{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Demo (Chapter 3 - ISLR)\n",
    "- Load Dataset: advertising\n",
    "- Linear Regression\n",
    "    - Simple linear regressions\n",
    "    - Multiple linear regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuany/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "## perform imports and set-up\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "import sklearn.linear_model as skl_lm\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') # emulate pretty r-style plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Datasets\n",
    "Datasets available on http://www-bcf.usc.edu/~gareth/ISL/data.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "        text-align: left;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>214.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>65.9</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>97.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>204.1</td>\n",
       "      <td>32.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>195.4</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>67.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>114.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>281.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>218.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>53.4</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>237.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>228.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.2</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>62.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>142.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>240.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>22.9</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>248.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>70.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>10.5</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>170</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>164.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>47.4</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>168.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>248.4</td>\n",
       "      <td>30.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>170.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>276.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>156.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>218.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>56.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>287.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>253.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>205.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>139.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>191.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>286.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>18.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>75.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>17.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3    9.3\n",
       "3    151.5   41.3       58.5   18.5\n",
       "4    180.8   10.8       58.4   12.9\n",
       "5      8.7   48.9       75.0    7.2\n",
       "6     57.5   32.8       23.5   11.8\n",
       "7    120.2   19.6       11.6   13.2\n",
       "8      8.6    2.1        1.0    4.8\n",
       "9    199.8    2.6       21.2   10.6\n",
       "10    66.1    5.8       24.2    8.6\n",
       "11   214.7   24.0        4.0   17.4\n",
       "12    23.8   35.1       65.9    9.2\n",
       "13    97.5    7.6        7.2    9.7\n",
       "14   204.1   32.9       46.0   19.0\n",
       "15   195.4   47.7       52.9   22.4\n",
       "16    67.8   36.6      114.0   12.5\n",
       "17   281.4   39.6       55.8   24.4\n",
       "18    69.2   20.5       18.3   11.3\n",
       "19   147.3   23.9       19.1   14.6\n",
       "20   218.4   27.7       53.4   18.0\n",
       "21   237.4    5.1       23.5   12.5\n",
       "22    13.2   15.9       49.6    5.6\n",
       "23   228.3   16.9       26.2   15.5\n",
       "24    62.3   12.6       18.3    9.7\n",
       "25   262.9    3.5       19.5   12.0\n",
       "26   142.9   29.3       12.6   15.0\n",
       "27   240.1   16.7       22.9   15.9\n",
       "28   248.8   27.1       22.9   18.9\n",
       "29    70.6   16.0       40.8   10.5\n",
       "..     ...    ...        ...    ...\n",
       "170   50.0   11.6       18.4    8.4\n",
       "171  164.5   20.9       47.4   14.5\n",
       "172   19.6   20.1       17.0    7.6\n",
       "173  168.4    7.1       12.8   11.7\n",
       "174  222.4    3.4       13.1   11.5\n",
       "175  276.9   48.9       41.8   27.0\n",
       "176  248.4   30.2       20.3   20.2\n",
       "177  170.2    7.8       35.2   11.7\n",
       "178  276.7    2.3       23.7   11.8\n",
       "179  165.6   10.0       17.6   12.6\n",
       "180  156.6    2.6        8.3   10.5\n",
       "181  218.5    5.4       27.4   12.2\n",
       "182   56.2    5.7       29.7    8.7\n",
       "183  287.6   43.0       71.8   26.2\n",
       "184  253.8   21.3       30.0   17.6\n",
       "185  205.0   45.1       19.6   22.6\n",
       "186  139.5    2.1       26.6   10.3\n",
       "187  191.1   28.7       18.2   17.3\n",
       "188  286.0   13.9        3.7   15.9\n",
       "189   18.7   12.1       23.4    6.7\n",
       "190   39.5   41.1        5.8   10.8\n",
       "191   75.5   10.8        6.0    9.9\n",
       "192   17.2    4.1       31.6    5.9\n",
       "193  166.8   42.0        3.6   19.6\n",
       "194  149.7   35.6        6.0   17.3\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1    9.7\n",
       "197  177.0    9.3        6.4   12.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising = pd.read_csv('../data/Advertising.csv', usecols=[1,2,3,4])\n",
