Startup 50 (Multiple linear regression).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective:- We want to prepare a model for predictions of profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup=pd.read_csv(\"D:/Data sets/50_startups.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will remove the state column beacuse it have nominal data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup.drop('State',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend     Profit\n",
       "0  165349.20       136897.80        471784.10  192261.83\n",
       "1  162597.70       151377.59        443898.53  191792.06\n",
       "2  153441.51       101145.55        407934.54  191050.39\n",
       "3  144372.41       118671.85        383199.62  182901.99\n",
       "4  142107.34        91391.77        366168.42  166187.94"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   Profit           50 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.7 KB\n"
     ]
    }
   ],
   "source": [
    "startup.info() #No null missing value present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73721.615600</td>\n",
       "      <td>121344.639600</td>\n",
       "      <td>211025.097800</td>\n",
       "      <td>112012.639200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45902.256482</td>\n",
       "      <td>28017.802755</td>\n",
       "      <td>122290.310726</td>\n",
       "      <td>40306.180338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>51283.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14681.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39936.370000</td>\n",
       "      <td>103730.875000</td>\n",
       "      <td>129300.132500</td>\n",
       "      <td>90138.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73051.080000</td>\n",
       "      <td>122699.795000</td>\n",
       "      <td>212716.240000</td>\n",
       "      <td>107978.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>101602.800000</td>\n",
       "      <td>144842.180000</td>\n",
       "      <td>299469.085000</td>\n",
       "      <td>139765.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165349.200000</td>\n",
       "      <td>182645.560000</td>\n",
       "      <td>471784.100000</td>\n",
       "      <td>192261.830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup.rename(columns={'R&D Spend':'rdspend' , \"Marketing Spend\" : \"mrktspend\"}, inplace=True) #change the column name "
   ]
  },
  {
   "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>rdspend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>mrktspend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rdspend  Administration  mrktspend     Profit\n",
       "0  165349.20       136897.80  471784.10  192261.83\n",
       "1  162597.70       151377.59  443898.53  191792.06\n",
       "2  153441.51       101145.55  407934.54  191050.39\n",
       "3  144372.41       118671.85  383199.62  182901.99\n",
       "4  142107.34        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup.head() ##This is our new dataframe we will continue with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rdspend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>mrktspend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rdspend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.972900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mrktspend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.972900</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>0.747766</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 rdspend  Administration  mrktspend    Profit\n",
       "rdspend         1.000000        0.241955   0.724248  0.972900\n",
       "Administration  0.241955        1.000000  -0.032154  0.200717\n",
       "mrktspend       0.724248       -0.032154   1.000000  0.747766\n",
       "Profit          0.972900        0.200717   0.747766  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup.corr() ##correlation b/w the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(startup.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ff3d572fd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style='darkgrid')\n",
    "sns.pairplot(startup)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will create a base model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf \n",
    "model = smf.ols('Profit~rdspend+Administration+mrktspend',data=startup).fit()\n",
    " #Probability of Administration & mrktspend are greater than 0.05 it mean both are insignificant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   296.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 19 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>4.53e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:27:34</td>     <th>  Log-Likelihood:    </th> <td> -525.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1059.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    46</td>      <th>  BIC:               </th> <td>   1066.</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> 5.012e+04</td> <td> 6572.353</td> <td>    7.626</td> <td> 0.000</td> <td> 3.69e+04</td> <td> 6.34e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rdspend</th>        <td>    0.8057</td> <td>    0.045</td> <td>   17.846</td> <td> 0.000</td> <td>    0.715</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th> <td>   -0.0268</td> <td>    0.051</td> <td>   -0.526</td> <td> 0.602</td> <td>   -0.130</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mrktspend</th>      <td>    0.0272</td> <td>    0.016</td> <td>    1.655</td> <td> 0.105</td> <td>   -0.006</td> <td>    0.060</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.838</td> <th>  Durbin-Watson:     </th> <td>   1.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  21.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.949</td> <th>  Prob(JB):          </th> <td>2.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.586</td> <th>  Cond. No.          </th> <td>1.40e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.4e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.951\n",
       "Model:                            OLS   Adj. R-squared:                  0.948\n",
       "Method:                 Least Squares   F-statistic:                     296.0\n",
       "Date:                Tue, 19 Jan 2021   Prob (F-statistic):           4.53e-30\n",
       "Time:                        18:27:34   Log-Likelihood:                -525.39\n",
       "No. Observations:                  50   AIC:                             1059.\n",
       "Df Residuals:                      46   BIC:                             1066.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept       5.012e+04   6572.353      7.626      0.000    3.69e+04    6.34e+04\n",
       "rdspend            0.8057      0.045     17.846      0.000       0.715       0.897\n",
       "Administration    -0.0268      0.051     -0.526      0.602      -0.130       0.076\n",
       "mrktspend          0.0272      0.016      1.655      0.105      -0.006       0.060\n",
       "==============================================================================\n",
       "Omnibus:                       14.838   Durbin-Watson:                   1.282\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               21.442\n",
