83   4.03  34.7  \n",
       "3  222     18.7  394.63   2.94  33.4  \n",
       "4  222     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Boston = pd.read_csv(\"../../datasets/Boston.csv\")\n",
    "Boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuals info count    5.060000e+02\n",
      "mean    -4.437382e-15\n",
      "std      6.209603e+00\n",
      "min     -1.516745e+01\n",
      "25%     -3.989612e+00\n",
      "50%     -1.318186e+00\n",
      "75%      2.033701e+00\n",
      "max      2.450013e+01\n",
      "Name: medv, dtype: float64\n",
      "coefficients: [-0.95004935]\n",
      "intercept 34.5538408793831\n",
      "MSE 38.48296722989415\n",
      "explained variance 0.5441462975864798\n"
     ]
    }
   ],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   medv   R-squared:                       0.544\n",
      "Model:                            OLS   Adj. R-squared:                  0.543\n",
      "Method:                 Least Squares   F-statistic:                     601.6\n",
      "Date:                Sat, 28 Mar 2020   Prob (F-statistic):           5.08e-88\n",
      "Time:                        09:39:54   Log-Likelihood:                -1641.5\n",
      "No. Observations:                 506   AIC:                             3287.\n",
      "Df Residuals:                     504   BIC:                             3295.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     34.5538      0.563     61.415      0.000      33.448      35.659\n",
      "lstat         -0.9500      0.039    -24.528      0.000      -1.026      -0.874\n",
      "==============================================================================\n",
      "Omnibus:                      137.043   Durbin-Watson:                   0.892\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              291.373\n",
      "Skew:                           1.453   Prob(JB):                     5.36e-64\n",
      "Kurtosis:                       5.319   Cond. No.                         29.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols('medv ~ lstat', data=Boston).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>33.448457</td>\n",
       "      <td>35.659225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>-1.026148</td>\n",
       "      <td>-0.873951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   0          1\n",
       "Intercept  33.448457  35.659225\n",
       "lstat      -1.026148  -0.873951"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18fc4f2130>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Grz+hxEwW1mwLAeFXOrXdThvFgb1aYsFU1KR+WGuT5F1F2OdwYTYKDXitWEBbVqCufEhCIIg6oZE3Y2GlnD5c3z3DYPVKu4W14TJUaozYn1WPlbPSkOV0RrQutw3CzmFNwEAq2elgQO8imwwxThYGx+CIAiibii46Ya/yWT1wVuJl0QiHjM31FiFNdhYLuB1icXQcwpvggM8Bsm4423gDEEQBNF8IVF3I5QZ2942DJDAIxlt9qRk7Mg+L/wslUgCXpe3RDWW5ahWnCAIohVCou5GKDO2vW0YOA7YdfACMiYkYe3s4Vg+fSh2HbyAs1d0iNeqseDZAVAqAl9XVLhStHnNpp2nqX88QRBEK4Ri6m6EMmPbfawq4BBapZzBM+MSXRLaMiYkYfoT/SCXMSi+dgmaDr0DXhfDSBAVoUTGhCRowuQw1FiF5jXTn6BacYIgiNYGiboboczY9rZh0IQpoQlTej3nxZqaeq+LYSTYtPN0vQbLBBPqAU8QBBF6SNRFCFXGdl3CXNc567Ou5lArHuqKAoIgCMIBiXoDaS4WqLAOKwuJxLEBkEklsLNAuEqO1bPSwDASh0u+kddIPeAJgiAaBxL1BhCoBcp/fvu+Mxg9sAuiIhQwW+yIi1JD5uQOd98oqMPCAl7HnycPgM3G4fVP8hpsHTd040I94AmCIBoHEvUGEKgFqq82Y/u+Mxg/vBs2fJYviO2iqamIjVLBbGWhlDMorzS7toydnOJzfrrYOgzVFmzccarB1nEwXOfeEgSpBzxBEERwoadqAwjUArXaWIwe2EUQdP7zK7fm4vzVCkxbsR/nr1Z4tozdlieUoInNQxdbh0ohC4p1HIxmPMEefkMQBEGIQ5a6E/85fAkHT97A6IGdkPZAByjr6K/uywIVc1nLZQyiIhSiYqtSOH4VvsRYzGpeODUV2gilxzpMFltQrONguM6pBzxBEETjQJa6Ex3baqAzmPBmVj6mLP8SH+w8jWulBq+f92aBatQK0XawGrUCMZEqny1hfc0vF7OaV23NxfVb1VgwxbXJjCZcgT8+ndIg65hlOUgArHkxDQunpKJXZ63LegKB2s4SBEGEHrLUnUjqFod3/jwKBRfLsO/wZew9dAlffHcR/brHYdyQrhjct72LmDGMBJ3iNVg9Kw02OweZ1CFcBqPFa6w9LsoRQ1+5tXZS2pz0ZGzbcwaAY375nPRkrM+qjbnPn5yCqHAlbuuNolazRAJkffWjMKhFgtrs97WzR8BmZ4XX9NVmv6xkMa/A7EnJ2HXwAp4Zl0iuc4IgiGYIibobEokESd3ikNQtDhUGM/bnFmHf0SK89vFxREcoMWZQZzw8uCvaxoSBZTlcLTV4JJFFhou72K02FjIZg67towRXtEzKoMZkhc5gAgDoDCbERKoEMeY7yjFMrFd3v6HGipzCm5j+hGNQizP1TXQT8wps+MwxLS42Sk2WNkEQRDOERN0H0RolfjW6Jyb+rAfyz5XiP4cvY0f2efwr+zxSerfF8AcS8Pf/nMGtCocg8xb56llpouIrkzLQGUxCXDnujjhGR3jvJgcAxYCQFOdu5c+elIyP957xcInzMX2zxY4yvRFajQqlOqPfWfBe+9QDJOgEQRDNFBJ1P2AYCVJ6t0VK77a4pTPiq5wifJVzGcfP3PT4LC987l3cFk1Nhc1mx5WbBmR9dQ46g8nFYuYF1j3BTqNWQB4ej7nrvxP6wq+cMQwcx+H6rSp8vPeMcCzeJe7Ndc73ffcn0Y3K0AiCIFoeJOoB0karxjPjemPSmJ74b95VvLvjFCxOAhkdoYDULdubZTls2nkaOYU3XQTW2WJmWQ4VVWaYzDbcuF0lCP+iqan47MB5QVxzCm/i0o1KrJ09At06RuPPkwd4WPfeXOcZE5Kw6sNcv8S5ObSXJQiCIAKDRL2eyKQMRg/ojO4do7HsgyMorzRDIgEqqixY8M4hjBvcFaMHdoJcxghWNuApsN5K1XjhX7k1FxkTkpBTWOsVKNUZYbOzHvFzHm+uc02Y3G9xbs5laM2lNS9BEERzg0S9AfDC9+YfHxTc2YUXy/Dl0SJs3V2Ij/9zBgMS430KrATAbb3Rp2UdFaFw+X5dlrY313l8TBjWzRnhtwiGarBNQ2juw2HENhwEQRCNBYl6A3EXvviUMPwspROKSiqx7/BlfH38qsd34rVqmCw2LJiSivc+P4XHR3b3KfyR4bXNZfyxtL25zuNaQdZ6cx4O423DUVfvfoIgiGBBoh4iurSLxAtP9sNvH0nEv7+9gH9+fQ42OwcAuLdDFCSQIOurH5FTeBOjB3YRtaxNFhtmT0rGjuxzyJiQ5BD5mLA6xbmxXOdN4QZvzsNhvG04lk1LaeKVEQRxt0CiHmLCVHKkj+mFnw/tivNXdPju5HUcPV2CowUlwmeOnLqOFb8bCp3BDH2VBV8fK8JTY3vDbLVhyxc/4OwVHQ4cu4p4rRrr5oxokOu8LiH2V6ibyg3enLPyvW04JBLf7YYJgiCCRaOJ+syZM3Ht2jUwDIOwsDD85S9/QWJiIi5duoT58+ejoqIC0dHRWLNmDbp27dpYy2oUeIFN7dMeqX3ao8poxd5DF/HJl2dhZzkcKSjB0cISGM12xGvVmDd5II79UILEe2KFpjTByD6vS4gDEeqmcoM356x8bxsOjrM34aoIgribaDRRX7NmDTQaDQDgwIEDWLhwIT7//HMsXboUTz/9NCZMmICdO3diyZIl2LZtW2Mtq0mIUMvxy1E9MSCxLU6eu4VPvjoLk9nx4C/VGbF80xH87sn7sevgBayelQaz2YLwcFWD3dsVVeJCvHb2CMREqgIS6qZyg4uFFjRqRbPIhve24TBU3ADaxzb6egiCuPtoNFHnBR0AqqqqIJFIUFZWhh9++AFbt24FADz66KN49dVXUV5ejpiYmMZaWkioy43NMBJ0bR+FMJUcW3f/4PLdymor3v7nSQxOag+LlcXN6xeQktLwuKzFahcVYovVsaEIRKib0g3u3qwnGGGAYOQHeMtlyC/+KaDjEARB1JdGjakvWrQIhw4dAsdx2LRpE4qLi9G2bVtIpY6Yo1QqRXx8PIqLiwMW9by8vFAsuV6ow8IgD4/H6m15LkNZrNWlMNbUuHw2PqGrhzhGRyjQNSEK/z1+FV8fu4pu7ZS4WHoMA/vdA5lUBo6zw1Dheay66Ni1l6gQS8AhLy9PdC3xWjXMJiPy8s54XOP8ySke11h87RIuBrguoP6/v/iErlixJU80Oa30xmW/jhHI76u+NKe/n6GArq9l05qvrzVfmxgSjuO4xj7pv//9b+zZswdz5szBvHnzsGfPHuG9Rx55BGvXrkWfPn3qPI7ZbEZBQQEABMWSDRY6g8ml4QwAIcnN3Y1ts7EoulmJVSL93J9/oi/yz97C7oM/QV9tAyMBWA6IjVJhacbggK1RfZUJRSUGYQLcoD5tMfXRvpBKJVAqpNCoFaIDarydJ1jZ73l5efX+/ZXqajBtxX6P1zcvHuO1OY87gfy+6kNDrq8lQNfXsmnN19car43Xvb59+0Kp9MwlapLs98cffxxLlixBu3btcPPmTdjtdkilUtjtdpSWlqJ9+/ZNsayg4a8bm5/y9umXPyJjQhKiIhSI1ihRWW3Bc4/1QYxGhV+P7onB98Vi8XvHoK+2AADK9A4Rmp2ejOH3d/BbSDVhSsREWjFjYj9ERyjBAVjy/mEXAe8Ur/FwHwNwGUTDi3dzaE4TjDBAcy6TIwiCCIRGqQOqrq5GcXGx8HN2djaioqIQGxuLxMRE7N69GwCwe/duJCYmtvh4Oi80zogJDZ+YllN4E6s+zMWWLwpxu8KItR8fx7y/fY/5b3+PopJKqFVKQdB5LDYW6/6eh4yV+/HhnkIUFevBsr6dLgwjQUJcBLp1jEZkhBKrPzrm4bY2GC3QalSI14YJgl1UUom567/DtBX7MXf9dygqqazzXGKwLAedwYRSXY0wda6h8Mlp/P2uTza8v78vgiCI5k6jWOpGoxFz5syB0WgEwzCIiorCu+++C4lEgmXLlmH+/Pl45513EBkZiTVr1jTGkkIGy3LgOODVF4a6DGYRExp3C3HiqB6CaxyoFdpVM4d5WKNtolV44sHu2Lr7B+zI/gk7sn9C/17x+OWoHrjvnhhU1lhcssMNRouLpX1bb/TLOg0kI96XO95XQltDCEajneZcJkcQBBEIjSLqcXFx+Mc//iH6Xrdu3fDPf/6zMZYRcsSEa+HUVMRFqaAJ8xQad9exJkwuKrT6KgtmT0rGhs/yXY776Zc/uohw/rlSnDhbirYxYag2WlFltCJeq8aCKalC9zpBsCKUHhuFQX3aQgJHnJoXx0BCCb6y0H1tDhpKQ8MAzXl4DUEQRCCQfzGIiAnXqq25YDmICoS769hksYm6gcv0JnyTdxVLM4bg3XmjsDRjCCLDFS6T2wCA44Cp4/ugTG9EldEqrOGVTUcxakBn4ecVW3IgZeBy7kF92iJ9bG/Mf/t7Fze7Uh5YKMFdtCuqzNAZTDCZxcvpmkvcmt8Y8GEHEnSCIFoiJOpBJNCEK2cLcfPiMejZORqLpqa6xIcXTU3FmUu38WBKJ0dTmjXZWL7pCGw2VlRshya1F3rM8+gMZny0t7YWvlRnhNnKupz7hSf6IfPDXA9RtrPwK2bt7dpNZhvmrv8Ol4srGzVuHYr4PUEQRHOHer8HkfpkYvMWIu++/sQpEz4mUoXYSBV+PvQeLH73sIvgbv6iAIumpmKlUync4ucGQSGXeqwhIkzusrFQK6UoKat2SYYr1dWIirLNzvrlmvZ27TduV6FUZ8SO7PMeIYRQxa2b+3hWgiCIUEGiHkQaknDl7L7m3ep8rTRr93Rd5xTexIyJ94uWn7mvYcGUVHz65Rnc0hmhUkhhtbFYtPEwenSKxsj+HTG4bzuoFJ6bAX5D4k/MWuzaF05NxcZ/nQIAnL2iw8d7zyBjQhK6JkQiTCmFnXXMko9P6AqW5YImuM15PGtLh+bFE0TzhkQ9iDQk4cqX655lbaKC601sxXqjv/jrZOFnjgU+//Yn7Dp4EeevVmDzFwX4Wf+OmPXL+/H2v/5XL0ta7NolEggDaQCHsG/aeRp//cMIlFeaQ2ZJU915aKB58QTR/KGYepCpb8KVr1ppQ0WpENfu1VmLpRmD8OoLQ8FxEI0Vu69BJmNcfpYwwOFTN2C/812OA7LzrmHpB0cRG6XGjIn9sPrFtIBF1v28EgBz0pNd4vFz0pNhs7GilrS+2uz3uXxBdecNw1s+gjcPiCY6vimXSxCEE2SpNxN8ue4v1tSgd69IvP6HEbitN7m0lK2PhStmyQLA2NTOOHD8Ks5cLkfWV0qMHdQFYwd38bvdqjtmK4ttexwud02YHIYaK7btOYM/PZMSUkua6s7rj698BJoXTxDNHxL1ZoK7+1py5zV9tRnqsDDHvHMOgqADtZbS638YAZaD16Yv7jFQb0ltA+5rh69yryAqQoEu7SPxj6/P4Z9fn0NKYlv8fEhX9O/dFtIANg9yGQOdwYRVH+YCAHp11iJ9bE9IGUlIJ7xR3Xn98ZWPQPPiCaL547eoHzhwACNHjoRcLg/leu5qGEaCqHClh6U0f3IKWJYTtZS0GpVX6x2AqNXVKV7jYcnyQ2QAQF9lwaxf3o/yShMOnbqBg/nX8coPOYiLVuPnQ7piTGpnaCNrR5966yLnbDFrNSpM/kUi1mflQ6tRYU56stA9LxSWdCj60gdrgE1zxlc+QlyUmubFE0Qzx29Rf+utt7Bw4UKMGTMG48ePx+DBg0O5rrsWMUtp9bY8r5ZS+tieotY736lt+z5X9/f2fWcw61cPCJas2WLHtVIDPt57Bmev6AA4rK9rpQYs35SDeK0aS6YNwvVbVfgq5wo+/s8ZfPLljxic1B7jBndBZLjCo6yODwe4W8wL3v4epTpHe9pte85gxsR+6BivgdViQoe2zbvc7G4pk/NVlknz4gmi+eO3v3Pnzp345JNPEBcXh8WLF2PEiBFYvXq1MPqUCA6+LCWx4SUJcRE+suY5jB/eDZt2nsaCdw5h087TGD+8m1A+xie1xUaphSx1Ppkt66tzwrEyPzqGfj3aYOWMYXh3/miMH34vThFZS18AACAASURBVJ2/hb+8dwR/fPM7jw2Fc8Kbs8XsvM6zV3RYvikHDCNB6Y3LzV4Yvbmlg5Xc11yoa0AOdd4jiOZNQDH17t2746WXXsJLL72EkydPYsOGDfjVr36FM2fOhGp9dx2BWkocB6+ft9lYodkL4BCiDZ/lY/WsNOGz7scEgNe2HResdv57/Hsd2kRg2mN98ZufJ+I/hy9h8xeFLusv1RlhsXrGWIMxIrUpuVvK5CgfgSBaNgE/UYuLi7Fp0yYsW7YMBQUFePLJJ0OxrrsWMUtp/uQUr5ZSdIR3y4rjICpE7kVwzsfkk9ucERNfpVyKkf07epSOAcArm3Lwn8OXUGOy+ryulpSRfjeVyZE1ThAtF78t9e3bt2P37t348ccfMXLkSLz44osYMWIEFApFKNd31yFmKRVfuwSG8Z6IFBOpROasNLAsB4VciugIh2UllwduHQdSDub+2bhoFUYP7IzcwhK8s+MUtu4uxMj+nfDzIV1xb4eoFm0BUpkcQRAtAb9F/ZtvvsGkSZMwZswYhIeHh3JNdz3umdsXa2pEP+cteSs6wiE0UeFKLJ8+BCVl1VApZDBZbGgXG+5TiAJxv3r77DMP98b5qxX4z+HLyD52BfuOXEa3DlEY2b8jhvZrjzbRYS1GzHnILU0QREvAb1H/4IMPQrkOog7Eyqnck