    "advertising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwNwVhr9he/bKExGGPYkPejTrgBAOw/6TX0L45jSsHYH4mxOKSk3FY2CeOBkFM+8EO2s+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AAEkqeYgXLEZ831up67c+CP799+D8/MCw72QjRdFlttRkLwuXsRqPsEwedHadywXVsGTLDnjv\nXofgYy3gPAGwJ1oN30etQ5V3WSMi2zeI93LNvlo0hsV7p3k5tZ6f5zhDzzcYKFlda8GCBoZEZj1e\nAISqziMpGqGN94HZswPo6RaNYSCtF/bshOuq6/LqfpnZEc/fvANkZSX4999D7PEHlbo1B0Yin3C+\nMMUg9vv9OHjwoOx3f//73+HzZd+4GYZBPB4HkKoo/eijj1BbW4vzzjsP77//PgDgvffew8UXX2zG\nNPOCFlE2Ok8X1fjL1TIx4fLAu2QVmD075cbtjodTHmPo7+rF9/aA7+8H8+IuxN9NFbm4rv4e7C5X\n6r7hTsTeeAnuufXw39WUMpjffFkMU6fG40fEIhtMmU24PPCtWDcosqenRWe2tqY5la5ZjUE05DfZ\n15dK23C5VO9jHzVK8VzSZ3bGo+iZOkPWllVhXGu1MI9GxOcnaS/oGxfBPe9m8F8eAd9hJIGgeCgH\nXWvBghSDKbNClzep/ku4PLBNnqpwBvnu+BnCOx8W17p9bI16SuHU6Xl7YQXGCiRiCK1uQGjtcnS9\n+hyoa68HSXs191pVPW6SM2IkwJSUiRtvvBEbNmzARRddJLq99+/fjzvuuCPrdeFwGE888QQ4jgPP\n87j00ktx0UUXYfz48diyZQteeuklTJo0Cd/61rfMmGZe4HgefO1EuOctBJxO2KuqwZ78EvzhT+EM\n1iDmcJl+Tz0hDo7nkeTUwzkgCW2KFA16lopJU1Ie529/XzSyiVeeFQv1OCYC5sVdoG9chK69LUp6\nF2EuJrcCLjUMpsxyPA9b3dlw33wb7MGxQHc32EgnYru3gFrZbFqxl96QmuC5CD7WomSZyFEIYhqF\nTw56IdX71DcgsqkRHBMRnwuAdlGqAFdGqpBGC3N4vEA0ApL2grr2+oH1U+mAfVwdyGnnD3loshx0\nrQULUgyWzJIEAfbjD8F/9ncAPAACzrOnIVE3FYmzzgHlcsO3shno60Hy+DEkoyGgr09c6+659ap6\ngXDTcLYdy7vATdXJkL4f8+IuxV7Lc5w6i1TNhBHHJ5wvTKNda2trwx/+8AeEw2H4fD5ceumlg97R\npli0a9TJVsQef1Cx2XmX3ofYtAvy2uyEfMOKRBz9TldGvmWXZtcZqRGkRKc/3QAAIABJREFURc/i\nX7sVSZcb4DggGs5NL7VgMRK/+z+gf3ALQg/fJxvPVlsH713rwHecRrL1C3R/+D9wfnMOGGnVbcNP\nwfx8V8rgGMKq/OHYQckXbkf/p3+TyR29YDFw9nTEgjWa1xmhyzFK86Oab6djDDtJgoq0gw93gvAF\nEPMGkTSYUpDNeBfydYVnt4U7kTx0UFF0R298GgBU5+uetxDxd99IcW6fNQXEaCci2zeAPdGaNYfY\nGY+C/9NvZQU4ud4jMDxl1qJdKz5GSg6xWcgms1RPHPjrHxV7GhGsBuv2oIfygoq0I7R6CfjeHtA3\nLgJIUlzrtmC10ja442dgo2EgEQNAgEgb2Fo6WE1fO9uOqXaKoxctBxIJ2KeeC9ZXJer2ANsr1jsI\nICodoB95BjhxzMoh1gHTaNfGjRuHf/qnfzJruNKCJ9WUQpGesPXBvAzAbJs6ANhOHddFu6LldYsF\nx8Fx6GPNBSDkJ9naTyF5+FMx/YGPx+TGcLAa1Le/j9DqBpmiYFkW9PxF4Pt6AY4Hm4gNeN+GMWn3\nUICoqFDIHbNnJ/zN2zWvMVxEkQfNTyb0NL1wHPoYoQKVsh56NoEqiIpGwLywU/25hP9nfuZ0Kje3\nZY3gPIEUzRLlVb13wuWBu25ywe/RggULgwt7vEukVQMGUg/dc+vRtbcF9PImJGmf+Hl831vwLPyJ\n+DPbfgqxN1+Ge2497GdPA+GvQvKLz9D1/JOyKJVWRFmzP4CKZ9dWWwebk0Lk+e1KPRoOqeufcGdp\ndyktIeRtEO/cuROLFy8GADz++OOygjopbr9decIpN5i52ZEEATragf7jR+GeWy/Sswh8hQCQbP1C\nV4hDk1c4FsnKhygaDEGgS+oRHj1adl/XnGtUc5Tdc+sRfXGXOC//2q1iEw9rkZkLviehnjMbjwN0\nQPUaZzyKWMuTcM+tF9lHYi1PaqZZ2F3qDAt2l/50oFyGqpk8v7q5MXOkV6g+c1U1wo/eryg6FefJ\ncar35ngebPV4KzRpwUKZQUvHCjUDzOYm+Ju3i2ubbT8FtvOMbK2z7adSxvMjz6CiOw4+EpLv7y07\n4J8yHVAxiDV14yPPKJwMvoaV6HxwhaoeJX1+Tf0z0viE80XeRXVjxowR/19dXY2xY8eq/hsOkG52\nUuTa7DIT3O0kCefnBxBavQTxd14HSBKehT+B54d3gKRTeYiIRhB/9w3QCxbLK95XrANAKIqeBEGP\n1dSJxq7eJPrMIqlk+2lZAQFsNvVx0hyQwumUqaoZuLcFU0H6AlmZHVQL4eJxUN/+PuK/eSdFxWez\nwfsvS4GeXtV7JHv7FPJGL1iMZFJZaJINqrIoYAgKO7IVAap+Vt+Qqg/Ic54JF60Y079ineEqcwsW\nLAweWF9QXcdyKf0lOCCkazv27hvw3r5arj9WNYNoP4m+v34A8BxAkqCu/yFswepU4XpPt/oEtHSj\nxLObYpvYDq6vV1s/BWs09V0+0FNoPdyQt4f4+9//vvj/H/zgB6ZMppQhbHZ6i4LUwiD+NZsQSlei\nZ4Zl6foGwBcAOBYcExFDMCAJwEkByaSYV5wz3Kyzr7nCq+cLgGg/mSogBA973WT1ArwLL4HnKzOs\n0MtgIK3kMuWuh/LCqZEWY68chcjbr8kLJCsd8N25BmRgjPLv5XIh9vZrMo9y4o+/BVU1FpEt61TT\negxDp0yaiVxea/lnPqDzNPjDn+Y9T7X72cafBU6jSYsFCxaGHgkXDd+KdQhLO4KmeYSBgfWfGDdR\nrksy0qdA2sD/9Y9ibrGwr7uuvQFde3aA9Wk0nciiG4V0LOfJ4wg9tEKzgA8eLwiSNC01oly4i81G\n3kV1H3/8sa7vzZgxI5/h80KxiuoEFFqoRC9oALNnhyZTA71xNxIuWt2QfuBu3cU6hQiz7Bl9ARAn\njiFaJotiuBV7AKlCg1Bnp7LgIh7VLGJDNAL+73/VkDGlzGge3jRkzlE32fAaGwwFW+jaJwkCzu4Y\nyNbDYqvyQueZa07DUWatorriwyqqM4ZcMhvw+9F7/CgQjcBOEmIhrZH17+08hdD9SxU603f3OnAc\nh7jGGLl0o9SWUCvgyywqNgNGC63NQFkX1W3fLi/qCYVCIAgCbrcbXV1d4HkegUAA27Zty/cWJQdD\neTgqYRCioiJ1mtPkMw2Dc7oVp7ykwaKnbJ6xXEZ95jOSknEqx4xF2O4oSWN4OENV7jJkQuhYaGs/\nBcIXQL9WuouKzKjJS1aZy/MZBrOww8jhVTrHmMMFctoFVgGKBQsjFEmPH9TKf1UwNAnQ0i1auchE\nRQXiE6Zo6pCculGiizML+NhgdXH0kwmF1uWIvA3iJ554Qvz/3r17EYvFcP3116OyshK9vb14+eWX\n4Xa7TZlkWSIjDGILVgMuN+j6BvDd3VnDspkGEAX1AiBpGFdtkWYaUaptm3O0XZbOxVFVBa6MvCjD\nGhL5yvQa2Grr4Fv8U0Oh/3xkziiEe5BpD3chHJ3ZQBIEXJ8fkEU2PMubND00WvO0ClAsWBj+IAkC\n7P7fi63ls+2LWt7cnqkzQNDqqQ9Jv0qaWgay6pwMW0Is4BO8tcXQT0OQ4lYKMKVT3X/8x39g/vz5\nqKysBABUVlZi/vz5+OUvf2nG8GWJzKId11XXgdn1aKolsmN0iudQZ/J7tuIgQLJItdrmpqHWTYtt\n+xL83z4YcW2Xyx1SmchkA2FPtCL6i2fgu3NN3gUWuWQuX+iV1ULgjDPoSrNsCK3Iu1qehDPOmHYP\nCxYsDA8441GE0sYwMECpyr//nkI3aTFCUCePIbKpEfTNt8l0pmd5ExKuwjyqhejifAvjiqX/Sx2m\n8BA7HA58/vnnOOecc8TfHT58WDSQRyIywyA2LilStjC7t8AWrIZ7bj0qpp6LZGBMVi+ZGZRWJEHA\n3p2A++YlKR7DHQ8PnIYbfgomCy2XhdJDJp90Znir/+P9YOcvzjv0X6wUBzPp1wB5ZIRne0HaHbB1\nRRQFhfSCxbB1RQHnCI5aWbBgQQmt9ADwSt2U/q6QoiYUIfcf+wLsiVbEXv95qjjZZkPFhZeA8VQV\nrDPz1cWF1G0MdopbqcAUg/j6669Hc3MzLrroIgQCAXR2dmL//v340Y9+ZMbwZQtpGIRKMKphj+Bj\nLYjYKnOGPbKGVHLk+wgLI7S5Ce659SKpt/A9gVt4uOcHDTdI+aRVw1suV0Gh/6KkDpiYm6al8MlA\nEGGVZiaBB4ZPPYMFCxZMglZ6AMcrdZPHC1ttneLA7W34KWzB6pTDK83R7/nKDHAaXPFGkY8uLtT5\nMBJTx0xJmZg9ezaam5tRW1uL7u5u1NbW4qGHHsLs2bPNGH5YQCsEgSztd3UjvaBtwWrQNy4CfdOP\nQd+0OEXjhoyFoVXQZ7MN+/yg4YqyCm+lZVUKrdy0XOE+LYXPxxhVGee64yY/jAULFsodCZcH/hXr\nFDzs8X1vKXRTwuWBd8kq1YZVrjnXiN/Tk29rJ0l4mU54jn0GL9MJO2mKOTaAIeB+L3eY1rp5/Pjx\nmDdvnlnDDTtohSAcJiyChMsDelUz0NYq68fumTAJ5JTz5AuD49W5hc+ZCcZqu1yWKKfwVq4WzwJ0\nhfs0FD7f368q46xXgwfUggULIxYcz8M26zLQG3fDdvo4kse+QOzNl8ExEYVu4ngeScFzLIFawyqt\nHgVAyhh2HtyP0LbmAS/z7auRmD4LSY4z58FGaGFcITDNIP7www9x8OBBMIy8cGU4tG7OBb0UT8UK\nQXA8DwTGgtmwWnZqjabDI9LWvPF9b4FesFge7lnWiFjNRHBmLUQLRYWWvJVDeEuv8a4r3Keh8JOn\n20DXN8gPh8ubEHcVqSLbwoiBEY5loHR4li1kB0GSiDndICdPh3NsLahpM7X3cg29Y6RhFRVpF41h\nIO1l3tYMf/N2RAymWQj7AfvJSVBOl3hvvc4HCwMwxSB+5ZVX8F//9V+47LLL8P7772POnDn4n//5\nH1x66aVmDF/SKJmOLtGwJi8t3B7Qi5aD2bU5xWP49mvwr3wQfEUlCBeFZDwORyxSsl5FCwMoGXkr\nALqMd61wXzwOKv05PD7Qq5rFg+BAh6mXAAD+tVuR7OsDPF7EDch2PhzGFixYKH+o6aZMfdBDeVUN\nTcZTNZAznEVfkAQBorcH7nk3AxyP+L63wKYLo/lwJ2DAIM61H5RL5LBUYIpB/N///d+47777UFdX\nh/feew+33HILLr/8cvz7v/+7GcOXNMyums8HJEHA7nKBXtAAsCy69/8BztlXgXn+yQEv8E/uAf2j\nO8FHwwDHg3nt56C+Mw/hh35atobVSEQpyFuxIN147C4XbLV1YE+0ip9XzJgFW6QD4bV3yjy//g1P\nof/D3wMsi8Tv/k8ql89mAyoqkKiqScmzEWO4zA8cFixYMAeq3P3LGtGdpXlPtgO1MF6nVL+k20Rz\nTASET58xLNzDFu5A8ssjIGmvaFRL94NyiRyWCkwxiOPxOOrq6lID2u1IJpOYMmUKDh48mPW6jo4O\nPPHEE4hEIiAIAnPmzME//uM/IhaLYfPmzWhvb0cwGMTy5ctBUZQZUzUfJlbN20kSVKQdfLgThC+A\nmDeYM59IyiAhLtglK8G88qw8HPPEevhXNSPyynPgmIiiNe9wMqyKiSGXWZM7CInKOx6HvXIUkvH4\nkHgSVDee21eDeelpsCdaYautg+eff4jO9AEOkKYF7QYx/izEWp6UV3+/8qxhY3Y4HjiGXGYtWDCI\nUpFZNX0Q2bIO/jWbwFTXgRNoTZFqd4x4HGS0E5Et61QP1Kr6Zc9O+O5eB9IxGjHfGIBls85J9dCe\nNqoFo9hijMoPphjE1dXV+PLLLzFhwgRMmDAB7777LiiKyimoNpsN9fX1mDx5Mrq7u3HPPffg/PPP\nx3vvvYeZM2fie9/7Hl5//XW8/vrrWLBggRlTNR8ZHcME75Td5QJJELo34nyT7FUX7PaNcM+tF+lf\nhN/3f/EZvHevQ3K0E+wIbc1YKIZcZtO0P67ZV4scmPHfvJNXoYSgWAVDMiTl7R1kr6iqHKdz6pLx\nOOwuF/r+8kcNmQ0jMeU8+Fc8iI57flyYMauxLmzhTlBlGnYccpm1YMEgBktmtfJvRWjog/5PPoLT\n7UHC5YEzzsB26jiSrV8AjtGI7NmhrYM0xkse+xxdrz4PenkT2Bx6V8uoFvZ8q3Auf5jC83H99dej\nq6sLADB//nz86le/QktLC26++eas1/l8PkyePBkAMHr0aNTW1iIUCuGDDz7AFVdcAQC44oor8MEH\nH5gxzaJASFy31daBuvZ6dO1tAbNnB0KrlxjqwEVF2hFRSbKnIu2y72VSUWl6DNMVrwKISgfAskj2\n9SHh8sBGErrprywMYKhltofygr7h1pScvfBUqoXnDbeihzL+dxMUq2v21QoaIWZzU8pzbAD5dkUC\noL1RxOOI1dSlPNcsqymzHM+D7+kunGZIgxYueehg0TrrFRtDLbMWLBjFYMistGtmx72L1de2Fk0k\nywLxePr6HyH0r6vQ9epzsFFukLRcF8t0kNZ4aeYKXXpXa89P7+klS7lZBjDFQzxr1izx/1OnTsXj\njz+Oo0ePYu/evbjkkkt0jXHmzBkcOXIEU6ZMQTQahc/nA5BaGJnMFaUEIXHde9dahFYvyds7xYc7\n1SmkJEn2JEHA2XoI/OFPAfAACFSc9/+oVryOmnqu+HsxpPL2a6C+fgWc8Sgi2zeos01YFai6MRQy\n64hFVA9OeYX0BcWqxU1tIFqQLfcWQO4itVwUQR4v4r95J6vMkj5/wTRDqpXZ9Q2pluso/xSKctWz\ngDGGh3JkdzDyfKcNjFuO70KKYsmsnvSohMsD77JGeQpEei/1XfBVMRdYuD6y42G45y0E88JO8T5S\nHaSqX9LpDsIYOfWuhq6sPP+rIL42u+wiWKWEggzi3t5evPbaazh69Chqamrwgx/8AF1dXWhpacHf\n/vY33Y05enp6sGnTJtxyyy1wOp26779v3z7s27cPALB+/XpUVVXBbrejqmrw+UbZ9jZVo6IiEUdV\n3WTN64T5crGIqpDbAmPE5+HOnERfWyu6Xn1uYDH5AvAtvx/hzWvF3/lWrAPvcMC/8iH0HzkEsCxi\nb78Gzy23wz7+LHCfHUi1mXzz5VSHunTo3RYYC39AO6l/qN5tKcJMmc2GzHfOfnIyLzlTA8/2it4J\nVQU7ZiwcKvNTkwP+9Am0q2wuVVtfANd6GKFHGkX59K9YB9usy0BIOLh5zg//inXK740/Cw6SBM/5\nYb/ldkSe3Sa2Rh117vkgps2EPx0NsZFk1jF0vxff5Qg+1gIuHAJBkgg9sgZs+6mBzw2871JaM4Ml\ns0aMNSPvpljjAvr/TkbmYHQeRscuxhxKDcWUWb26lPN+E4E1m9D3yUfiXkp9+/vo/+JT1evt4ybI\nnFGZOkjUL2dOou/TA2LuL5Bd74r30NCVFefMBMlxcGheWdooBV1ZkEG8e/duHDlyBBdccAH+93//\nF62trWhra8MVV1yBH//4x6Dp3B6UZDKJTZs24Zvf/KboTfZ4PAiHw/D5fAiHw5rjzJkzB3PmzBF/\n7ujoQFVVFTo6Ogp5rLxAOV2qRkW/04VwlvkI87XTfnhvXy16/8SiItqPZEcHSIKAJxoG39MD99x6\nkaqF2fUo/A8+Kal49YElSaCLgT0QRIXLDb6nG9TXr0SXiwYXColzlbaZJCodoL9+ha65lirGjRs3\nKPcxW2azIfOd5ytnaiDtKS9urOVJheeVXt6EsN0BTmVMNTmgzpxWbA4k7QUfDaP/2BcymQ090qjq\nYSUnnSur3GZcHnChkOxzamWzKOf9JAkc/KvodfYHAmByjKEbtkqgqgZUggHHyFMujLzvXGtmOMqs\nERRLnxgdt1i6rRT0pdlzGC4ya0SXUqQ9lSZBEnDNvhqxN1+G59bl6hEpx2j412xCkuO1dZCtEuS4\nSXAmEuCYSKr+6KrrYK+bjN7ePiQ6O7N6edV0pZ/jDP+tS4lispj2hV6ZLcgg/utf/4qNGzfC4/Hg\nH/7hH3DbbbehqakJ5557rq7reZ7Hjh07UFtbi+9+97vi7y+++GL8+te/xve+9z38+te/xle/+tVC\npjkoKJQEO8lxSEyfBX/zdgXLRDaqFrGqdNQowF6Biu44+j/9GHxPNyK/eQdU/W0DxVHpeViE3flj\nqGXWzL+dkO5DrWwG4nGxgC0vxZgRxrMFq0FddwM619yuKrNqYUEpRZCWok64PHCCEItY4u++IXaU\n4n2Xm04zNBzWylDLbDYYbXRhYWRgMGTW0Np2udC1t0Vm/CY7zigdCQsWA5WjwVRPGKBaA8R6H0GX\nAek0MicF/7/uANd5BhGJxzdXUbMZes6imFSC4Pn8n3zhwoV47rnnNH/OhU8++QSNjY2oq6sDkU5k\nv/HGGzF16lRs3rxZ9PjedddduqhV2trahtSLmc9pS2u+mZys0vxkIHUSdc+tT+dVNoDZs0NOOSXN\nG17ZrPTGmTjXUsFgeC6KIbPZoPbOh/pUL52TTE5JApHtG8CeaAV902IxtUeAILNde1uy5uBqKeqe\nqTPgOPSxav4dx0QQfKwFnbZK05+3kPddCh7iwZbZUjByjebN6tVtxexUV6z3ZnYO8XCSWWFtVyTi\n6FdjmZB+L0Mn+ddsQmT7BgXjj3S/VbvOs7wJ/KhR8oZC6ToFaeqEf+1WJEc7desbo/szlWDArLxV\noaOHqj6i7D3ELMvi448/lv0u8+cZM2ZoXn/OOefgF7/4hepnjY2NhUxtSFDIqU2+6fqAztPigqEX\nNGgySXiXrELogbtTlCuZTAFpKpZc3jijcx3JKAWZNftvl6/Bp0Vaz3kCsPVpMD7YbDk9rFrFLv7m\n7SLftvh7Cd0QFw4BVTUFvQs1lPtaKQWZtWDBCAZLZoW1XVU3OZUmkcUjm5hyHuhHnoE93gW+JwHW\n7QX1o2WI7d6SMorT+3GM8gJpqlQ1XRbd3AT3vIVyPdayQ0aVyvf2oP+jD0UqtqJ4bS3qVQUKMog9\nHg+2b98u/kxRlOxngiCwbdu2Qm4xIqDqEatvELvPCHRTmSe5igsvQTIez84UYLOhosIOKtGFhIse\nsaEQC+ooJGymRVpPb3warEs9P6/iwktSLU6zja2hqLWYWIjKShCVDpA+f+5nzTD8hecohRw6CxYs\nlDhOHJM1wfIsbwL9L0sR2XifMpoVi8AmpDRKkPqZV/6OHKB7y6RiK4rXNhezzwhEQQbxE088YdY8\nRjRUPWKSE2N831uqRU+MpwpOe0VWpoCKSVMRfrQJHBOBZ3kT4iM4P8iCEgV1ZsviYUiMm6ian5fT\nGAY0FTXp9akzsYyrA728CQjWABoFdKqG/6pmEH19iFo5dBYsWMgB3d7ezU1iJ1j33Hp1oxNyHnPp\n7/KiYssDw6E+wmyYwkM8ElAs7xLPcbCFO9QNi/SJkW0/hdjbr6kWPSVcHniX3gfm508pjGbfT+4F\n84tnxLykaBnzp1ooUpvlQsJmWTwMYohRUgmtd55airqfhXoRi5NConpCVmo1ZzyKWMuTMppB/uRx\nMM8/md9hwMKwAbvo2qJRnlkYRtDSlSreXoFZBxQFb8NPEdnx8IDOWvgTkGNrZNRsnuVN4Gsnwj91\nOpKHDiqo2OyjRoFKMAodaidJUJF28OFOcLEI7LQ/a2dbKQrR0cMVlkGsA8XyLpEEAXb/75E8ejjr\nKZKodICqvy3lXUs36QDPiwYS4fHBs/B2sOEO+O5aC/bkl+D7+0GOHQfnpVeCn3WpSHk1kvODyhnF\naLNMEgTs6e5G+YTNcnkY8s29zVTUdpcLyd4+2O0kmD/+VmbUCkWjOZ89HlcUnXrvXAN6/iLwfb2p\nghhrjViwMCLBc5yCCUJ386AMb6+ttg42pxOR558A39sDW20d/CsfAngOPMchefokOMonM0Tj6fuR\n4yfD2dMtUj0K6ZORTY0im46g6+0kCefB/QhlULUmps8yZBSXc32E2bAMYh1QC5Xwhz8FI6miz8e7\n5IxHEXqkESTtVXi+hBOjZ9pM1QWqlXccfXozOCYC75JVCG99AOyJVhnjxEjODypnCDKoWjyZp1ez\n0I6FxfQwiBRrJ48j9NAKOTf3S08PyLXOEJ+9cpR4iABSHMlcqB3Mz3cpWFmsNWLBwsiB4JhictCe\nqTkABMYIqbfXt2QVOh+4W9Q17IlWhDb+TOxgJ+rYqTPBZRiiUp1qC3emvMUS9gmprqci7aIxDAx0\nLfU3b0eE1m6wZUEblkGsByqhEoAvvEIzPS7bfmqgaxxFYdTU89CfZGHv60FS41KtvGPf3WsBHog+\n/wTYE60Dn+3ZCf+aTWBGcH5QWcPENsvSMdU6FnKegMKolaYM8WwvSLsDnMQLTLo8cMajcLYdM80w\nVi3aSyt8o+kiYvFpGq4514Bp2aFgrLDWyPBAKVC/WSgPCI4p6WGZ//II3A4HWF+VqGPUHADxdOqk\n9HfJHKkV0uJjNSeG4AyguxMAeLjmXCNGr6S6XqvImA93ApZBnBcsg1gPVEIlQP6hZrVx2fZTiO97\nC9R1NyD82DpFWNy3dA3Ys6agx+HKWr2aPHYYAERjWPYZx4/o/KCyRlpWtIon8/JqpsdU61goRVZe\n4FgEiMdBRjsR2bJO08MizXWTNp3JCo2NJRmPI1ZTl/6FTnnOXMMaBwtrjViwMMIg0TO2YDWoa69X\nFLCLuk6IhI2bKGt2JU07oABVHW2feDboGxcNpGbF46DS95ce7gV9K2WzkPKtC7qe8AVU70P4LGM4\nX2hXoVgQIYRKUvlCaaE7exo8Gb8Tw7cGxvWvWCeO4brqOjAtO+CafbUiLB7e+gDsp47DefgAYhtX\nI3n4U/E6AWI+U9posgWr4fnhHfAvb4J/9UbY6VQHMAvlB0EGU41YFhckd5lj5hpLi4nC3d4G/oPf\nwdYVEY1h6efOeBQABnLdVi9B6MEVCK1eAufB/bBnKYIDMHAIkCBf4z/zWYUDbSFjkwQBKsGAaj8J\nL9MJ6mQrqARjrTELFsoJaT1jC1bDc+ty8L29cM+thy1YLeoy6vSX4P/0W/Cf/g38n34LZ+shzXWu\nqlfrGxDdtRlde1tAXXs9KmbMAhntBLPyVkQbbwez8lY4Pz8AO0mCjnaAP35UPoc9O+G66jqZfo55\ng/Devlp2H+/tqxHzBgfnvQ1DWB5iHdDKlQRQUP4kx/OwzbpMHMPGsVnD4v1HDgE8D9fsq1Wp2IS8\nY3Qx8E6ZBu70STDPPSHLMXaOq0OibqrlBSszmNpmOWPMnDKs4ant+3g/mD07tBvHpEN7+ea6FaNN\ntfisvgA8EyYpi2J1jl2MIkcLFiwMPhIuD/yr1yP55VGEH71f4ZVl20+BD3WInTfFvTRYg5jDpRgv\nZx7wnp0IrN2KzvuXynRirOVJ0AsaEJJG2iRzsE+djq7xk0W9kuQ4JKbPgr95O/hwJ2yBMWAMsExY\nUMIyiHVCqxqz0ApNgiTFMbxMZ/awOMum8jxJQpZ3bD97GthgtVipCocLVIICkyYLByTcxvMWwhkY\nY9FKlSEUMihhHDFtTLWxtKqrWTb1g0bjGMHbmm+um9lFe5nPShYwdjGKHC1YsDD44Hge5JhaMM33\nKGoKhFbzyZPHFXupf8p0QMUgFsaMOWlQ0QiYF3bKPuN7e8DFGIVOdM2+Whlpk8yB9SlrO5Icl3Iq\n0AFUVVUhWaTWxyMFVsrEIEMMs6bDq7zkNJfs7QO9YLF6WDz9eyElAkjxE3ftbQEbrEbMmdGFLhrW\nTuyPRor9mBZKGJkymCvErxoCXLAY8X1vAYAYrdBKvRBy3aTQm+smbCyxmjqljBeIgsbWU+RowYKF\nsgAfCamvY5sNvjt+hvi7byg+43u6xZ81dapG2peaToTNpjmHfNPiLBiD5SEuAGrNOrJtqmrFSf4V\n60BOOjd1ncuF2Nuvpfqij6pE4P7N6P/87+C7u1N8q/8wF+SYGjDYa/S5AAAgAElEQVTPpToEZg3z\nZuNMtGilyhpG5U5xrcFWzZme2lFuGqFH7hNDgELjmMDarejv61PMSch1i2TwZca8QaBcw3vFKHK0\nYKEIMMK4Ydv1ZhFnUrogfX71Tq8XXoLkKIfICyz9jPVVpa7NUnQMEPDdtRbJ1i8Qf/cNkUs45g0q\n0sEqzpmpOQddHT4tFAzLIM4T+RgWzngUiV/+Ar671gK93UDlaMTeewe0P5jOB/WB+tEyMBtWi4Te\n3iWrwLNJeC/4KpIuNxJOdyqPNIcxlHB54FneJM+RrG8AxtWN6NaM5Y585E4KZ3csRSk072YQoyrB\nkzbwx4+CDo6VKV01o1tINwj4/alGMbIq6AZ0BccN5K9J5pKZ66abZaKEIXjNU3l/yrbq1hqzYKGM\nEKzRbDUPQLGXepY3Ie6iAZ5XLToW8oGlzDu+O34GYvxExNypPN/MdLAY5VW9jzCHnI1DLBQMgueH\nz1tta2tDVVUVOgzk0eTrbaMSDJiVtypOc9Lcwcyx0Z8EefwIIts3DCySn9yL6Mu7xUYDYmFcuLNg\nwU/dn4Et3AHCMTplUI+m8h7P6LsdbIwbN26op2AYbW1tmp+RBAFfsge9Z06LsuCMR3PKndo4ghxW\nEEB4x8aBxhb1DYi98ZKsCxKArEZ3VVUVQp2dBbctNxuDLZ96WmnnmtNwk1nA4gCWwojHtRTem575\nDkeZzdRpQndMuFxiqoKWvqNOtiL2+INwzblG5HInHA6x6Y8AotIB97yFICZMUnVgkAQBZ+sh8Ic/\nRYqzmABx9jT0TPwKHIc+zukEKfX9OReKOX+9MjuiPcQFeds0Ku9t4U6QwgLKGNt35xpEX3lGTqf2\nxL+mCnNeTC2eqFCQY5RnVQWpHEk34HRLJjlszj/DGoJstmfIJpxUVrlTVbKZMi6pXGZadojyJxSD\nAVClWZMa3VbLz+IUOVoYXigFI9eCPmh1xxRsAk195wuAuu4GsdGP4A3WquFhNjepsgQ541ExOiyA\nqHTA37xd5CQWxrEKd4uDEV1Up8WvKvCnZoVGsnzy0EE4Pz8AZ5xRjB1+7AG4Zl8NW7Aa9I2LQN/0\nY7E7nQCtghw9hVBGi6UslC60ZNPucmWVu8y/ueo4e3amvBnpn0ESsAWrU7yX7adg706ApOU5sENZ\nKFbKcl3Kc7NgwYIxCPrSftYU+O/dAN9P7oGNJOHsTWhfxHGKrpfJk8fVec65VIfb/r/8UcY/TBKE\nppNNi6VHrz62dJR+DLmH+Mknn8T+/fvh8XiwadMmAEAsFsPmzZvR3t6OYDCI5cuXg5IYjaZBq8Wi\njja4qhypkm4yvnvXq49N0YpOON6Gn8IWrAbbfkq1IEePJ5skCLg+P6DMc7L4UE3HoMisVpe23r6s\ncqfwGmjJOJlSikSlA3A4ld2Z0qkUQuHcUBWKFZozXUzYSRLOz/6WtUNfqWBI9awFC3lgSGQ2GoH9\nrCmg/uGfZJzEvtvvhX36Rep1DyqMTvF334Dvjp8h/PhDCj0tpayUenu1CuG1OtLp0celrD9LEUPu\nIb7yyiuxevVq2e9ef/11zJw5E1u3bsXMmTPx+uuvF+fmBXTCEirv/Wu3ip5ekcS7tweEw6k6duXZ\nX1HwlkZ2PAzXnGtEI1bRKUxSCEXfuAgk7VV4sp1xRjSGhXGjm5vgjDN5vRoL2hgUmdWSTZcrq9wp\nvAZa46TZEej6BhAcq+TSbdkB11XXid/3LmtED1V8gzjTm+HsjuUfxSn2PE8ey9qhr5QwpHrWgoU8\nMCQy6/HC/U83i3U+QDq6u+1fQUXa1a/xBUDftBj0TT8GfeMi2ILV4JgIkuFO0AsWw3/PerjnLRxw\nWkgoK4XxEY2gh/LCu6xRQV8pMFLk0520oCj4CMSQG8TTp09XnPA++OADXHHFFQCAK664Ah988EFR\n7q23da0WOJ5HcrQTXa8+D+bFXTJvWtLlVoztWd4EtrdH1WNXMXEyfHevg83rk4U0SIIA2XoY8fd+\nleIfttngbVgB+1lTZMaPLdyukVtavkn2pYrBkNlssplN7jIPc2rjeJc1gph5MeiNu4ELL4V9/CR1\nmZz8FdALGtLNJ3bAcewzUIkusJ98VJTQm+jNkLQzJU99CXr+ItlmUwo8v854FP2ffFQ2HMRDqWct\nWMgHgyWzJEGAP30C1MlW2HkOBHiQtFdMaxScUHy4U/Va4sQxdL36HJgXnkq1Zr7uBtCLliP+5kuI\n7n4M0ZYnUTHr66DuuA/+5u2Ivf2aqLMBQW/7UoVze1I1HfSCBvjXbELP1BkyRgrPum2gNz6t38Ob\nLQpuQYEhT5lQQzQahc/nAwD4fD4wTHG8nGZ0wtJsLzuaAmRj+8B3nkbys4Oq4Y/+L4+CeWGnaLDE\nps4cSLT/+VOgvv19RZoFJ2lsIHikFeEWx2hzXpaFrDBbZgXZDD7WImOZEGRTb1tjNRmPpccRDND+\nL4+oy+QXn4ldlmzBaqCtVSz6KEboLdObQdJecKfbxGptMez49mtDz/MbjeTs0FfqGCw9a8GCWTBb\nZtWKlwMPbFMUydH1DSCqxiiud8ajisgs07ID9PxFAzztJ1qRjMcRq6lLRZYyKSuXNwEkKf6OeXEX\nADl7UN5FzFr9CMpERw02StIg1ot9+/Zh3759AID169ejqqoKdrsdVVVVxgYKDBiWjixf0wLvuxzB\nx1rAhUMgfX4gWAMHScrG5k+fQPuG1amTZyZvaTpfE0inUGxZh+BjLSDG1oL95CRcs68Wv28LVsM1\n5xqwne2oPKsfAb8fBEmC4/pB1zcoFjHpCxh/HxrI691akEFNZrPBbreDGFsLQCmbWeUuEyoyzp8+\ngfbNTaoy6Vu6BtFnHxevcc25RlE4wmxuEuXUDLCfnJQpbtV77tmJQOOjIMefpXhWvfLJcxzQflL2\n3git96Y1BtuLzp3vKN/bsvthl8xtOKwZozJ7ejAmZaEoKHdZFaBXZgUdKNUx/UcOiXpH2G/5nh6Q\n/z977x4mRX3l/7+rumem6e7p+8BcYFBgNHJJIkIkxoiJ85hssomGZTVuQImrgogEEAFZHS5GggYE\nCXJRWUAw0YRHULOJuhNX/Lr+kkXxgqAoiIwwA8xM36a759Zd9fujp2v6UtVdfa/uPq/n4XmYvn1O\nV7/r1KnP53zO4Xnhehsi2meFPoPv6xX+Zio0qBg6DJoBG8T8NvfZUfFVOp8XtvpRsr6zmK/hOQss\ni1fDvq5J8FGWxauhEvGf+UYJvlKRAbHRaITD4YDZbIbD4YDBIL7BrbGxEY2NjcLfHR0d+avFp6oA\nbDXB/9vtMU/rL5wH39sT7Or18gvB6hIsg/JLJ8Dxu0cillBYgwkBpwP+C+eh1umElo6qqurIzU9/\n2iVsnIOqHNraelROvw2hGoaorYdTVQ4uQ8dD6XUO81kfMx3NxiPhMU+gu3hIabJswiT4dZWR3Zkk\nWhT3XjgPj6pi4CWpd9ADAL1WFzmbITFmf4CDR+S7Wi0W9J75UnT88JrBrKsz7Y1wrFoD/cy58OzZ\nEjxuKhXKvjYBnpqR8IfZpuQ6xNnSLFG4yPlti0mzIR8YDu92SF5vo31FjM9CWEdYDKa6ucqGQNPy\nRaRvCvPbUp/Tr9XBIWF7tL+tGH4ROsN8j/B8hTZY5m2grrJbZwSX5LUiF1AdYgkmTZqEgwcP4sYb\nb8TBgwcxefLkfJuUPmFLF4H2c3D/4WkwFRrYVm8C53YKd6LQ66Ey29B/5D0APPq1epRfOh5MhSY4\nYxa1+SlYt3gHPNpK+OoboLUOVVSzhFKhIDUroUnDt66Bb4g+IiUDYCSX3liGgbbbA7blJJybfp1y\noBmdBhJvzGhYhkHg8Dtwh82EiDUaqZw2E84X98TMdCdb0zOUihLeNdKtM4IroO57BalZoqTJuGZF\nUgpCfkfsehvtK8RS10LNtSwNY4WGWJrTn8VNN+vRm2B5aD36Pz0CBALwvvUa9DPnSna8FKseYVm8\nGuzFl0Wkw8Wk1A2tpZggDnnvVLdx40YcO3YMXV1dMBqNuOmmmzB58mRs2LBBmPFdtGiRrNIqqXSq\nyxViAjUtaIL6iu/Af+QQuLYzwSYJM+aAYRCZ+jDvP6BiAP+5s3Dv3Rbz2ZaVT8BVd3HWv4NSj22I\nXM1cZFqz8cjmMU9UkidiBsJsDXZZjJpZ7WkYD83nH4P/6hS69u2OCV6TDTSF7ornz8Dffh4MAPez\nWxIG2fE6RwIQnjP84i64n3sqZlzLg+sQqKrO+E2kUmaIc6lZakZRuCipU10uNCvqA+9cCPT1gfd5\nRa+3xtWbBxtnIXamtkdviu0sJ1LGMuQbpWID3yVfl2xvn6hTrpxOukqDZogBLFiwQPTxpqamHFuS\nXTieR0/D+Ii7QPfebTCp1eCH1sH92IPge3ugHlYDx/oVkXelmx+B+YHfoHzsN8Q3zg3R5utrlSTF\notlEm0pDGzlYnTHosAd2QIenB2g8zuDM6/RbpXczJznzCkA4B0INQ6BSoezyK+E22sQD1gS7qYXn\nBsrNRZ9D/pPH0fXbB4u2RmexaJYoHXKh2ejNy0LL5koDyrs9or5CrdND39YS4S/DN7zpB3xi9Ea7\nUEfQ0GMh3yhWGs25cXX84DVRD4U0eiyUMnkPiEsJjccJ+8P3RQjVvq4psolHX5+4kL1ecDxEN86h\noiKXX4MoIuTsXg532NE7oAXHKxFoJrubmWUYqM6dET4nlMoBAMZLxoMzWMXfmGA3deg5b/MrsZta\nBwrmp5o+QRBE4cLxPJhhdcJeCCA4A9v3+Scx11vT3UvhfLwJgbMt0itWCZohAVG+MZXgNVH1CKou\nkRLK2mZY7EgIP7yJB6MZItpIgbUNQ6DlFDwvPR+sUxhqyvDS8/BTuSQim8Rz2AOONxRoplrTO4TW\n64K/5YukG+b4dEZYFq8WHT+8FnOg/Rw8r+6H5aH1sDy4LqKxScT3IgiidHE5AZ8n4nprXrQK7j/t\nROBsC4A4TS6kmiFFbbQTfGMKDcLE6stbFq8WPjPdHgulCs0QZxk1y0LvbAfv6ARrMkNVVy+cUKqq\nauiuvwF8TzcsD62Hc+uj6D9/VnQGqz/AQT3+cnC/f0qYMQPoro+Qj2ium8eZeBNm1GxDSLcqzo8A\nGBiWroH70eXBShXTb4N65Chww4aDZ1loW08nt8HT5YT39ZdizgHT/AfhkdhgAgRneVQTr5JM/4hO\nDXHrjNB6Xej67YM0i0IQRCRGEwAGnNspXG8Nv7hLuHaHEJvJFdtoZ1rQBG74xTBeOgEwW8FwHCrP\nnASj0cJvNMO0oCm28k0Cfxft01TDLxKqR2Six0IpQgFxFlGzLLTHDsO+ec1gndJ7HoDrhR1AX19M\n8W/TnPvBGExw/ecTwbxJvR5qWzUC585CXVEutHBM1IyBIKIR3bgxbznczz8Tf/kPkQ6eNZhidGtc\nuBKGdTsBRydgNMErtqlEbm6u0QTO7YwoAwcw4OpHJ3wvw7Ix6R8xpeBqRwY/h+dlNzchCKK08OmM\n0I6+NCJlQm7VG6n9Qvp/XwBU1cRU4zHdvRSe5ldi9mckqlgTne4WXVc45WYeJUzeq0xkEqVVmTC5\nO2FffnfECaSqq4dlwQpwbhf6vzgO7+svRew8NS/7DXinHe59u2O7081/CNxFYwC/H3A5cn7Xp6Rj\nK0Y+62OmSq6qTEjtOg7f6CG1C1mo/OBoB6vVw/HE6oiZkuj3hY8llBNMtCkufKw4lS/iEV2HWHS3\nd3QVjW4P1N4u8D3dCJht8OkMRVllIpNQlYniRUlVJjJJKn6WZRhoe30o83aBdznAmCwIuB1wDmyA\nj1eVR63Twb3zdxgy4YrgTb1GC0alAu/1iFbjkeOHE6H063MiqMpEkcM7OiOD4apq6H/4M3Q8eE/M\nhp5A+znwvT3wnzwO72sHYJqzGPbHBpdz+d4eODc9DMOM2UBVzWCAUDz3M0Q2kbHRQ2z5TzRADdOs\n6PsGxpJT2D6aVJf6xOoQWx5aD3v0bu+BTXO+UOWM6BaqA3WLCYIgmK9OoTPcR9x2DyyrnoC/rz/C\nN4muwN29VMg5NvxiNtz7dktX40ngh4ncQJvqkoRlGOh9bujbWqD3ucEyjORrGbM1IlletND33u3B\nGTQMLL8EAsEXBwKiJ466ejj4r05B2+3J8DcjCoFk9BeB1MYNjo/8O2r5T6wkULhmRd83MJZUYfuY\nTShRhJb6PDX18GjlzdhqvS6hPWlorP5Pj0huBgx9L9ZgguGWO4MXqjTPq5R/G4IgFIfW64Ir2vft\nfhL9Rw4DRlOEbxItnbb1Ueiu+cHAp/ER1XiA4ASZ4ZY7YZgxB+r60VBVVQMYKO1WXi7Lh4T7HP78\nWfI5aUIzxEmQVCMDowle81CY5i2HcyCHONSCOZzQ3WFo5s339t+g/+nN4CXylRjNEHjf/CsMtSPA\nXvoNSpIvIeSkE0i1Txbd6DGQQwxE7UKWU0JIpZJ8X2gs/syXuauFKWInAgHxOqIsA7/XG8yHDp/B\nrtDAlOJ5JfXb8OarM/HtCILINVJVocrKoO72QR/mYxOuwA0EwqFqPJ5X98ekRAqP/9M0ONc3gXM7\n466opZNeRohDM8RJIDpbNjDjJYhzyR1wNc2De8kdqPjsCLrHXQHLmq2wPLgO5d+cLDpLVzZhEixr\ntsHz6n4MuboRfE8PAD64yS68bMrMOehr+QK6a34A56ZfJ5xpI4qLePoDIKpB7YmjYBkmIhXBuHpz\nMG1g7ETol6wZ/FvMkUrMLJddfqXk+0JjlV1+ZdLlhJIlNEOiLi+PGcv71msw/+qhiHPINOd+uJ/f\nAbV2CIx3LgLf24vKaTOhqqoeSEuKPK/kzvpK/TZob8vYdyUIIjewDIMyrXgJVHX9KNhXzBd8rO7E\n0WDliDgrcN7mV2CYOUfYMGy8bZ7oarHxtnlCR7tEK2qJrge5pFhWxyggToY49VilxFnR5YDTYIVr\n5CVwmYaK1g5022rgNlqh//cFUFUa0LVvN+xrlsK9bzcsSx6BcdY8VE6/DaxtGMBxgF5P9VJLkQTd\n2BI5yOhUBD/HJUxNkKp36Tba4r6P43m4jbas1sIMvwFwrm+CYeaciLH0M+ciMLQOliW/hmHGnODG\nlX27of32teDaz8GxvgnuvdvQ9eIe6H96sxAUh45n6PM9jy0H/8mH4A+9DcO5FqhZEbcp8dtwDntG\nvitBELkhdN47NqyKqa1uunspnDs2RvhY14aVYHgOpnnLI15rvucBdB95DwDAuZ1AbT0Mj+2A