       "Skew:                          -0.949   Prob(JB):                     2.21e-05\n",
       "Kurtosis:                       5.586   Cond. No.                     1.40e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.4e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RnD</td>\n",
       "      <td>2.468903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mrkt</td>\n",
       "      <td>2.326773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adms</td>\n",
       "      <td>1.175091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variables       VIF\n",
       "0       RnD  2.468903\n",
       "1      Mrkt  2.326773\n",
       "2      adms  1.175091"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsq_rd = smf.ols('rdspend~Administration+mrktspend',data=startup).fit().rsquared  \n",
    "vif_rd = 1/(1-rsq_rd)\n",
    "\n",
    "rsq_mr = smf.ols('mrktspend~rdspend+Administration',data=startup).fit().rsquared  \n",
    "vif_mr = 1/(1-rsq_mr)\n",
    "\n",
    "rsq_adm = smf.ols('Administration~mrktspend+rdspend',data=startup).fit().rsquared  \n",
    "vif_adm = 1/(1-rsq_adm)\n",
    "\n",
    "d1 = {'Variables':['RnD','Mrkt','adms'],'VIF':[vif_rd,vif_mr,vif_adm]}\n",
    "Vif_frame = pd.DataFrame(d1)  \n",
    "Vif_frame\n",
    "##VIF is less than 10 than we can say no much collinearity b/w independent varible "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "qqplot=sm.qqplot(model.resid,line='q') # line = 45 to draw the diagnoal line\n",
    "plt.title(\"Normal Q-Q plot of residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([49], dtype=int64)]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.where(model.resid<-20000)) ## Outlier in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will check the Homoscedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(get_standardized_values(model.fittedvalues),\n",
    "            get_standardized_values(model.resid))\n",
    "\n",
    "plt.title('Residual Plot')\n",
    "plt.xlabel('Standardized Fitted values')\n",
    "plt.ylabel('Standardized residual values')\n",
    "plt.show()\n",
    "\n",
    "##The residuals is fillowing approx Homoscedasticity but one outlier is there we will remove it  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will check the cook distance for outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_influence = model.get_influence()\n",
    "(c, _) = model_influence.cooks_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the influencers values using stem plot\n",
    "fig = plt.subplots(figsize=(20, 7))\n",
    "plt.stem(np.arange(len(startup)), np.round(c, 3))\n",
    "plt.xlabel('Row index')\n",
    "plt.ylabel('Cooks Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 0.2880822927543267)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(c),np.max(c)) ##It is the outlier in our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Will check the laverage influence plot for detection of outlier in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "influence_plot(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30000000000000004"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = startup.shape[1]\n",
    "n = startup.shape[0]\n",
    "leverage_cutoff = 3*((k + 1)/n)\n",
    "leverage_cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rdspend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>mrktspend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>116983.8</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>14681.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rdspend  Administration  mrktspend   Profit\n",
       "49      0.0        116983.8   45173.06  14681.4"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup[startup.index.isin([49])] ##We will remove the 49 from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup1=startup.drop(startup.index[[49]],axis=0).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "startup1=startup1.drop(['index'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rdspend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>mrktspend</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rdspend  Administration  mrktspend     Profit\n",
       "0  165349.20       136897.80  471784.10  192261.83\n",
       "1  162597.70       151377.59  443898.53  191792.06\n",
       "2  153441.51       101145.55  407934.54  191050.39\n",
       "3  144372.41       118671.85  383199.62  182901.99\n",
       "4  142107.34        91391.77  366168.42  166187.94"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will create a new model after removing influence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = smf.ols('Profit~rdspend+Administration+mrktspend',data=startup1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   372.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 19 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>8.85e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:40:22</td>     <th>  Log-Likelihood:    </th> <td> -506.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1021.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   1028.</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> 5.238e+04</td> <td> 5542.657</td> <td>    9.451</td> <td> 0.000</td> <td> 4.12e+04</td> <td> 6.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rdspend</th>        <td>    0.7830</td> <td>    0.038</td> <td>   20.470</td> <td> 0.000</td> <td>    0.706</td> <td>    0.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th> <td>   -0.0222</td> <td>    0.043</td> <td>   -0.518</td> <td> 0.607</td> <td>   -0.109</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mrktspend</th>      <td>    0.0252</td> <td>    0.014</td> <td>    1.825</td> <td> 0.075</td> <td>   -0.003</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   1.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.082</td> <th>  Prob(JB):          </th> <td>   0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.706</td> <th>  Cond. No.          </th> <td>1.41e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.41e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.961\n",
       "Model:                            OLS   Adj. R-squared:                  0.959\n",
       "Method:                 Least Squares   F-statistic:                     372.8\n",
       "Date:                Tue, 19 Jan 2021   Prob (F-statistic):           8.85e-32\n",
       "Time:                        19:40:22   Log-Likelihood:                -506.28\n",
       "No. Observations:                  49   AIC:                             1021.\n",
       "Df Residuals:                      45   BIC:                             1028.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept       5.238e+04   5542.657      9.451      0.000    4.12e+04    6.35e+04\n",
       "rdspend            0.7830      0.038     20.470      0.000       0.706       0.860\n",
       "Administration    -0.0222      0.043     -0.518      0.607      -0.109       0.064\n",
       "mrktspend          0.0252      0.014      1.825      0.075      -0.003       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.082   Durbin-Watson:                   1.598\n",
