Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Unnamed: 0'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.scatter(data['TV'],data['sales'],c='black')\n",
    "plt.xlabel(\"Money Spent on TV ads\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data['TV'].values.reshape(-1,1)\n",
    "y=data['sales'].values.reshape(-1,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg=LinearRegression()\n",
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is: Y=7.0326+0.047537X\n"
     ]
    }
   ],
   "source": [
    "print(\"The linear model is: Y={:.5}+{:.5}X\".format(reg.intercept_[0],reg.coef_[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RwAIAACRP2AKesEulUmr03d4Yo2q1GkCPBgu6f2vWSHfcIc2cKb35ZvN222/vzGM97riRVwhG5wKfA2utXWitfbz/329LelbSDpI+JWlOf7M5coJaAAAAYJDe3t6Otidd2NcFDaJ/v/2ttO++TlrwuHFSV1fj4PUrX3G2Wyu9/LKUzxO8hpUvL4sxJifpXyT9UdK21tqFkhPkStrGjz4AAAAgWsIekIVN2NcF9aN/lYo0Y0ZteZuDDpL++Mf6dkcfLf31r7Xlba64Qtp8c9e6AQ95HsAaYzaRdLekc6y1yzq4X5cxZp4xZt7rr7/uXQcBAAAQSmEPyMLGi3VB3Syi5UX/+vqkq66qBay5nDR3bn27979fuv9+qVp1Ata77pJ23XXEu0WAPF0H1hgzTtJ9kv7HWnt9/7a/SjrYWruwf57sb6y1u7V6HObAAgAAJFOpVFKhUFBvb68ymYyKxaLrBZz82EcUhbGIlrXSPfc481hfeKF5u3HjnHmsZ5xBheCoCEMRJyNnjutSa+05623/mqQl6xVx2spaO7PVYxHAAgAAwAthDNLCIixFtBYskC68ULrvvtbtTjtNuvhipwgToifwIk6S9pc0XdIhxpg/99+mSbpa0qHGmOclHdr//wAAICZYtxNu8Os8KhQKg4JXSerr61OhUPBkf1ESVBGtpUulL3+5lhb8gQ80Dl4PPlh69NHaPNZbbiF4TYKxXj2wtfb3kkyTP3/Uq/0CAIDgDB3NqlQq6urqkqTEj2ahfX6eR1Q6bi6TyTQcgXW7iNaaNdKcOU5a8NKlzdtNmuSkBR9/vDRmjKtdQIRQHBoAALiG0Sy4wc/ziErHzXlZROv3v5f237+2vM3nPtc4eJ01S3rjDWeEdeFCafp0gtekI4AFAACuYTQLnWqUKuzneUSl4+bcrBr84ovSKafU0oI/8hHpkUfq2x11lPTss7W04GJR2mILF54MYsPTKsRuoYgTAADREJaiL4iGZgWUNtpoIy1ZsqSuvVfnEVWI3bdihfTtbztpwa3ssYeTFjxtmhPYIrnCUMQJAAAkDKNZ6ESzVGFJvp5H+Xxe5XJZ1WpV5XKZ4HUErJXuvVfabTcnEE2nGwevqZR07bVOgGut9PTT0ic+QfCK9hHAAgDQAJV0R8bNlMO44xxrnhK8dOlSzqMIeOYZ6VOfcoLPVEo68kjpuefq233uc04KsbXS2rXSf/2XtOGG/vcX8UAKMQAAQ7AuJLzGOeYg5Txa3nhDuvpqJ+W3lQMOkK65RtpvP3/6hXhoN4WYABYAgCH4Ug2vcY45COTDbe1aae5cJxV48eLm7SZOdALWz36WCsEYOebAAgAwQlTS9V/S0mmDOsfCdpxJOQ+fRx5xKgQbI40dK518cuPg9fzznWVvrJUWLZJOOongFf5gBBYAgCEYHfNXEkfhgjjHknicMbyXX5YuvVS67bbW7T71KenKK52qwYAXGIEFAGCEqKTrr2aVaAuFQkA98l4Q51gSj3OQgh7tbrb/lSul665zRkuNkd7znsbB6267OVWFq1VnlPWeewheEQ6MwAIA0ADrQvonlUqp0fcRY4yq1WoAPfKH3+dYUo9zEIIe7a7f/zQZ8zVZ2zoCveYa6eyzpY028ryLQB2KOAEAgEggZdsfHGf/BH2sd9jho3rllc9LOqplu5NOki67TJo82fMuAcMihRgAAEQCKdv+4Dj7x+8iXW++Kc2a5aQEGyO98sqDahy8/kG/+52TEmytdPvtBK+IHgJYAAAQKCrR+oPj7J9MJtPR9k6tXSvNmSNtu60TsG65pXTVVY1aLpF0iqSxkoyy2bwOOMCVLgCBIYUYAAAAcJEXc2AffdRZuua3v23d7ktfknbe+S6dd96JVJxGpJBCDABAzARd1RRAe9wY7X7lFem002ppwR/+cOPg9ZOflJ56qpYW/LWvSaeddgyj7YgtRmABAIiAoKuaAvDWu+9KN90kzZwprVnTvN0uu0izZzvrshrjX/8ArzECCwBAjLCGJ8KEbIDRs1b6+c+lD3zACUQ33FA677zGwetVV0nLlzv3ee456cgjCV6RXASwAABEgN9VTRFuQQaQA9kAlUpF1lpVKhV1dXURxLbhueekY45xgs9USpo2TVqwoL7djBlSuVxLC77gAmlIAWkgsQhgAQCIAK+rmiI6gg4gyQZo37Jl0kUX1eax7rab9KMf1bfbZx/pN7+pBazf+56UzfrdWyAaCGABAIgA1vDEgKADSLIBmqtWpZ4eafvtnYB1882lr361vt3mm0u33iqtWuUErI8+Kh10kP/9BaKIABYAgAhgDU8MCDqAJBtgsP/3/6RDDnEC1jFjpOnTpYUL69ude670+utOwPrmm1JXlzRunP/9BaKOABYAgIjI5/Mql8uqVqsql8sErwkVdACZ9GyAV1+Vzjyzlhb8oQ9JDz1U3+7ww6U//7mWFnz99dLWW/vfXyBuCGABALFBZVTvcYyDF3QA6VU2QFjPrXfflb75TadKsDHSdttJN99c326nnaS77nLSiK2V7r9f+ud/9r+/QNyxDiwAIBZYJ9V7HOPwKJVKKhQK6u3tVSaTUbFYjPRrEKZzy1rpb3+THnjAuf3kJ83bfvWr0jnnSBtv7F//gLhqdx1YAlgAQCzkcjlVKpW67dlsVuVy2f8OxRDHGF4J+tx64QWpUJB++MPW7U44Qbr8cmnHHT3vEpA4BLAAgERJpVJq9JlmjFG1Wg2gR/HDMYZX/D633n5buvZaJxht5phjpEMPlT72MQJWwA/tBrDMgQUAxELQhW2SgGMMr3h9blWr0p13SpMnO/NYN9uscfC66abO/NZVq5zR2FNPJXgFwoYAFgAQC0EXtkkCjjG84sW5NX++M4I6sLzN8cdLL71U3+6cc6RFi5y5r8uWSaefzvI2QJgRwAIAYoF1Ur3HMYZX3Di3Fi2Szj67trzN1KnSr35V3+6ww6Q//am2vM3Xvy5NnOjik4mwsFaCBtbHHFgAAABEzqpV0ne+I82cKa1XvLhOLifNni39539KKYZumgpTJWgkE0WcAAAAECsPPugErI8/3rrd5ZdL554rbbKJP/2Kg6ArQQMUcQIAAEDgRpOW+ve/S/l8LS34Yx9rHLwef7yzdutAWvBFFxG8dqq3t7ej7V4hjRnDGRt0BwAAABBPQ9NSK5WKurq6JKlhWuo770jXXy9dcknrx91rL+maa5yAFu7IZDINR2D9rDLe6fmCZGIEFgCAkGIkIvqS/hoWCoVBcyolqa+vT4VCQZKzvM0PfuDMUzXGWcamUfCaTkvf/rb07rvOCOv8+QSvbgtDlfHhzhdAIoAFACRYmIOLgZGISqUia+26kYgw9RGt8Ro2Sz/dU5XKreuWtzn2WKnBwJ/OOkt69VUnYF2+XPr856Xx4z3vcmKFocp4WNKYEW4UcQIAJFLYK25SUCX6eA0HjsFySV+R9MWWbT/2Menqq6UpU3zpGkKIaybZKOIEAEALYU9VYySifWEdSU/qa7h6tXTzzU46cKVSlvS6GgWvEyYs1513SmvXOqOsDzxA8Jp0YUhjRvgRwAIAEinswUWzwil+FlSJgjCn6SbpNXzoIWnvvZ15rOPHS2ee6RRkqneppE0lGa1YsY3Wri2xNivWCUMaM8KPtwwAQCKFPbhgJKI9YR5Jj/NrWC5L06fXlrc55BCp8WyvO7Xhhh/QhAlbSzKSLpPkRLZheZ0QLvl8XuVyWdVqVeVymeAVdQhgAQCJFPbgIqwjEWFL1w3zSHpYX8ORWL5cKhZrAeuOO0o9PfXtxo17WtKhcoJVI+l4rVy5QEuWLGn4uGF4nQBEC0WcAACJVSqVVCgU1Nvbq0wmo2KxGMngwi9hLHxF0RdvWCvdfbd0/vnS3//evN2GG0qzZ0unneakDqdSKXXy3ZLXCcAAijgBADAMUtU6E8Z03bCPpEfJk09K06Y5I6yplHTMMY2D1zPPlBYudILcFSuks8+uLW/TLAV/woQJvE4AXEEACwAJELa0T0RTGNN145Sm67fFi6XzzqulBf/zP0s//3l9u0MOkR57zAlYrZVuvFGaNKnxYzb7QeGGG27gdQLgClKIASDmwpj2iWgiXTfa1qyRvvtdaeZMadmy5u22395JCz7uOI2oQjCp+QBGghRiAICkcKZ9IppI1w3OSLIoSqWSJk36jIz5o4yRxo2TTj+9cfB60UXSW285I6wvvyzl8yMLXiVS8wF4a2zQHQAAeCuMaZ+IpoFAhNE1fw3NohhY61ZS3bGvVKSLL5bmzpWkfP+t3jHHSF/9qrTrrh52HAA8QAoxAMQcaZ9AtLW6hp95pqwbbpBmzRruUZ6UNFOZzF9UqZQ96CUAjA4pxAAASaR9AlFXny1xpKTnVKmUtfHGzYLXVZLOkbSBnPVY/1nS/+jFF8m8ABBtBLAAEHNUaQWibdKkj0n6qSTbf/uJpF3q2p1+uvTKK8481mx2V0k3yAlka5otcwMAUUEACwAJQFEVIDqWLpW+/OXa8jYLF/5S0ifr2v3TP72qRx+tLW9z883Sdts5fyPzAkBcEcACAAAEaM0a6bbbpAkTnIB1wgTp2mvr240Zs0jSZ5XJ7KSenpKeeWaS9tmn8WOSeQEgrghgAQAAfPb730v77ad1y9uceqoz8jrUrFnSG284I6xr1mwja+eqUvl7W4EomReAN0ayrBXcwzI6AAAAHnvxRemSS6Q77mjd7qijpGJR2n13f/oFoDOdLGsFbzACCwAJwq/GgD9WrJBmz67NY81kGgeve+wh3XefVK06o6x3303wCoRZoVBYF7wO6OvrU6FQCKhHyUMACwAJMfCrcaVSkbV23a/GYQxiCbQRNdZK994r7babE7Cm09L559e3S6Wc+a0rVjj3efpp6ROfcO4TdlyX7uJ4RlP9slattw/F6z56xlobdB+GNXXqVDtv3ryguwEAkZbL5VSpVOq2Z7NZlctl/zvUxND0LMmpnkoBGoTNM89IF14o/fSnrdt97nPSpZdKO+zgS7c8wXXpLo5ndI3ms5TXvTVjzHxr7dRh2xHAAkAypFIpNXrPN8aoWq0G0KPGohJoI3neeEO6+monNbiVj3zEabfffv70yw9cl+7ieEbXaIJQXvfW2g1gSSEGgITIZDIdbQ/KaNOzALesXevMW5040Unx3WqrxsHrNts47dascdKCf/vbeAWvEtel2zie0TWaJap43d1BAAsACVEsFpVOpwdtS6fTKhaLAfWosagE2oinRx5xRlCNkcaOlU4+WVq8uL7dBRc4y95YK732mnTiidKYMb531zdcl+4YmP/YLAOS4xkNI12iiuvIHQSwAJAQo/nV2E9RCbQRDy+/7KzBOlAteP/9nTVahzrySKfgkrXO7aqrpPvvT04xFq7L0Vu/kF4jHM/44zpyibU29LcpU6ZYAEBy9PT02Gw2a40xNpvN2p6enqC7hJhYscLaa6+1NpUaCEUb33bf3dqf/tTaarXx4/T09Nh0Om0lrbul0+lYn6tcl6OTzWYHnS/r3zieycF11JykebaN2JAiTgAAjFCpVFKhUFBvb68ymYyKxWLoRrSTzlrpZz+TZs6Unn22ddvZs6WzzpI22sj5/1avL8VYoi2IazcqhfSAoLRbxGmsH50BACBuhlaiHFhXVxJBbMCefVaaNUu6557W7U4+2VneZvLk+r8N9/pSjCW6grp2M5lMwx89mP8IdIYRWAAARoARuPB4803pmmucpWta2W8/p90BBwz/mMO9vrz+0RXUa8caoEBrLKMDAB4aqCSZhOItaIwRuOCsXSvNmSNtu61TeGnLLRsHr1ttJd12m7R6tZNK/Ic/tBe8SsO/vhRjia6grt2oFNIDwq6jANYYs7ExJsZF4gFgeOtXkrTWrks/I4htXxx+AGA5BH89+qh00EG15W1OPFFatKi+3Ze+JC1Z4gSsS5ZIp5zitO/UcK/vaIOROFwDURXktTvS5VcArKdVhSc5Ae7xkn4maZGkF/v/+7Skr0napZ1KUaO9UYUYQJg0qySZzWaD7lokxKV6a1yeR1i9/LK1XV2tKwVL1n7yk9Y+9ZT7+/fy9eXcCRbHHwgnuVGF2BjzsKRfSbpX0gJrbbV/+1aS/rU/uP2JtbZw1YtgAAAgAElEQVTHjWC6GebAAggTKkmOTpzmDlKF2D3vvivddJNTLXjNmubtdt3VqRZ8xBHOaKyXvHp943QNRBXXLhA+7c6BHS6AHWetXT3