/t4H\ngfJyUX/hP31C2JsRegwup3jAmeB6kCviTcQUGpQykQzxur/I6DYjtmGIra2HoeXkQJ1iCxxhJ1vg\nbAvsj/0HzPetgv/0Fwi0n0fX3m0wzbkfqrp6qpdaaiTqPpSFdp2J6l0m+97oDXJSKR5yCL8BCLSf\nCxbRn34bykZdAr+tGj6dAQZXR8TmVABwP7sFldNvi5mdqZw2E10v7hGOp9brgmfPlthqLwua4GmY\nEGmnxG/Dmi2yvgtBEMog3K/43v4bzItWAb09UA2rAdfTLVqLWNV5Ae7XDgyWiuR4uF7YAfPCFej3\ndUf6Nm0l9D63uC9HZBDJVGgAs1V8A3DdSEV0o5OaiCnEjp80QxyH6LuyHr1JesZLYmlZrdNF3CmF\nz9L1VprBf/Ih+t//B/wnj8OxYSX0P/wZyi4dH0y2H+hG528/B/dz24EeX3BZd9tvYbp7KXWdKRFC\nOoTXC9OCJukZ1zgdj9JZ0oqeWWbEZkgTvNdXOxIAoG09LYyf9sxC1A1AYOA86T/1uTC23+sVv0kA\nH/uYShV5PF1O6K75gcjS5jYYXB0Rx1KqMxSqamQfK4Igcg/LMODPnxXO5/AqOdqrr4Pj8RWwP74C\nHU3zwTCsqI/l+/uhvfK78Da/Au/rwdJ1uh/9C9DbC1VvN8CqoPUmiCVunQvGbIn1IRwnno7Fccro\nRqeQmepMQDPEEkglrPc0jI+Y8erRm6D1OIVgJaLbzIzZcD6+AvqZc2PyLFmGgfbTD9D5xMMxSfWm\n2+cLs1qhvEdVXb2Qj8T39sDP8ZQ4XwLwHBehQ1VdPSwPrYef4yP153ICZiuMC1cKO6MFzepN0Kba\nKCMDsAwD3YmjEXYZF64EXzcyvZkFqRnzQAAqRwegrZR+jchMTEydZKMpZiNsqHRiqL54+LEUmw2P\nLpZPEIRyCF3n28N8k+Wh9ZJVchzbfwvTnPvh3PbbwfN/5hz4z7eia+92GGbNg3poNfrPtUGl1aJz\n9SLRZkbhsYSq/Rz8J4/Dc+D3ACDMMpdNmAS3rQba1tMSAacjwudUDB0Gh1qT+7gg0cplAUHeWgKp\nZQCNxynMlvl0xmDh/yV3wLNhBQL2DlgfWg/L4odh+Lc7g7Vaz7ZEbLzT+7pgPPsFjM52OAaCYeHz\n926H7pofoP/UicjyLdt+C9Pt8+FtfgVA4YqNSIH2tggdBs62wP7wfYMB1+cfD+a4/uMgVCYzjI/t\niNjwpvE40958oWZZmNydMJ7+DNyXJ8TzaCXQet0x5YtcG1aizONKa2bBpzPCPD9y05xhxmx433oN\njGaI8BrT/AdlzcRENw3x6Ywo+9qExKUTB45lKuXiCILIH1Ll0kwLmkSrQgXOtoAxmlE5/bbgCu70\n2wDNEHhffj44YWGywP7Yg4DHLQTNusafhHW7G/QZ+vZWgFWBMdsAlUooZen+w9Po2vcs/EO0QR8S\nZ+Uv3Ocww+ry4nOkVscKcQWbZoilkJGPGV7LVP/Tm+F+dkvEbG/0+3SdF8CdPA6/Vosy61Dpclah\nWsRhj/vPtiDQfk66PBZRlHAOu/RGTkAyx9UbynHleVlajpfLK9aC3DRvOXxjJ8KfoL0oAKgc7eIb\nztrPpzWzwPE8AheNgWXJI8E0iUBAKFvk11UKr+HqRwcvXOABjg/OxJSXw7JmK/xer2TuMsfz8NSM\njFj5kSydSIX0CaLwEPGNgbMt4CzDUG62ifqnwNnTKL/8SvR98H9Bn/PCTuHa7D8zMJvLMoPvC///\nAHxvD/yffgSo1bCHzxyHSq/NnCtc45XeYj7VZkpKhAJiKcKWAcLbz4ZygrmwQEOq4UaoHSNToYHa\nYID/48Po2rcbldNmot/nEz3Zyi8dD8e2xyJMYSo0UI+bCOPqzQUtNiJ52IGZTDEd+r1e0RxX58bV\nkWkHCZa0EtWz1DvbhWBYGGPzGljWbIXTYE34HRiNVvzC4uiAYcbsyFqcSTp6X4UWWoYJvp5loLv2\nn4DaeviG6AfrIg/RQzvi4pjv5zbawIXslxjPz3HwNEwQnL1apwPzp11FsTxIECWPhG9Uq1k4Hl8V\nmx4x0KXTWD0cjEYTkQZhmv8Q3Lt+F/yQgbrDfG9P5P/DxlDVjIDj8RUxcYNlzdaI1apCCDhDM9XC\npICCbEsGRQfEH3zwAXbu3AmO43DdddfhxhtvzNnYobuymBm4sPazwlKGxB0gWAZl4yfCOGMO+M52\n8N3dYA0mgGXgff2lmGDAPG85+m3DUDlzbky+pdtoBWcY2LFeoGIrBTKu2aoaSR2aFjQhoDcknLFM\nNMMQvmwYCrr5M1/CUDUMHlMVmN4eVE6/FeB4eJtfEWpk8o5OQEZA7NdVwjBzTmQO3cCFBYjNmUvG\n0XM8D199A7TWoSjzedGv1cVcLNK9oIQ7e5ZhFD1bkwr59LMEkQqZ0qyUb/R7vQicbUHA0xW5uvTy\nC+DcTvi/PAHv6y8NVpUAA7ZuRLC8GoDuw/8fzPc8AMeTvxFqEEf4v1vnwt9xTnzm2OsdvFEfoFgC\nTqWj2ICY4zjs2LEDDz74IKxWKx544AFMmjQJw4cPz834AxdR06JVwgYaIHLjj9CR66tToneAZVd8\nB+rq4ehcMT8ylYLjhALd4ScUUzUMvjINEHXx9irsbpAQJxuaZVhWUofOjathXbUpYdpBwoAwbFd1\nROe2P+2Cad5yOJ5/Jpg7FxbIcm4nGHPiYBgYmKGtrRcuLOqRY+DavVmotxlaRTF865qUdB66WNjq\nR8HR0SF6scjUBaUQZmuSId9+liCSJZOaDZ3PVU/sQe+F88L5rPW6wFRo0P12M/T/9C9wbn00bOLq\nAXT9+U/BqjZ/eFr4LOOlE4TJC+3V18Hf5RJ8Hs8Dhn+7E3x/P9T1o+A/3wr4vEWzGa1YUOymuhMn\nTqC6uhrDhg2DWq3GVVddhUOHDuXUhrhlm1xO4WRiplwbu3Fn4UqA5+GIWmp2790OnlXBMGM2OLdT\nSKBXDR8JT1UwKZ425xQm2dJsPB36+/vjl2IL+wxJTQ2sdIil/jg3r4Humh8If7v3bofu+htgmrcc\nHlOVbPt99Q1gvvVdMJd+HYGaEaicObdgN2EU0/mpBD9LEMmQac1yPA9mWF3E+Rya7Boy6Ttw/2kn\nKqfNFMqgup7fgSGTvhPxGaFANjR54d67Hd4Dv4fKNhRd+56Fe8dGuH//NJiKCriefhzo7RFmjgvV\nDxYjip0httvtsFoHZ6CsVis+//zziNc0NzejubkZALB27VrYbDao1WrYbLaM2cEHekXv4iqGDoNG\nGMcKvnYEqkZfCs5hDxbjr6pB4IN/iAfTPT543nwV1ofWAyq18HqTwks0ZfrYFhupajYeoWMupcPy\nqqHA2G+g6ok9EdpLptwXz1lgWbwa/ae/kEz9Cf+7vOEyMOOvgEmlkj0GACDs2PC1I9KyWQwl6lOJ\nNoWTDc2ez7yZRI5QslZDZNPPhsObrwan1cL93PaImWAAKBs5WvDHTEWwe6dq+EXQsCwC7a3ge4PN\ngqRSLiquuArl4y4HY7HBdsV3wDvT94NK9zWJUIL9ig2IeZFZFyaqYH9jYyMaGxuFvzs6OmCz2dDR\n0ZExO1i1RjTHyKHWgIseR1UB2AYK8dvtMJks4kHM+IlgvnUNXDojLFZr0F4Znb/yTaaPbaapra3N\n6/ipajYeoWMeV4d2e4z2koW9+DIYLFXiG8Y4PuJvtroOnYFnuYIAACAASURBVA5H0mPEkKbN0ShR\nn4lsKkbNEoWLnN+2GDUrdZ7qjeLXcP+wuoi0KbfOKHTv1Gt1wnu8Lz8fmYY24LedlWZw+oHUCBYZ\n8YNK9H/JkE375WpWsQGx1WpFZ2en8HdnZyfMZnPO7UgnZ9BjqoJp3nI4o8pVdQ0dHixXVcBLrUQs\n2dRstnNXOZ6H22iLCbpN85bD/fwzAKK6rxXADRyRGKX4WYKQSy41K70heSBVSmRPQvh7Au3n4Hl1\nf0QzpULec1DsKDYgHj16NNra2nDhwgVYLBa88847mD9/fl5sSXVDjp/j4Bs7EZY1W8E7OsGYrfCY\nqmTVbiUKj2xrNts7jUWDbr0J+iVrqPtakaIkP0sQcsilZlOZiBB7jzv8PRQMKxbFBsQqlQq33347\nHnnkEXAch+9973sYMWJEvs1KGj/HBWu1hsqoUDBctBSDZmOCbo6jcj9FTDFoligtcq3ZVCYiqExa\nYaLYgBgAJk6ciIkTJ+bbDIKQDWmWKDRIs0ShQZolsgGtfRIEQRAEQRAlDQXEBEEQBEEQRElDATFB\nEARBEARR0lBATBAEQRAEQZQ0DC9W5ZogCIIgCIIgSoSimyFetmxZvk1IikKyt5BsLRaUeMyVaBOg\nTLuUaFO2KcTvXGg2F5q9SqcYjmehfwcl2F90ATFBEARBEARBJAMFxARBEARBEERJo1q5cuXKfBuR\naUaNGpVvE5KikOwtJFuLBSUecyXaBCjTLiXalG0K8TsXms2FZq/SKYbjWejfId/206Y6giAIgiAI\noqShlAmCIAiCIAiipFHn24BM8sEHH2Dnzp3gOA7XXXcdbrzxxnybJLBlyxYcPnwYRqMR69evBwB4\nPB5s2LAB7e3tqKqqwsKFC6HX6/NsaZCOjg48+eSTcDqdYBgGjY2N+NGPfqRom4uJfGo5Ga3yPI+d\nO3fi/fffR0VFBebOnZuVZa9k9ZgLu/r6+rBixQr4/X4EAgFMmTIFN910Ey5cuICNGzfC4/Hg4osv\nxr333gu1Wo3+/n5s3rwZX3zxBSorK7FgwQIMHTo0ozblGyX7YKCw/RrHcVi2bBksFguWLVsmqTNC\nPkrXqxT33HMPNBoNWJaFSqXC2rVrFa9hJV5XYuCLhEAgwM+bN48/d+4c39/fzy9evJj/6quv8m2W\nwNGjR/mTJ0/yixYtEh7bs2cPv3//fp7neX7//v38nj178mVeDHa7nT958iTP8zzv8/n4+fPn8199\n9ZWibS4W8q3lZLT63nvv8Y888gjPcRx//Phx/oEHHsiKTcnqMRd2cRzHd3d38zzP8/39/fwDDzzA\nHz9+nF+/fj3/9ttv8zzP89u3b+dfe+01nud5/tVXX+W3b9/O8zzPv/322/zjjz+ecZvySb51K4dC\n9muvvPIKv3HjRv43v/kNz/O8pM4IeRSCXqWYO3cu73K5Ih5TuoaVeF2JpmhSJk6cOIHq6moMGzYM\narUaV111FQ4dOpRvswTGjh0bc7d26NAhTJ06FQAwdepURdlrNpuFO7IhQ4agrq4Odrtd0TYXC/nW\ncjJafffdd3HNNdeAYRhccskl8Hq9cDgcGbcpWT3mwi6GYaDRaAAAgUAAgUAADMPg6NGjmDJlCgDg\n2muvjbDp2muvBQBMmTIFH3/8Mfgi2sKRb93KoVD9WmdnJw4fPozrrrsOAMDzvKTOCHkUgl6TQeka\nVuJ1JZqiCYjtdjusVqvwt9Vqhd1uz6NFiXG5XDCbzQCCjtrtdufZInEuXLiAU6dOYcyYMQVjcyGj\nRC1L/e52ux02m014XS5slaPHXNnFcRzuv/9+3HHHHZgwYQKGDRsGrVYLlUoFALBYLMK44b+rSqWC\nVqtFV1dXxm3KF0rUbTwKya/t2rULM2bMAMMwAICuri5JnRHyKDS9RvPII49g6dKlaG5uBlA48UQ4\n+fbf0RRNwpHYTEvIeRCp09PTg/Xr12PWrFnQarX5NqckKCQt59pWuXrMlV0sy+K3v/0tvF4v1q1b\nh7Nnz+bdpnxRSN+vkPzae++9B6PRiFGjRuHo0aP5NqdoKCS9RvPwww/DYrHA5XLh17/+NWpra/Nt\nUkbJ129TNAGx1WpFZ2en8HdnZ6dw56FUjEYjHA4HzGYzHA4HDAZDvk2KwO/3Y/369fjud7+LK6+8\nEoDybS4GlKhlqd/darWio6NDeF02bU1Gj7m0CwB0Oh3Gjh2Lzz//HD6fD4FAACqVCna7HRaLRbCp\ns7MTVqsVgUAAPp9PUZte0kWJuhWj0Pza8ePH8e677+L9999HX18furu7sWvXLkmdEfIoFL2KEfqt\njUYjJk+ejBMnTihaw1IoxX+HKJqUidGjR6OtrQ0XLlyA3+/HO++8g0mTJuXbrLhMmjQJBw8eBAAc\nPHgQkydPzrNFg/A8j23btqGurg7//M//LDyuZJuLBSVqWep3nzRpEt566y3wPI/PPvsMWq02K44r\nWT3mwi632w2v1wsgWHHiyJEjqKurw7hx4/D3v/8dAPDmm28Kv90VV1yBN998EwDw97//HePGjSuY\nGSk5KFG30RSiX/u3f/s3bNu2DU8++SQWLFiA8ePHY/78+ZI6I+RRCHoVo6enB93d3cL/P/roI9TX\n1ytaw1Lk+7oSTVE15