7e0GhWqaqy4WV4DqVQClYKB1cZi445TLqJfV2kWX1rHf0ZfbfYp7GKlYz07a9GzsxZTH70PO775CTu/vYALuwod1ntyRzwytAtiox3u/pbSZjQUZXIEQRDBxKeov/zyy5BI6rZEXnvttaAtqDURrLpmdViYuEWuUQrlaizHQSGTYu3fj9cOcnk2FVn7f3RJlNu+7wyeeri3R117p3gNDEYLrDYWSjkTtN7sNpbD9VID/vzbAbhdYcShUzfwbf41fHPiGgAgMlyOJdMGo0xvFDYTZP0SBEHUD59ZPl26dEHnzp3RuXNnaDQaHDhwAHa7He3atQPLsvj6668RGRnZWGttUfCu8bnrv8O0Ffsxd/13KCqpDHiuN8tyiInvBKPZhowJSejVWSuIc3mlSShXUytlqDJa8NJT/bFwSiq0GhUyP8rFo2n3uhxv9MAuonXtt/VGYa3nr1YErXzLZmfx+Mju2PxFAT7YWYDbFUYsfHYgnvxZdwBAZbUVL791EO9/fhovvf5Nve5RqKCZ7ARBtDR8Wuovvvii8Odp06bh/fffx4ABA4TXjh8/jo0bN4ZudS2YYIz/FIuZ853fnMX5oYGdwEgkLm52/nNxUa4Z21ERCtGM+PJKk/B6RJj4Z/wt33L2UEgAvPHpCZf78MHOAmTOTMP//dfRtITjgCMFxeA4YO767zDzl/fjwZROLi1pfXk9QtHprT7NZu6GjnMEQTRv/I6pnzx5Evfff7/La/fffz/y8/ODvqjWQDDqmsU2BrsOXsDsScmQMkDGhCTkFhZj4qieWPr+YY969BkT+7lkwMdr1YiKUIpmxFusdiyckoo20SqEq+X1ril3F8ONfx4leh/sbO19iNeq8euHeuFv/zwJi43Fm1n5+Pu+H/Hw4C6OlrQalVeBBcRb4Ta001ugmzJfmwCCIIjGwu8i2/vuuw+vv/46TCZHDbPJZMIbb7yBxMTEkC2uJdPQumaW5WC22F0EsVdnLcYP74blm47gjU/zIZdJ8MvRPSFl4CE0pToj2sWGQ2cwI2NCEt6dNwozJvZDtcmC2ZNcx6EunJKKhDbh+PpYEW5VmLD5iwKPzyyamuqRNS/mnnYXQ8md4S3u98H5z7MnJWN/ThEAoE20CrMnPYBO8RHYvu9HPLdiP17ZfBT/O38L0x7rK4Qf+HBAqDq9Bbopu1s6zhEE0bzx21LPzMzE3LlzMWDAAERGRqKyshJ9+/bF2rVrQ7m+FktD6pp5q69Mb3SxmCeO6oENnzkGovz2kUShWxzf2nXbHtf+7TIpAxkjgVwmQVmlCcs35SBz5jDsOngBr/7OMY+9+HYVNu44BZ3BhJee6g+VQooKgwUcx2HZ80PAMBKU6Y2IinB1JXuzTCPDa133vTprAQ5CWV3WV+egM5gwJz0ZCrkUmxeNActx2LTzNM5e0blYt2NSu+B6aRX+mX0O2cevIu/HUkgZCcakdobNzuLCdb0gsL7a5NbXHR5oB7y7peMc0fKgsNDdhd+i3rFjR2RlZaG4uBilpaVo06YNEhISQrm2Fk196pr5f3xmix1leiMO/e8GXnqqvxCT5uPhGROSPNq/rs9yuNv5ISwLp6Ri76GL+Pzbi4jXqvHSU/0xqE9baCNVGD2wCyoMZvx1e56LEL3x6QmsmjlMmKbmvGGQSV0FvUxvFLVMM2emIV6rFjYeS+6EBeK1asx/NhVWmw1SKYONO/6HWb96AFHhSjz78x6Y/kQ/j3sUppbh9E+3wd3JT7OzHPYdLYKUkUCpkOJskQ733RMjKr4yKdMgt3ygm7KW3gaXaJ3cLYOIiFoCeuLodDrk5OQgNzcXCQkJuHnzJkpKSkK1thZPIO02nbPln191ABt3nMKDKZ0gkzGYMbEfMmcOQ4RagXitGpowuahV2DYmHGteTMOK3w3Ftyeu4vNvLwrvvfHpCUx9tC/e+OQEVn2YC42XZDgAgqDzr63PyoeddV3nrQqj6PcrqsyYPSkZ6WN7emw8Vn+Uiwi1Eh98XoCcwpt1WrFi1i8ADOrbDgDw2sfHsfjdwxiR3BFx0Y7wA//QkjJosDuc79T3wcKHsHb2CI8HoXP4gbvjkWiJbXApy7/1QmGhuw+/LfXc3Fz8/ve/R9++fXHixAk8//zzKCoqwpYtW/Duu++Gco13BWL/+DZ8lo9lzw/ByxsOAnC4s2dPSobJYhO1CpUKKSLDw2GxshiR3BEPpXZFjdkKXaUZO7LPAxIgWqPAwimpUHhpIcuynKhY2+6oOr/OjAlJot8v05uwI/s85qQnix6nstosuNqVct6adngMBvVpi+cfTwLHOTZEChmDpRmDoFLIYKixYkf2eegMJjzzcCJulRtx/loFZFIG/8o+D4VciuEPdMBDqZ3QpV0kbuvFNx11bSRYlkNFlRkmsw03blcJIQPnTn3858QsoLW/Hw6zjYXszoausa2hQF2tZMm1bigsdPfht6ivWrUKb775JoYMGYKBAwcCcGS/nzp1KmSLu5tgWc5l7vmO7PM4e0UH5k6iWanOiLNXdPh47xk899h9WDglFas+dG0go5AzqDbaUKY3QaFgsHzTERcXukrB4KmxvbHqw1xoNSrMSU/2cLMzjLjYy6SOQS8ms13Iup89Kdklrr9oaio++fJHnL2iw7XSKtHjGGqswnrtbK013auzFo+P7I6F7xwSBD59bG+XMr056clQKqTY8Fk+zl+rQLxWjeXTh6BMb8S+I0X4Nv8aDp68ju6dojEyuQPaRKtwq8Lkcn4A0BlMomLnq4TQPfPdmwXkHAJp7Oz3+gh0MEovieYLhYXuPvwW9evXr2PIkCEAIHSZk8vlsNs9x2wSgcGyHPRVZmzaedpFTHYdvACVQuoi4DqDCTY7h/05FzFjYj8kxEVApZCixmzDn+7MNucFUKtRoVRnFFzoy6cPEY5TqjNi254zmDGxH9rFhoPjALmcgUTCucTxeWGoMVmx9P0jLuv7Ju8qMiYkITZKhWiNEnIZg6cf7o1LNyoFa91507BoaiqiIpRYOWMYFHIpbE5WxMRRPVzq2UcP7ILMD12b5KzPyseffztAsPTnPTsALOuYM58+piceH3kvjhaU4Ju8a9j8RSHUSinUSimMZrtwT17bdhzRGgUyJiSBkTiG3vAC781bkjEhCas+zHWxbrxZQCqFTPgzL46NRX0Emiy51g0NIrr78FvUu3XrhoMHD2L48OHCa4cPH0bPnj1DsrC7CX21GSvdurxt+CwfmTPTYLdzUMs5vP6HETCa7bilM4IDh9EDO8NQY8WWXQV44Yl+guDy31+fVStG/GsSicTlAX72ig5ZX53DjIn9XKz+ec8OwKqZaSjTG9EmWg2FnMEf72wYnNeXMSEJXx8rQvrY3ljw9veChb1yxjAwjARKOYO1s0fAZmchkzKw2Oy4dEMvDJfp3E4jWBHueQLe8gYiwhR44w8jUGO2wWJhMe9v37tsNM4WleNPz/RHjcmGfUcu49CpGwAAlVKGv/3jf4gIk2P88G5YtPGQ8L2FU1MRF6WC1SoucJowuYd1480CMtRYXb5bn4Y99c1Q9iXQOoNJ9NhkybVuaBDR3Yff/3Lnz5+PuXPnYt68eTCZTFiyZAnmz5+Pl19+OZTruyvw9jC+rTdi2sr9+MsHx1BeaYZcxmDhxkNYtPEwPtz9AwDg8ZHdYbOLx8E1YXLh53itGnKpZ+18+tiegqDz31vz0XGwLAt9lQUMI4HZi9h1bR+J6U/0c7GocwpvYtHGQ46HR4QKMZGOREEpA5SW12DjjlNY8M4hbNxxCrd0Jvxl2iAXtzyP+8/8NZTrTVApZYjXhnl0qtvwWT5GD+yClVtz0SE+AnN/MwAfLnkYvxrdA1dKDCguq8a10iqPNrmrtubiSkkVWI4TPafJYvOwbsTmw89JT3bkLjjf8wAa9jS0pbC33ggsy3k9dkufc0/UTSAJu0TLx29Rf+CBB/DFF1+ge/fumDhxIjp27IgdO3agX79+oVzfXYG3h7G+ygKg1o3Ksg7R6dVZi98+kij0fb9+yyD6fYYBFk5JFTLijxbc8GgqkxAXISrYZXpHX3l9lVlIquvVWYuFU1KROXMYlmYMwq2KGtz2kgXvbKGyLAezlfXIqn/9kzwo5VJkTEhCm2gVFk5JFdb29bEizH92oMtaX3qqPxQKBkvfP+L1vLyFz58/KkKJ8cPvRZs72fH3dohCjdnm8T1tpBJ7D13EoqmpLudcODUVPTpFe8SleQto7ewR+GDhQ1g9Kw3aSCV0BpPwXX/FMVgZymICvWhqqhDWETu2syW3efEYrJvjmeVPEETLwW/3u8FgwL/+9S/88MMPqKmpQVFREY4ePQoA2LJlS8gWeDcgFvfiE7R4tBoVOACvvjAUdpbDR3sKhQd11lfnPOLXy54fDKuNxV931sbG500eiBM/3kTGhCRERSjQJloNqdS7G7lUZ8TKrbnInJmGJRmDoK+yuJzjT8+kwGZnfbpveSvUaLaJijAgEUTn+Ql9sGrmMNhsLOwscPLcTbzywlAYaizQVZrBchxe2+aYQsdb8mLrHtSnLSQASnU1kMsYaNQK/GXaYKzYkoMJI7qhpKwaZfraBDpGAmR9dRZJ3dsgKkLp1VXp7iLXqBXQV9UKsnPoIRA3Z7Di2mKuVpblkFN40+exaaQsQbQe/Bb1OXPmwG63Y8yYMVAqyTUXTNwfxhIA731