Mjhq2McbcLunfJS2y1r6/f9ulkk6V9Hp/s1nW2vuH6yQBLIAw4cvn6MT9BwC+GLfHWukXv3AC1gULWre9+mrp7LOlIVNOIyvu1wAAB58HnXGliFOTwHS8MWbjVm36fU/SYQ22f91au2f/bdjgFQDChuItoxPnuaPMj27tueekY45xRk5TKWnatMbB64knSpVKLVH4/PPjE7xK8b4GADj4PPDOsEWcjDFfNMbs1v/vg+TMg33eGHNmq/tZa38raakrvQSAEKGS5OjE+QeAQqEwaIkMSerr61OhUAioR8Fatky66CInYDVG2m036Uc/qm+3777Sww/XAtY77pCaxXJhLH7UaZ/ifA0AcPB54KHhJslKelzSmP5/PyBpH0kbS3qqjfvm5MydHfj/SyWVJT0p6XZJW7a4b5ekeZLmZTIZF6cHAwCC1tPTY7PZrDXG2Gw2G5viKcaYhgW+jDFBd80Xa9daO3eutZMmtS68tMUW1nZ3W7t6dWePH8biOyPtU1yvAQCOpH8ejIRcKuJ0iaQzJN0kabyk0yR9S5KRdFJ/EPob64y2Nrp/TtJ9tjYHdltJi/tfwCskbWetPbl1iM0cWABANCRxfvRjjzkpvr/5Tet2550nXXihtPXWI99XGI9vGPuExpiPCDe0ex7x3tA5t+bAXiZnBHaspK0k3W2tvVxO8LnQWnt5s+C1yeO9Zq1da51qxt+R9KF27wsg2sKY9ge4LQmpoa++Kp1xRi0teJ99Ggev06ZJTzxRG3e97rrRBa+S1Nvb29F2r5VKpYZfUKXg+oTGmI8IN3RyHiXh8yAwww3RStpO0jVygtZN+7ftLumUNu6b0+AU4u3W+/e5kr7fzjAx68AC0RbGtD/AK3FLDV250tpvfMPa8eNbpwXvtJO1P/qRtdWqd30J0xrMjd7Xgu4TmgvTuYPo6vQ8itvngdfkRgrxaBhj7pR0sKStJb0m6ZL+/9+z/8UuSzrNWrtwuMcihRiItnbTaEjvQhKF7by3VvrlL53lbZ58snXbYlH64heljTdu3c4tA6Mf6xdGSafTgRRRa/a+FmSf0BxLF8ENnEfeciWF2BjzFWPMVi3+fogx5t8b/c1ae5y1djtr7Thr7Xustd+11k631n7AWvtBa+0R7QSvAKKvnbQ/0ruQRGE57194QfrMZ2rL2xx2WOPgdfp06R//qI27ZrMlve99/k0NCFMF8FYpwgSv4cPSRXAD51E4DFfE6VOSZkpaKWcu7OuSNpS0i5yR1F9JutJa+7qXnWQEFoi2dkZgKXaAJArqvH/7benaa6XLL2/dbu+9pWuukf71X+v/FqbR0CDwnhUtST9f4Q7OI2+5VcTpXmvt/pJOl/S0pDGSlknqkfQha+25XgevAKKvnUIGYSvOAvjBr/O+WpXuvFOaPNkZZd1ss8bB62abSTffLK1a5YywPvZY4+BV8naNwygUfaNAS7SEafQe0cV5FA4dz4E1xqQkbWKtXeZNl+oxAgtE33Dz/BjNQBJ5ed7Pn+8sb/Pgg63bnXOONGuWNHFiZ4/v1VywKI1whG3+MgBEWbsjsG0FsMaY/yNnFHatpPmSNpd0vbX2a6PtaDsIYIH4i9KXVsAtbp73r70mXXGFdOONrdsddph01VXSnnuOpMc1XgXf/JgFAMnkSgrxevboH3E9UtL9kjKSpo+ifwAwCGk58ROFNNCgjea8X7VK+ta3pI02ctKCJ01qHLzmctIPf+ikEVsr/fznow9eJe9SaJlOAABopd0AdpwxZpycAPZea+1qOUvhAIBr8vm8yuWyqtWqyuUywWuEhaW6bhR0ct4/8ID0L//iBKwbbCB94QvSypX17S6/3CnUZK1TNfiYY5z7uN1vL350arfKJz+QAEAytZtC/AVJ50t6QtIn5IzA9lhrP+Jt9xykEANAtJAG6o6//U36ylek73+/dbvjj3fSh3fayZ9+eamdtGqmHABA/LiaQmyt/aa1dgdr7TTrqEhqUpcQAOCGKI8wkQY6Mu+8I112mTNaaoy0886Ng9e99nJGYwfWYy2V4hG8Su2N7HpZARkAEG7tjsBuK+lKSdtbaw83xuwh6cPW2u963UGJEVgAyRP1ESZGYNtTrUp33SXNnCm1iu3TaWn2bOnUU6Xx4/3rX1h5VQEZABAct4s4fU/S/0javv//n5N0zsi6BgAYTtRHmFgjs7k//Un6t39zRljHjJGOPbZx8HrWWdKrrzojrMuXS5//PMHrgHbnyQIA4qfdAHZra+0PJVUlyVq7Rs6SOgAAD0Q9BZeq0jWvvy598Yu1tOC99pJ++cv6doce6qzdOpAW/K1vSdtu639/o4AfSAAguca22W65MWaC+isPG2P2lfSWZ70CgITLZDINU3CjNMKUz+cTGbCuXi3ddpuTFvzOO83bZTJOWvAxx0ipdn9OhiStO68KhYJ6e3uVyWRULBYTeb4BQNK0G8CeJ+mnkt5rjPmDpImSjvasVwCQcMViseEcWEaYwunXv3YC1vnzW7e79FLpvPOkTTf1pVuxltQfSAAg6dqtQvy4pIMk7SfpNEnvs9Y+6WXHAIRDlCvhRhkpuOH2j39IJ5xQSwv+6EcbB6/HHis9/3wtLfiSSwheAQAYjZZViI0xR7W6s7X2x673qAGqEAPBiHolXMAty5dLX/+6dNFFrdvtuaeTFnzoof70CwCAuGi3CvFwAewdLe5rrbUnj6RznSKABYLBUihIKmulH/3ISQtudapvuKETsJ52GhWCAQAYDVeW0bHWntTi5kvwCiA4Ua+EC3TiiSekww93UoJTKenTn24WvN6k97xnb/X0lLRihXT22QSvQBi5NQWGqTRAuLRbxEnGmE9Iep+kDQe2WWsv96JTAMIhDpVwgWYWL5aKRekb32jd7pBDpIMO+rmuueboden0L70kdXV1SRLp9EAIDZ0CU6lURnTNuvU4ANzTVhEnY8wtkj4j6WxJRtIxkrIe9gtACMRhrUV+OceA1aulW2+VNt/cGWWdOLFx8Lr99lJPj7R2rZNK/OCD0u23nzFoLrgk9fX1qVAo+NR7AJ0oFAquXLNuPQ4A97S78tx+1trPSnrDWnuZpA9LmuxdtwCEQdQr4Q78cl6pVGStXffLOUGsP8Lw48HDD0v77OMErOPHS6efLi1bVt/uyCOf0uTJ75cxKY0bl5NUGrQ2K+n0QLS4dc16ee2H4T0SiKJ2A9gV/f/tM8ZsL2mNpB296RKAMMnn8yqXy6pWqyqXy5EJXiV+OQ9SUD8eVCrSjBm15W0OPlh67LH6dp/+tPTcc84Ia09PSb/85b568cWnm/a1Wdo86fRAOLl1zXp17fMDKzBy7Qaw9xljtpA0W9J8Sf+Q9H3PegUALmDULDh+/XjQ1ydddVUtYM3lpLlz69t98IPSL34hVatO0PqDH0i77NJ+X+OQTg8kiVvXrFfXPj+wAiPXMoA1xuxtjJlkrb3CWvumpE0kPSXpLklf96ODADBSjJoFx6sfD6yVfvITJ/g0Rtp4Y2nWrPp248c781tXrnTu88QT0r/9m3OfkfQ16un0QNK4dc16de3zAyswcsOtA/u4pI9Za5caYw6UM+p6tqQ9Jf2TtfZoPzrJOrAARmJo9UjJ+eWcwMN7bq4h/NRT0oUXSj/7Wet2p58uXXyxtN12HT086x0D8B3vO0A9V9aBlTTGWru0/9+fkdRtrb3bWnuRpJ1H20kA8NJwv5xTQMM7o0m7W7JE+q//qqUFf/CDjYPXgw+WHn3UGWG1Vrr55lrw2slrS3pwsnDdt8bx8QfvO8AoWGub3iQtkDS2/99/kXTg+n9rdV83b1OmTLEA4Kaenh6bTqetpHW3dDpte3p6gu5abPT09NhsNmuNMTabzTY9tqtXW9vdbe0WWwyEoo1vkyZZO3eutWvWDL/fTl/bdvuKaOO6b43j4y/ed4DBJM2zbcSGw6UQFyRNk7RYUkbSXtZaa4zZWdIca+3+rkfUDZBCDMBtpG8F63e/k2bOdEZQW5k1y2m3+ebtPzavbfKUSiUVCgX19vYqk8moWCw2nCbAudEaxwcIp3bf46Ku3RTilgFs/wPtK2k7Sb+01i7v37arpE2stY+70dnhEMACcFsqlVKj9z9jjKrVagA9irfeXumSS6Tvfa91u6OOkopFaffdR74vXttk6WSuO+dGaxwfBC0pgVonklTPw7UANgwIYAG4jZEGb61YIX3zm9IFF7Ru9773SbNnS4cf3rhC8Ejw2iZLJ68350ZrHB8EKUmBWieSdF26VcQJAFwTpuIgFNBwl7XSvfdKu+3mBKLpdOPgdcwY6brrnADXWmnBAmnaNPeCV4nXNihBXd+dLEfCudEaxwdBYm3cxlhyqYF2JsoGfaOIExAMNwtMhLE4CAU0RmfBAmuPOKJ14SXJ2lNPtfall/ztG6+tv4K8vrPZ7KD9Dtyy2WzTvnJuNMfxcQ/HsjPGmIbXsjEm6K4FqtP3uChTm0WcAg9O27kRwAL+c/sLaZLegONq6VJrZ84cPmA98EBrH3kk6N7CT0Fe32H8cQzgvOwc3xMaS9K51G4ASwoxgIbcTuUhBSZYI0nvXLNGuv12aeJEJ8V3q62c+apDbbONU5xpzRonhH34YenDH3b/OSC8gry+h1vvGQgC6bCdI4W9Md7j6lHECUBDblejTFIRgrDppDDGH/7gLFvzyCOtH/OCC5x2W27pRY8RNVzfwGDDfYZSbbcxjkuyUcQJwKhkMpmOtg+HX1aD02ok4KWXpFNOcUZYjZEOOKBx8HrkkdLTT9cSha+6iuAVNVzfwGCtPkMHflSsVCqy1qpSqairqyvQwoZhkc/nVS6XVa1WVS6XCV7REAEsgIbc/kJKCkxwBqdxbijpS5KsKpWyJk920oSH2n136ac/lapVJ2D9yU+kPfbwqcMhFaYq2mHD9R0PQZ/jQe/fTa0+Q0kvBkapnYmyQd8o4gQEgwqK0VetWjtx4slWembY4kuzZ1vb1xd0j8MpSUU0kExBn+NB798LzT5DqbYLNKY2izgxBxYAYubZZ6VZs6R77mnd7qCDXtB///fOmjzZn35FGXM8EXdBn+NB799PSXquQCeYAwsg1OKUKha0N9+ULrywNo91jz0aB68bbDBP0keUzebU01PSb35D8Nouqmgj7oI+x4Pev5+SPmecz3+MFgEsAN9RwGJ01q6V5syRtt3WCVi33FK6+ur6dhMmSLfdJq1e7SQJr1w5Vdb+jsIYI+B2UTMgbII+x4Pev5+SPGecz3+4gQAWgO8oYNG5Rx+VDjrICVjHjpVOPFFatKi+3Ze/LC1Z4gSsixc7FYbHjm1vH538Kp60X9CTPmKC+Av6HA96/yMxmvfBpFbb5fMfrmhnomzQN4o4AfEqqEQBi+G9/LK1p57auuiSZO0RR1i7YMHo99dJAZU4FltpR5yuQaCRoM/xoPffiaS+D45WmD//o3T+xZUo4gTEx0DKzfq/WqbT6cimHG299dZasmRJ3fYJEyZo8eLFAfQoeCtXSjfeKJ1/vpMi3Myuu0qzZ0tHHOGMxrqlk6IiFCABkHS8D45MWI9b3L5nRRVFnIAYIeUmfqyVfvYz6X3vcwLRjTaSvvSlxsHr1VdLy5c79/nrX6VPfcrd4FXqrIBKkoqtwD2N0i2Tloo+GhyrwYI+HrwPjkxYU8X5nhUx7QzTBn0jhRhh53XaSZhTbkYibs+nXX/5i7VHHTV8WvCJJ1pbqfjbt2w22/A1yWazo2prLWlZaJxuOW7cODt+/HhSMNtAuupgzc6nCRMm+PY+0+n7IGrC+JmQ1O8lYaM2U4gDD07buRHAIsz8+GIRtw/KuD2fZt5809pZs4YPWPfd19qHHw62r17NgeWLN6xtfs0n4X3ADUl5z2xXO+eT1+8zvLfFC9dYOBDAAj7x400vbh+UcXs+A9autXbuXGsnTWodsG6xhbXd3dauXu3u/kf7q3Yn92+3LV8KYG3z0Q1GPNrD6NBg7Z5PXr/PhHEkESMT1+8lUUMAC/jEry8WcfugjMvz+eMfrT344OFHWc87z9rFi73rR1g/fPniDWsZgR2tVj8ExeW9tBMTJkzgxxC4LonXUti0G8BShRgYpbBW1IM3Fi6ULr9cuuWW4Vr+TNKFkp7ypZJhWM/DsPYL/mpU4XPcuHEyxmjVqlXrtlH1s7FmFVJnzJihOXPmJK5yarNK9kPxPgNEC1WIAZ+EtaKeF0Zb9THoqpEj8e670g03SBts4FT+3X77xsHrzjtLd98tZTI5SUbSv0t6SpI/lQzDWhEzSdcHmsvn8+ru7lY2m5UxRtlsVnfccYduv/32QdviHniNVKPj193drfvvvz+RlVOXLl06bBveZ4AYa2eYNugbKcQIuySknYw2RTWsKa5DVavW/uIX1n7wg8OnBV95pbXvvDP4/kGlzIZ5rmkSrg8gCElN0W/2fjdmzBjeZ4AIEynEANw02lTQMKeSPv+8VChId93Vut1nPytddpmUyzVvE9TzZBH29pVKJRUKBfX29iqTyahYLHKMEElhfl/1Eu93QDyRQgzAVaNNUQ1TiuuyZdLFFzspwcZIu+7aOHj90Iekhx6qjbnOmdM6eJWCS5ltlmLIl7nBBr74VioVWWtVqVTU1dUViXR2YKikpujzfgckGyOwANoS5RHYalW6805p5kzplVeat9tsM2n2bOnkk6Vx40a+P0b4wiupI1aIL95vAMQFI7AAXDXaX/r9HimYN0/66EedEdYxY6QTTmgcvJ5zjrRokTPC+tZb0mmnjS54lZzRgXK5rGq1qnK5HJsvk2EswtVpn8KUCQC4Ia7vNwDQDAEsYimMX7SjrtOUraGvgSRPU75ee036/OdracF77y39+tf17Q47TPrTn2ppwV//ujRxoitdiLUwpt6OpE+ZTKaj7QAAIFxIIUbsUNwheH68BqtWSbfe6qQFr1zZvF0u56QFH320E9hiZMKYejuSPvH+AABAOLWbQkwAi9gYmAfU6AutJI0ZM0bVapU5Qj7wKth54AEnYP3zn1u3u/xy6dxzpU02GfGuMEQqlVKjzwtjjKrVagA9GnmfmDMIAED4MAcWibJ+KmEza9euDU3qY9y5Nc/wb3+Tjjuulhb88Y83Dl6PP95pO5AWfNFF0Qleo5LuHsbU25H2iTmDAABEFwEsYqFQKAxKCRxOX1+fCoWChz1KtpEGFm+/7ayzOhCw7ryz9P3v17ebMkX61a9qAWupJO20kxs991cY55U2E8blOsLYJwAA4C0CWMTCSCqIUnXUO+0GFtWq9IMfSNmsE7Butpl06aX1j7fxxtKNN0rvvusErAMVhkei1Yin36OhjX54CeuPK2FcdzGMfQL8FpUsDgBwjbU29LcpU6ZYoJVsNmslNbyNGTOm4fZsNht0tyOtp6fHZrNZa4yx2WzW9vT0tPX3xx+39uMfHxg7bX77whesfe019/ucTqcHnQfpdNr29PS0/JtXjDENz01jjGf7RDINd70imoJ43wIAr0iaZ9uIDSnihFhoVVlUElVHXdZJJddFi6SvflX61rdaP+bHPy5ddZW0115e9NjRqriUJN+r7Iaxsi/ih8rL8cV7CIA4oYgTEqVVKuFo0gxJzWqsVerrqlVOuu/GGztpwdtu2zh4zWSc+a1r1zpjrv/zP94Gr1Lr4lJuFZ7qxEjmcHJOolNRSlVHZ4J43wKAoDECCzTBqEVz9cuXHCJptqQpLe936aXSeedJm27qYedaCNsIrNTZki6ckxiJMC6BBHcwAgsgThiBBUaJUYvmtttuP0k9qk27elCNgtdjj5Wef742s/WSS4ILXqXWI55BVbTtZEkXzsngDYyAG2M0duxYGWNCPxIexiWQ4A4qcQNIIgJYoAlSs2reeUe64ora8javvPJ7SfWBVja7VL/8ZS1gvfNOZymcsPAq1dwvnJPBGrre9Nq1ayUp1MsfSQQ5cRaF9y0AcBspxEATSU7Nslb60Y+kmTOlVk/VmJWydqYymZ/ryisv5UuTx5J8ToZBs+M/IMyvQyep6gAABIEUYmAE1i+Q88477yiVGnyJjBs3LrajFk88IR1+uDPCmkpJn/504+D1zDOlhQudILda3VDWflOVyvN8GfYBI2nBGm6k24uRcLeKdnWSqg4AQJh5FsAaY243xiwyxixYb9tWxpgHjDHP9/93S6/2D3Rq/fRAa62WLFlSV+DEGBNQ79y3eLF07rm1tOA995R+8Yv6doccIj32WC0t+MYbpUmT/O8vSBcM2nBzRt2eUzr0PSnsqcoAAPjBsxRiY8yBkt6RNNda+/7+bbMlLbXWXm2MuUDSltba84d7LFKI4Yfh0gMHhDlNsJXVq6Xbb3fSgpcta95uhx2ka66RjjvOGYkF4GhUBXqAF9WgSRkHACRJ4CnE1trfSlo6ZPOnJM3p//ccSUd6tX+gU+2m/0WpYM5DD0l77+2MsI4fL51+euPg9eKLpbfeckZYX3pJyue9C15ZxxRRtf4IuCSNGTNGkjwbCadoFwAA9Twt4mSMyUm6b70R2DettVus9/c3rLUN04iNMV2SuiQpk8lMaWdkDBiNOIzAlsvSRRdJPT2t233609JXvyrtsosv3VqHdUyB9jECCwBIksBHYEfLWtttrZ1qrZ06ceLEoLuDBGhUIGeosBXMWb5cKhZr81h33LFx8PrBDzrzW6tVZ5T1Bz/wP3iVWMcU6ARFuwAAqOd3APuaMWY7Ser/7yKf9w801ahAzhlnnBGqgjnWSnffLb33vU7Auskm0le+Ut9u/HjpG9+QVq6UenpKeuutnA4/PKUddxw+ZdfLFN+4pUS2c6xImcZIUbQLAIB6Y33e308lzZB0df9/7/V5/0BH9t9/f910002B9uHJJ6ULL5Tuv3+4ljdLulzp9LJ1X3KHpuwOVDGV1PBLcKftO5XJZBqmRLpdvdUP7RyrM888U7fccosGpmq4fTwRf/l8nnMFAID1eFmF+E5JB0vaWtJrki6RdI+kH0rKSOqVdIy1dmihpzpUIYYfwjI/c8kS6corpeuvb93u4IOlv/zlSL36av3vQANz5DqdQ+f1nLuwHGM3DHesSqWSpk+frkbvscxhBAAAGCzwObDW2uOstdtZa8dZa99jrf2utXaJtfaj1tpd+v87bPCKaBltumSQ6ZZBzc9cs0b6znekLbd00oK33rpx8DppkjR3rtPeWqfC8Guv/bThYw6k5Haasut1im+cUiKbHZNKpaJUKqUZM2Y0DF5b3RcAAACthbaIE6JnYHStUqnIWrsuXbLdIHS09x8tP+dn/u530oc/7ASs48ZJXV3Sm2/Wt5s1y9lurbRwoTR9utS/coek5qm3A9uH+/tot49EPp9XuVxWtVpVuVyOZPAqtT4m1lqtXbt2RPcFAABAcwSwcM1oRzCDrlDrZfDW2yuddFKtWvCBB0qPPlrf7j//U3r2WSdgtdapMLz55s0fd7gqpZ1WMfWz6mnUixu1U7W6EWMMVWQBAABGylob+tuUKVMs3NPT02Oz2aw1xthsNmt7enpceVxjjJVUdzPG+HL/0erp6bHpdHrQvtPp9IiOz/Ll1l555UAY2vz2/vdbe//91laro+t3q9ez09e7WXs3zxs3j3WQ1j8mjc7dRufyGWecEXS3AQAAQkfSPNtGbBh4cNrOjQDWPV4GDtlstuGX9mw268v93TDSIK1atfbHP7Z2551bB6xjx1p7/fXWrljh8RNxmdvnTRhea7c1e05jxoxx/cciAACAuGk3gCWFOGHcTNMdmgI6bdq0UaWf+pm+2kwn8zMXLJA++UknJTiVko46Snrhhfp2XV3SSy85Iezq1dK550obbujhk/CA2+ndcVsPVmp+/s6ZMyfy830BAADCggA2YdwKHBoVXJozZ45mzJgx4gqzYa9Qu3Sp9OUv1+axfuAD0n331bc78EDpkUdq46633irtsIP//XWT2wGnH8Wi/Bb28xcAACAOPFsH1k2sA+set9b59Hq90DBYs0aaM0eaOdMJXpvZZhtp9mzphBMGVwiOE7df7zitBwsAAIDRC3wdWISTW2m6cUwBlaQ//EHaf//a8jaf+1zj4PWCC6Q33nBGWF97TZoxI77Bq+R+ejejlQAAABgJAtiEcStwiEsK6EsvSaecUksLPuAAJ/13qCOPlJ55ppYWfNVV0hZb+N/foHgRcMZlPVgAAAD4hxRijEhUU0BXrJC+/W0nLbiV3Xd30oL//d+dwBYAAACAd0ghhqfaGZEbWqW4VCr53k9rpf/7f6V/+icnEE2nmwevs2dLfX3OfZ59tlZhGMELw7kEAACA4BHAJoybgUCrFNBGVYq7urp8CTyefVb6j/+oLW9zxBHSX/5S3+6UU6QXX6ylBX/5y9JGG3nevY4QuAVzLnHcAQAAwokU4gTxM+3XzyrFb7whXXONc2tl//2dNvvv7+ruPRPVNG23+V3xmuMOAADgv3ZTiAlgE8TPQCCVSqnRuWWMUbVaHdVjr10rzZ3rpAIvXty83dZbO2nB06dLY8eOapeBCCJwKxQK6u3tVSaTUbFYDEXA5uW51EgSlogCAAAIG+bAoo6fS9+4XaX4f/9X+shHnLTgsWOlk09uHLzOnCktWeKkBL/+unTSSdEMXiV/X68gU76H43fF67guEQUAABAHBLAJ4nUgsP68wXfeeUfjx48f9PdO1g19+WXp1FNry9vst5/0+9/XtzviCGnBgto81muukbbayo1nEzw/A7dCoTAoZVaS+vr6VCgUXN9Xp9xeg3Y4cVkiCgAAII4IYBPEy0Bg6AjekiVLZK3VhAkT2lo3dOVK6brrpDFjnID1Pe+Rbrutvt2uu0r33CNVq07Aeu+90vveN6qpei0AABfPSURBVOrue2K0hYD8DNzCPOroxRq0rfgdMAMAAKAD1trQ36ZMmWLhjp6eHpvNZq0xxmazWdvT0+PK42azWSup7pbNZhu2r1atve8+a/fYY2DstPnt6qutXb7clW76pqenx6bT6UHHIp1Od3y8vXq9hur09Ys7v447AAAAHJLm2TZiQ4o4wRXtFNr561+lWbOkH/+49WOdeKJ02WVSlDM2o1YIiMq7AAAACFK7RZwiWt4GYZPJZBoEbJtp002LMqb1fffd15m7euCBnnXPd2FOyW1kIEgNYxViAAAAYABzYOGKYrGojTbaRNIJkl6Rk4H6lpYtO6uu7RZbSN3d0urVTpLw//5vvIJXKZqFgPL5vMrlsqrVqsrlMsErAAAAQocAFqPy2GPSv/6rdMIJea1Y8bak/5a0XV27885zlr2xVnrjDafCcFSXt2lHu4WARlvoCQAAAEgSAtgEG0nwtHChdMYZteVt9tlH+s1v6ttNmyY98UStFNN110kTJrj/HMKqncq5YV57FQAAAAgjijglVLtFe959V7rpJmnmTGnNmuaPt/PO0uzZ0pFHatg5r3BErdATAAAA4JV2izgRwCZUs+Apk8nqllvKmjlTWrCg9WNceaX0hS9IG2/sUSdjrp3KzQAAAEAStBvAkkKcUIOr4e4i6YeSrHp7y5o2rXHw+tnPSv/4Ry0t+MILCV5HI4qFngAAAIAgEcAm0LJl0mabfUNOpWAr6TlJx9S1+9CHpIceqgWsc+ZIuZy/fY2zdgs9AQAAAHAQwCZAtSqVStIOOzjzUzffXHrrrS80aPmWTjrpj1q1yglY//hH6eCD/e5tcrRT6AkAAABADXNgY2rePOn886Vf/7p1u003/a7efvtCZbPOyB/BEwAAAAC/MQc2YV59Vfr852vL2+y9d+Pg9bDDpD/9qZYWvGzZKbJ20bqqt3FZk5T1VQEAAID4GRt0BzAyq1ZJt97qLG+zcmXzdrmcs7zN0Ue3Xt5m6LI6A2uSSorcqGycngsAAACAGlKII+SBB5yA9c9/bt3u8sulc8+VNtmk/ceO05qkcXouAAAAQBK0m0LMCGyIvfCCVChIP/xh63YnnOAErTvuOPJ9DV5WZ/jtYRan5wIAAACghjmwIfL229Ill9Tmse6yS+PgdepU6cILf6VsNidjUvrd73J65JHRzfH0a01SP+amsr4qAAAAEE8EsAGqVqU775QmT3YC1s02c0ZSh9pkE+mmm7RueZtzzinphhs+pUqlImvtujmeowkG/ViTdGBuqpv9boT1VQEAAIB4IoD12fz50qGHOgHrmDHS8cdLL71U327TTe+QtK2y2ZxuuaWkM86Qxo1z/lYoFNYVKBrQ19enQqEw4n6Ndk3SdkZWveh3I6yvCgAAAMQTRZw8tmiRdMUV0re/3brdxz8uXXWV9OyzgyvoSs7o4foBWCqVUqPXzRijarXqav/bMbTqr1TfZyl8/QYAAAAQDu0WcSKAddmqVdJ3vuNUCx4y2DhIJuMsb3PMMVJqvXHwdirohq3Kbrv9CVu/AQAAAIRDuwEsKcQuePhhacoUJy14gw2ks85qHLxeeqm0bJkzj7VSkT7zmcHBq9ReBV2353iOtrBSu1V/mZsKAAAAYDQIYEfpwQelgw+WHn+8/m/HHis9/7wTsFrrVBjedNPWj9dOBV0353i6UVhpuD4PBMjTp0/XRhttpAkTJjA3FQAAAEDHSCEepSeekPbc0/n3nns6acGHHjryx2t3Pqlb3EjrbdVnSb4+HwAAAADRwxzYCCuVSioUCurt7VUmk1GxWPQs2HOrsFKzPjPvFQAAAMBwCGDRFq8DTCoPAwAAABgORZzQlmKxqHEDC8z2GzdunGuFldqZ0wsAAAAA7SCADZHRVgMeKWPMoP9fvXq1vvjFL7qyfyoPAwAAAHALAWxIuFENeCQKhYJWrVpVt33JkiWu7N/NiskAAAAAko05sCERVLGjZnNU/do/AAAAADAHNmJ6e3s72u6W4eaier1/AAAAAGgXAWxIBFXsqNEcVT/3DwAAAADtIoANiaCKHQ3MUZ0wYULd3yi2BAAAACBMCGBDIshiR/l8XosXL1ZPTw/FlgAAAACEFkWcAAAAAACBoogTAAAAACBWCGABAAAAAJFAAAsAAAAAiAQC2AQqlUrK5XJKpVLK5XIqlUpBdwkAAAAAhjU26A7AX6VSSV1dXerr65MkVSoVdXV1SRIVhwEAAACEGiOwCVMoFNYFrwP6+vpUKBQC6hEAAAAAtIcANoS8TPHt7e3taDsAAAAAhAUBbMgMpPhWKhVZa9el+LoVxGYymY62AwAAAEBYEMCGzEhTfNsdtS0Wi0qn04O2pdNpFYvF0XUcAAAAADxGABsSAwFopVJp+PdWKb6djNrm83l1d3crm83KGKNsNqvu7m4KOAEAAAAIPWOtDboPw5o6daqdN29e0N3wzNDKwI1ks1mVy+WGf2sW+La6DwAAAACEhTFmvrV26nDtGIENgf/f3r0HSVaWdxz//pbFwBKVIJdSZGe9kHhnNSuhgiGgiSIxQRMSIZN4o5zEYIy5VIJOElfNpNQYMUklVCYRRDOCREWNIkpxCZWyuCywLDcvBFlECCwFojjKzSd/9Gloxp6dmV13zpyd76dqqvu85+3up3ufenufft9zzrBlw4PmWuLriZkkSZIkLQcWsIts2LGqWys057PE1xMzSZIkSVoOLGAX0WzHqu61115D+/eXAM91fOpRRx21oHZJkiRJ6iIL2EU02xmGge06M/DZZ5+9oHZJkiRJ6iIL2EU021Lhu+66a7vODOwxsJIkSZKWg5VtB7CcrF69eujZglevXs3o6Og2X8pma88rSZIkSTuLVmZgk9yU5OokG5PsvNfHmWFiYmK7lgov9vNKkiRJ0lLS5hLiI6pq7Xyu9bOzGB0d3a6lwov9vJIkSZK0lKSqFv9Fk5uAdVV153z6r1u3rjZsWDYTtZIkSZK0rCS5fD6Tm23NwBbwpSSXJxkb1iHJWJINSTZs2bJlkcNr17BrxUqSJEnSctfWSZwOrapbk+wLnJvkK1V10WCHqpoEJqE3A9tGkG3oXyu2f3md/rViAZcES5IkSVrWWpmBrapbm9s7gLOAg9uIYyma7Vqx4+PjLUUkSZIkSUvDohewSfZI8tj+feClwDWLHcdS5TVdJUmSJGm4NmZg9wP+J8lVwKXA56vqnBbiWJJmu3ar13SVJEmStNwtegFbVTdW1UHN37OryouVDvCarpIkSZI0XJvXgdUQXtNVkiRJkoZr5TqwC+V1YCVJkiRp57XUrwMrSZIkSdKCWMBKkiRJkjrBAlaSJEmS1AkWsJIkSZKkTrCAlSRJkiR1ggWsJEmSJKkTLGAlSZIkSZ1gAStJkiRJ6gQLWEmSJElSJ1jASpIkSZI6wQJWkiRJktQJFrCSJEmSpE6wgJUkSZIkdYIFrCRJkiSpEyxgJUmSJEmdYAErSZIkSeoEC1hJkiRJUidYwC6Cqakp1qxZw4oVK1izZg1TU1NthyRJkiRJnbOy7QB2dlNTU4yNjTE9PQ3A5s2bGRsbA2B0dLTN0CRJkiSpU5yB3cHGx8cfLl77pqenGR8fbykiSZIkSeomC9gd7Oabb15QuyRJkiRpOAvYHWz16tULapckSZIkDWcBu4NNTEywatWqR7WtWrWKiYmJliKSJEmSpG6ygN3BRkdHmZycZGRkhCSMjIwwOTnpCZwkSZIkaYFSVW3HMKd169bVhg0b2g5DkiRJkrQDJLm8qtbN1c8ZWEmSJElSJ1jASpIkSZI6wQJWkiRJktQJFrCSJEmSpE6wgJUkSZIkdYIFrCRJkiSpEyxgJUmSJEmdYAErSZIkSeoEC1hJkiRJUidYwEqSJEmSOsECVpIkSZLUCRawkiRJkqROsICVJEmSJHWCBawkSZIkqRMsYCVJkiRJnWABK0mSJEnqBAtYSZIkSVInWMBup6mpKdasWcOKFStYs2YNU1NTbYckSZIkSTullW0H0GVTU1OMjY0xPT0NwObNmxkbGwNgdHS0zdAkSZIkaafjDOx2GB8ff7h47ZuenmZ8fLyliCRJkiRp52UBux1uvvnmBbVLkiRJkradBex2WL169YLaJUmSJEnbzgJ2O0xMTLBq1apHta1atYqJiYmWIpIkSZKknZcF7HYYHR1lcnKSkZERkjAyMsLk5KQncJIkSZKkHSBV1XYMc1q3bl1t2LCh7TAkSZIkSTtAksurat1c/ZyBlSRJkiR1ggWsJEmSJKkTLGAlSZIkSZ1gAStJkiRJ6gQLWEmSJElSJ1jASpIkSZI6wQJWkiRJktQJFrCSJEmSpE6wgJUkSZIkdYIFrCRJkiSpEyxgJUmSJEmdYAErSZIkSeoEC1hJkiRJUidYwEqSJEmSOsECVpIkSZLUCamqtmOYU5ItwOa245jF3sCdbQehJc0c0VzMEc3FHNF8mCeaizmiubSZIyNVtc9cnTpRwC5lSTZU1bq249DSZY5oLuaI5mKOaD7ME83FHNFcupAjLiGWJEmSJHWCBawkSZIkqRMsYLffZNsBaMkzRzQXc0RzMUc0H+aJ5mKOaC5LPkc8BlaSJEmS1AnOwEqSJEmSOsECVpIkSZLUCRaw2yjJkUm+muSGJCe2HY+WhiQ3Jbk6ycYkG5q2vZKcm+Trze1PtR2nFleSU5LckeSagbaheZGef2zGlk1JXtBe5Foss+TI+iTfasaTjUmOGtj3tiZHvprkZe1ErcWU5IAkFyS5Psm1Sf6oaXcsEbDVHHEsEQBJdktyaZKrmhx5Z9P+lCSXNOPIx5M8pmn/iWb7hmb/mjbj77OA3QZJdgH+GXg58CzguCTPajcqLSFHVNXagWtonQicV1UHAuc121pePgwcOaNttrx4OXBg8zcGnLxIMapdH+ZHcwTgpGY8WVtVZwM03zfHAs9uHvMvzfeSdm4PAn9aVc8EDgFOaHLBsUR9s+UIOJao5z7gxVV1ELAWODLJIcB76eXIgcDdwPFN/+OBu6vq6cBJTb/WWcBum4OBG6rqxqq6HzgDOLrlmLR0HQ2c1tw/DXhli7GoBVV1EXDXjObZ8uJo4CPVczGwZ5InLk6kasssOTKbo4Ezquq+qvoGcAO97yXtxKrqtqq6orn/XeB6YH8cS9TYSo7MxrFkmWnGg3ubzV2bvwJeDHyiaZ85jvTHl08AL0mSRQp3Vhaw22Z/4JsD27ew9QFCy0cBX0pyeZKxpm2/qroNel8uwL6tRaelZLa8cHzRoDc3yz9PGTj8wBxZ5pplfM8HLsGxREPMyBFwLFEjyS5JNgJ3AOcC/wt8u6oebLoM5sHDOdLsvwd4wuJG/KMsYLfNsF8evB6RAA6tqhfQW7p1QpLD2g5IneP4or6TgafRW+Z1G/D3Tbs5sowl+Ungk8Bbq+o7W+s6pM08WQaG5IhjiR5WVQ9V1VrgyfRm3J85rFtzuyRzxAJ229wCHDCw/WTg1pZi0RJSVbc2t3cAZ9EbGG7vL9tqbu9oL0ItIbPlheOLAKiq25v/aPwQ+DceWdpnjixTSXalV5hMVdWnmmbHEj1sWI44lmiYqvo2cCG946X3TLKy2TWYBw/nSLP/8cz/cJcdxgJ221wGHNicsesx9A6A/2zLMallSfZI8tj+feClwDX0cuO1TbfXAp9pJ0ItMbPlxWeB1zRnED0EuKe/PFDLy4zjFV9FbzyBXo4c25wd8in0TtJz6WLHp8XVHHf2IeD6qvrAwC7HEgGz54hjifqS7JNkz+b+7sAv0TtW+gLgmKbbzHGkP74cA5xfVa3PwK6cu4tmqqoHk7wZ+CKwC3BKVV3bclhq337AWc2x7SuBj1XVOUkuA85McjxwM/CbLcaoFiQ5HTgc2DvJLcA7gPcwPC/OBo6idzKNaeD1ix6wFt0sOXJ4krX0lmvdBPweQFVdm+RM4Dp6Zx09oaoeaiNuLapDgd8Frm6OXwN4O44lesRsOXKcY4kaTwROa842vQI4s6o+l+Q64IwkfwNcSe+HEJrbjya5gd7M67FtBD1TlkARLUmSJEnSnFxCLEmSJEnqBAtYSZIkSVInWMBKkiRJkjrBAlaSJEmS1AkWsJIkSZKkTrCAlSQtGUkqyUcHtlcm2ZLkcy3F84YkVyfZlOSaJEfvgNd4+w54ztcn2dj83d+8h41JzkhyS5IVM/pvTHLwNr7WhUnWLaD/W5O8Zkbb+hnb+yQ5Z1vikSTt3CxgJUlLyfeA5zQXWAf4ZeBbbQSS5MnAOPCiqnoecAiwaQe81I+9gK2qU6tqbVWtBW4Fjmi2jwW+CfxCv2+SZwCPrapLf9xxzJRkJfAG4GPN9rOSXAS8KckVSY5r4t8C3Jbk0B0dkySpWyxgJUlLzReAX2nuHwec3t+RZK8kn25mRC9O8rymfX2SU5rZwBuTvGXgMb+T5NJmlvFfk+yS5PgkJw30eWOSD8yIY1/gu8C9AFV1b1V9o+l/YZIPJvlyMzN7cNO+RxPHZUmu7M/YJnldkk8lOSfJ15O8r2l/D7B7E9vUzA8iyXHN7Ok1Sd470H5vkokkVzWfw34L+HxP59EXoz928DMeeI2Dm/d3ZXP7M0377s1M7qYkHwd2b9p3SfLhJtark/zxkNd+MXBFVT3YbK8HPgKcDBwKXDbQ99PA6ALelyRpGbCAlSQtNWcAxybZDXgecMnAvncCVzYzom+nV/z0PQN4GXAw8I4kuyZ5JvBq4NBmNvIhekXRGcCvJdm1eezrgVNnxHEVcDvwjSSnJvnVGfv3qKqfB/4AOKVpGwfOr6oXAkcAf5dkj2bf2iaW5wKvTnJAVZ0IfL+ZHX1UsZbkScB76RV9a4EXJnll/7WBi6vqIOAi4I3DP8qhzgRe2cyG0sR0xpB+XwEOq6rnA38N/G3T/iZguvk3mAB+duD97V9Vz6mq5/Kjnyf0itTLB7bvp/dDwYqq+n5V3TCwbwMDM8WSJIEFrCRpiamqTcAaerOvZ8/Y/SLgo02/84EnJHl8s+/zVXVfVd0J3AHsB7yEXoF1WZKNzfZTq+p7wPnAK5oltLtW1dUz4ngIOBI4BvgacNKMYzVPb/pdBDwuyZ7AS4ETm9e6ENgNWN30P6+q7qmqHwDXASNzfBQvBC6sqi3NjOUUcFiz736gf1zw5c3nNS9V9X/AtcBLkqwFHqiqa4Z0fTzwn0muAU4Cnt20Hwb8R/Ncm3hkWfWNwFOT/FOSI4HvDHnOJwJbBrb/gl5B/+Yk/5XkoIF9dwBPmu/7kiQtDyvn7iJJ0qL7LPB+4HDgCQPtGdK3mtv7BtoeovcdF+C0qnrbkMf9O71Z3K8wfLaQqirgUuDSJOc2/dbPeN3BOAL8RlV9dXBHkp+bJb6tGfZe+x5oYpvvc83UX0Z8O0OWDzfeDVxQVa9KsoZeQd43871TVXc3BejLgBOA36J3vOug79Mr6vuP+RZwXJJ30Vs+/Cngac3u3Zr+kiQ9zBlYSdJSdArwrpmzovSWy44CJDkcuLOqhs309Z0HHJNk3+YxeyUZAaiqS4ADgN9m+DGgT0rygoGmtcDmge1XN/1eBNxTVfcAXwT+MEmafc+fx3t9YGAp86BLgF9MsneSXejNSP/3PJ5vPj4JHMXsy4ehNwPbP4HW6wbaB/8NnkNvmTdJ9qa3FPiTwF8Bg59d3/XA0/sbSfqzuj+kN5O8x0DfnwaGzQxLkpYxZ2AlSUtOVd0C/MOQXeuBU5NsAqaB187xPNcl+UvgS82lYx6gNzvYL0TPBNZW1d1DHr4r8P7mWNQf0Fv6+vsD++9O8mXgcTwy0/hu4IPApqaIvQl4xdbfLZNN/ysGj4OtqtuSvA24gN5s7NlV9Zk5nmtequrbSS4G9uufmGqI9wGnJfkTesut+07mkX+DjfRmqAH2b9r7P44Pm/X+As0S8MavJ/kQvaXCxwBvGdh3BPD5BbwtSdIykEdWIEmStLykd33Zk6rqvAU+7kLgz6pqww4JbCeW5Czgz6vq6wNt66tq/Yx+FwFHz/LjgiRpmXIJsSRp2UmyZ5Kv0TsD8IKKV223E+mdzGnQhYMbSfYBPmDxKkmayRlYSZIkSVInOAMrSZIkSeoEC1hJkiRJUidYwEqSJEmSOsECVpIkSZLUCRawkiRJkqRO+H8b19j770SaNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions=reg.predict(X)\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.scatter(data['TV'],data['sales'],c='black')\n",
    "plt.plot(data['TV'],predictions,c='blue',linewidth=2)\n",
    "plt.xlabel(\"Money Spent on TV ads ($)\")\n",
    "plt.ylabel(\"Sales($)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.612\n",
      "Model:                            OLS   Adj. R-squared:                  0.610\n",
      "Method:                 Least Squares   F-statistic:                     312.1\n",
      "Date:                Fri, 07 Jun 2019   Prob (F-statistic):           1.47e-42\n",
      "Time:                        11:10:00   Log-Likelihood:                -519.05\n",
      "No. Observations:                 200   AIC:                             1042.\n",
      "Df Residuals:                     198   BIC:                             1049.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
      "==============================================================================\n",
      "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
      "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
      "Skew:                          -0.089   Prob(JB):                        0.716\n",
      "Kurtosis:                       2.779   Cond. No.                         338.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\revanth\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X=data['TV']\n",
    "y=data['sales']\n",
    "\n",
    "X2 =sm.add_constant(X)\n",
    "est=sm.OLS(y,X2)\n",
    "est2=est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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