jh8+DB2794NjuPwve99D9OmTcu3SQIbN27EsWPH0NXVBaPRiJtuugmTJ0/G\nhg0b0NHRAZvNhkWLFilmxujTTz9FU1MT6uvrhYv2LbfcgoaGBsXaXEzkU8vJaJXneezYsQMffvgh\nysvLMXfuXIwePTrjNiWrx1zYdfr0aTz55JPgOA48z+Pb3/42pk+fjvPnz8eUwyorK0NfXx82b96M\nU6dOQa/XY8GCBRg2bFhGbco3SvbBQOH7taNHj+KVV17BsmXLJHVGyEfpehXj/PnzWLduHYDgZt6r\nr74a06ZNQ1dXl6I1rMTrSjRFFRATBEEQBEEQRLIUTcoEQRAEQRAEQaQCBcQEQRAEQRBESUMBMUEQ\nBEEQBFHSUEBMEARBEARBlDQUEBMEQRAEQRAlDQXEBEEQBEEQRElDATFBEARBEARR0lBATBAEQRAE\nQZQ0FBATBEEQBEEQJQ0FxARBEARBEERJQwExQRAEQRAEUdJQQEwQBEEQBEGUNBQQEwRBEARBECUN\nBcQEQRAEQRBESUMBMUEQBEEQBFHSqHMxyJYtW3D48GEYjUasX78eALBnzx689957UKvVGDZsGObO\nnQudTgcA2L9/P9544w2wLItf/vKX+OY3v5kLMwlCgDRLFBqkWaLQIM0SSoLheZ7P9iDHjh2DRqPB\nk08+KYj+ww8/xPjx46FSqbB3714AwIwZM3DmzBk88cQTWLNmDRwOBx5++GE88cQTYNnEk9mtra2w\nWCyw2+1Z/T6ZpJDsVbqttbW1GfusXGo2Hko85kq0CVCmXYlsIs0qg0KzOZ/2kmaVSaF/h2zaL1ez\nOUmZGDt2LPR6fcRj3/jGN6BSqQAAl1xyiXAgDh06hKuuugplZWUYOnQoqqurceLECdljyTk5lEQh\n2VtItqZLLjUbDyUecyXaBCjTrlzaRJpNnUKzudDslYI0mzkK/Tsowf78WwDgjTfeEJY+7HY7rFar\n8Fyh3/UQxQlplig0SLNEoUGaJXJJTnKI4/Hiiy9CpVLhu9/9LgAgmQyO5uZmNDc3AwDWrl0Lm80G\ntVoNm82WFVuzQSHZW0i2ZpNMazYeSjzmSrQJUKZdSrGp1DWbiEKzudDsTQXSbHIU+ndQgv15DYjf\nfPNNvPfee2hqagLDMAAAq9WKzs5O4TV2ux0Wi0X0/Y2NjWhsbBT+7ujogM1mQ0dHR3YNzyCFZK/S\nbc1kbpsU2dBsPJR4zJVoE6BMuxLZRJpVBoVmcz7tJc0qk0L/Dtm0X1E5xGJ88MEHeOmll7B06VJU\nVFQIj0+aNAnvvPMO+vv7ceHCBbS1tWHMmDH5MpMgBEizRKFBmiUKDdIskS9yUmVi48aNOHbsGLq6\numA0GnHTTTdh//798Pv9QkJ9Q0MD7rrrLgDBpZL/+Z//AcuymDVrFi6//HJZ47S2thbcXZIce1mG\ngdbrAlxOwGiCT2cEl/2fLQalH9tMzlzkUrPxyOYxT1VXStWBEu3K5QxxKWg2WyjJZjnnZbHMECtF\ns4E7fyrbZtXTL8t+bS5RkoZTQQkzxDkJiHNFMQbELMNAe+Io3BtWgu/tAVOhgWHhSvjGjMt5UKz0\nY5uLpbxMk6/gIh1dKVUHSrRLCSkTmYYC4uwh97wsloA4V1BArHyUEBArosoEIY3W6xKcIwDwvT1w\nb1gZnEEgiBQhXRGE8qDzkiDyBwXESsflFJxjCL63J7icRhCpQroiCOVB5yVB5A0KiJWO0QSmQhPx\nEFOhAYymPBlEFAWkK4JQHnReEkTeoIBY4fh0RhgWrhScpJBTpjPm2TKikCFdEYTyoPOSIPJH3htz\nEPHheB6+MeNgeOyZvFeZIIoH0hVBKA86Lwkif1BAXABwPA+P1gBoDcEHyDkSGYB0RRDKg85LgsgP\nlDJBEARBEARBlDQUEBMEQRAEQRAlDQXEBEEQBEEQRElDATFBEARBEARR0lBATBAEQRAEQZQ0FBAT\nBEEQBEEQJU1Oyq5t2bIFhw8fhtFoxPr16wEAHo8HGzZsQHt7O6qqqrBw4ULo9XrwPI+dO3fi/fff\nR0VFBebOnYtRo0blwkwii7AMA63XVTC1NUmzRKqEa50P9IJVa3KmddJtYVNofjITkGYJpZCTGeJr\nr70Wy5cvj3jswIEDmDBhAjZt2oQJEybgwIEDAID3338f586dw6ZNm3DXXXfhmWeeyYWJRBZhGQba\nE0fhXnIHXE3z4F5yB7QnjoJlmHybJglplkiFaK23/2pmTrVOui1cCtFPZgLSLKEUchIQjx07Fnq9\nPuKxQ4cOYerUqQCAqVOn4tChQwCAd999F9dccw0YhsEll1wCr9cLh8ORCzOJLKH1uuDesBJ8bw8A\ngO/tgXvDyuBMiEIhzRKpkG+tk24Ll3xrJ1+QZgmlkLdOdS6XC2azGQBgNpvhdrsBAHa7HTabTXid\n1WqF3W4XXksUIC6n4ORD8L09wWXBUDemAoA0SyREgVon3RYICtROvlCyZgN3/lT2a1VPv5xFS4hM\no7jWzbxIvhQjsWTU3NyM5uZmAMDatWths9mgVqsjThilU0j2pmorH+gFU6GJcPZMhQYVQ4dBUyDf\nPR7pajYeStSHEm0ClGFXIWldrm6LQbOJUILNyWhHCfbmg2xp9nxmzIshl79RoWtCCfbnLSA2Go1w\nOBwwm81wOBwwGIJ3wFarFR0dHcLrOjs7Je/+Ghsb0djYKPzd0dEBm80W8X6lU0j2pmorq9bAsHCl\nsBzIVAT/dqg14DL43WtrazP2WWJkS7PxUKI+lGgToAy7ktV6tjULpK/bYtBsIpRgczLayae9xajZ\nbJHLcZWg4XTIpv1yNZu3smuTJk3CwYMHAQAHDx7E5MmThcffeust8DyPzz77DFqtlpbwChyO5+Eb\nMw6Gx56BcfVmGB57Br4x4wpu9zRplkhEtNarntiTd62TbguDYvGTmYA0S+SDnMwQb9y4EceOHUNX\nVxfmzJmDm266CTfeeCM2bNiAN954AzabDYsWLQIAXH755Th8+DDmz5+P8vJyzJ07NxcmElmG43l4\ntIbBXDiFO3nSLJEq4VrX2GwZXQVJBOm2sCk0P5kJSLOEUmB4saScAqW1tbXglg0KyV6l25qLpbxM\n09raGvd5JR5zJdoEKNOuRDaRZpVBodlc7CkTmSaRZpPZKJcMudxUV2gajqakUyYIgiAIgiAIQglQ\nQEwQBEEQBEGUNBQQEwRBEARBECWN4uoQFxul2JueyB6kJ4Ig4kE+giBSgwLiLCL0po+qK1mqpXSI\n9CA9EQQRD/IRBJE6lDKRRUq1Nz2RHUhPBEHEg3wEQaQOBcTZJF5veoJIFtITQRDxIB9BEClDAXE2\nMZrAVGgiHmIqNIDRlCeDiIKG9EQQRDzIRxBEylBAnEV8OiMMC1cKDkrI59IZE76XZRjofW7o21qg\n97nBMky2zSUUTjp6IiKh8yv30DHPPuQjCCJ1aFNdFgnvTZ/Mjl/aGEGIkaqeiEjo/Mo9dMxzA/kI\ngkgdWTPEHMdh8+bN6O/vz7Y9RUeoN72nph4erUGWY6KNEYQUqeiJiITOr9xDxzx3kI8giNSQFRCz\nLIuPPvoIDC1x5QbaGEEQ2YPOr9xDx5wgCIUjO2Xixz/+Mf74xz/ipptuglpNmRZZZWBjRPgFhDZG\nEESGoPMr99AxJ0qQwJ0/lf1a1dMvZ9ESQg6yI9tXX30VTqcT//Vf/wWDwRDx3NatW1M24M9//jPe\neOMNMAyDESNGYO7cuXA6ndi4cSM8Hg8uvvhi3HvvvSUVhIc2RsTk2+mMAC1/5R3SbGFTiudXvjVb\nisecSI98a5YoPWQr6d5778344Ha7HX/961+xYcMGlJeX4/HHH8c777yDw4cP48c//jG+853v4Kmn\nnsIbb7yB66+/PuPjK5VMbYygFp6ZhzSbGKXrrtQ2HilBs6V2zNNB6edPLlCCZonSQ3bZtbFjx0r+\nSweO49DX14dAIIC+vj6YTCYcPXoUU6ZMAQBce+21OHToUFpjFCLpbowQdnUvuQOupnlwL7kD2hNH\nqdRRBiDNSlMouiu1jUdK0GypHfNUKJTzJxcoQbNEaSF7hri/vx/79u3D//7v/6Krqwu7d+/Ghx9+\niLa2Nvzwhz9MaXCLxYKf/OQnuPvuu1FeXo5vfOMbGDVqFLRaLVQqlfAau92e0ueXMlK7ug2PPQOP\n1pDg3YQUpNn4kO6UB2m2cKDzJwhplsgHsgPi3bt3w263Y/78+VizZg0AYMSIEdi9e3fKAbHH48Gh\nQ4fw5JNPQqvV4vHHH8cHH3wg+/3Nzc1obm4GAKxduxY2mw1qtRo2my0le/JBtuwNfNomuqu7zOeF\nrX5USp9ZaMc2G2RDs/FQ4jGPZ1M2dJcJu/KFEmwizSZGKTbLPX+UYm+2yLVmz6dlbWZI9/csdE0o\nwX7ZAfH//d//YdOmTdBoNEL5tXTv0I4cOYKhQ4cKm/SuvPJKHD9+HD6fD4FAACqVCna7HRaLRfT9\njY2NaGxsFP7u6OiAzWZDR0dHyjblmmzZq9fqRHd192t1cMgYTyyPzWK1KvrY1tbWZn2MbGg2HkrU\nczyb0tWdXApFn4l+P9KsMsi3zSE9q3q7YfjFbHhffwmB9nMAxM+ffNpbjJpVAunamG8Np0s27Zer\nWdk5xGq1GhzHRTzmdrtRWVmZnGVh2Gw2fP755+jt7QXP8zhy5AiGDx+OcePG4e9//zsA4M0338Sk\nSZNSHiMXKLElabpto8Xy2Pio378UyYdmlagvKXLROpb0mRzF4meLlXA921cuQNe+3dDf8HOoqqqz\n3npZqb6FNEvkA9kzxFOmTMHmzZsxa9YsAIDD4cCuXbtw1VVXpTx4Q0MDpkyZgqVLl0KlUuGiiy5C\nY2MjJk6ciI0bN+L555/HxRdfjO9///spj5FtlNqSNJ1d3VJ5bLa1T4GtNJf0Zphca5bnOEXqS4pc\nVBOQ0mfVE3sAVUXc95biDv5i8LPFgJT2RPW8ZxssqzbBP0SbNY0q9doFkGaJ/MDwvDzl+/1+7N27\nF3/729/Q19eH8vJyXHfddZgxY4Zi6gC2trbmfNlA73PDveSOmCViuZsg5Nqbywu5vq0FrqZ5MY8b\nZswBM/wiRThMMXKxlJdpWltb4z5vDfSi/VczU9ZXOkhpLt9Lc1L6tP1mOxy2Gsn35SMAUELKRKZJ\npNl86yMVsu2H42lP23paVM/G1ZvhqalPy954pHrtKkbNJtNAI1uk25ijEM+7cJSQMiE7klWr1Zg1\naxZmzZolpEpQK2fEb0maoYAl5xdyqa5SgUDcHc+ZCNpLcQYvHpzDnnV9iRFPc3lHQp+sWTyfMISc\nHfyp6I80Wxqk44fjaU9Kz+rycuh97uSC7mR0mINrF0EUErJziAGgra0NL774Iv74xz9i//79aGtr\ny5ZdhcOAMwsn0y1JpZyp1uvK2BjhiOaBzpgNb/Mrgw4zikzUz6QanLGwZkvW9SVGrjWXDFJ5yqiS\nnh0GED8AQGr6I82WDmmdE3G0J6rnmXPgXN8kW08p6TAH1y6CKCRkB8Rvv/02lixZgtOnT0Oj0aCl\npQVLly7F22+/nU37FE8uNhElupBnmlAeqGXNVhhmzEHltJnwvPwCAu3nJB1mJgIoJQdheaOqJvv6\nEiPHmkuG8Dxl4+rNMDz2DHxjxoFhE7izBAFAKvojzZYQ6ZwTcbQXrmfLyidQOf02eF56HoH2c7L1\nlIoOc3LtIogCQnbKxPPPP48HHnggojPdJ598gs2bN+Pqq6/OinGFQDKbiMSWtGQhlcKQxTt5jufh\nNtqgHX5R7BKhzghEf79MLL/REl4MDMtG6csMsCy0raezuzyfB80lQ6jrmaALGcfApzPCuHAlXGF6\nNi5cCW9Iz6nojzRbOqRxToSCTylfGtKz3uWE+7ntEe/le3ugcnSCjXeuJ9ChVDoFtdMmiEFkB8Td\n3d245JJLIh5raGhAT0+PxDtKBzkXZ6n8M96c+GYikTPNFtEOs2LoMDjUmhiHyTIM1CyTfgCl8CAs\nX4T0xeqMOcslj6c5TeK3Kxa+vByV028DwANgwJeXDz6Ziv5IsyVDOn5YdvApoSf/58eg7emWPtfj\n6DBR7nOyN5ZEdkhmY1+6G/AIcWRXmdi/fz88Hg9uvvlmlJeXo6+vD3/84x+h0+nws5/9LNt2yiIf\nVSbkIrWjt+qJPehMUCoKUMbGHaljq/e54XlsOfQ//Bnce7cLTte0oAmehgmy7Ux382Ax7n4OP+bp\nVjRJFqVWmZAikV2Jjl8q+kv0HqoyURgopdqPqJ5mzIbn5RfAuZ2CVqPtjVvFwuvKqN8oRs0qocpE\nMogFxIV43oVTUFUmXn/9dTidTvzlL3+BXq+Hx+MBAJhMJrz++uvC67Zu3ZqkqSWCxJIW57ADcUpF\nhVD0nbzLicDZFnhefgGV02YCLANwPDijNfnd0TUjYFi3E3B00hJeNDlenle05lIhwfFLeQm5ZgTM\nD6wFo9HCr6uEb4ieNFukZPucEPZvrNqE/iPvAhwv7N8AIHquC/5Tq4dlzVb4e/sAnW5Qu5TWQxCy\nkB0Q33vvvdm0o/gxW2H4xWwAPMDx8Da/As7tBGu2ZHTWIS8zyQPLdYH2c3D/4WkAAzMQU6bKszfe\nrBwFFoNELYuqqqqhu/4GqDi/UJ4JQN5XEnJBSvn4MtIbkgl4pLQLJZSmI0RRwkqbHJv8Q7To2vds\nMH+4qhqGW+4EVCqodbqIyhGS/nNo7eD3orQegpCF7IA4fDMdkRwsw4A5exrufbsHndbMOUBtPXhb\nNbTv/m9G8kLz1Xkondw6ObVhiSDhx5k1mKC/4edw79k2eMyXrgHT1xexaUwpnacySar5+JnOxSft\nFhZKrK0tZVNPw3gYFq6EZ8+WyFS0P+2K0LocDeZrDwpBFBpJtZj78ssv8cknn6Crqwvhqcc333xz\nxg0rJrRelxCkAIOtOQ2P7QDTcS5jF9V8XaDT2q1My3myCT/O6m4f7CvmR/zW/Mnjwk1X6LFiDNBS\nbd2c8V31pN2CIm5zDKtVcTb5xoyDadEq2JffLa11GRqkahIEIQ/ZAXFzczN2796Nr3/96/jggw/w\nzW9+Ex999BEmTZqUTfuKA0mn5QBXVpa5i6rIOKzBBHW3D/osO8KUc+toOS8pwsszRf/WAJ/TAC1v\ny89p5ONnNAeUtFtYKLG2dhybOK0Bfq9XUut6rQ7q8nJZGiy6/QAEkQVkN+Z46aWXsHz5ctx///0o\nLy/H/fffj0WLFkGlUmXTvuIgTlH2jHYiixpHVVUN/Q0/h33FfMV20aLi8CkioimAyVnnqbx2aJM4\nnxK1bs40pN0CQ4md2RLZJPU8F4B7yR1wrm+CYeYc0iBBZADZM8RutxuXXXYZAIBhGHAch8svvxyb\nNm1KywCv14tt27bhq6++AsMwuPvuu1FbW4sNGzagvb0dVVVVWLhwIfR6fVrj5JN4OVwVZnPG8rui\nx9Fdf4OQYwoocwm9EJfzlKBZMU0xoy+NaTyRrVzBfObPSp1PqKoB7Pasjh1OIWlXCZrNN0qsrZ0o\nv1fsedOCJji2rAXf24NA+zl4XnoeldNvg7phLAJmq2I1mCykWSLXyA6ILRYLLly4gKFDh6Kmpgbv\nvvsuKisroVYnlYYcw86dO/HNb34T9913H/x+P3p7e7F//35MmDABN954Iw4cOIADBw5gxowZaY2T\nT+JdOGM7kaV+UY0eR8X5s7KEnuxSeaLXF9pynhI0K6UpALkJ0PKYPxv93dU6XbDUVHsbWJHGMZlE\nTMuFoF0laDbf5OIGJlnfmMgmsec5rxeBsy3CZwTaz8H93HYYV28OajGVzdgKq7wBkGaJ3CM7ZeKG\nG27A2bNnAQDTp0/H7373O6xevRr/+q//mvLgPp8Pn3zyCb7//e8DANRqNXQ6HQ4dOoSpU4Mlu6ZO\nnYpDhw6lPIZSCAV9npp6eAZqnsp5Lp1xAmZbykuELMNA7+uC8ewXMHWeg77HC57jkl4qz+vSehZQ\nkmbFdJNJLcUlieXnoJbc0Le1QO9zZ+S35wZmz+DzwL78briWz0b7r2ZGaCvT4xaqlpWk2XyTzfMj\nGX2Ea1PrdQVvrCRsirYZOl1Kfl3sfFCqpkmzRD6QPb177bXXCv+//PLLsXPnTvj9fmg0qS82Xbhw\nAQaDAVu2bMHp06cxatQozJo1Cy6XC2azGQBgNpvhdrtTHqNYSOUuPtlyO8IYXi9YVyecG1dHlIkL\n2M9Da6qSWCrfAY+2MuYzi600FWk2iFxtJSp1JUfTLMNA2+2B2tsFvseHgLkKPp0h/g79LLS5LlQt\nF4tmlTqTGUKuPtItj+nTGWFZvBr2dU0RaRQ+vQngQtMW7wAAIABJREFUONH3qFkW2s8+ivTpC1cC\ndSMVqeli0SxRWCSV7+D1enH48GE4HA6YzWZMnDgxrcEDgQBOnTqF22+/HQ0NDdi5cycOHDgg+/3N\nzc1obm4GAKxduxY2mw1qtRo2my0tu3KJHHt5jkPg8DsRDtCyeDVUE68Cw8af5OfNV6PqiT3gHPbg\npqOqGlQAQHtbxGMAhDEqp82E88U9MWXiKqffhvJLWdGlcvX5s7BeEWtP4NM20deX+byw1Y+ScYSU\nRTY0Gw8l6lmtVsNitYpqSxP1+/Pnz6Jd5IJr27gH3JlTCTXNcxwCH78L/1dfwh5Wc9m8eDVYvUFS\nWybNEPR+dQqV028VGuGEylUxw+pS+t6paFkJv18xaDYdHygHKZt5jovxlVLjydWH1DmRjDZZ69Ww\nNj2Ovk8+AgIBuPdug2nWPNHjwXMcuE8/QudAMBw+pm317xTpn3Ot2fNpWZt7xL6PEnxNOijBftkB\n8ccff4x169ahtrYWNpsNnZ2d2LFjB+677z5MmDAhpcGtViusVisaGhoAAFOmTMGBAwdgNBqFoNvh\ncMBgEL9TbWxsRGNjo/B3R0dHwfXzlmOv3ueGe+BCoKqqhq7xJ+g//QVgqYLbaEs8q6CqEMpRsQ6H\neHetupHCGGAZ8fxQ8ODLNaJlfvpPn4R/WG3MrIJeqxN/vVYHR4Z/J7n9ytMhG5qNhxL1HGFTmLbE\nNrTpL5wX1VLgfCscIb0NPGZf1xQzM6X3ucF/9gm6ouorO9Y1wbJmq7i29AawJwbfw1RoYLh1LniO\ng//MaQR6e1OaXUxFy4l+P9JsLGIzwVqva9A/QVovqSJmc7IzuXL1IXVO9F44D0+cOtrhWAO96Fy9\nKOJzpI6H3ucG/8lH4udhty9pTRejZgsNse+jxGtFMmTTfrmalX1rvWPHDtx1111Ys2YNFi1ahEce\neQSzZ8/Gjh07UjbSZDLBarWitbUVAHDkyBEMHz4ckyZNwsGDBwEABw8exOTJk1MeoygY2MCkqqqG\n/qc3o+vFPXDv3Qb78ruTzveSWtZTe7sGnSLHi+eogYFfVwnT/Acjy/zMmA3v6y+J1vMUK01lWtAE\neL0ZyyfNJaTZJJHINWY0Q+TVhHU5IVVf2d/bJ1r2DBwH56ZfR2r82S1ATzfsv16ccp5koWq5kDQr\nldMKiXq82awhLOUrtV6X6Otll+FLsfxbeA4w73KCNUS+XvJ4uJxAICA6ZsBsU2TpwELSLFE8yJ4h\ndjgcmDJlSsRj3/rWt7B9+/a0DLj99tuxadMm+P1+DB06FHPnzgXP89iwYQPeeOMN2Gw2LFq0KK0x\nCp4BB6pr/InQwhNIMd9LojoA39MtzBR4m1+B4da5cD+7JSKHWD3iIriH6KGtH43K6bcB4AGOh+fl\nF8C5naIOPWKXtNeLMpZB//GPwbecgvet16CfObfgWguTZuUjlWvs11XKa2phNCFUXznmtTodfENr\nhR34FUOHwaHWQNt6WnKFI/R/uedN9GxlT8P4weoWLAPn1kcRONui+DbZhaJZqSBUajUgqzWEk6yk\nIreKRSqtlEVnq2fOgeel5xFoPxdcObz+Bqg4P/Q+d+S4RhO8b70Gw4zZgy2gB27mPDoDoNDSgYWi\nWaJ4YHhenvL/8z//E9XV1fjRj34kPPbXv/4VbW1tuP3227NmYDK0trYW3LKBHHtDzpA/8yXce7fF\nPG9cvRmemnpZ4+l9briX3BFzYbGs2gT4+4ULvKquHqZ7HgDf1wdGMwR+XSUqakeg025PaVMIyzDQ\nf34kclPHjNnwvLof+iVrMrLsmYulvEwTmgGRQol6TtYmsSVwALI0xDIMtC2fA60tQk1tpkID48KV\n8Ea9NmSXlMYrp82E+w9PC48lOm/i6VzrdYmOER1kKyFlItNkU7P6tha4mubFPG5cvRnweTK6STIc\nMZuldCS6SS7JzX7JvkdS09Nvg/f1l6C/4ecR50f4sQnp2LNnC3TX/ABQqVD2tQnw1IyEX2ITXjyK\nUbOBO3+aI0syg+rpl2MeU+K1IhmUkDIhe4b41KlT+O///m+8/PLLsFgssNvtcLlcaGhowIoVK4TX\nrVq1KnlricTUjEBZRQUMvwimJwTazwFIfpakR2+CaUFTTAUJ5/omcG4nTAuawBmtgE4Hd5STDm2Y\nijcTIuXotV6XMCYwMPOzdzsqp83MSe1aIj7Z3MEvVWc6WkM9ehO0HmeMDb76BmiramAZMxZ8TzcC\nZhu8uuBn6X1u4fU8F+xUJzoDNzCbFkLOeROvakA+6zAXNXHaYftqR+Z0JlPOTK7cyYF49atlnXsS\nelM3jIVpwhWwr5gvuXIY8tf6JWuEMTx6EzQi5xpBlDKyA+LrrrsO1113XTZtIUSIt1TGuZ1JdSIL\nld5x790WDERVKpRd3AD3H3cKAbZz4+rBGZAEBeWjg5y4JbakAgiVKr+tU4mUy0ClG0SHa4hlGGg/\n/1jSBo9GB2h0g2MjdobZsng12IsvEwIAy5qt6H//H2DKygDNkGBaDyBriRpA/KA3TuBGpE68IDTX\nDXzkpEDIKbWWsPSgnHNPQm8Bs1XWzVnMuXb6M/AnjyOYRsRAO/pS+OobKCgmSpqU6hATQcIDAqFb\nlk6X0bttUYe7ZxssqzbBP0QrOpbUErW+7TTsA7O0oaXj0FJy//GPhc9PdZYr7oyahEMv+9oEePQm\n6Gm2QpKYPNYMz+4kuqhHj89zlrRrqSZrg5zXh++y53gefq9XSDFSVVUHbwJZBmUTJsFtq0lsZ7zZ\nyqjATVVXD9PdS+F3OaEHSMMpku922El3IpQRjMb1i4As3ffoTbA8tB79nx4BAoHB/Rc6I7SsCoZf\nzAbAAxotGC4Avr8fap0OLMPEHDtttwdobYmswjJzDrRVNcEbT4IoUWQHxG+//TYuuugiDB8+HK2t\nrdi+fTtYlsUdd9yBurrU6noWEqJBSfSMVignNs2NYuFjSbVf9vf1wWOtjm2C0O0B23JS2GUfXlat\n/9Mj4s6bHdwdn9YsV5yLg692ZMzMj2lBE7y1F0Hz2ZGs5QYWOjzHxQSe5nkPwPX8jsxt5orzu7Ei\nDS4si1dDWz0iswX9k01BkPP6gYCWNZiga/wJmIoKqGrrwfv7he5g8Y5ZotnK8M2irKsT9ofvE50B\nJCLJRSv34BhuqBztYDRa+HWV8A3RJ17xSPYmT85KQTythv4v9lzYDLPm849hD/cBC1bA2zAe4Hkw\nZ0/DvW83WIMpmEv8+6eDr/vTLlH71d4uoaZ3aDz3nm2wjBkbsQpDEKWG7LJrL7zwAvR6PQDg2Wef\nxejRo3HZZZfhmWeeyZpxSkGsFJC+7XRsQLB3O3TX/CBuaR5ZY7V8Dv7//h/44x8BfX1Q1Q1u/FFV\nVcPwi9nCbuLwNrXaE0fB//3N2JJTA2XVpErvAIzw/7RK7sQpJxQeQBhXbw4GTg0TUNHlSKq0UcnR\n3hZzfBybfxPcHIMMHS+R301VVw+1TofKMyfBf3VKKPEUmomNKNM3AGswQd3tS61VcrKlqGS83qcz\nwrB0DfQ3/BxdL+6Ba9dmONY3IXDqM3geW56w9JqYZsODCyFw0+li8+NJw6LIbRWcTtttlmGgO3EU\n7iX/DvvKBbCvmA98+A9oWz6P+znJllkDZJZai6fVsOdUVdUw3HInDDPmCLO7UnY5Nq6CxuOE1uuC\na+A5XeNPhI118ezne3yiQTjf0y35PQmiFJAdELvdbphMJvT19eH48eO45ZZbMH36dHz55ZdZNE8Z\niDmkeLOt4Xf/SY8Vtpzlfu4pONY3wfCvv4Sqrj5Yh/iGn6Nr327YVy6IuJiEbJSq2cr3dAuld6Lr\nqDJTrhW94CdLootDKIDw1NQLy9qJZk9KHc5hTzirn+7xiv7dVHX1MPz8DtiX3w37ygXo2rcb+p/e\nDFVVtTBeqExfiJA27Svmxw105NqQ6OZM7PWWxasjXs/xPGAdFhskJHHjKqrZaEjDspETdMoNmuON\n4YoeY8828CePx/+9U/gdE900AfG1HXpOVVcvXWM+nl3hz0k1VIqyP2CukqxJTBCljOyUCYPBgHPn\nzqGlpQWjR49GWVkZent7s2mbchBxSKHZ1pilslBTixTTDsSWs5xbH4X14c3gOU58N/G6nVB3+1A5\n/Vao60dDVVePwNmWCLsCZhv0M+fCs2fL4Ia68NI7NQNLZWmkKaSU/0ebk+LCmi2SOov4O43jFf27\nqXU62JffHRNEhsqWhRf0DwU3uutvEJ2dkptCwfE8ehrGw7JmK3hHJxizFR5TFTiJslBiWlMNvwhc\ndLc8lyPxjWu6VSFIw/JJM+dWVjqO1BjgJX9vnuOg1ulgmDEnmKPb/AoC7edk/Y6JUjwS+UXfmHEw\nLVoVe84l2H8Rskt4buDak0iHPp0BxoUrhZsGoYyhLv5GaoIodmQHxP/yL/+CpUuXgmVZLFy4EECw\ne8zIkSOzZpxiEHFI3rdeiy1fNpBDnEzlh2iklrM4nxcBViW6TM22nIQ9LGfYdPdSuP+0U8gxNS1o\ngk9vBDfGECy94/VCXVEOv9cLjccZE7SmUz0g2fy/VIrUlxRVNbG51wO/L5BExYQEhP9u+rYWySAy\nNBPrjiroL5XrjoFNZnA5AbMV4DjA5YjRlVieZKL8zeid8xXtbdBfOB/52VLBRJo3ruHE07Am8dtL\ni3RzbuUExFJjgBH9vVmGQeDwO7APtIaO2Q8icW4l4ydjqjxEvc8fpxOf2P6L4DloBMswg5vtKjQw\n3HYP3LufjOtLOZ6HNypA99ImUIJIrsrEt7/9bQBARUWw33pDQwMWLFiQHcsUhNgFTz9zLnzhXasM\nBqC3F8YRFyNgrpL1uSzDgD9/NuIiHlrOinbmweWs2BkA3fU3xOQMO7c+CuuKDeg79iEQCMC9d5uw\n0c+nM0LbdkYy8AjfWMIaTNBdfwMq60chUD1cqPOaSfK9q1zpMCwbPD7rdqLM7QDX2Y6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UhsY1SdqmH10Z1VkBKSSeLk2UA2s7ycqkGmNg01UvsyT2d0reFyQNMmHy61FhRFyXMo9Xru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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10947a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(advertising[['Sales','TV','Radio','Newspaper']]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple linear regressions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2/hGv6yz60sXQjTNvMkYNVS9GFZUM24490j/GZH4+Q61oO5LPIJKh8mS2JptY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109df4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(advertising.TV, advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})\n",
    "plt.xlim(-10,310)\n",
    "plt.ylim(ymin=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter estimation and uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV', advertising).fit()\n",
    "est.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QoNMJ/u/+Lqnfimqs6qG361RKcuWdbloQka4H45UrAM+Dt03tMaNa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\nBQhCCCGiKEAQQggR9f8B4XSvLvQJIxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0aeba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(advertising.Radio, advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})\n",
    "plt.xlim(-1,50)\n",
    "plt.ylim(ymin=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>    9.3116</td> <td>    0.563</td> <td>   16.542</td> <td> 0.000</td> <td>    8.202</td> <td>   10.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.2025</td> <td>    0.020</td> <td>    9.921</td> <td> 0.000</td> <td>    0.162</td> <td>    0.243</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ Radio', advertising).fit()\n",
    "est.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DnE4Et22DOKwgEBoaINTXQywtHfKe5OtlZcDOnUB9PVBaivxhv2XEPFDCaHk0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109fa5a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(advertising.Newspaper, advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})\n",
    "plt.xlim(-1,50)\n",
    "plt.ylim(ymin=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>   12.3514</td> <td>    0.621</td> <td>   19.876</td> <td> 0.000</td> <td>   11.126</td> <td>   13.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>    0.0547</td> <td>    0.017</td> <td>    3.300</td> <td> 0.001</td> <td>    0.022</td> <td>    0.087</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ Newspaper', advertising).fit()\n",
    "est.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Linear Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 29 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:33:59</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</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>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Sat, 29 Feb 2020   Prob (F-statistic):           1.58e-96\n",
       "Time:                        21:33:59   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "Radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "Newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV + Radio + Newspaper', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the p-values of TV and Radio coefficients are small, while that of Newspaper is large (0.860). It suggests to drop the variable Newspaper in the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   859.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 29 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:34:07</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   778.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   788.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>    2.9211</td> <td>    0.294</td> <td>    9.919</td> <td> 0.000</td> <td>    2.340</td> <td>    3.502</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.909</td> <td> 0.000</td> <td>    0.043</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1880</td> <td>    0.008</td> <td>   23.382</td> <td> 0.000</td> <td>    0.172</td> <td>    0.204</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.022</td> <th>  Durbin-Watson:     </th> <td>   2.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 148.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.323</td> <th>  Prob(JB):          </th> <td>5.19e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.292</td> <th>  Cond. No.          </th> <td>    425.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     859.6\n",
       "Date:                Sat, 29 Feb 2020   Prob (F-statistic):           4.83e-98\n",
       "Time:                        21:34:07   Log-Likelihood:                -386.20\n",
       "No. Observations:                 200   AIC:                             778.4\n",
       "Df Residuals:                     197   BIC:                             788.3\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9211      0.294      9.919      0.000       2.340       3.502\n",
       "TV             0.0458      0.001     32.909      0.000       0.043       0.048\n",
       "Radio          0.1880      0.008     23.382      0.000       0.172       0.204\n",
       "==============================================================================\n",
       "Omnibus:                       60.022   Durbin-Watson:                   2.081\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              148.679\n",
       "Skew:                          -1.323   Prob(JB):                     5.19e-33\n",
       "Kurtosis:                       6.292   Cond. No.                         425.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV + Radio', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this two-variable model has R-squared=0.897, the same as three-variable model in training error."
   ]
  }
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