+SugO16uzFpN/kSjErXnRrzBYcPaKDmev6LBtzxm88sJQ6CpNMNRYUWGw4M0sV/f0mm3HsHz6ULz56QmcvaLD5sVjEB3hfUPRq7MWE0f1gI1lIWOk+OK7Cy7H++v2PPz+1w94ZMEvfm4QGIlDVCWAzxI4uUyCV58fCKVKBX2VWch+561MqVSCtR87hHzNi2nC93dkn/c477zJA/H9yWtIH9sb853uFT9adt2cEWBZDn95bpAQcogKV6BtTBi+zb+O705exwM92mBkSkc80KONh6C7Z5bz2f7OoYdLNyr9zhznNwn8vahPXFssFu98bp3B1Gxj5tTpjCCCT0ADXY4ePQqFQhHK9dy1OFtLLMvhmXGJuHSjEqU6I9LH9vRwXTtnZQOOh/eVEoPwc+bMYaLWX2W1GRNH9cCmnachlzEuGwqzxY5rpQbBQ+DeitZ5I8EfTyZl8O9vf8LqWWngAEgAcByH81crkPXVOTz3WB+U6oyiIvzSU/3x7v+dwqSHekCllngkC67cmou1s0e4bHbcy/tmTOyHtjHhuFZahehIJcYP7yYIOn8c9wxwmVSCGRP7CfXvuYXFeGxENxRcvI1D/7uB/HO3EK1R4sH+HfHEg90RE6kSdZGv3JqLpRlDXO6JVqO6Y3nXCEIlhvMmQay80B/XfV0lbCzLgeMc3h33mvumjplTfTxBhAa/RT0lJQUXL15E7969Q7keAp6Wu81mFxXoqAjHBsu59zuPtwY1+ioLoiIULg92fkPBshyMZht0BpPQilarUQn18yaLDZN/kYhFGw8LxzNZbHhmXCK0GhWulho8LH6L1VFOxosw7/qPUCuw4bN8nL2iw6UblVjxu9pNCO8h0ITJYbOxiItWCyLl7FXQGUyQy6RYn5UPncEkZNqL3SvHcJxad3xslBrb953Bo2n3YuKoHigpq8al65VQKaR4ZOg92Hv4Ev797QXs+v4ihiUlYHBSO9HjVhkt+O0jicJGyN2jsvi5QVCHhXn8jp03CaW62vLCjvEaKBVSv6xWXyVsUeFKD9Hks/w1YU1vEVN9PEGEBr9FffXq1Xj++edx//33IzY21uU957nrRHBwttyvFpeJCnSbaDU2Lx4DmZRBjcnqkqTVLjYci6amCtavc+37C0/0Q2yU2uPB7ryZMJntooNjFjybil6dtdAZTFg0NRWxUSqEKeUoN5hcHtIOi9WODvEazH92IFZ/dAxnr+iwaedpwWJ3tvj5Jjti53S24Lq0i8SaF9NgtrIovl2Fj/eeEYbESBkAEM8RuKUzYuHGQ0LDmpgopdCIxz3s8N+8q/j9r5Px7v/9Dym92+Lw6WJ8d/I6pIwEdqeMdH6TtGnnaWRMSIJcJvHwqKzYkoNl01I8fr/ucfSzV3RYvikHmxeP8VvUfMXixURz1dZcrJszoskFHaD6eIIIFX6L+htvvIGSkhJ07NgRVVVVwut8IxoidBgqSkUbSDgLc7RIghfLclg5YxjKK02wWO2QyyR4bnxfSKXe46n8ZkIHk2j/9syPHIlzMhmD6AjHOS6XVMJirfUm8Nn5/HcH9WmLV6YPhcFoQZhSjm17CwVBB4BBfdpCJpVgwbOpqKgyeZzT2YJjGAlYDnjjkxOYOKoHpjx6Hww1VmzbcwZ/njwACjkj2imPg0OMtRoVyitNKCmrFrrV8edxDmlowuQorzTj1w/1xK8f6omjBcU4kHsFRSUGSCTA/T3a4OFBXaBSMNBqVOiaEHknOc9TqCQSqcd9DkZ9uK9jNHfRpPp4gggNfov6nj178OWXXyI+Pj6U6yFEMNbUoHevWne8TMpAygC39UaXBCN3C89h/YZBrZTitt4k1Gf7E7+MCld6LXeDxLGJ0FebYTLbUWEwQSGXCpb27EnJqDJakDEhCTuyzwsJZEszhuDzb85j/PBuQr7AEyPvxcj+nfDntw5Cq1HhD156xjuLkVzmaFm7I/u84KZPH9sTSjkDo9mObXvOuLTc3bbnDKY8eh8AR+e69Vn5eOmp/qLn0UYqsTRjEKIiHP+XySSY91ZtY51nH03E/pyrOHq6GCfP3YJMKsEv0u4BAw4KhUxUqDjOs+tiMDp9+TqGvtrcrEWTOp0RRGjwW9Q7deoEmczvjxNBxjnuHUiCEW/Zujdc8RW/5LOSZV7K3aSMBJeL9S6u/Zee6o/F0wbBUG1x6TnPu7TPXtGhymjBgymd8E3eVfzpmf6Iiw4Dx3FCxnupzoirXnrGy5y8C1HhSiyfPgTllSaP5LLIcAV0BpOQMMh/n+/0xtexeyuJ06gVQrZ9vFaNPz6dIrTbzSm8iXFDuuLCtQrwTnibncPOby/iyyNF+FlKJzw3vg+27Cp0WZOh4gbQPtYj27tTvAav/2EEzFbH7HeF3NOir+vvhLduYRq1AgumpAqNgeK1aiyYkgqNunkkulKnM4IIDX5v2ydMmICZM2di9+7dOHLkiMt/daHT6fD888/j4Ycfxvjx4/Hiiy+ivLwcgCOr/rHHHsPDDz+M5557DmVlZfW/mruA+jQqCcQV69zd7LWPj2NOumuzmjnpybh60+CRqf7GpycglzKiWfoTR/UQ4s8bPsvHmEGdoZDLsODt73HrjpjzjW3aRKswb/JAj3M6RwwYRoIwlVw0fs2ynMeaF0xJRXyMCu/OH4020WrEa9VCNr5Lo5kpqfjy6CVkTEhC5sxhyJiQhM+/cXgDeFQKmce9BIAHerbBgWNXsHrbcUSGKzD10T549YUh6BSvgbGmRrRrXEl5NcorzVjw9vd4ftUBvLwh8E5y3rqFGYwWZH31o8u1ZH31IwxGi9/HDjXU6Ywggo/fpvf27dsBAK+//rrL6xKJBF9//bXP70okEmRkZGDQoEEAgDVr1mDdunVYsWIFXn75ZWRmZmLAgAF45513sG7dOmRmZgZ6HXcN9YmVBhK/9JaV3SFeg+ulBsGVLRo7hnhMOSpCgXmTB+L9zx1NZuKiwrDgne8Fi3lQn7YYP7ybSwx+xe+GQl/taDrDx8udsXm5D2WVJkRGKLDs+SFgJI6/exIGuKUzYtXWYy7lY3xJXLvYcOgqzYjWKJCS2M6jjC9cVfvPxGSxYVCfthg9sIvg3v/6WBEeG94NUx5NxL4jRdh18BJ+ulaID/cU4mf9OyLlXpnoZkwsrr9iSw5e/8MIsBwa3Ac+p/CmR+OZ6U80j5h6MKF6d4KoxW9Rz87OrvdJoqOjBUEHHC1nP/30UxQUFECpVGLAAMcDOz09HaNHjyZR90F9EowCiV96y8p+f+FDWL4pBwB8dJGTir4eoVbAYrUJ09VYrrZX/Y7s8/jDU/2x9P3Dwmt8DJ5PWnO/PpblhD7t7ucymW2w2VihL/yaF4dBE6YUrHp+o7Ls+SEw1Figr7LgzU8dmfjv/HmUR5IeP9OeP35stBrpY3u7uLXnPzsQapUMjESCw6eKhQx5jgOy864hOw9IPFbpsQnhrX7nEj6W41CmN7mENupTv90cE9FCIb5U704QrjT6v3CWZfHpp59i1KhRKC4uRkJCgvBeTEwMWJZFRUVFYy+rxVCfARyB9Pf21odedqfkjD+emFterWQcrm6n1+c/OxAHci+jymgVysmkjARrXkzDwimpAIAqo0XU6uano7lfn77aLJTGOZ9r3uSBYBiJIOi9OmsRrlZ4eBDOXtFBX2XGli8KAQBTHnXMpzdbxfsByGQMNi8ag9d+PxwKqdRjJOzqj46BYwGdwSzqmn8srStKdTUer/NWv3Mff4vV7hHaWLElB2V6Y0BueX//nrAsB53BhFJdDXQGU8BDZPwlWENr3AlW33yCaC00eubbq6++irCwMPzmN7/B/v37g3bcvLy8oB2rOeJ8feqwMCyblgKJRAqOs8NQcQP5xT8F5TzqsDDMn5yCzw6cx+iBXRAVoYBWo4SpRof5k1OwelseGIkEH+7+wSPD/OXf9BfiuPzrn+0/i6mP9gXLscicORiV1a5z2ec/m4pwlXjWeJtoFZZNS/G4vrYduiGn8CYqDBbhXDY7i6gIBcJtMiHrfvIvEqGvMgsNcJyPzzCORjHOiXavTB8qug5wHBiZBKW6GnCceIiBkQD6Kovo98cN7oghPeW4cFuObf/5ERarwwX++TcXMCK5A9ZnncCtCkePAbGYfanOiFsVRlQYjLBWl8JY47lB8Pa79PX3RB0WBnl4PFZvy6v9fUxOCegcPHX9+4tP6IoVW/I8xHfZtBSU3rgc0Lmcaduhm+j9qq424eK5QpfX1WFh0ETHO90P/6/zbnq+tDZa87WJ0aiivmbNGhQVFeHdd98FwzBo3749bty4IbxfXl4OhmEQHR0d8LFTUjwbfLQW8vLyfF9f+1jv79UDm43F0w/39nAB35OgEbKVxTLMOUhE47i/HN0Taz8+jtWz0pD50VE3KzcXf3qmPxY8m4rMj5zON3UQ2mjDwTARHtfH9zM/e0WHVR/mCr3xnfvGz5s8ENEaBa6UGHDofzc8WtTGRKqFmer8WrbuLhAa5Ti71v+6/QTSx/bExh2nvPawZznHZDn+PFqNCuljeyIhLgJgrbgvMRH3ARie3AnXSw04cOwKjv1wE6d+uu1ybd6y8vkmN/XtuMayHPQR4S6ub321GXPXf+f6+9iWF/A56vz7CcccADHxlStUSE7uX29Xubfe9uHhKpc18Z6CZZtd3fS9e9Xtpvfn+loyrfn6WuO1mc1mFBQUeH2/0dzvr7/+OgoKCvD2228L/eP79u0Lk8mE48ePAwCysrIwbty4xloS4QWD0SLqAjYYLdBqVIiLUou6dvladWfitWroKh1uaZudg1ajEsa3LpySeqdMD8ja/yOWPT8EmTOHYcbEfpDJvT9o3V3LYr3x12w7hgvXKrFxxymhjI7PBH/1haGoqPJ0lecU3oQEcMkYl8DhructaLGs+TnpyZDLGTwzLhG7Dl7AnPRk/O7Jfti44xReWP01/vLBMRSVVAIAJBLgzax8DO6bAJVCiicf7O4S537rH/l4NK12VCyfrLcj+7xLQiTLctBXOdzmJWXVKK/07jr35foONOmyvngL61wrNTTIDe9vmIHc9MTdQqNY6ufPn8d7772Hrl27Ij09HQDQsWNHvP3223jttdewdOlSmM1mdOjQAWvXrm2MJRE+sNpYl57vhhordmSfFx723mqMAbgk5A3q0xZTHu2LKqMFSzMGQaWUeri856Qng+Uc40EfH9kdC945BMDxcOaHxLgnVbmf35s48TXpzp3i4rVqZExIEs7hbuHdqqj1QDh/lreg3XvYx0WpoVAwiAxzdNfLmJDkmAT33mEPAeHXy6/tVoUJhRfLsDRjMF7/JA/llWZUGa3YsqsQbWPC8MufdUePztFCnT+f6MayHEoramCotrh4FRY/NwgxkUqYra6JaN4EbfWstEZLpvM1XlhnMNXbA+FvvXtz77BHEMGiUUS9R48eOHv2rOh7/fv3x65duxpjGYSfKOWMqPgq5bUPe28zuLu0i3QMVrHZoa+2CFnt8VpH33j38a3rs/Lx+18/4NIghs8Gt9pZlJbXYPf3F/HMuESX5D7n8+sMyGZ17gAAIABJREFUJtEyM/54zkl3znPq3V3y858diM/2O/6e8tfMD8nZkX1eKIXje9gvfm6QMGwGcFiDizYe8tqtjhcd/lqdNwm/e/J+REUoEBkux7q/5+HC9Ur8678/QSaVoG+3OMyc2A8P9GwDjVoBQ40ZdhsnCDp//BVbcjBjYj8s35TjkgXuTdBsdkcb4U07TyOn8GZIu7rx4ps5Kw2l5TUw1FiFzQqABomrP/Pgm2M1AEGEAmoRR3hgZ+HiztZqVLBY7TCa7WA5k89SJIaRICbS0V/dXXQyP8pFxoQkl5h7qc4IhVwqCKh733i+W932fWcw61cPCF31nEujIlRy0TKzvYcuAXA8vONjwrB6VprLnHq+Tj2hTQRu3KrC3kOX8GjavZjyaB/oKs1QKBhhSI7OYII2UonVs9LuzJd1lKzpq83C/eDF01tcnGU5aNSOCXnb953BS0/1xxt3Zttv2nkai6am4sPdP+DCdYervks7DdrFhiO3sERoSfvEg90xakAnVFaLVwyoFDLhz7x3wJugXS6uFM47Y+L9YBhJSGu8GUYCuYwRqhOc1xJqcaW2tMTdAok64YHz+FIxkfWnDtjbCFR+XCxPvFaNuCgVzFa7y8hX583AG5+eQMaEJMHV7l6XvHLGMNEys4wJSTj1022HRR3liLk6z6nnR7fWGK1CDf6BY1cFT8G9MQ7LEoBLiMFbXTQvnjuyz2Pe5IFYs63WNT57UjI27TyNF57oh3CVHL97sh+sLCvMdTdZbJDLGVQYaju+PTPOUerm3JL2n1+fxxcHL2JgYltoNUroDLUxYWdvB38frDZWyIEQc32X6hxz4Rtr5GlTiSu1pSXuFkjUCQ+cLbuJo3r4nJrmzzF44rVqRIUrhdf5B7rdbsVHe37EKy8MBTjx+HhUhAJyGSMaHy6vNIl+p2tCpDBbnH94i7mA+Ta2/DF4y3nljGFoE+06olbnNmLW+X44CxYjgUtOAu9qfvJnRsz72/eCez/rq3OC5yBeqxbc50Btn3rAsbl68mfdoa8y43/nb+HIaUeTG5lUApudQ5toR6e8/x6/ioVTUqEJk8NksUEpZ1wEzWyxw2JlYbHaMHFUD+zIPo+zV3SNFltuSnH1x03f3KHuebXQvRCHRJ3wwFmcnIWFx58EI28WmSZchsxZacIAk+gIJW6U6pFTeBOjB3aBXCYR3QzERKoQFa7Ebb3RYz3e6sNVCqnLQ5x/CLAsB5PFJgiac7ycX+uiqamIE5k5X1fCVVSEEitnDAPDSJC1/6xLqIEvTeO/sz6rNoGPfy0hLkK4FpPFhnit+Iz5+c8OxD+/PocyvQllehOqTTb8N+8q+naLxaadp13veZhScK1frnQdxDN7UjJ2HbzgMjAn1LQGcW0KqHteLXQvvEOiTnjgbk3VJ8HIl0UWGe76WY6zC27r55/oi3nPDkBllUVwS7eNDRMEVswD8PWxIiyamupRV+/s0hV7CPBx/GiNAm1jwgQxVsoZQQjd8eaBkAAek+sWTElFQlw4Eu+Ju5MEp8SO7HPC9/gEPufjqJQy4Z4p5QwWPzcIZXqjh7fkg3+fxtKMIVifdQJlehP63huL/x6/hgO5V12O7+xV0VebPUoVN3yWj1emD0UjajpRT7xVMTRW6KQ5QffCOyTqhCjOo17rGwP11yIzVJQK59h3+DJ+MexeYdAJfz5eYMU8AM+MS0SneI1Pl67zQ4CPmcukDP48eQDMVptL4xreuhVD7Pxz0pNRXFbtUSuf9dWPeOrh3i5z7GdPSsbVm1VCiZrJYgNQW18dHeG6bk2YEmqleJe5KqMFk39xH3YdvIBZv3oAZXoTXnrjW4/P3bhVDa1G5dXLYDBaIJc3jqq3ZJdpIGsPxXVSWV4tdC+8Q6JO+KQxYqDGmhr07lV7jgVvf+91B+5rPb42EHwNeZtoFVgOLklsc9KThZnpde343ePT13xMrhs9sIvHHHu+Zp4viYuJVGLz4jFe7yvDSKBUiA/K4bvMrZwxTNhkuX8OABa8/T36947H8Ac6oE20SmhJy3++qsaCtjFhPu9dMASqJbtMA1l7qK6TyvJqoXvhHboDRJ00xtxrZ1Guawce6Hoc3dccQ2BuVZgEQeePvT4r32Vmel07fv78chkDq43DlEfvQ2S4EoP6tHX5XFSEQvRa7rmTwNelXSSiIuq+DrGuac5d5hhGIsTM3T+3fPpgTBrTE5duVOLNrHxYbZwwSpbf0LSLDffqeQnmIJaW3NUtkLWH6jrrM8yptUL3wjtkqRPNilDswJ1jyd4S/9xj23Wdz3mj4BxDByA0ctFqlKLXIg0wUYz3DqyelYZbFUboqyweXeacP+fuxejfqy3Sx/RC7g8l2Hv4Mk6euwVGIkHHthoYqs1IaBOBUl2NaD5BMGOXLdllGsjaQ3WdVJZXC90L75CoEw0i2LHDUNQxOz9kvTWGcY9t13U+saSzzA9zsTRjCB4f2R02Owuzze6RVf/SU/4PL3G/t1qNClVGK/66Pc/rvfEWhpBKGQxJSsCQpAQU367Gl0cvY3/uFZz4sRRSRgI7yyEuSoWXnu6PmEgrEuIiXBrqOFNfgWrJLtNA1h7K6wxW5YDz3634hK5gWa7FCSJVUYhDok7Um1DEDgPZgXvbULi/rpTXPmT5gSzuzXTqim27403sKqvNWPDOISyckoq3NuW49NA3WWxgGPh1b7zdW+eEQJmUgUwK3L4za50vEazr+O3jwjHl0T54ZNg9eOmNb1BZ7WhYc1tvwiubc/D02F54MKUjYiLVPgWKv89tO3SDzuC70yDQsru6BbL25n6dLTm3gagbEnWi3oSqrMSfHbgv0btaavB4ffn0IVj6/hGcvaLDroMXsHz6UNSYrIgMdwxlkQVoRXkTO76jG+/mL9UZXUbUvjd/tF8P97ruLctyuHG7CuWVJhdPQKAPZ17QecwWO7bu/gFf5hRhfNq9GJncUbzfgFoRsDC0ZJdpIGtv7tdJ5WCtm+bv9yKaLU0ZI/X2YPLW8S1MJcfqWWlY82IaRg/sgjc/PYE/rf8OizYegsFo8XUqUcQSdRZMScXXx4oAQGgc4wxfh+7Pw72ue6uvNqNEpIwukIQssXGobaJVeGz4PVDKpXjv89OYuuIr7Dp4EbN+dT82Lx4jJPgZjJZ6JYM1RtJlqAhk7f5+lmU56AyOEbo6g/fxucGkJec2EHVDljpRb5oyRupr8pj4644H1ry/fe/xXn0eZo7BNUqhd7uhxoo931/EuCFdMeUXfVBuMGHub1Kw7u+u8e/oiLqtdJblIIH4aFjJnfetNlaY8V7f6/FWcx8TqcK0x5Jw4XoF9h0pwrf517A/9wq6d4rGz4d0xYgHOpAw1IE/uSZN5QZvybkNRN2QqBP1piljh94eTDKpeJtZmZSBRCIulPV9mJmtrNCnnefAsav4YOFD6NAmAjVmq+vAFj/Owz/ot+874xH7nz0pGe99fgrPjEtETKQSHCd+Pf62fOXdxGtnj4DFagfDSCCRAFLG8f0enbTo0UmL58b3wTd5V7H3yGW89Y+T2PJFAYb2S0BMpBLlla4DZQK5ly25EY0v/BXrpnKDN/eYP9EwSNSJetOUsUNvDyatRiVqfdaYrGgXEx7Uh5m3jYVSIQUALHv/qMd7dT2wnR/0FQYLlmYMQZXR4lLGdulGJf76hxFIaBOOBc+mIvOjXJdrlUkdg2f434lGrYDBaPH6O5IyQLXJ6tL5zlmEwtVy/CLtXjwy7B78cKkc+45cxn/zrsFmZ+/U6rNoE60K6F625mQtf8W6qbwd7v9uzSYjOrRt+fedcECiTjSIhpaVsCyH+ISuKNXVBLQp8LWhcHeLb9tzBjqDSYgH17UJ8deC9GXxiA2e8eeBzXe+46e7Waw2j5CBVqNCmd6EVVtzodWoMGNiP7SLDcftCiP+e/wqHh1+ryDQg/q09Zg1z4snANy4XQWOcyTIZUxIEobciImQRCJBn3tj0efeWGRM6IsDuVew9/AllOqMMFns+CbvGsYO6oIwtazOe9eak7X8FeumdIM7/7vNyzuDTu1jQ35OonEgUSeajFprLa9e1pq3DYWYWxxwPGz9aSfrrwXpa2NRnwe2WEObeZMHYlCfti7T3tLH9hREu1RnxPJNOYjXqpExIQnD7k9waU07emAXj1nzvHgyEnhkz/Nz1usaxxoVocTEUT3wxIPd8a+9h/HTbTn+/d0F/N83P0EhY2C5Y73/Zdpg0XvXmmPy/v7uyQ1OhALKjCCajFC10xTL6vYlqM4ZyGV6Y0Br8pblXJ82lmINbdZsO4Zpj/V1OU5CXISoIEZFKO50h6t9z9foXLOV9cie3/BZvjBf3p/7pa82o+//t3fvcVGW6f/APzPDnDkOOJxhlAQRVBAEAcUkU3EprXa/mZuWm7mV2WG3NjqppabkrtW+fpbf0s3cdbVvW6uJB/CQVoqIoCliaMpREOR8HA4z8/uDZpwjDjDDMzNc778KxuG6n2ee55r7fu77ukMkeOPJOHzwUjLEAid0/5qYbzfJkb7lR5RUNRu8x0DPkT0x99xrfynUXllg6WFwJmbYE+ZQT50wxlq9tYH0gPR75hnPT7NITNo37PZ2OcRiwV0fLZg6Hhw2W2c0wNQEOQ8XAZQqlc7vehXGt85lASZXCrg588w+XlIPIdKXxECpVMFZxEO7vFfn9R3yXrz84UnEjfdBaqIM0aFSnTr1jthLHeiadms+bhjq3AVbn8xo6/ExgZI6YYy1nikO5KaqP1rQ3NZtsZjUN+wbVy8jJiZG53fGbkZOHBMz+n8tEav9b/UT4guPRqOmoR2ZP97Aa0umaDat4XHZBqVqX1wYjZaObjS2yI3+vVHuQnj+un+9fsz1zZ3o7OrVef6+cWc+/vpistHz6enKR+JEP/xwoQq5l2/BWyLCnKnBuD8u2KYLtAyVrZQwHcrcBVufzGjr8TGFs2bNmjVMBzFYCoUCtbW1AAA/Pz+Go7Ge6upqh2wfn8vBpLGjkP9zDdrlvZqL0lsiBos1tIuSxWJByHeCWMiFkO9k8v1aO7qxO7tY8/+1DR1Y/tAEFF6vs1hMTc3NcBK4oLWjG929CnA5bJTXtOKtraexO7sYp36qwqSxoyASOCFcJtH52y8ujIariIfWzh509yrA53I0Pd2kiX5InOiHiDFe2JNdjMnjpPji4BUolUo899soJEf7w0XEx6f/vYTHU8MxL1GGyJC+1yZH++N8cS0emD7GoK2+v9Z916a+ga76NAf7vr+B4rIGLH9oAm7WtqG8phVp08ZA4iIwOJ+rlk1FSmwQHkwOQbCvK27VtyM7txz7f7iOypo2SCUijPZ1hZDPHfI5VypVaGrr0hxnPpdj9D3NfZ0aE9ffQGM0Rf/zDQDt8l6kTRsDsbBvEyNT7Wtq68JbW09rvhC0y3uR/3MNZkwOgJDPfH/QWHwNLZ0YP9oT7fK+60Xe2Y5Ro0YxFqOlzqM2dd6TSqVwcjI8D8yfGTJiqXvUa56KAV8gZKS3pt+7VJeR3bhiGlS//n4oMSmVKnDFUrzy0fea3sT6Z5OM9p7WP5uEnQeu6Mx+33ngCv7wYARe+38/GvREPN2EOhu8qKvYHc2rQEVNGx5JGQt3Zz7cXXg6Mbm78KBSAjMmB+Jvu/KxYcU0zbEYyGx17b3huU7su46QRIZ4IizYA7cbO3DqYjWOn6vA9xduIkDqjNQEGVJiA+Es4hn8bXOPszm9Nnvo3VkyxqGMhtn6ZEb9+MKCPPDA9BCkb/nR4NEQE+eWqc+a/c9KIXaNzWahtqqUsbKhxiY1/X5uODzdhBaJqbm9Cxt35uskw4YWudGbJZvNQmOrHO/tOIvXPz6F93acRWOrHM1t3ZrX7Dp8BfXNnZpJauoNXnasmg2ZryvW/jERq5fFAwC27bsENoeFhbPHYdu+S3j941PYtu8SHr0/DFlnSsBm961n77vx999WUzd4N2ce0pfEwEXIQ2OrHHXNfa/xchNq3k9/T/bN/y7A/XFB+Pzt2XhpYTTEQi4+21eIJ97Jwod7CnC1vBEq1cAmczW3d+G7c+VYvSwBW19LweplCfjuXLnBBEcm93Q3d8KaJWMcyr7jtj6ZUT++R1LGaoo1AX3HbePO/GE5t8Yw9VmjnjoZ0axdQMdYMjT13J7PZRt9Vv7Pg1cAGO+JmNrE5o2lcfByE0ChBDbsOKV7o/siD8vmT4Di12fzA7nBG3v+3tJUg4paJ5M9Eu2bW1iQBx5PHQcuh43W9m7EhksxMyYQJVXNOHymDCfyK3AsrwJj/N2QmiAb0FDv9OgAvLMt504v7YkpBq/RrwOgnhdg7d7nQHptPT2W6yEP5fNt65MZ9eNzc+bZ1MgCUyMdlNTJiGfNSU3GkuGxvDK8uTROs3xNfbMUC/qGnjesmAalUgUnDgtbv7mI4vJGAMZ7Iuv+kYsNK6YZ9Aje+/ws/vpiMnoVpnvYXA4bUqmo381G1JP5nDhszU532jF7ugkhl7tizXbTk7HUN7ewIA88mTYeH+wu0Jm0J3HtwWg/N6z47SQsTRuPkwWVOHi6FFv+8xP+sf8y7o0JQGqCDKP93Ewe555eJTZ+kWfw5WXDimma6np8LtugDsALj0Zj/w/Xrd77NHfCmlKpMljBAAythzzYz7et7zanH5+p/RKYGllgqrgQJXVCrMhNzEf6khjNELx6eF97X3R1KVdjW8Y+mRaBkqqWu/ZETP3c1I3F3ZmvqSNvrLdoqme5+aVkdPXo3uBZLE6/PRJ1DI+kjNUkdPVrPtpzHs8+MhFiIRceLgKIBFykJo7G3AQZissacSinFEfPluPQ6VKEyySYmyDDtEl+4HE5Ol86VKq+SnvacdQ2dkKhUOL1X0c2Vi+LxydfXzSYF7D+2SSr9z7N7bU1t/d96dCv+//m0jhGesi2MovfFO34jK0KSV8Sw9jIAlMjHZTUCbEiNpuFnvZao70d7Zul/paxHi4CdHb1QCwQYd0zSXDisExuSKNQmO7ZGbuxvLZkCr46dhW5l2tQUtWis0e7OkmyAOw6fMVoz1LqIdJpo0ql6LdHoo6hs6vXaGIT8JwMkhuLxcI4mQTjZBIsmx+JY3kVOJxTgg92F+DTvZcwbZIfYsOl+GxvoU6vf+eBK5qRDamHUGctvqld7dhsltV7n+b22np6lci9XIOm1m6dxwRuzrbTQ7ZVxkYWqitLwGYzUwKXqZEOSuqEWFlnR8ddezvaPbmwIA88/VAkuroVeP3jO8/P31meYPSZ+39PXDPo2al7BNo3Fnm3AqVVLfj0v5c0iU/dWzTWM3/h0Wg0tXYbvFZfa1Ntvz0SdQz1zZ1GE9vddrBzEfGwYEYIHpg2GkfzyvHZ3kIcyS1H1pkyzWu0e/3qsrmvPxmHb767difOjh7GhmfN7bWpk39xeSPe23FWE+NfX0y2eoyOQP/L8o2ODgajYWakg5I6ITZAuyf3SMpYtLZ3GwwVr/40B5tfSsbGFdNwu6lTZ+e21o5uk8vw1DeWxla55nmymjqp9bdkTTu5KJUqgyVCnR0dGBfWf49EvQTP1P7t5gxJtnR04/+OXkVXjwJvPDkF7+3I0/l9bWMnBHwuPn1jFpzYLPB5bFz8pU7z+6+PXzMoxKNOrNauTGZur83WJ6cR20dJnRAboH0zdxH1FQUxNlTc1aOEl976dO1leP0losHsKufm3Dd5T91z37bvElb8LmpIE6+092/nc9lwEZmXQHWfS7MMet0A8MbHPyI23BupCTJEhUp12tvYKofEVYBNLyRrto1VJ0v9UYp3lidAJOD2ldn1k1lkrbM5vTZbn5xGbB8ldWIS1VUePvo38/JbLSaHigd74x/MrnLOQh42PJeE1o4ezajA8od0h+CFItGAdraTuA5uOFI7xq+PGz5yeO63k3D5ej2yz5Yhr6gGnm4CzJka3LeyQMgFn8uGQgmdhK6uDaA/n6GhRW4w03+4CtRYasiWrt+RiZI6McoeKm/ZC6FIpFlWZc7mHkqlCt09CpNDxerXuon5mpt2c3uX2YndWMIw1ot/c2kcdh68rLPtq7Fn0C7u0n6XtFmKdozqyn/rn03SfClxEfIgcRXgREEFAKClvRv/zirGl0euYmqkL6LDRuHLI8W43STX+Tzrz0x/JGWswe519rbXO12/IxcldWLUUDaCIHcYKxN7t5srm82Cn5czXERczZp1HpcDd60Z0Ja+aRvrxbsIefj93HDNkjpTz3f7W9Jmyd7i3UYo1D3u201yAH3D9RJXPmLH++DUT1U4dbFKJz7151l/lKK/7WrtBV2/I5dt1PsjNsfW6z7bC2NlYs0pFclms+Dm3LdPu4+nGBJX3RKu1ihBqb83vJMT26z9vtVL2rRJPYRw4rB1ysO+8tH3KLvVMqT9vE3tXw8Y/8w2tHRh5uQAvG6kulxtYyeulDbAVcTTKaWqrqGv3x5bKY9qDrp+Ry77+ZSSYWXrdZ9tSX81va11cx2um3Z/SVRNvaRNv744hw2jXzzqmjs1x8nceujmMPWZbe3oQYfcMFGzAGzYkYeXPjiJohv1WPvHRGx/636MDXQfdL10W0HX78hFw+/EKFpaY567DYNbq1QkUyUojTG1pM3UjPrahg58sLsA7yxPQE+v0mKPEIx9Zl94NBon8iswMzYQ6U/EYeMXd0rzvvp4DEqrW3EopxRb/3sJQj4HMyYHIjVBBpnvnfZ0yTvh721fz6Lp+h25KKkTo2hpjXnu9uzSWJlYS9xcbe2mbWwCnqkvHq0dPaht7MSt+naDtfjqY6c9CXAws/u7uhWorG3FifwK3BsTiI/2nIeHiwDPPjIRfl7OEPCd4O7MxziZJ+ZMDca1iiYcOl2K4+cqcDinFGFBHn0laaP8cPnGFQT6MlOVTNtA5ifQ9TtyUVInJtl63WdbcLdh8P7KxA4Fm81CoNQFG1dMQ69SBadfz5Ut3bRN9ZzVu86ZKts6lEmA2isIOrt6kTTJT7PsrbaxU1Nt7q8vJmvei8ViITTIA6FBHnjqwQgcz+9L7B99eR7bvi3EhCA+pAGtCPR2GdRxsMRkwcEcE7p+R6ZhSeoZGRnIysrCzZs3sX//foSGhgIASkpKkJ6ejqamJri7uyMjIwMymWw4QiLEIswZBjdWJnaoN3qlUmV0AxhbWrJkrOesXusO3JmQZlDLXqka8sxt9d8W8o1/cTA198BZxMOD00PwwLQxKLxRj8OnS/HjxZs48/5xTAjxQmqCDFMn+Jr9mMNSqxSGczY7rW+3b8PyAO6+++7Drl274O/vr/Pz1atXY9GiRcjKysKiRYuwatWq4QiHEItR90YHMqlKfaMfyqxwa8x+twbtiXaebkI0tvYtN5N6COHjKTY4di8ujEZDi9wikwDZbBb4PM6gJoyxWCxMCPHCq4tj8acFvnjiN+NR29iB9/91Dn9Ym40vDhThVn37XWOw1HkaromRlvhsEmYNS089NjbW4Gf19fUoKirC559/DgBIS0vD2rVr0dDQAIlEMhxhETJkg3l2aYlel70tWTJ1nADo9OR3HriCR1LGWmwSoCXmHjgLOJiRNBYP33sPzl+txaHTpfjmu2v4+vg1RIR4YtaUICRH+YPL5Rj8W0udp+GaGEnr2+0fY8/Uq6ur4e3tDQ6n70LgcDiQSqWorq6mpE7sykCfXVriRj+Qm7ytDKeaOk59+6B34J1tuQBgtATsYCcB6n+ZcOKwwWEDdc2dAz4WbDYLMeO8ER0qxYVrtcjYeQ6F1+tReL0eOzKLkJoow5ypwfB0uzMyYKlkPFwTI+3tyyIx5DAT5fLz85kOwaqoffZNu31SP5nRG32XvBP5+VfMej+hSGQwqz59SQyqK0t0tpsUikTgiqUGr+tpr0WnBbelHOr50z4mxeWN+OfBK30z1Uc5Q9HThdamKpyv/mVIf2Mox0L//G356id0yHs1P+uQ92B3djH2HClGmL8AU8Y6Y7QPH2Kx2KzzZG78a56KAYvFgUqlsMgx0W+fJT6btsbR7y36WCqVatgelqSkpGDr1q0IDQ1FfX095syZg9zcXHA4HCgUCsTHxyM7O9vsnnpXVxcKCwsBADExMdYMnVH5+fnUPjum3z5LTZ4ypwfe2CrXlKhVU8/+ttRwqiXO33DUKh/ssdBvX21jB55ad8TgdRueS8K5KzU4crYcLe3d8PUUY25CMGbGBAIsMD5SYop2+xytZrwj3lvUeS8yMhJ8vuFIDWM9dU9PT4SHhyMzMxPz589HZmYmwsPDaeidODxLrSE2Z9ifqeHUgQ75D8e6ams/3/aXOiMyxAu/nzsOpy5W43BOKT7PLMI/D/2MpIl+SE2UYfxoCVgs202OprbHJfZjWJL6unXrkJ2djbq6OixduhTu7u44cOAA1qxZg/T0dHz88cdwdXVFRkbGcIRDCOOGaw0xE5XnBtvbs/YxGa7n21wnDu6dHIDkKH9cLqnHsbxy5FysxsnzlQjycUFqggwzYwIhFnIt2j5Lam7rcpje+kgzrMPvlkbD746B2mc91hpO1e6J95VRlejslmbNIf/BTvwb7LEwdv7uFoP+3xrlLsCsuGCcu1KDaxVN4PM4SI7yx7zE0Rjj78boREb99g3HI5vBGui5d8R7i80OvxNCrM/Sw9pKpQpNbV2Qd/Wiqq4Ne7KvorFVrpMcrTnkP9Rqc8a2lx1MQr3bqIL+0rDbTXIcyyvHX19MRn2THIdySnHyfCWOnC1HkLcLGlvlaO3osYlesa3OgHe05/3WQg9LCHFw5uy0Zg71TfXVv3+PP248hk++vojF88Lh4SLQKahizR3ChlrMRftYuIn5qKhttUqhlf4S4z2B7lj5P1H4YtUcLJkXjpu329Da0aN5zZrPcqxSRMjcHfFsdYc3eym4xDRK6oQQsxi7qf79y/N4JGWsTk9uMFX2zGXJXqQ1k4Q5iVEs5GLG5AAo9JJrQ0sX1m7IOHJfAAAS/ElEQVTPxYmCSvT0KoYcCzCwSnHWPH9DYasjCLaGht8JIWYxdVN1EXF1EpY1Z7JbcuKfNZOEucVijLXHWeiE5rYu/G1XPj7by8OsKUGYkxAMPy/nQcfTX6U4fba6w5stbTdsyyipE0LMYuqmKu/uNUhY1prJbsnKakNNEv1N2jI3MZpqT6DUBYU36nDwdCn2fn8d35z4BdGho5CaKEPceB9wOANLZAP9AjOU82etCoa2tt2wraKkTggxi7Gb6htL4yDiAaM8XIZlBrcle5FDSRLmTNoyJzH2156oUCmiQqWob+7EkbPlyMopxXs78iBxFWB2fDBmxwdjlN4Qv6mEOtgvMANN0NaczGarIwi2hpI6IcQspm6qPxf/DHkPa9hmJQ+2F2ksQQ02SVhy45O7tcfTTYiF94fhdyljkf9zLQ6eLsGXR4vxf0eLMWW8D1ITZYgOlQKAyYQ6mC8wg0nQ1t4Qxhb2iLeVvRRMoaROCDGbsZuqi7sUa7bb9s5e/SWowcTIxKQtDoeNuAgfxEX44FZ9O7Jzy3Aktxy5l2/BWyJCcrQ/vjtXgbpmuSYe7fMwHLsJOvpkNntYVkczDAghQ8JicWz+Rm7pme5ML/vy8RRjybzx+Mfbs/GXxbHwlojw1bFrmoSupn0eBrq0cTAJmunjYm32sKzOMY40IYQxKpXC5m/klu5B2sqyL64TG9Oj/LH+2SRsXJEEIV93T3dnIRfdPYbL4rTXrEv9ZEaXtg0mQdvKcbEWexiJoOF3QsiQtDbV2vysZEsvh7LFSVvhMk9kPD8da7efwe0mOZw4LLR19uDFv53A9Oi+krRjA92hUpl+9q4d/2Cew9vicbEke1hWR0mdEDIknR0dGBdm2zdyayyHsoVJW9rUCfVvL83QnIf6JjmycstwIr8Cx/IqMMbfDTOi/bH/xxuoazL+7F3//QZ6Xm3tuFiSPSyro6ROCBkyW7+RO3oPUk3/PHi4CHBPoDuWpo3HyYJKHDzdtx2sPlNDyLZ+XoebPXyOKKkTQkYEJhMU08ugRAIuUhNHY26CDHlXarDpn+cg777zrN1FxIUdb9g5rGz9i47tPAgghBAHNJC669bGYrEQO84b76+cDi+3vsTEYbPQ2tGDlz84ie3fFuLm7bZhj4tYDvXUCSHEiqxdkGWg1EPIm1/ue/Yu7+xAQ5sSh8+UYf8PN7D35HVMvMcL8xJHIz7SB04DLElLmEVJnRBit5ge1jaHLS6D0h5Czr9xBTExMYgKlaKxRY7s3DIczinFxp15cHPmYXZ8MOZOlUEqETEWLzEfJXVCiF2yh+pegH0sg1Jzc+YjLsIHWWdKAQCdXb34z7Fr+M/xa4gZ5415iTJMHucNjg0dX6LL9j5VhBBiBnuo7gXYV0EW9TG9/etyt+4eJSSufDwwfQyuVzbh3e25ePq9I/jyaDEaW+R3eTfCBOqpE0Lski0OaxtjD8ug1Iwd0/qWLsxPDsHStAjkXr6FQ6dL8K9DP2N3VjGmRvoiNUGGiWO9wGLZXntGIkrqhBC7ZE/D2ra+DEqtv2PqxGEjaaIfkib64ebtNhzOKcWxvHKculgF/1FizE2QISU2CK5iHnMNIDT8TgixT/Y0rG0vzD2m/qOc8dSDkdixag7+tGgyXMV8bP/2Mp58Nwub/52PKyUNtO6dIdRTJ4TYJXsa1rYXAz2mPC4HM2MCMTMmECVVzTicU4rv8ivxXX4lZL6uSE2U4d7JARAJuMPbkBGMkjohxG7Zy7C2PRnsMR3t54ZnH5mEJ9Mi8P35vpK0n3x9EZ/vv4wZkwOQmiBDSIC7FSIm2iipE0IIsRgh3wlzpsowOz4Y1yqacOh0X+8960wZwoI8MDdBhmlRfhDwKP1YAx1VQgghFsdisRAa5IHQIA889WAEjudX4HBOKT768jy2fVuI+2IDMTdBhkBvF6ZDdSiU1AkhhFiVs4iHB6eH4IFpY3D5Rj0O5ZTi4OkSfPvDDUwI8UJqggxTJ/ja5MoFe0NJnRBCyLBgsViIDPFCZIgXmlq7cDSvHIdzSvH+v87B3ZmPWXFBmDM1GD6eYqZDtVuU1AkhhAw7dxc+fpsyFg/few8uXL2NQzkl+Oa7a/j6u2uIDpNiXoIMseHe4NCGMgNCSZ0QQghj2GwWJo+TYvI4KeqaOpGdW4asM2VY9/lZeLkJMHuqDLPjg+DpJmQ6VLtASZ0QQohN8HIXYtGccXh0VijOFtXgcE4p/p31M/YcKUZ8hA9SE2SYNHYU1SLoByV1QgghNoXDYSNhgi8SJviiuq4