       "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.232\n",
       "Skew:                          -0.082   Prob(JB):                        0.890\n",
       "Kurtosis:                       2.706   Cond. No.                     1.41e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.41e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.summary()\n",
    "##Here Administration have pvalue is greater than 0.05 means it is in significant so we will try transformation for better result "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We will apply the cubic transformation on the for better result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_model = smf.ols('Profit~rdspend+np.square(Administration)*Administration+np.square(mrktspend)*mrktspend',data=startup1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   190.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 19 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>8.08e-29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:44:41</td>     <th>  Log-Likelihood:    </th> <td> -504.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1022.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th> <td>   1035.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</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> 3.856e-05</td> <td> 5.97e-06</td> <td>    6.458</td> <td> 0.000</td> <td> 2.65e-05</td> <td> 5.06e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rdspend</th>                                  <td>    0.7758</td> <td>    0.039</td> <td>   19.779</td> <td> 0.000</td> <td>    0.697</td> <td>    0.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(Administration)</th>                <td>-1.073e-05</td> <td> 2.97e-06</td> <td>   -3.613</td> <td> 0.001</td> <td>-1.67e-05</td> <td>-4.74e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>                           <td>    1.3333</td> <td>    0.206</td> <td>    6.474</td> <td> 0.000</td> <td>    0.918</td> <td>    1.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(Administration):Administration</th> <td> 2.645e-11</td> <td> 1.13e-11</td> <td>    2.348</td> <td> 0.024</td> <td> 3.71e-12</td> <td> 4.92e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(mrktspend)</th>                     <td>-3.931e-07</td> <td> 3.53e-07</td> <td>   -1.114</td> <td> 0.272</td> <td>-1.11e-06</td> <td> 3.19e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mrktspend</th>                                <td>    0.0807</td> <td>    0.067</td> <td>    1.201</td> <td> 0.236</td> <td>   -0.055</td> <td>    0.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.square(mrktspend):mrktspend</th>           <td> 6.416e-13</td> <td>  5.2e-13</td> <td>    1.233</td> <td> 0.224</td> <td>-4.08e-13</td> <td> 1.69e-12</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.312</td> <th>  Durbin-Watson:     </th> <td>   1.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.315</td> <th>  Jarque-Bera (JB):  </th> <td>   1.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.170</td> <th>  Prob(JB):          </th> <td>   0.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.213</td> <th>  Cond. No.          </th> <td>9.64e+17</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.64e+17. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.965\n",
       "Model:                            OLS   Adj. R-squared:                  0.960\n",
       "Method:                 Least Squares   F-statistic:                     190.6\n",
       "Date:                Tue, 19 Jan 2021   Prob (F-statistic):           8.08e-29\n",
       "Time:                        19:44:41   Log-Likelihood:                -504.12\n",
       "No. Observations:                  49   AIC:                             1022.\n",
       "Df Residuals:                      42   BIC:                             1035.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                 3.856e-05   5.97e-06      6.458      0.000    2.65e-05    5.06e-05\n",
       "rdspend                                      0.7758      0.039     19.779      0.000       0.697       0.855\n",
       "np.square(Administration)                -1.073e-05   2.97e-06     -3.613      0.001   -1.67e-05   -4.74e-06\n",
       "Administration                               1.3333      0.206      6.474      0.000       0.918       1.749\n",
       "np.square(Administration):Administration  2.645e-11   1.13e-11      2.348      0.024    3.71e-12    4.92e-11\n",
       "np.square(mrktspend)                     -3.931e-07   3.53e-07     -1.114      0.272   -1.11e-06    3.19e-07\n",
       "mrktspend                                    0.0807      0.067      1.201      0.236      -0.055       0.216\n",
       "np.square(mrktspend):mrktspend            6.416e-13    5.2e-13      1.233      0.224   -4.08e-13    1.69e-12\n",
       "==============================================================================\n",
       "Omnibus:                        2.312   Durbin-Watson:                   1.640\n",
       "Prob(Omnibus):                  0.315   Jarque-Bera (JB):                1.501\n",
       "Skew:                           0.170   Prob(JB):                        0.472\n",
       "Kurtosis:                       2.213   Cond. No.                     9.64e+17\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.64e+17. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Residual Vs Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(Final_model, \"rdspend\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(Final_model, \"Administration\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NN7BarXh7e5OQkMAtt9xCfn4+U6dO5cCBA1x11VXo9Xq6du1a4Rg2m41evXrx+uuvc9NNNwEwbtw4brvtNrp3787UqVOxWCwopRg0aBDDhw+vNmZ/f3+WLFnC8OHDadWqFbNmzSIlJYUWLVpc9vkLIYQQzlJSUsLJkydp2rSpo+2NN97g888/x263c8011zBjxgyCgoL4/PPPeeONN9A0Db1ez8SJE7n11lsZMWIEw4cP5/7772flypW8++67+Pv706FDB8c+k5OTOXfunGOkZPnv9+zZw8KFC7FYLJw+fZo777yTuXPnVojz8OHDl/zbu2jRIgoKCnjxxRcB+PLLL1m6dClr167l+++/JzExkaKiInQ6HWPHjqVnz568//77rFu3jqKiIvz9/Vm0aBEJCQmcO3cOgLvvvptx48bx/vvv89lnn/Hmm28CVPj+u+++Y/78+Y5rmr/+9a/06dOnQmy7du1i0aJFtGrViiNHjuDj48Po0aNZvnw5R44coXfv3kyZMoVdu3YxZ84cfH19KSgoYOLEiY59fPfdd8THx7No0SLWrl0LwOOPP86yZcvYsmULqampGI1GTCYTL7/8Mtdddx29evUiKiqKr776iry8PP7yl78wbNgwgCqvk+r6Ok4Il1FCiEbhX//6l3r44YfVQw89pL744otKtzl27JgaMWKEKioqUkop9fHHH6t+/foppZRasmSJuueee1ReXp5SSqm//vWv6rXXXlNms1ndeeedaseOHUoppT766CPVoUMHlZWVddH+ExISVEREhHrwwQcd/61Zs8bxs7///e9KKaUeffRRtXHjRqWUUuvXr1ejR49WSil15MgR1a9fP3X27FmllFI///yzuuuuu1RBQYGaM2eOmjhxorLb7erMmTOqR48easmSJRfF8Nprr6mZM2cqpZTKyclRt912mzp//ryaPHmyevPNN5VSSv36669q3Lhxymaz1ahvU1NTVYcOHdS//vWvGm0vhBBCOFOHDh1Uv379VL9+/dRdd92levXqpWbNmqV+++03pZRSGzZsUOPGjVNWq1UpVfp366mnnlJKKXXPPfeo//73v0oppbZt26aSk5OVUn/8Lf7f//6n7rjjDvXrr78qpZR68cUXVc+ePZVSpdcGZX9TL/x+/Pjx6uuvv1ZKKZWfn6+6d++ufvjhB5WVlaU6d+6slFI1+tt79OhR1b17d2U2m5VSSj3//PNqzZo1KicnR/Xu3dtxvXHq1CnVo0cPdfz4cbV+/Xp16623Oq5Zli5dql588UWllFIFBQVq3Lhx6vz58xWuMZSqeM3x2GOPqY8//lgppdT+/fvVSy+9dFG/f/3116pjx44qPT1dKaXUyJEj1ZAhQ5TZbFZnzpxRnTp1UqdOnVJff/21uuGGG9SxY8ccj4uKilI7d+5U9957r9q/f3+F5/LMmTOqpKREderUSWVnZzuew9TUVKWUUj179lQvvviistvt6uTJk6p79+7qwIED1V4nOeM6TghPICMwhGgkRowYwWeffUa7du24++67K93mmmuu4ZVXXuGjjz4iMzOTvXv3UlBQ4Pj5bbfdhr+/PwA33ngjubm5/PzzzxgMBu644w4A+vXrV+2aFVVNIamJr776il9//ZUnnnjC0aZpGkePHmXnzp1MmTIFTdNo3rw59913X6X7GDhwIIMGDWLSpEl8/PHH9OrVi4CAAO677z4SEhLYt28fd9xxB9OmTUOnq9kyQV9//TUtW7bkiy++YPjw4TV+nBBCCOEsZVNI0tPTGT16NN27d+dPf/oTAFu2bOGHH35g4MCBANjtdoqKigCIiopi7Nix3H333dx1112MGjWqwn537tzJXXfdRcuWLQEYMmQI27dvv2Q88+fPZ+vWrfztb3/jl19+wWw2U1hYSGBgoGObmvztDQ4O5vrrr2fz5s3ccccdfP3118yZM4fvvvuO06dP8+yzzzq21TSNn376CShdF6TsmiUyMpLRo0dz8uRJ7rzzTuLi4ggICKg2/r59+/Lyyy+zefNm7rzzTl544YVKt2vTpg033ngjACEhIQQEBODl5UXz5s3x8/MjNzcXgFatWnHNNdc4Hnfq1Cmefvpphg4dyg033HDRfvV6Pffffz8xMTH8+c9/JiIiosL127Bhw9A0jauvvprIyEi++uorTCZTlddJ4JzrOCHcTa6yhWhE2rRpQ0hISJU/T09PZ8iQIeTn53PXXXfx1FNPVfi5t7e342tN01BKATj+LWMw1E1u1G63c8cdd/D//t//c/y3Zs0a2rdvf1EclU2PgdIkzY033sgXX3zB+++/z6BBgwDo2bMnn332GX379mX//v3079+fU6dOXTKmt956i19++YUPP/yQkydP8vrrrzvhTIUQQoja6dSpE5MnT2bSpEmOhRjtdjtPPfWU42/n+vXrWbVqFQDjx49n5cqV3HTTTbz//vuVTp+s6u9r+WsBoMIC1o8++ihffvkl1157Lc8++yxXXXXVRdcLNf3bGx0dzQcffMDHH3/Mvffei5+fHzabjXbt2lW4Jli9ejUREREA+Pr6Oh4fHh7Opk2bGDJkCMePH2fw4MH8+OOP1cYfExPDhx9+yF133cX27dt58MEHMZvNF8Xm5eVV4fuqroHKx1PWj//4xz/YsGEDe/furfQxiYmJ/O1vfyMkJIRly5ZVSKKUP47dbken013yOsnd13FCOIMkMIQQDt9++y033XQTf/nLX7jtttvYtGlTheollbn++utRSvHll18CsGnTJsenDbWl1+spKSm56Os77riDr776isOHDwOl82AffPBBiouLiYyMZN26ddjtdnJzc9m0aVOV+4+Ojuatt96iqKjIsU5GXFwcn3zyCVFRUcyYMQN/f3/HJxZV2bFjB3//+99JTk6mefPmLFmyhH/+859s27btis5fCCGEuBL9+vUjPDycefPmARAREcG6devIz88H4LXXXmPixImUlJTQq1cvioqKGDp0KDNmzOCnn37CYrE49nXXXXfx1VdfORILGzZscPysWbNmpKeno5QiPz+fLVu2AHD+/Hl++OEH4uPj6d27N6dOneLo0aMXra1Q07+99913H+np6axZs4bo6GgAOnfuTGZmJt9++y0A+/fvp0+fPpUu/J2YmEhKSgr33nsvU6dO5brrruPgwYM0b96cgwcPYjabsVqtfPbZZ47HxMTEsH//fh555BFmzZrF+fPnOX369OU/GVVo2bIlXbp0ISEhgYkTJzpGxJRd95w9e5a7776bwMBAnnjiCcaNG1ehqkxZ5bYTJ07w1Vdf0aNHj2qvk6pSF9dxQtQlSa8JIRz69evH559/Tt++fbHb7fTs2ZPc3FzHBU9ljEYjr7/+Oi+99BKLFi2iY8eOjiGrtdWrVy8WLVqE1WqlS5cuvP7664wdO5alS5fy8ssv88ILL6CUwmAw8MYbb+Dn50dsbCwzZsygb9++NG/evMIiY5Xtf+bMmRWGyY4ZM4apU6eyevVq9Ho99957L7feeivZ2dmMHj2aZcuWERQU5Nj+2LFjvPDCC8yZM8cxquW6665j+vTpTJgwgQ0bNtCqVasr6gchhBCitl588UUefPBBtm3bxuDBg8nOziY6OhpN02jVqhXz58/HYDAwZcoU4uPjMRgMaJrG3LlzK4wquP7665kwYQKPP/44fn5+hIeHO35Wtv/evXsTFBTEbbfdhlKKJk2aMHr0aB5++GF8fX0JCgqiS5cuZGZmEhwc7Hh8VX97L+Tl5cUDDzzAjh07HMcv++DglVdewWw2o5TilVdeoU2bNnzzzTcVHv/4448zadIk+vXrh5eXF9dffz1RUVHodDpuvfVW+vbtS8uWLenevbtjCkp8fDxz587l1VdfRdM0xo4dS5s2bZz6HAE8/PDDfPbZZ8yfP5+ZM2dy//33M2LECJKTk3nmmWd44okn8Pb2Rq/XM3v2bMfjjh07xiOPPEJxcTHTpk3j2muvBajyOqkqdXEdJ0Rd0tSFY4aEEEIIIYQQQnikXr168dprr/F///d/7g5FCJeTKSRCCCGEEEIIIYTweDICQwghhBBCCCGEEB5PRmAIIYQQQgghhBDC40kCQwghhBBCCCGEEB5PEhhCCCGEEEIIIYTweI2yjKrdbsdmc/7SH3q9Vif7FRVJP7uO9LVrSD+7jvR13TAa9e4Owe3q4tpCXq+uI33tGtLPriN97RrSz3WnqmuLRpnAsNkUOTmFTt9vYKBvnexXVCT97DrS164h/ew60td1o2XLAHeH4HZ1cW0hr1fXkb52Deln15G+dg3p57pT1bWFTCERQgghhBBCCCGEx5MEhhBCCCGEEEIIITyeJDCEEEIIIYQQQgjh8RrlGhiVsdlKOHfuNCUlllrvIztbQynPX8TFYPCiWbOW6PXy9AshhBBCCCGEqB/kDvZ3586dxtvbFz+/q9E0rVb70Ot12Gx2J0fmXEopCgrOc+7caVq0aOXucIQQQgghhBBCiBqRBMbvSkosV5S8qC80TcPPrwn5+TnuDkUIIYQQ1fhw7wkWfvYT2XlmggJMjIkMpW/HIHeHJYQQQriNJDDKaejJizKN5TyFEEKI+mrj/mzm/vsgxdbSkZ2n8szM/fwggCQxhBBCNFqSwPAg33//HdOnTyY0NAxN0zCbzfTufT+DBsVc8rH/+9+PzJ37MhERPTh58jjTpr3MmTNnOHToZyIierggeiGEEEI4S8q2DEfyokxxiZ2UbRmSwBBCCNFoSQKjltLSDCQmepGVpSM42E58vIUHH7zy9S+6du3GzJnzALBYLAwbNpA+faIICAio9nHffPM1AwY8UiHZ8f3335KZmSEJDCGEEA7r169h4MBod4chLiE7z3xZ7UII59q4P5uUbRkyhUsIDyMJjFpISzMQF2dCKfD2Vpw4oREXZ0Kns9C3r/MW8SwsLESn0zFu3BhatWpNXl4eCxe+yoIFszh+/Dg2m42YmOG0atWajz/+fxgMRlq2DCI5eRHLl69hxYp3KC4u5v/+L5yIiLudFpcQQoj6a8OGdZLAqAeCAkycqiRZERRgckM0QjQuG/dnM/fzgxSXyBQuITyNJDBqITHRC6XAy6v0ey8vsFhg4UIjfftar2jfu3d/x9ixo9HpdBgMBsaPn8B77/2L++67n7vv7sn69atp2jSQF1+cRWFhAU8++Sh/+9s/6du3H3/605+4++6eJCcvQqfT8eijT/w+AkOSF0IIIUR9MiYytMIaGADeBh1jIkPdF5QQV2Dj/mz+9lUmJ3OLPX5EQ8q2DEfyooxM4RLCM0gCoxaysnR4e6sKbUYjHD2qu+J9l59CUua99/5FSEhbADIyMujW7TYAfH39CA0N4/jxY1d8XCGEEEJ4jr4dg/DzNUkVEtEg1LcRDTKFSwjPJQmMWggOtnPihOYYgQFgtUJIiPOmj1xIpytNjoSGhrJv33+5++6eFBYWcPjwYVq3bl3pYzRNQ6m6i0kIIYQQdefBm1vTo22gu8MQ4orVtxENMoVLCM915UMGGqH4eAuaVjptRKnSfzUNJky4sukjNfHgg4+Qm5vLM8+MZOzYv/Lkk6No1qx5pdu2a3cd27Z9yX/+81mdxyWEEEIIIURl6tuIhjGRoXgbKt4myRQuITyDjMCohaioEoCLqpD062fHZqv9frt06UaXLt0ual+6dJnja6PRyLRpMy/aZuTIvzq+XrfuIwA6dLiBVaver31AQgghhBBCXKH6NqKhbFSIVCERwvNIAqOWoqJKHImMP8iAFiGEEEIIIcobExlaYQ0M8PwRDX07BknCQggPJAkMIYQQQgghRJ0pSwTUlyokQgjPJQkMIYQQwgOsX7+GgQOj3R2GEELUib4dgxh6Rxg5OYXuDkUIUY/JnAchhBDCA2zYsM7dIQghhBBCeDQZgSGEEEIIIYSokY37s2VxSyGE29RJAsNqtTJlyhSOHz+OxWLhmWee4brrrmPSpElomkb79u2ZMWMGOp2ONWvWkJqaisFg4JlnnqFnz54UFxczYcIEzpw5g5+fHwsWLKB58+bs2bOHOXPmoNfriYiIYOzYsQAsXbqUL774AoPBwJQpUwgPD6+L0xJCCCGEEKLR2rg/u8JinKfyzMz9/CCAJDGEEC5RJ1NIPvzwQwIDA1m5ciVvvfUWs2bNYt68eYwbN46VK1eilGLTpk2cPn2a5cuXk5qayttvv82iRYuwWCysWrWKDh06sHLlSgYMGEBKSgoAM2bMICkpiVWrVrF3717S09NJT0/nm2++Ye3atSxatIiZMy8uMVpXdJkZ+CTOR5eZccX7stlsvPDCWB5++AE2bvwYgPXrVwNgNpv56KMParyvGTMm8/33311xTEIIIYQQQpRJ2ZZRoZIIQHGJnZRtGe4JSAjR6NRJAuP+++/n+eefd3yv1+tJT0/ntttuA6BHjx7s2LGDffv2ccstt+Dl5UVAQAAhISEcOHCA3bt3ExkZ6dh2586d5OfnY7FYCAkJQdM0IiIi2LlzJ7t37yYiIgJN02jdujU2m42zZ8/WxWlVoMvMwDd5Mfqso/gmL77iJMaZM7+Rk5PDhg2f0LdvPwDeffcfAJw9e+ayEhhCCCGEEEI4W3ae+bLahRDC2epkComfnx8A+fn5PPfcc4wbN44FCxagaZrj53l5eeTn5xMQEFDhcfn5+RXay2/r7+9fYdusrCxMJhOBgYEV2vPy8mjevHmV8en1GoGBvhXasrM19Pqa5XN0mRl4L10MPt6oq69Gy83Bb+liip+Pg7ahNdrHhRYunMuxY1ksXDiXDh1u4Pz5HM6fz2XRogWUlJSQkXGEd975O0OGDGXu3JfJzc0FYPz4CVx3XXvWrVvNhx9+QIsWLTh37ix6va7a89G0i/ugvtDrdfU29vpG+to1pJ9dx5P72mDQuyQ2Vx1HCNHwBAWYOFVJsiIowOSGaIQQjVGdLeJ58uRJnn32WYYNG0b//v1ZuHCh42cFBQU0adIEf39/CgoKKrQHBARUaK9u2yZNmmA0GivdR3VsNnVRCSelFDabvYpH/EGXmYEpeTF2kwkV0BTsCgKaotkV3q8lUTB2PPZaJDFeeCGBGTOm0Lz5n1DKzogRT7J27WpeeCGBkydPcOjQQZ544ilSUpbQpcutPPzwILKyjjJ37kwWLnyN1atX8a9/paLT6Rg58lFsNnu156PUxX1QXwQG+tbb2Osb6WvXkH52ner62t1lTEtKbHX+OtBlZhD900+c3/u/Wv2tqkrLltX/3RVCNAxjIkMrrIEB4G3QMSYy1H1BCSEalTqZQvLbb7/x5JNPMmHCBAYNGgTAjTfeyK5duwDYunUr3bp1Izw8nN27d2M2m8nLy+Pw4cN06NCBLl268OWXXzq27dq1K/7+/hiNRo4ePYpSiu3bt9OtWze6dOnC9u3bsdvtnDhxArvdXu3oiytlWpsKViuqaWCFdtU0EM1aUvrzOvTLL4dIS/uQsWNHs2DBbPLy8sjMzCAs7Fq8vLwwGAx07NipTmMQQoiGqKGXMS2b+hhUVOiUqY9CiManb8cgpvRuz9UBJjTg6gATU3q3lwU8Rb20cX82/Zft4rakrfRftouN+7PdHZKogToZgfG3v/2N8+fPk5KS4liAc+rUqcyePZtFixZx7bXX0qdPH/R6PSNGjGDYsGEopRg/fjwmk4mhQ4eSkJDA0KFDMRqNJCUlATBz5kzi4+Ox2WxERERw8803A9CtWzeGDBmC3W5n+vTpdXFKDubBMfgmL0bLzamQxNByc1BGA+bBMU47llKqdN+aDqVKM91t24bSu/eN9O59P+fOneWjjz6gdetryMj4BbO5GIPByM8//0Tv3n2dFocQQoj6rSx5oUwmTnv7oEwmfJMXUxhbu1GDnmbv3r0kJiayfPlyMjMz66zqmRCiNIkhCQtR30lFnfqrThIY06ZNY9q0aRe1r1ix4qK26OhooqMrDtn18fFhyZIlF23buXNn1qxZc1F7bGwssbGxVxBxzdnbhlIYOx7f5MXwexJDy81BM5spHheHvU2I044VGhrGyy+/SELCNKzWElJSlvDYY08yf/4sPvzwfQoLC3jyydE0a9aMp556mqeffpLAwGb4+Pg4LQYhhBD1W/nkRVniXTUNhNycBpHEeOutt/jwww8df/vKqp51796d6dOns2nTJjp37szy5ctZv349ZrOZYcOGcddddzmqnsXGxpKWlkZKSgrTpk1jxowZJCcnExwczOjRo0lPT6dTJxndKIQQDUV1FXUkgeHZ6mwNjIasfBJDO34MjEYKY8ejtQ2FGqyjUZlWrVqzbNk7FdqSk990fP3OOysdX8+bl3TR4++5pzf33NO7VscWQgjRcJVNffw5txU7PtDz22/RvPuugTvvbM71vkcxrU2lKH6Su8OstZCQEJKTk5k4cSLARVXPvvrqK3Q6naPqmZeXV4WqZ0899ZRj25SUlApVzwBH1TNJYAghRMMhFXXqrzpZA6MxKEti2IJD6v2nV0II0ZilpRno2dOXzZvX0rOnL2lprs3t1/XxzYNjOHrSxM6N+eTlaWhaCXl5Gjs35nP0pMmpUx/doU+fPhgMf/SZUsrpVc/y8vJcdDZCiPpM1lSoP6qqnCMVdTyfjMC4Ava2ofX6UyshhGjs0tIMxMWZUAp0OjMnTvgSF1d68RIVVdIgjm9vG8pzxycRwyvoNfhVM9FMy8FIEc8dn8SKtg1rqKxO98dnM86senYplZVov1KeXPa3oZG+dg1X9POHe0+Q9O+fOZlbTKum3sTd14EHb25dp8csO+7cfx+k2FpuTYV/H8TP1+SS419IXtPVm9Dneqb+vx8dzxeAt1HHhD7XX1a/ST+7niQwhBBCNFqJiV4oBV5eUFhY+q