dWWdKceRsOXIuVcPXU4y5CcG4b0oQ3JypeqA+SuqEEEJslq+XGE+mReD3c8fh9MVqHMopxeeZRfjnoZ+RNNEPqYkyjB8toQ1lfkVJnRBCiM3jOnEwY3IAZkwOQNmtlr6StOcqcPJ8JYJ8XJCaIMPMmECIhSO7JC0ldUIIIXYl2McVf3xoIp6YNx4/XLiJQzml+N//XsKOA0VIjvJHaqIMYwM9mA6TEZTUCSGE2CUB3wn3xwfj/vhg/FLRhEM5pTh5vhJHzpbjngA3jPdnIyKyFwL+yEl1I6elhBBCHNY9ge5YGRiFPzwQgRP5FTiYU4pvc5tx9KcspMT0laQN9nVlOkyro6ROCCHEYYiFXPxm2hjMSxqNvVk5uFHPx+EzZcg8VYLxoyVITZAhaZIfuE4cpkO1CkrqhBBCHA6LxULQKD4emhuDZfMjcSyvb0OZv/27AJ/tK8SsKUGYkxAMPy9npkO1KJtI6iUlJUhPT0dTUxPc3d2RkZEBmUzGdFiEEEIcgJszHw/PvAcLZoTg4i+3cfB0KfZ+fx3fnPgFUaGjkJogQ1yED5wcoCStTST11atXY9GiRZg/fz727duHVatWYefOnUyHRQghxIGw2SxEhUoRFSpFfXMnjpwtR1ZOKTZ8kQeJqwCz44MxOz4YozzstyQt419L6uvrUVRUhLS0NABAWloaioqK0NDQwHBkhBBCHJWnmxAL7w/Dtjfvx9t/iMdoP1d8ebQYy9ZnY90/cnHuSg2UShXTYQ4Y4z316upqeHt7g8Ppm7TA4XAglUpRXV0NiUTCcHSEEEIcGYfDRlyED+IifHCrvh3ZuWU4kluO3Mu3IJWIMHdqMGbFBcHDRcB0qGZhqVQqRr+KFBYW4rXXXsOBAwc0P5s3bx42bdqEiIiIfv9tV1cXCgsLrR0iIYSQEaRXocLPlZ0490s7Smu6wGYD4QFCxI4VQybl20RJ2sjISPD5hrXvGe+p+/r6oqamBgqFAhwOBwqFArW1tfD19R3Q+8TExFgpQubl5+dT++wYtc++Ufvs11DaFh8HPAGgoqYVh8+U4lheBS6X1yFA6ozUBBlSYgPhLOJZNmAz3K0zy/gzdU9PT4SHhyMzMxMAkJmZifDwcBp6J4QQwrhAbxc8PX8Cvlg9By8tjIZYyMVn+wrxxDtZ+HBPAYrLGsDwgLcOxnvqALBmzRqkp6fj448/hqurKzIyMpgOiRBCCNHgczm4b0oQ7psShOuVTTh8pgwn8itwLK8CY/zdkJogw4zJARAyXJLWJpJ6SEgIvvrqK6bDIIQQQu4qJMAdK37rjqVp43GyoBIHT5diy39+wj/2X8a9MQFITZBhtJ8bI7HZRFInhBBC7I1IwEVq4mjMTZChuKwRh3JKcfRsOQ6dLsW4YA+kJo5G0iQ/8LnDV5KW8WfqhBBCiD1jsVgYJ5Pg5ccm44vVc/DUg5Fo7ejGB7sLsPTdLFwtbxy2WKinTgghhFiIi4iHBTNCMD95DC5dr8Ppi9UQ8Iavp05JnRBCCLEwFouFifeMwsR7Rg3r36Xhd0IIIcRBUFInhBBCHAQldUIIIcRBUFInhBBCHAQldUIIIcRBUFInhBBCHAQldUIIIcRBUFInhBBCHAQldUIIIcRB2HVFOe09bLu6uhiMxPqoffaN2mffqH32y9Ha1t3dDQAm93BnqWxpd/cBam1txdWrV5kOgxBCCBlWoaGhcHFxMfi5XSd1pVKJ9vZ2cLlcsFgspsMhhBBCrEqlUqGnpwdisRhstuETdLtO6oQQQgi5gybKEUIIIQ6CkjohhBDiICipE0IIIQ6CkjohhBDiICipE0IIIQ6CkjohhBDiICipE0IIIQ7CrsvElpSUID09HU1NTXB3d0dGRgZkMhnTYVlMSkoKeDwe+Hw+AOCVV17B9OnTGY5q8DIyMpCVlYWbN29i//79CA0NBeA459FU+xzhPDY2NuIvf/kLysvLwePxEBwcjHfffRcSiQQXLlzAqlWr0NXVBX9/f2zatAmenp5Mhzwg/bUvLCwMoaGhmkIf77//PsLCwhiOeOCee+45VFZWgs1mQyQS4e2330Z4eLjDXH+m2ucI19+AqOzY4sWLVXv37lWpVCrV3r17VYsXL2Y4IsuaOXOmqri4mOkwLCYvL09VVVVl0C5HOY+m2ucI57GxsVF15swZzf9v3LhR9frrr6sUCoVq1qxZqry8PJVKpVJt2bJFlZ6ezlSYg2aqfSqVShUaGqpqa2tjKjSLaWlp0fz3kSNHVAsWLFCpVI5z/ZlqnyNcfwNht8Pv9fX1KCoqQlpaGgAgLS0NRUVFaGhoYDgyYkpsbCx8fX11fuZI59FY+xyFu7s74uPjNf8fFRWFqqoqFBYWgs/nIzY2FgCwcOFCHD58mKkwB81U+xyJdp3wtrY2sFgsh7r+jLVvJLLb4ffq6mp4e3uDw+EAADgcDqRSKaqrqyGRSBiOznJeeeUVqFQqxMTE4E9/+hNcXV2ZDsmi6DzaH6VSid27dyMlJQXV1dXw8/PT/E4ikUCpVGqGcu2RdvvUFi9eDIVCgeTkZKxcuRI8Ho/BCAfvzTffxKlTp6BSqbBt2zaHu/7026fmSNff3dhtT30k2LVrF7799lt8/fXXUKlUePfdd5kOiQyCo53HtWvXQiQS4fHHH2c6FKvQb9+JEyfwzTffYNeuXfjll1+wZcsWhiMcvPXr1+PEiRN4+eWX8f777zMdjsUZa5+jXX93Y7dJ3dfXFzU1NVAoFAAAhUKB2tpahxr+VLeFx+Nh0aJFKCgoYDgiy6PzaF8yMjJQVlaGDz/8EGw2G76+vjrD1A0NDWCz2XbbS9dvH3Dn/Dk7O+N3v/udXZ8/tQULFiA3Nxc+Pj4Oef2p29fY2OhQ15857Dape3p6Ijw8HJmZmQCAzMxMhIeH2+WQkTEdHR1obW0F0LfV3sGDBxEeHs5wVJZH59F+bN68GYWFhdiyZYtm+DkyMhJyuRznzp0DAOzZswdz585lMsxBM9a+5uZmyOVyAEBvby+ysrLs8vy1t7ejurpa8//Hjx+Hm5ubw1x/ptrH5/Md5vozl11vvXr9+nWkp6ejpaUFrq6uyMjIwJgxY5gOyyIqKiqwcuVKKBQKKJVKhISE4K233oJUKmU6tEFbt24dsrOzUVdXBw8PD7i7u+PAgQMOcx6NtW/r1q0OcR6vXbuGtLQ0yGQyCAQCAEBAQAC2bNmCgoICrF69WmdJm5eXF8MRD4yp9i1btgyrVq0Ci8VCb28voqOj8cYbb0AsFjMc8cDU1dXhueeeQ2dnJ9hsNtzc3PDaa68hIiLCIa4/U+1zdXV1iOtvIOw6qRNCCCHkDrsdfieEEEKILkrqhBBCiIOgpE4IIYQ4CErqhBBCiIOgpE4IIYQ4CErqhBAAfbvJnT59mukwCCFDQEmdEGKWyspKhIWFobe31+x/Q18UCBlelNQJIYQQB0FJnRCi4+LFi3j44YcxefJkJCYmYsOGDQCg2eBkypQpiI6Oxvnz51FeXo4lS5YgPj4e8fHx+POf/4yWlhYAwKuvvoqqqio888wziI6OxmeffcZYmwgZKaiiHCEEQN9Q+bp16/DRRx/hsccew4IFC9De3o5r164hKioKlZWVuO+++3D58mU4OfXt2lxWVobKykpMmTIFbW1tWLlyJcaPH48333xT5z0TExOZbBohIwb11AkhOpycnFBeXo6GhgaIxWJERUWZfG1wcDCSkpLA4/EgkUiwdOlS5OXlDWO0hBBtTkwHQAixLevXr8ff//53pKamIiAgAM8//zxmzpxp9LV1dXVYv349zp07h/b2dqhUKri6ug5zxIQQNeqpE0J0yGQybN68GTk5OXj66afxwgsvoKOjAywWy+C1mzdvBovFwv79+1FQUIBNmzaBnugRwhxK6oQQHfv27UNDQwPYbLam181msyGRSMBms1FRUaF5bXt7O0QiEVxcXFBTU4Nt27bpvJeXl5fO6wkh1kVJnRCi44cffsBvfvMbREdHY/369fjggw8gEAggFArxzDPP4LHHHkNsbCwuXLiA559/HkVFRYiNjcXy5csxe/Zsnfdavnw5PvnkE8TGxmL79u0MtYiQkYNmvxNCCCEOgnrqhBBCiIOgpE4IIYQ4CErqhBBCiIOgpE4IIYQ4CErqhBBCiIOgpE4IIYQ4CErqhBBCiIOgpE4IIYQ4CErqhBBCiIP4/wMXkgVF/45KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residu = results.predict(Boston[\"lstat\"]) - Boston[\"medv\"]\n",
    "sns.scatterplot(\"lstat\" ,\"medv\",data=Boston)\n",
    "sns.lineplot(Boston[\"lstat\"], results.predict(Boston[\"lstat\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18fc475340>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(residu, Boston[\"lstat\"])\n",
    "# sns.lineplot(Boston[\"lstat\"], 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}