/FUtruigRG2fGv0x8hyvYWafpRHLKFOf34X2e342xAArGFC2ijTqPDn+SABH7OvhbId9pxPEFZ1bPu3buzdetWbr/9dsLDw3n11Vcxm81YLJaLqp6Fh4dXWvUsODiY7du312gRz8pKtF8pKbHsOtLXrlHX/XzhwowncouZ+sGPFBSa63xdg4Wf/VThZhig2Gpn4Wc/0aNtYJ0euzLymq5ej7aBTLmvPSnbMsjOMxMUYGJMZCg92gZeVr9JP9edqkq0SwKjnPLDThuysuomQgjR2GVl6fD2rvieaDSWtrvq+B2MvzC2YAF2lc/YggUk+ybwc9a1Tj1OcLCdwyfCWOqXQJTlLdL8RnHYFkZwcO3WbfJkCQkJvPjii3VS9UwI4dncuTCjrKlQ/0hFnfpJEhi/Mxi8KCg4j59fkwadxFBKUVBwHoPBy92hCCGE2wUH2zlxQsOr3Fui1YrLbuxvDzpMTOYrFOt9+FUL5CrMjMlbQGrbiYDzLqri4y3ExZk4ZAtjrn4CfrZmaFppe0PQpk0bR5WysLCwOqt6JoTwbO5MIgQFmDhVyXFkTQUhnEsSGL9r1qwl586dJj8/p9b70DStXoxuMBi8aNaspbvDEEIItyu7sbdYQKnS6SOuurHXZWaw5JrX2Jzpx3kViFJFnFOBNAGWXDMfXebzTlsgumw6SmKiFz/9ZKJ1a0V8vMUl02SEEMJV3JlEGBMZWmH6CoC3QceYyNA6P7YQjYkkMH6n1xto0aLVFe1D5kAJIUTdWr9+DQMHRl96wxpy5429aW0qIa3M3NE3iB07FL/9ZiAgQHHHnf6E+J7F5uTyplFRJURFlfDoo9GsWCGjC4QQDY87kwhlUxEuXFNBpigI4VxSRlUIIYRLrV9f+5vnDRvWOTGSUlFRJWzZUkivXoPZsqXQZaMSzINjwGikw1VnefxxK2Fha3j8cSsdrjoLRmO9L28qhBCu1rdjEFN6t+fqABMacHWAiSm927ssidC3YxAfje7ON3E9+Gh0d0leCFEHZASGEEIIl9qwYZ1TR1E4gy4zg2GHD6HLzHDatI1LsbcNpTB2PL7JiyE3BwAtNwfNbKYwdrzL4hBCiIZEFmYU7rZxf7aMxKlDMgJDCCGEx0tLM9Czpy+bN6+lZ09f0tKcl3/XZWbgm7yYoKJCfJMXo8vMcNq+L6UsiaGZzbQsLpLkhQBKL377L9tFhxc/pf+yXWzcn+3ukIQQQtRAWSnfU3lmFHAqz8zczw/K+7gTSQJDCCGER0tLMxAXZ+LECQ2dzsyJExpxcSanJDHKkhfKZOK0tw/KZHJbEiPbx1eSF0IufoUQoh6rrpSvcA5JYAghhPBoiYleKAVeXqUVQry8SiuGJCZeWTno8skL1TQQANU00G1JjJXtrpPkhZCLX9GolY0+ui1pq9tHH3lSLKL+cGcp38ZCEhhCCCE8WlaWDqOxYpvRWNp+JUxrU8FqdSQvyqimgWC1lv5cCBeTi1/RWHnS6KPKYpn+yU/cKskMcQlVlex1RSnfxkISGEIIITxacLAdq7Vim9Va2n4lyqqAaL8voFlGy82RKiDCbeTiVzRWnjT6qLJYysi0LlGdMZGheBsq3mK7qpRvYyEJDCGEEB4tPt6CpoHFUjp1xGIpnUoSH2+5ov2WX0BTkyogwkPIxa9orDxp9NGljinTukRV3F3KtzGQMqpCCCHq3Pr1a2pdOjUqqgQoXfPip59MtG6tiI+3ONqvRPlSplIFRHiCsotcKcEnGpugABOnKkkcuGP0UVWxlCfTukRVpJRv3arTBMbevXtJTExk+fLljB8/nt9++w2A48ePc/PNN7N48WJmz57N999/j5+fHwApKSkYjUYmTJjAmTNn8PPzY8GCBTRv3pw9e/YwZ84c9Ho9ERERjB07FoClS5fyxRdfYDAYmDJlCuHh4XV5WkIIIS7Thg3rap3AgNIkRlRUCY8+Gs2KFWucGFm5KiBfbZPkhfAIZRe/gYG+5OQUujscIVxiTGQocz8/WGHqhrtGH1UWy4VkWpcQ7lFnCYy33nqLDz/8EB8fHwAWL14MQG5uLo899hiTJ08GID09nb///e80b97c8dh//vOfdOjQgdjYWNLS0khJSWHatGnMmDGD5ORkgoODGT16NOnp6QB88803rF27lpMnTxIbG8v69evr6rSEEEI0QGVVQB6Q5IUQQriFJ40+Kh9LZSMxZFqXEO5TZwmMkJAQkpOTmThxYoX25ORkHn30Ua666irsdjuZmZlMnz6d3377jUGDBjFo0CB2797NU089BUCPHj1ISUkhPz8fi8VCSEgIABEREezcuRMvLy8iIiLQNI3WrVtjs9k4e/ZshYSIEEKI+k+XmcGww4fQZWbIKAkhhGiAPGnofflYNu7P9ojEihCiDhMYffr04dixYxXazpw5w86dOx2jLwoLC3n00Uf5y1/+gs1m47HHHuOmm24iPz+fgIAAAPz8/MjLyyM/Px9/f3/Hvvz8/MjKysJkMhEYGFihPS8vTxIYQgjRgOgyM/BNXkxQUSG+yYtlqscVePjhQe4OQQgh6hVPSqwI0di5dBHPTz/9lH79+qHX6wHw8fHhsccec0wzuf322zlw4AD+/v4UFBQAUFBQQJMmTSq0lW83Go0XtZclP6qi12sEBvo6+/TQ63V1sl9RkSv6edWqVQwdOrROj1EfyGvaNRpDPxsM+tJzPHKER48cJvBcNoSF1ezBR46gezMZmvhzxtcPryb+eL2ZjH3CxJrv43fV9bUjRjdx1fFHjnyizo8hhBBCCFEXXJrA2LlzJ88884zj+4yMDMaPH8+GDRuw2+18//33PPzww5w9e5Yvv/yS8PBwtm7dSteuXfH398doNHL06FGCg4PZvn07Y8eORa/Xs3DhQkaOHMmpU6ew2+2XHH1hs6k6WRRLFttyDVf083vvvUffvg/V6THqA3lNu0Zj6OeSEhvn9/6vtNpHQQElc+bWaBRF2cgLZTKhvP2w2+0Ue/uhma1oNdxHedX1dUmJza3Pg7uPfyVatqz+gwMhhBBCCGdwaQLjyJEjBAcHO75v164d/fv3Jzo6GqPRyEMPPUT79u1p06YNCQkJDB06FKPRSFJSEgAzZ84kPj4em81GREQEN998MwDdunVjyJAh2O12pk+f7spTEkIIUQNBhYWORMRpbx+UyVSjqSCmtalgtaKuqjh0VzUNRDt+DNPaVIriJ9Vx9EIIIWrjwrUjJvS5nh5tA90dlhCiHqvTBEabNm1Ys+aPcndpaWkXbTNq1ChGjRpVoc3Hx4clS5ZctG3nzp0r7K9MbGwssbGxTohYCCEajvXr11xR6VJn0WVmMOTIL6iON6KaBgKlCQhycy6ZxDAPjsE3eTFabo7jsQBabg4YjZgHx9R1+EIIIWph4/7sCqVIT+WZmfr/fmTKfe1lPQkhRK3p3B2AEEKIurFhwzp3hwCUjqLQK3uFBAT8nsSwWktHWVTB3jaUwtjxaGZzadKC0uSFZjY3uIU8ZXFNIURDkrItw5G8KFNstZOyLcM9AQkhGgRJYAghhAdav/7i0Wb1lXlwDDZN50hAlKnpKIrySYyWxUUNMnkBeMRoGSGEcJbsPPNltQshRE1IAkMIITyQp4yecAZ721BWh11L1kEr7/+jgCNHonn/HwVkHbTWOBFRlsTI9vFtkMkLIYRoaIICTJfVLoQQNSEJDCGEEHVuX969xPx3MiX5xQSTQUl+MTH/ncxHP15X433Y24ayst11krwQQoh6YExkKN6Girca3kYdYyJD3ROQEKJBkASGEEKIOqXLzGDQ/86gFLzRJIGjuhDeaJJApi6MxEQvd4cnhBCiDvTtGMSU3u25OsCEBlwdYGLOQzfJAp5CiCvi0jKqQgghGhddZga+yYu5xlrCeP0CXvdKYJ5hAoH6Zhh1kJXlOXl0WURTCCGcq2/HoAoJi8BAX3JyCt0YkRCivvOcK0chhBANSlnyQplMnDQGUmj3YWzBAkJVJgBWKwQH2y+xF9eRRTSFEEIIITybjMAQQoh6ZP36NfXiRrt88kI1DaR586/Jze0BNogreY3Xi6aTZQgjPt7i7lBFAzVgwAACAgIAaNOmDU8//TSTJk1C0zTat2/PjBkz0Ol0rFmzhtTUVAwGA8888ww9e/akuLiYCRMmcObMGfz8/FiwYAHNmzd38xkJIYQQQkZgCCFEPVJfqpOY1qaC1YpqGgiAn18WffqUoJo2xWBXjDQtJynJTFRUiXsDFQ2S2VxapnH58uUsX76cefPmMW/ePMaNG8fKlStRSrFp0yZOnz7N8uXLSU1N5e2332bRokVYLBZWrVpFhw4dWLlyJQMGDCAlJcXNZ+RaG/dn03/ZLm5L2kr/ZbvYuD/b3SEJIYQQgCQwhBCNxPr1a9wdQqNiHhwDRiNabo6jrX17O08MOM1V12xh2CcDJXkh6syBAwcoKiriySef5LHHHmPPnj2kp6dz2223AdCjRw927NjBvn37uOWWW/Dy8iIgIICQkBAOHDjA7t27iYyMdGy7c+dOd56OS23cn83czw9yKs+MAk7lmZn7+UFJYgghhPAIksAQ4gK6zAyGHT6ELjPD3aEIJ6ps5IIkNeqOvW0ohbHj0cxmRxJDy81BM5tZHXatlEIVdcrb25uRI0fy9ttvM3PmTOLj41FKoWkaAH5+fuTl5ZGfn++YZlLWnp+fX6G9bNvGImVbBsUlFdemKS6xk7Itwz0BCSGEEOXIGhhClFM2bz+oqBDf5MUUxo6XG60GbMOGdfViPYnaKJ+Ic9dr2N42lLSbJmCft5iSc3fy8TkbuskTyD71glviEY1HWFgYbdu2RdM0wsLCCAwMJD093fHzgoICmjRpgr+/PwUFBRXaAwICKrSXbXsper1GYKCvU89Dr9c5fZ+Xkp1nrrLd1bG4kjv6ujGSfnYd6WvXkH52PUlgCPE7XWYGv014jW/2+XPk3OOc/jif246+RouFz0sSQ9QrnpKIS0szEDf/eoJtCTyse4sNtlFkzQ8jOPhOl8ciGpd169bx888/89JLL5GdnU1+fj533XUXu3btonv37mzdupXbb7+d8PBwXn31VcxmMxaLhcOHD9OhQwe6dOnCl19+SXh4OFu3bqVr166XPKbNppxeHtIdJSeDAkycqiSJERRgatDlL6W8p2tIP7vOpfp64/5sUrZlkJ1nJijAxJjI0Aolb0XNyGu67rRsGVBpu0whEYI/khebd/hxorAZmlbCicJmbN7hx28TXpPpJMKlrmQaU9lred3H/nxz8nHWfezvttdwYqIXSsEpnzDmGSdwyicMpeDw4WEuj0U0LoMGDSIvL4+hQ4cyfvx45s6dy9SpU0lOTmbIkCFYrVb69OlDy5YtGTFiBMOGDePxxx9n/PjxmEwmhg4dysGDBxk6dCirV69m7Nix7j4llxkTGYq3oeLlobdBx5jIUPcEJIRwOlnrRtRnMgJDCEorJqTvsXFeF4heD5oGej2cJ5D0PefpvTaVovhJ7g5TNAJXMnqifCLuvC4QTSv6PRGn0WuC60cTZWXp8PZWFdqMRsjLCwJU5Q8Swgm8vLxISkq6qH3FihUXtUVHRxMdXXEqmY+PD0uWLKmz+DxZ2Sew8smsEA1XdWvdyO+68HQyAkMISism5BR4EUhOhfZAcsgp8CqtqCBEHStLXiiTidPePiiTCd/kxTUePVFlIk4XSPoeW2lpUxcKDrZjtVZss1rB27v2n/A8/PCgK4xKCHEpfTsG8dHo7nwT14OPRneXGxohGpjq1roRwtNJAkMIShcbXB06AS97EU3sOQA0sefgZS9idegEWQND1LnyyQvVNBAA1TTQkcT48p1j9Ozpy+bNa+nZ05e0tIsH0HlaIi4+3oKmgcUCSpX+q2nQrt3KWu+zoS66KoQQQrhKUIDpstqF8CSSwBDid0OnXMMSnwSMtiKusR/DaCtiiU8CQ6dc4+7QRCNgWpsKVqsjeVFGNQ3k2C8lHJy+hhMnNHQ6MydOaMTFmS5KYnhaIi4qqoSkJDOtWyvsdhOtWyuSksw8/XRrl8YhhBBCiD/IWjeiPqvTBMbevXsZMWIEAOnp6URGRjJixAhGjBjBJ598AsCaNWt45JFHiI6OZsuWLQAUFxcTGxvLsGHDGDVqFGfPngVgz549DB48mJiYGJYuXeo4ztKlSxk0aBAxMTHs27evLk9JNGBRUSU8/2prUttOJJNrSW07kedfbU1UVIm7QxONgHlwDBiNaLk5Fdq13Bz+m+7Naq9H8fIqHcHg5VU6oiEx0eui/XhaIi4qqoQtWwrp1WswW7YUEhVVIqMohBBCCDfq2zGIKb3bc3WACQ24OsDElN7tZbpYA7Jxfzb9l+3itqSt9F+2q0Et0Fpni3i+9dZbfPjhh/j4+ADwv//9j7/85S88+eSTjm1Onz7N8uXLWb9+PWazmWHDhnHXXXexatUqOnToQGxsLGlpaaSkpDBt2jRmzJhBcnIywcHBjB492lHT/ZtvvmHt2rWcPHmS2NhY1q9fX1enJRq4qKgSoqKCePTR3axYkQBI8sIV1q9f47SbWmfuy5XsbUMpjB3/eynfAo6ci+b9f+RzW7iV2dYpZPuGopXb3mgsXSTzQqUJt9asmjuROw69w862T/D8FEnECSGEEOIPfTsGScKigSqrMlO2UGtZlRmgQTzndTYCIyQkhOTkZMf3P/74I1988QXDhw9nypQp5Ofns2/fPm655Ra8vLwICAggJCSEAwcOsHv3biIjIwHo0aMHO3fuJD8/H4vFQkhICJqmERERwc6dO9m9ezcRERFomkbr1q2x2WyOERtCiPphw4Z1HrkvV/vox+uI+e9kSvKLCSaDkvxiYv47mXOBbbFaIcR2hMklCwmxHcFqLV0kszJRUSWs+CqInfftZsVXQZK8EEII4XEa8ifEQrhTdVVmGoI6S2D06dMHg+GPAR7h4eFMnDiR9957j+DgYF5//XXy8/MJCAhwbOPn50d+fn6Fdj8/P/Ly8sjPz8ff37/CttW1CyFEfZOY6EWmLow3miRwVBfCG00SyNSFoWkabe1HeOb8AkLsR3nm/ALa2o8QH29xd8hCCCGcoLHdzJd9Qnwqz4zij0+IG/p5C+EKDb3KTJ1NIbnQfffdR5MmTRxfz5o1i27dulFQUODYpqCggICAAPz9/R3tBQUFNGnSpEJb+Xaj0VjpPqqj12sEBvo68/R+36+uTvYrKnJFPxsMenkucd1r2pn9Xem+jhzh0SOHCTyXDWFhVbe5SVk/Hzumw8cHjmnXMs8wgebG5pgM0CznCOu6L2Dnf3345WwLrg0oYN0tCwi5cyIEVh27J72OPSUWeZ8WQniahj7cuzLVfULcUM9ZCFcJCjBxqpJkRUOpMuOyBMbIkSN58cUXCQ8PZ+fOnXTq1Inw8HBeffVVzGYzFouFw4cP06FDB7p06cKXX35JeHg4W7dupWvXrvj7+2M0Gjl69CjBwcFs376dsWPHotfrWbhwISNHjuTUqVPY7XaaN29ebSw2myInp9Dp5xgY6Fsn+xUVuaKfS0ps8lxS932dlmYgMdGLn35K5ZZbSstuXul0hwufu7LypC0LCiiZM5fC2PEAF7W5s1RuWT+3aePLiRMaXr+vzWm3K64uOsIUwwJahel5uLMv77+/hocfGYyWa7lk7J70OvaUWOR9um60bFn9BwdCiKo1xpv5hv4JsRDuNCYytEJSFBpWlRmXJTBeeuklZs2ahdFopEWLFsyaNQt/f39GjBjBsGHDUEoxfvx4TCYTQ4cOJSEhgaFDh2I0GklKSgJg5syZxMfHY7PZiIiI4OabbwagW7duDBkyBLvdzvTp0111SkKIK5CWZiAuzoRS/F4a1Je4uNLMcG2SGH8kQ9bSs6cP8fEW+t90CN/kxSiTidPePiiTCb+5L6MA1aKFo803ebHbkxhQmsCJizNhsZRWGbFYYIhlBbd0K0Y1rVhFRDUNRDt+DNPaVIriJ7kpYiGEEFeqMd7MN/RPiIVwp7LEZ8q2DLLzzAQFmBgTGdpgEqJ1msBo06YNa9asAaBTp06kpqZetE10dDTR0RUrBvj4+LBkyZKLtu3cubNjf+XFxsYSGxvrpKiFEK6QmOiFUqUlQQsLS/+1WErbLzeBUVky5LVxJ7jjltcIbm9CNQ0s3VCBfn9p9SJr5N2lTU0DITfHI5IYZeddmogx0bq1ov3j0bT5cSEqN+eP86C0vCpGY2n51Xrg4YcHuTsEIYTwSI3xZr6hf0IshLs15CozdbaIpxBCVCcrS4fRWLGtqtKgl1I+GaJppf8ONq8gfY+twk2/4X8/gt4ABgOG9B8d7appIFitmNZenGR1taioErZsKaRXr8Fs2VLI3U+0oTB2PJrZXJq0oDR5oZnNbk+4XI76WNpWCCFcYUxkKN6Gin/7GvrNfN+OQUzp3Z6rA0xowNUBJqb0bt9gb7iEEM4jCQwhhFsEB9uxWiu2VVcatDpZWTpsNjhzRsNsbsaZMxqphkfJKfBy3PQDlNx4E9hKoKSEkk43OdpdPZph/fqLR5JVx9421JHEaFlcVO+SF0IIIarWWG/m+3YM4qPR3fkmrgcfje7e4M9XCOEcksAQQrhFfLwFTaPCeg+aRq1KgzZpYicvT8NmA1DYbPBjwbX8/U8TKoxcQANbx06UdOzkeKwzRjNcbkJiw4Z1l32MsiRGto+vJC+EEKKBkZt5IYSoGUlgCCHcIiqqhKQkM61bK+z20vUekpLMtVrAU9M0lKrYphQcM1570ciFginTKZwy3amjGWqTkKgNe9tQVra7TpIXQgghhBCiUZIEhhDCbS5c76G2JVRzczWaNgW9HkBDrwe9Pp/cXK3SkQsymkEIIYQQQoj6RxIYQlRCKia4ji4zg2GHD6HLzKj1PoKD7eh0ij/9SaHT/fr7v3bHehqVjVyQ0QxCCCGEaEg27s+m/7Jd3Ja0lbsTv2Dj/mx3hySE00kCQ4hKSMUE19BlZuCbvJigokJ8kxfXOolRtp7G1UVHmGpfxNVFRwBVq/U0GhJJxAkhhBCNw8b92cz9/CCn8swo4ERuMXM/PyhJDNHgSAJDCOEWZckLZTJx2tsHZTLVOokRFVXCm5N+Yop+AaGcYIp+Afe2e7nWU1IaCknECSGEEI1DyrYMiksqVnIrLrGTsi3DPQEJUUckgSGEcLnyyQvVNBAA1TSw1kkMXWYGUT8upN8gPb/5bKDfID3P5q29omkp7iajJ4QQQghRU9l55stqF6K+kgSGEMLlTGtTwWp1JC/KqKaBYLWW/ryGLkyG2GzteeeDlhw63p9NvZP58p1jzg3eRWT0hBBCCCFqKijAdFntQtRXksAQQriceXAMGI1ouTkV2rXcHDAaS39eQ+WTIQcP6rBYepOXp3Fe54+lwMr+qWvo2tWPzZvX0rOnL2lpBueejBBCCCGEm42JDMXbUPHWztugY0xkqHsCEqKOSAJDCOFyZWVMNbPZkcTQcnPQzObLLmtaPhmyY4ceKC2nGkguVmXkn9YRnDihodOZOXFCIy7OJEkMIYQQQjQofTsGMaV3e64OMKEBrZt6M6V3e/p2DHJ3aEI4lSQwhHCC9evXuDuEeqd8EqNlcVGtkhcX7oecXMBOE3sO3hQz1z6JDC0Mux00Dby8QClITPSqk3MSQgghhHCXvh2D+Gh0d76J68GX8X+W5IVokCSBIYQTbNiwzt0h1EtlyYdsH99aJS8u3E9bn2z68xkt7NkkGZ7nsD0MpUpHZJQxGuHwYR09e/o6ppVMm7bLOSdUA7rMDIYdPuRYYFSSX0IIUX9t3J9N/2W7uC1pK/2X7ZKSlUIIUcckgeFEq1atcncIQtQ79rahrGx3Xa2TF+V17KgADbtdQynQ/f4O5+//xzb5+WC1UmFayT//2a3W00ouTEhcalvf5MUEFRU6qq1cbvJLqpMIIYRn2Lg/m7mfH+RUnhkFnMozM/fzg5LEEEKIOiQJDCdavbrmlROEEM5Tlhi4Orw5n3npKApoSVzJUm5reRh/f9A0hVJgsYDZrOHjUzqdpGxaCWi1mlZSWULiUtsqk4nT3j6OkrFBhYWXlfyU6iRCuIbdbmf69OkMGTKEESNGkJmZ6e6QhIdJ2ZZBcYm9QltxiZ2UbRnuCUgIIRqBGiUwtmzZUuH7Tz75pE6CEUKIy3VhGVW9/iADH7ESEfA+m++YzD+nH6B1a4XdbqJ1a4WXF/j6KgBsNiNnzmhYrQHs36+7rFEYVSUkKktiXBgjlJaMVSYTQ478whf//Idj2/o4paQ+xizEpfznP//BYrGwevVq4uLimD9/vrtDEh4mO898We1CCCGuXLVX61u2bOH7778nLS2N//73vwDYbDY2b97MAw884JIAhRCiOo4yqleVLlTV1G7H+M1OjHY7hn176NtyKXdvmc/69WsYODCanj19OXFCw24Hm+2PuSWaBnFxpbXSo6JKqj1mVQkJcnPwTV580XoeF8ZYRjUNRK/s9Dx+zNG2YcO6ejfKoj7GLNzjxIkTVf6sdevWLozk0nbv3k1kZCQAnTt35scff6x2+8OHDzJy5CinxmAw6Ci54BN+UTdq09dnTpzHYrv4MV56HQO2NXFWaA2KvKZdx5l9fbbQwvGcYiw2O156HdcEetPcVxZEB3lN16WvvtpWaXu1CYwbbriBnJwcTCYTYWFhAGiaRr9+/Wp00L1795KYmMjy5cvZv38/s2bNQq/X4+XlxYIFC2jRogWzZ8/m+++/x8/PD4CUlBSMRiMTJkzgzJkz+Pn5sWDBApo3b86ePXuYM2cOer2eiIgIxo4dC8DSpUv54osvMBgMTJkyhfDw8Bp3jBCXo+wmWHgO8+AYfJMXl5ZjVdC9xIrSGyjRaSijEd3pX9FlZjiet/h4C3FxJvLztQr7CQj4o0LJpRIYZQmJn3NbseMDPb/9Fs277xq4887mXO97FNPaVIriJ1UaY1nCA0pLx9o0HVuuacP9TusRITzX+PHjAcjJyaGgoID27dtz6NAhWrRowYYNG9wcXUX5+fn4l1tAR6/XU1JSgsFQ9aWTweDcmbmapjl9n6Jytenr4Oa+HPmtALtSjjadphHc3FeetyrIa9p1nNXXv+VbyDxb5HidW2x2Ms8WodPpaOEvSQx5TbtetQmMli1b8vDDD9O3b190ust7Yt566y0+/PBDfHx8AJgzZw4vvvgiHTt2JDU1lbfeeovJkyeTnp7O3//+d5o3b+547D//+U86dOhAbGwsaWlppKSkMG3aNGbMmEFycjLBwcGMHj2a9PR0AL755hvWrl3LyZMniY2NZf369ZfbD0LUiHzS7HnKKpD4zX0Z/f50SigdTaFXCmvk3QAVRkWUJSdGjvRGKSi9FynAZPJDKcjKuvR7nXlwDL9NeI2dO/LJ0wWiaSXk5RnZuTEfnztNtHghptIYf5vwGt/sK+DIuWje/0c+t4VbWR12LWd8fStsL4ky0VCtXr0agGeffZYFCxbg7+9PYWEhL7zwgpsju5i/vz8FBQWO7+12e7XJi3bt2rNu3cdOjSEw0JecnEKn7lNUrrZ9vXF/NinbMsjOMxMUYGJMZKiUrqyGvKZdx1l93X/ZLq6qZFpUQICJdaO7X/H+6zt5TbtetVfqCQkJADzwwAP07duXvn37cv/999O3b99L7jgkJITk5GTH94sWLaJjx45A6TQUk8mE3W4nMzOT6dOnExMTw7p1pavxlx+22aNHD3bu3El+fj4Wi4WQkBA0TSMiIoKdO3eye/duIiIi0DSN1q1bY7PZOHv2bO16o5bS0gz07OnL55+n0rOnb62rGQghasfeNhR7y6vQrFa8FFBiIz2wWek6E00DwWotHTXxu6ioEjp2tNOsmeJPf1Lo9RagtDpJcPClhwHa24by3PFJeFNEMy0HTYNmWg7eFPHc8UmVVlT56MfriPnvZEryiwkmg5L8YmL+O5l9efdetK2U5RUN3alTpxyjG3x9ffn111/dHNHFunTpwtatWwHYs2cPHTp0cHNEwhP17RjER6O7801cDz4a3V2SF6LBkbVehKep9k67TZs2AEyZMoV77734Irs6ffr04dixP+Z1X3XVVQB8//33rFixgvfee4/CwkIeffRR/vKXv2Cz2Xjssce46aabyM/PJyAgAAA/Pz/y8vIuGsrp5+dHVlYWJpOJwMDACu15eXkVRnRcSK/XCAz0rfLnl+ODD2DCBN3vJRvNnDrlx4QJ3vj52RkwwCmHEBfQ63VOe/4ul8Ggr/TYVbXXd67qa6f0X9x4tNyzmDMz0PWIpPDTT/H2NkJODvj5YB/5F0zljjFjBowZo1FSUjp1pKREQ6+HGTNUjWLZ9Ws7zjWZxNiCBbRRp9ERwNImk/j51zACAy9OgixerOO44Vr+1nQSD5xdxidNR5NlD8Pyy3DatPnGcUyDQQ9Qr15P9en17873D/GHiIgIHn30UW666Sb27dvHQw895O6QLnLffffx1VdfERMTg1KKuXPnujskIeqMjCQRVQkKMHGqkmRFUIDJDdEIcYkExqZNm7jqqqtYvnw5Z86cqfCzIUOGXPbBPvnkE9544w2WLVtG8+bNHUmLsmkmt99+OwcOHKgwbLOgoIAmTZpcNJSzrN1oNF7UXpb8qIrNppw21GfmTF9sNuUoyWgwKCwWmDkT/vxnGU5UF9w5VKukxFbpsatqr+9c1ddO6b9mQejip5D+wQd0Mlux2+2Ys0+jmc2l00eaBUG5Y/z5z7BwoYHERC9yc01cfbWd+HgLf/5zCTk5lz5cmza+HDoRylK/iURZ3iLNbxSHbKG0aWOv9FwyMvzx9lZkaqHMM0wgUNcMvaYoKroKpf54TyopsQHUq9dTfXr9y1DPutGyZfV/dy80fvx4Dh48yMGDBxkwYAA33HBDHUVWezqdjpdfftndYQhR5zbuz2bu5wcdJWFP5ZmZ+/lBAEliCMZEhlZ4fQB4G3SMiQx1X1CiUat2CsncuXM5f/48FouF06dPV/jvcv2///f/WLFiBcuXLyc4OBiAjIwMhg0bhs1mw2q18v3339OpUye6dOnCl19+CcDWrVvp2rUr/v7+GI1Gjh49ilKK7du3061bN7p06cL27dux2+2cOHECu91e7egLZ8vK0mE0VmwzGms2j14I4Vz2tqEk+/qimc20LC76I3lRyZQOKJ1KsmVLIb16DWbLlsJLLt5ZXny8BU2DQ7Yw5uoncMgWhqaVtlcmONiO1VqxzWoFb+/sGh9TiPpu7dq1ACQlJfHRRx9x4MABPvnkExYtWuTmyIRovFK2ZVS4OQUoLrGTsi3DPQEJj9K3YxBTerfn6gATGnB1gIkpvdtLcku4TbUjMMLDwwkPD+euu+4iJCSEo0eP0qZNm8tOENhsNubMmUOrVq2IjY0F4NZbb+W5556jf//+REdHYzQaeeihh2jfvj1t2rQhISGBoUOHYjQaSUpKAmDmzJnEx8djs9mIiIjg5ptvBqBbt24MGTIEu93O9OnTa9MPtRYcbOfECQ2vcovw1nQevXAfWSSx4Tqq11MYO57sr7ZVm7y4UmXJjsREL376yUTr1or4eEuVSZCy6icWS+mUFYuldNRWu3Yr6yQ+ITzR1VdfDcC1117r5kgEyLSBxqS651rWOBCX0rdjkLw3CI9Ro9Umjx07xoQJE2jXrh0HDx5k7NixNZqv2qZNG9asWQOUVgqpzKhRoxg1qmLddB8fH5YsWXLRtp07d3bsr7zY2FhHYsTVqropqepTWOEZnFlNRJeZwbDDh9BlZtTZzbK4PPa2oaxsdx0P1OD5uJLnLyqqhKioEh59NJoVKy5+b7pwW/gj4eHnpwixZXDfPjPrD00gLc1wWSNAhKiPyhbo7tOnD+fPn0ev17NmzRoGyKJRLifTBhqPSz3XssaBEKI+qdE8h3fffZf333+flJQUNmzYwL/+9a+6jqveiIoqISnJTOvWCru99FPYpCSz3IhcgfXrq78R9BTr169Bl5mBb/JigooK8U1ejC4zw91h1UsPPzzIaftq1apVjbd19fNXNmWlU6fFtMjL4KkzC2jLL8QWvsNr405IBSPRaMTHx/Pjjz+ycOFCjEajy0dPCpk24Eob92fTf9kubkvaSv9lu9i437VTBy/1XI+JDMXbUPGWQNY4EEJ4qholMDRNw8/PDyiti24ySUa2vLKbkt69Yy57Hr242JWUkHRl8mPHe//CN3kxymTitLcPymSSJEYtOXM6T6tWrWu0XVnywh3PX8nBSJ4rWoBV78NxXRvMmhfPFS1g1dzjdX5sITzB+fPnueeeezh16hSjR4/GYpFRi64m0wZco2z0w6k8M4o/Rj+4Molxqeda1jgQQtQnNUpghISEMH/+fP7zn/8wf/58QkJC6jquekeXmUHMwZ89/ua1voxuqK0rSX5cDl1mBkOO/IIymVBNAwFQTQMliVFPlE9euPr502Vm8FzRv7DofDivCyRQ5fB/Kh0TFkYeeZHRB/YTVFh/qmSUn4IjRE1ZrVb+8Y9/0KlTJw4dOlShmphwjaqmBzSEaQPuHvFQnieMdKnJc923YxAfje7ON3E9+Gh0d0leCCE8Vo0SGLNnzyY4OJgdO3YQHBzMrFmz6jquesUxDL2wyONvXl11g9/Qmdamold2x81vGdU0EKxWTGtT3RKXqBnT2lSwWt3y/JnWpuKtO08OgTS1n+MOtYtAlcvN1u+4nV2EnzvLkCO/ePT7SBmZQiVqKyEhgTNnzvDMM8+wa9cuXnrpJXeH1Og01GkDnjDioTxPGOnSUJ9rIUTjVKMExtNPP83w4cOZPn06w4cPx3hh3dBGrMIwdB+ZRtCQlf+k2Tw4BpumQ8vNqbCNlpsDRiPmwTFuidET1IdRPubBMWA0uuX5Mw+OwbdpJtfYMulm/gqrMmDGRJA6yVVe5/jFPwCzTufx7yPunIIj6r8uXboQERHBp59+SteuXbn++uvdHVKj01CnDXjCiIfyPGGkS0N9rhsqZ48g8qQRSUI4Q40SGAEBAfznP//h8OHDHDlyhCNHjtR1XPWCO4ehNzSeftN74SfNAKvDrkUzmx03wVpuDprZXKelO+uD+jDKx942lMLY8W55/uxtQ9lxrUZUi68xGWxo6AjjCH4BOvRhbQjLz0MDj34fkfc+caUWLVrEBx98wOrVq9m/fz+TJ092d0iNUkOcNuCsEQ/OuunzlNEPDfG5boicPYLI00YkCeEMl0xg5Ofnc+zYMd59911eeuklpk+fzowZM1wRm8dz5zD0hsaTb3qr+qQZcNwEtywukuRFPVM+ieHq5+/mc+fw6XYDV7XSEUIGOhQ/W6/lTEkgXjYbt5/+FRQe+z4i733iSu3evZtXXnkFX19fHn74YY4dO+bukEQD4YwRD8686ZPRD+JyOHsEkaeNSBLCGaqt2bdixQr+8Y9/oNfref755+nRo4er4qoXzINj8E1ejJabU+FCXqYRNBxVfdJMbg5DjvwClCYxsr/aJsmLeqgsieHq58/wxEgyNx3iu5OBtEOPH/kUF8PJn/Jogw+/0QzTxq343najR76PyHufuFI2mw2z2YymadhsNnS6Gg0IFeKSxkSGMvfzgxVu2i53xEN1N321STz07RgkCQtRI85eM8UT1mARwtmqvWL4+OOP+fTTT1m9ejX/+te/XBVTveHOYejCNar7pFmv7JjWpmJvG8rKdtfJ8+0hHn540GVt747nr/dfn+W545Ow4MW3ui5s4c80IYcQjnKMazhPU06d0DhxwjNv6uS9T1ypxx57jEceeYSDBw8yePBghg0b5u6QRAPhjBEPctMn3MXZa6Z4whosQjhbtVfHXl5eeHl50bx5c6xWq6tiqlcqDEMvkmkEDU11iz3aNB2ftniUnj192bx5LT17+pKWVu2gJuECAwdGu/yYl5s0Afg6ux0pAQl4U8zVnMZACb/SAoWGHsV/1D1s/jHIY9eUcOcUHFH/vffee6xatYo333yTv//97zz44IPuDkk0IFe63oPc9Al3cfaaKZ6yBosQzlTjj/eUUnUZR73mGIbu6yMX8A1MZZ80Z+49z8frbDx34t/ETL6BI0c0dDozJ05oxMWZJInhIWqTVKit2iRNgoPtHLaHkWR4nj9xhu/pwmf0JYdAtnMX5wjkeEEzjv1S4rFrSjje+3x85b1PXBZN05g8eTKff/4577zzDosWLXJ3SEI4yE2fcJcrHUFUfvHZuxO/AJA1WESDU+2d1qFDh4iLi0Mp5fi6TFJSUp0HV5/Y24aS2r4D98sFfINTdpPmm7yYgHNN+OZLK696J/CLags2KCrS0OuNeHmBxQKJiV5ERZW4O+w6s379GreMcrhcnh5jfLyFuDgTv6h2jGIZE0kEYCt3A6ABgeTw33Rvmi/23DUlyqbgPCDvfeIyDBw40N0hCFGlspu7lG0ZZOeZCQowMSYyVG76hEvUds2UssVny9ZvOZFbzNzPDzKld3s+Gt3d2WEKwcb92W55n6w2gfHqq686vo6J8dwLaCHq2kc/XseqnVO47dwKVukew08ppjGHldoIjhCGzeYLgNEIWVmeuW6Bs2zYsM7jkwP1QVmSKzb2NzJtbVhgTyCBBQSSQw6BNCWHJsZiZlsn0bNtSzdHK4RzPfzww+4OQYhqycKbor5x9uKzQlTnwoRZWbUmoM5fb9UmMG677bY6PbgQF9JlZjDs8CF0mRmXPRz9Sh5bnbQ0A3FxJpS6ls+ZTpg9gzF5r2DAwkS1gAUkcITS41mtpVMDhKiJqKgSVq0ai8Gg54cfPiTxeALx9vmEcB7N6MVrPhOxB7cFCt0daqXS0gwkJnrx009r6dnTh/h4S4MefSSEEEJ4Kll8VriSOxNmDfujYlGvlJUsDSoqvOyFC2v62LQ0A4/elc0d/+7Ko3dl12i9isREL5QCLy8I4zAJLKAIb7IIpghvJrKAMA5jsYCmlU4NEOJyvfyymXOBoSwyPEuWLojXfCaSZQjz2NdTWWLvxAlZA0YIIYRwN1l8VriSOxNmksBoRMqPUPA0QYWliQdlMnHa2wdlMtU4iVGWvLjUY9PSDLw27gQxma/Qll+IyXyF18aduOQNV1aWDqMRQmxHmKTNowhvcggENAqNgZjxJoEF3NriF5KSzPIJtKiVqKgSkpLMnPT2Z7ZuGiXBoR79eiqf2NO00n+VKm0XQgghhGvJ4rPCldyZMJMERiNxJaMb6pouM4MhR35BmUyopoEAqKaBNUpilE9eXOqxq+Ye57miBVj1PhzXtcGq9+G5ogWsmnu82viCg+1YrTCoaAVeWjHFpkB0GoAiMFBx90P+tG29kfUP/9Njbzbr0vr1axxfe3KSrD6IiirhzjvH0qvXYLZsKfTo11NZYq+8xrAGjBBCCOGJLqxg0rqpt1QcEXXGnQmzOr3S3Lt3LyNGjAAgMzOToUOHMmzYMGbMmIHdXjpnZs2aNTzyyCNER0ezZcsWAIqLi4mNjWXYsGGMGjWKs2fPArBnzx4GDx5MTEwMS5cudRxn6dKlDBo0iJiYGPbt21eXp1Qv1XSEgrt4v/U3OuTmwAWVelXTQLBaqy0haVqbClYrP//anHffNXLkSDTvvmvk51+bV3isLjODIRkLseh8OK8LBOC8LhCLzochGQur7Yv4eAuaBiv1j2LFSFOVg8kEVwdt4/HHrXS46iw2TYd5sHsWui2fQHCHDRvWAZ6dJBPOV5bYK0/WgBFCCOEs5UuC9l+2i437s90dksfr2zGIj0Z355u4HnwZ/2cA6UNRJ6605O+VqLMExltvvcW0adMwm0vnwcybN49x48axcuVKlFJs2rSJ06dPs3z5clJTU3n77bdZtGgRFouFVatW0aFDB1auXMmAAQNISUkBYMaMGSQlJbFq1Sr27t1Leno66enpfPPNN6xdu5ZFixYxc+bMujqleulyRii4Kz7d6V8x2WwUb9zK+//IdyQhMveeB6Ox2sSAeXAMR0+a2Lkxn7w8DU0rIS9PY+fGfI6eNDkea1qbSqCf5fepH3/IIZBAP0u1SZKyof0lwaG8ok3gT35F9I/8DT+/LLTcHDSzmdVh1zp14dDLUZZAcCdPT5IJ5ytL7FkspVNHZA0YIYQnkpvg+qmswsGpPDOKPyocyPNXcx/uPXFRH07/5Cfm/+dnd4cmGojyCbOPRnd32WifOktghISEkJyc7Pg+PT3dUdWkR48e7Nixg3379nHLLbfg5eVFQEAAISEhHDhwgN27dxMZGenYdufOneTn52OxWAgJCUHTNCIiIti5cye7d+8mIiICTdNo3bo1NpvNMWJD/DFCoSx5UaYmoxvqmuOmt0ULtvnezKkTGl1yvqAZv6Hl5vLNl1bSbppQbWLA3jaU545Pwpsimmk5aBo003Lwpojnjk9yPNY8OIZOnfU0sedgs5XecNls0MSeQ6fO+kuOnoiKKmHLlkKuvWcC93weS1irIloWF6GZzRTGjifb19eJPVO/lF+/xBOTZDXx8MOD3B1CvVOW2GvdWmG3m2jdWnn0mh1CiMZHboLrr+oqHIiaSfr3zxf1IcD6vafkd0DUa3WWwOjTpw8Gwx+LIyql0DQNAD8/P/Ly8sjPzycgIMCxjZ+fH/n5+RXay2/r7+9fYdvq2kUp8+AYMBrRcnMqtGu5OZcc3VCXypIXR0768M4HLTl45j7+o+4BpeitNhOkZfOqdwIvvdvhkvv6OrsdKQEJeFNEG3UMb4pICUjg6+x2jm3sbUNpsfB5et1ZQGvfcyhloLXvOXrdWUCLhc9f1ugJe9vQ0qSFjy+FseOxtw1tUDfAlzsl5Z6TJzw2SVZTAwdGu+3Y9fm1U5bYqw9rdgghGh+5Ca6/pCTolTuZW1zlz+R3QNRnLqt3p9P9kSspKCigSZMm+Pv7U1BQUKE9ICCgQnt12zZp0gSj0VjpPqqj12sEBjr/E3NNq5v9XpHAG2HqFI4++wo7/1vIkbPRbPhnAXfcYiPk9Sk0CQtzS1i6pevJPKr4eFcLR9s5AvmPuodIvuC0vhXZvtdSdIyL+lSv11VoCw2FI8ev5XX/STxgWcYn/qM5Yg8jNPSCxwbeSJM3J3HdwlfYtHED9/Tti33CJLiMPjAY9KX7DLyRNddfz7CbbwRg5MgnatMNTuGIyUk++miD43zK9/UHH8Ds2Tr+97+13HuvH/fdZ+ff/9ZRmLEB33PJBLUtIP14M06fjuZf/zJyT9dztLvGB/vIv2DytN8LDzJy5BN8/PEHjn42GPTAxa97T+bs12BduvD9QwjRcDW0m+CN+7NJ2ZZBdp6ZoAATYyJDG+wCjUEBJk5V8jxJSdCKqntNtGrqzYkqkhj19XdACHBhAuPGG29k165ddO/ena1bt3L77bcTHh7Oq6++itlsxmKxcPjwYTp06ECXLl348ssvCQ8PZ+vWrXTt2hV/f3+MRiNHjx4lODiY7du3M3bsWPR6PQsXLmTkyJGcOnUKu91O8+bNq43FZlPk5BQ6/RyVqpv9Xqm0Hdfw2rcJPFe0gGBOY83zZ9C3CTy/ozVRzdwTr67/QL5LTibAlkOBMRANBb///1u687bvWMxmRZs2F/dpYKBvhbbx4w3ExZk4WBLKXP0E/EqaoWmK8ePN5ORc8IlwsyB0f43l5JYt5Pw1FnuzILiM56ykxOY4dvmv3cnZcZTfX1lfp6WV9rFSCp3OzC+/+LFokQ4fH0WJvhUzixOI/e8CbPocNM0EOQXs2FTMvtnjufsy+7gxKv/e0b//w2zYsM4jXls15Sm/CzVx4fuHcI6WLav/4MCVlFL06NGD0NBQADp37kxcXBx79uxhzpw56PV6IiIiGDt2LFC6EPgXX3yBwWBgypQphIeHc/bsWeLj4ykuLuaqq65i3rx5+Pj4uPGsPJOn31A3pJvgsukwZSNKyqbDAB7V584yJjK0wvmClAS90KVeE3H3dSBuXeXFDerj70BVPP19SDify+rdJSQkkJyczJAhQ7BarfTp04eWLVsyYsQIhg0bxuOPP8748eMxmUwMHTqUgwcPMnToUFavXu24yJg5cybx8fEMGjSIG2+8kZtvvpmbbrqJbt26MWTIEGJjY5k+fbqrTqneSEz0IlMXxhtNEjiqC+GNJglk6sJITPRyW0z2tqHMtk7CV1dEE3sOmmalKTl4U8x8EjhkC6vxgoCXOxff3jaUle2uc9uim/VRYqIXSoGXV+lCjcXFZQs3amga/GQJYz4JeNmLCeYYfvqiGk8BEjBkyB9Tudw5naW26vM0GNHwHD16lE6dOrF8+XKWL19OXFwccHkLgaekpNCvXz9WrlzJjTfeyOrVq915Sh6pPqwv4c4yf87W2KbDuLPCQX1xqdfEgze3ZuDNV1/0uPr6O1CZ+vA+JJyvTkdgtGnThjVrSufTh4WFsWLFiou2iY6OJjq64gW7j48PS5YsuWjbzp07O/ZXXmxsLLGxsU6KuvbK34R4kqwsHd7eiqNaGPMMEwjUN8OoK213J3vbtizOSuAF6wJCtNMoQwBzbAlk2NvSqbUiPt5S4zn1UVElREWV8Oij0axY4d6yog1R2WsoxHaEZ0re4l3baDK0MGw20OtLF0TN1MKYrxJ4XLeMNL9RnNSFUZyluTv0emHo0KH1elRAfUy6iIYrPT2d7OxsRowYgbe3N5MnT+aqq65yLAQOOBYC9/LyqnQh8N27d/PXv/4VKF1MfNGiRTzxxBNuPCvPU93Nk6fcZJbF0RA+nW1o02Fqom/HoHr5XLlKTV4Tk+7twM3XNG0QvwOVqQ/vQ8L5XDaFpDHw1JuQ4GA7J05oeJUbcGG1lra7U3y8hbi4MBaRwMPmt9jgM4pzhlBuDp7Dv/89zq2xVaWxftIcHGzHkJXBWOsC7CqfSdoC5qkEjhlK1w/R66GkBLIMYczTlybJrBb3v8aEEA3b2rVreffddyu0TZ8+ndGjR9O3b1++++47JkyYwOuvv37Rgt9ZWVmYTCYCAwMrtF+4yLgsDl65+nJD3VBughvSdBjhHDV9TTSU34HK1Jf3IeFcksBoBEoTBSYslrJh/9R4ekZdKhtdkZgYyuyfpnF9sA9J8WZWrdoBjHNrbFVx5SfN69evqfZ4l/q5M730+M/kT1tMAT78RiDN9WYmlSzgNcNEDtsD8faGggLw8lKUlHjOa6y+aqyJMiEu1+DBgxk8eHCFtqKiIvT60sVwu3XrRnZ2Nn5+fpe1EHjZwuHe3t6ObS+lLhYI9+RFZ6taILBVU2+Pjbk6ntzXABP6XM/U//cjxdZya0IYdUzoc71Hx30hT+/n+uRSr4nG0Nee8D7UGPrZ00gCoxH4I1HgxU8/la4RcTnTM+pSZVM/Vq1yc1AeYsOGddUmKC718yuVlmZg8WIdlp+OM9N3MW1v8GL/yaao30rQmnnToZWNl7IWMPX8c5jaBvLnP5fwxRcGj3uN1UcyJUOI2lu6dCmBgYGMGjWKAwcO0Lp1awICAi5rIfCyxcQfeeQRx2Lil1IXC4R78qKzT9/VttJFFp++q63HxlwdT+5rgB5tA5lyX/uLpgL0aBvo0XFfyNP7uT651GuiMfS1J7wPNYZ+dpeqFgiXBEYjIWtEiEtJSzP8nuRaS9euvpw9C97eEGt9B0uBlW9+bkWfPiX88MMaHnlkMBCA/qf9tN9+NyHvfIG9bSgzZljkNSaEcKvRo0czYcIEvvzyS/R6PfPmzQP+WAjcZrMRERHBzTffDOBYCNxutzsWAn/mmWdISEhgzZo1NGvWjKSkJLedj6dqSOtL1BcNeSqAqJ3G/pqQ96HGSRIYQtQxV071qK0/yqSCTmfmxAk/7HYwGmGVIZop+r/RxJbDjh1N+X1aOLpjRzEc2M95owHf5MUUxo6Xyi5CCLdr2rQpy5Ytu6j9chYCb9GiBW+//XadxdhQNPabJyGEe0jp1MbNvWUohGgENmxYV2f71mVmMOzwIXSZGVe0nwvLpNp/H4mXnw8ZWluW+iXgqyuCnNzS4x47inH3d1i7dOOofwDKZMI3efEVxyGEEEIIIURVpHSqkASGEPVUUGEhvsmLCSoqvOLkQVaWDqPxj+/1+tIFX2220u+P6sNY7JVAC/8iQvLzHckLe3BpSULVNNCRxAgqlHmAQgghRH20cX82/Zft4rakrfRftktuCoXHqa50qmgcJIEhRD2ky8xgyJFfUCYTp719rngERHCwHav1j+/Lqg3qdH9UrskyhKGbPJ4mVislN9zoSF6UUU0DwWrlnpMnandSQgghRCPiackC+WRb1AdSOlVIAkN4nPpSQtJdceoyM/BNXoxZpytNGlBxBERtkhjx8RY0DUepXU1T+PvDNdeA3V5aVSQpyczdT7RhcaebUM2aoeXmVNiHlpsDRiObWrW+4nMUQgghGjJPTBbIJ9uiPggKMF1Wu2h4JIEhPM6VLHjpyqSCuxbmNK1NBauVgvJzPvhjBIRpbepFj1m/vvqqIFFRJSQlmbm1xS9Mti7m1ha/kJxczKFDdnr1GsyWLYWOkqjZvr4Uxo5HM5sdSQwtNwfNbKYwdjzZvlILWwghhKiOJyYL5JNtUR+MiQzF21DxFtbboGNMZKh7AhIuJwmMRqa+jG6oLU+v9uEM5sExYDTiV37OB3+MgDAPjrnoMTVZSLT/TYf44I653N7qn3xwx1z633Soym3tbUMdSYyWxUWO5IVUIRFCiCtXNrWgw4ufesTUAuF8npgskE+2RX3Qt2MQU3q35+oAExpwdYCJKb3bSxWSRkTKqDYyjeEGv6ErSx6YNv270hEQtUkilE1LuXBNDaZOuWQc2V9tq3Dchp4kE0KIulQ2taDs0/myqQWAXKA3IEEBJk5VkqxwZ7JgTGRohdceyCfbwjNJCefGTUZgCFEP2duG8nrAYD5eZ6Pklzv5eJ2NtJsmXJS8SEsz0LOnL5s3r6VnT1/S0i7OWZZPXly4poZu4SvVVhWxtw1lZbvrKhxXkmRCCFF7nji1QDifJw6Dl0+2hRD1gYzAqGfWr1/TIG8QXf2pvaePEtBlZjDs8CF0mRmVjqj48p1j3PazN+/4Pkh33adssI0ia34YSS3NjrUq0tIMxMWZUAp0OjMnTvgSF1f6yU7ZNvDHmhrqqooXKKppIJw+JVVFhBDChTxxaoFwvrKkQMq2DLLzzAQFmBgTGer2ZIF8si2E8HQyAqOeqclaBvWRq5MynpwEKhsREVRUWGlVEV1mBvZ5i2mrjhJjW8kqQzSnfMJQChITvRzbJSZ6oRR4eYGmlf574Tbwx5oalVYVKS7G32qtdXlWIYQQl0fWIWg8+nYM4qPR3fkmrgcfje4uiQMhhKgBSWA0IJeqNCE8X1VrUZQlEHSZGfw24TVOnPMhU7XjXJEPL1iTCbEdwWiErKw/fqWzsnRcUKjkom2g4oKcFdbU+O03QOFXYq11eVYhhBCXxxOnFgghhBCeQhIYDUhDHZ1Rn5WfClKTbX2TF3PkpA/vfNCSI0eiS/896YNv8mIMO7bz24TX2LzDj/NaIAA5BFKomvBM7gJaFR8hOPiPedPBwXYuKFSC1UqFbcpcVFXkt9/QAFpeVWkiRQghRN2QdQiEEEKIqkkCQ4g6cqmpIBcyrU3l2C8lfLStBXl5GppWQl6exkfbWnDslxJ8F84jfY+N87pAvMrNAskhEKxWhlhWEB9vcbTHx1vQNLBYSqeOWCylU0nKb1NeWRKjwGBEA+wtWkBgIPDHop6+yYurXdRTCCHElSubWvDzrPtlaoEQQghRjksTGO+//z4jRoxgxIgRREdH83//93+kp6cTGRnpaP/kk08AWLNmDY888gjR0dFs2bIFgOLiYmJjYxk2bBijRo3i7NmzAOzZs4fBgwcTExPD0qVLXXlKQlTqUlNBKmMeHMN/071pYs9Bry9NNuj10MSew3/TvSmcMJmcAi8CycFgAJ1WmqAIJAcLXrR/ObrC4pxRUSUkJZlp3Vpht5to3VqRlGSusM2F7G1DyTcaUd7ejookZVTTQLBaZVFPIYQQQgghhFu4NIHxyCOPsHz5cpYvX06nTp2YNm0a//vf//jLX/7iaH/ggQc4ffo0y5cvJzU1lbfffptFixZhsVhYtWoVHTp0YOXKlQwYMICUlBQAZsyYQVJSEqtWrWLv3r2kp6e78rSEqKC6sqTVJTHsbUOZbZ2Er66IJvYcoDR54asrYrZ1EiV3RrA6dAJe9tKf63Q2rvbOwVc7z/vtJ3D3E20u2mdUVAlbthTSq9dgtmwprDZ5UWZTq9ZVL+ppNGJ4YuRl9IYQQgghhBBCOIdbppD88MMPHDp0iCFDhvDjjz/yxRdfMHz4cKZMmUJ+fj779u3jlltuwcvLi4CAAEJCQjhw4AC7d+8mMjISgB49erBz507y8/OxWCyEhISgaRoRERHs3LnTHaclBFCuLGkVIxhMa1OrfKy9bVsWeyXgTRFt1DG8KWKxVwL2tm0BGDrlGpb4JGC0FXGN/RhGWxGJ+nEMnXKN0+LP9vV1rIdBTg7w+6KeZjOFsePp/ddnnXasxk4W3hVCCCGEEKLm3JLAePPNN3n22dKboPDwcCZOnMh7771HcHAwr7/+Ovn5+QQEBDi29/PzIz8/v0K7n58feXl55Ofn4+/vX2HbvLw8156QEOVUW5bUaCz9eRXi4y1kGcJYZEwgkxAWGRPIMoQ51q2Iiirh+Vdbk9p2IplcS2rbifh2+rBGIysuR9l6GBQXly7q+Xvywt421KnHaexk4V0hhBBCCCFqzuDqA54/f55ffvmF22+/HYD77ruPJk2aOL6eNWsW3bp1o6CgwPGYgoICAgIC8Pf3d7QXFBTQpEmTCm3l26uj12sEBvo6+9TQ63V1st/yDAZ9lceo7mcNiSv6+YoE3ghTp3D02VfY+d9CjpyNZsM/C7jjFhshr0+hSVhYlQ8dPhz8/BSzZ1/L7P9N48ZQP/42zc6AAV6Al2Ob4cPDGDBgDx9/EMaAAbsu2R+X89pwbBt4I9qkSZz+8gsMU6uOu7G87upCWd95/Gu6AZG+FvXBxv3ZpGzLIDvPTFCAiTGRobKQpxBCCIEbEhjffvstd955p+P7kSNH8uKLLxIeHs7OnTvp1KkT4eHhvPrqq5jNZiwWC4cPH6ZDhw506dKFL7/8kvDwcLZu3UrXrl3x9/fHaDRy9OhRgoOD2b59O2PHjq02BptNkZPj/EoKgYG+dbLf8kpKbFUeo7qfNSSu6OcrlbbjGl77NoHnihYQzGmsef4M+jaB53e0JqpZ9bH/+c+l/z36aDQrVpROMfh9JkcFZc/3pZ53XWYG0T/9xPm9/6vRCIry+wsMacuKsHbc3ywIGvnrri6U9V19eE03FNLXdaNly4BLbyRqZOP+bOZ+fpDiktKS16fyzMz9/CCAJDGEEEI0ei6fQnLkyBHatPljscGXXnqJuXPnMmLECL7//nvGjBlDy5YtGTFiBMOGDePxxx9n/PjxmEwmhg4dysGDBxk6dCirV692JCpmzpxJfHw8gwYN4sYbb+Tmm2929Wm5VVqagZ49fdm8eS09e/qSlubyvJS4QGKiF5m6MN5oksBRXQhvNEkgUxdGYqLXpR/sRJdbyrU2Hn54kNP3KYQQjVXKtgxH8qJMcYmdlG0Z7glICCGE8CAuv9N96qmnKnzfqVMnUlMvXtQwOjqa6OjoCm0+Pj4sWbLkom07d+7MmjWNczG8tDQDcXEmlAKdzsyJE77ExZkAnL4ugqi5rCwd3t6Ko1oY8wwTCNQ3w6grbXeVqkq5Onsti4EDoy+9kRBCiBrJzjNfVrtoWGT6kGjs5HdAXIpbFvEUtaPLzGDY4UMVPkVPTPRCKfDyAk0r/VcpXP5Jv6goONiO1VqxzWotbXe2ykZA1LaUqxBCCPcKCjBdVrtoOMqmD53KM6P4Y/rQxv3Z7g5NeLiN+7Ppv2wXtyVtpf+yXfX2NSO/A6ImJIFRT1Q1FSArS4fRWHFbo9G1n/SLi8XHW9A0sFhKE0oWS2mCqayaiDNVNgLiSkq5CiGEcJ8xkaF4Gyr+Dfc26BgTGeqegITLyPQhURtV3fR/uPeEu0O7bPI7IGpC7nKdbP16509lqWoqgC4zw/FJf4jtCJNLFhJiO1Jnn/SLmouKKiEpyUzr1gq73UTr1oqkJLPLpvVcSSlXIYQQ7tO3YxBTerfn6gATGnB1gIkpvdvLEOpGQKYPidqo6qY/6d8/uymi2pPfAVETksBwkrQ0A9266YiNHeLUhTTLkhdHTvrwzgctOXIkuvTfkz74Ji/mpcd/pq39CM+cX0CI/SjPnF9AW/uROvmkX1yeqKgStmwppFevwWzZUujSNUnsbUMpjB2PZjY7khhabg6a2ez0NTCEEEI4V9+OQXw0ujs/z7qfj0Z3l+RFIyHTh0RtVHVzfzK32MWRXDn5HRA1IQkMJyhbSPOXX8y/L6SpERdnckoSw7Q2lWO/lPDRthbk5WloWgl5eRofbWvBsV9KeODQUlJvmYfB35ssQjH4e5N6yzz633TICWcm6rPySYyWxUU1Tl5IVREhhBDC9WT6kKiNqm7uWzX1dnEkV05+B0RNSALDCcoW0rRaC5y+kKZ5cAz/TfemiT0Hvb50HQW9HprYc/h5nxXd6V8Jbm/kkSf9CAtbwyNP+hHc3igLNQrgjyRGto9vjUdeSFURIUR98u9//5u4uDjH93v27GHw4MHExMSwdOlSR/vSpUsZNGgQMTEx7Nu3D4CzZ8/y5JNPMmzYMMaNG0dRUREAmzdvZuDAgQwZMqTRVjkTrifTh0RtVHXTH3dfBzdFVHvyOyBqwuVlVBuispKZ5TlrIU1721BmWycRp5uP3g7FmGhiz8GkK+JIURDK+0ylCzVqx49hWptKUfykK45B1G/2tqGsbHcdD8i0ESFEAzN79my2b99Ox44dHW0zZswgOTmZ4OBgRo8eTXp6OgDffPMNa9eu5eTJk8TGxrJ+/XpSUlLo168fjzzyCMuWLWP16tUMHz6cefPmsW7dOnx8fBg6dCg9e/akZcuW7jpN0Yj07RgkN2vispS9Xi4sPfrgza3JySl0c3SXT34HxKXICAwnqOuSmfa2bVnslYA3RbRRx/CmiMVeCXwUOlYWahRCCNFodenShZdeesnxfX5+PhaLhZCQEDRNIyIigp07d7J7924iIiLQNI3WrVtjs9k4e/Ysu3fvJjIyEoAePXqwY8cODh8+TEhICE2bNsXLy4uuXbvy3XffuekMhRDi0srWzfkmroesmyMaPBmB4QTx8Rbi4kyUlOjQ6+1OL5lZuv8wFpHAw+a32GAcRZYhjHFTzBTeNB7f5MUgCzU2OJdai0LWqqjfdJkZDDt8qHSqV+CN7g5HCI+2du1a3n333Qptc+fO5YEHHmDXrl2Otvz8fPz9/R3f+/n5kZWVhclkIjAwsEJ7Xl4e+fn5BAQEVNlW1p6fn3/JGPV6jcBA39qeYhX71Dl9n6Jy0teuIf3sOtLXriH97HqSwHCCsuoSo0Ydx24PpXVrRXy8xWlVJ8r2k5gYyuyfpnF9sA9J8aUlOe2UrnHgm7z4shZqFJ7vUmtRuGqtCkmUOF9ZdaGgosLSBOTUKdBMPi0RoiqDBw9m8ODBl9zO39+fgoICx/cFBQU0adIEo9F4UXtAQIBje29vb8e2le2jfEKjKjabcvpw7cBA33o5BLw+kr52Deln15G+dg3p57rTsmXlf3tlComTREWV0Lz5PXVWMrO6kpy1WahRiJqSRT2dqyx5oUwmTnv7oEwmdAtfkUV3hXACf39/jEYjR48eRSnF9u3b6datG126dGH79u3Y7XZOnDiB3W6nefPmdOnShS+//BKArVu30rVrV9q1a0dmZiY5OTlYLBa+++47brnlFjefmRBCCCFARmA0GLJQo+eSEQyiTPnkRdniu6ppIBQX4Ju8WBKQQjjBzJkziY+Px2azERERwc033wxAt27dGDJkCHa7nenTpwPwzDPPkJCQwJo1a2jWrBlJSUkYjUYmTZrEyJEjUUoxcOBAgoJkhJQQQgjhCSSBIUQdkxEMooxpbSpYrairLrgZCgyEM+ekcpAQtdC9e3e6d+/u+L5z586Vlj6NjY0lNja2QluLFi14++23L9q2V69e9OrVy/nBCiGEEOKKyBQSIYRwEfPgmEorB5GTI5WDhBBCCCGEuARJYAghhIuUrVejmc2OJIaWmwPFxTJ9RAghhBBCiEuQBIYThdhsf5RFFEKISpRPYpRVDrJPmCjJCyGEEEIIIS5BEhhOosvMYGxBgaMsoiQxhBBVubByEGFh7g5JCCGEEEIIjycJDCcoqyxQDI6yiJLEEEJUp6xykIy8EEIIIYQQomZcXoVkwIABBAQEANCmTRuefvppJk2ahKZptG/fnhkzZqDT6VizZg2pqakYDAaeeeYZevbsSXFxMRMmTODMmTP4+fmxYMECmjdvzp49e5gzZw56vZ6IiAjGjh3rsvMpS15gNtOppITTVmtpWcTcHCmLKDyGlHIVQgghhBBC1HcuHYFhNpsBWL58OcuXL2fevHnMmzePcePGsXLlSpRSbNq0idOnT7N8+XJSU1N5++23WbRoERaLhVWrVtGhQwdWrlzJgAEDSElJAWDGjBkkJSWxatUq9u7dS3p6usvOybQ2Fe3cOQz702miFJ1yzqHl5JQmMazW0rKJQriZlHL1LGlpBnr29GXz5rX07OnLBx+4OyIhhBBCCCE8n0sTGAcOHKCoqIgnn3ySxx57jD179pCens5tt90GQI8ePdixYwf79u3jlltuwcvLi4CAAEJCQjhw4AC7d+8mMjLSse3OnTvJz8/HYrEQEhKCpmlERESwc+dOl52T9c4IDAf+BzYbeZpGCRrGb3aiyzoqZRGFEBdJSzMQF2fixAkNnc7MiRMaY8boSEtz+YA4IYQQQggh6hWXXjF7e3szcuRIBg8eTEZGBqNGjUIphaZpAPj5+ZGXl0d+fr5jmklZe35+foX28tv6+/tX2DYrK6vaOPR6jcBA3ys/oSNH0H28Ae64A90PP2BSihKDAT2g3/M9ttdTaHLzjVd+nHKGDx9eZewGg9455+Xh9HpdozhPTyB97XyLF+vQNDCZoLAQvL01LBZYvNib4cPt7g6vwZPXtBBCCCFE/eXSBEZYWBht27ZF0zTCwsIIDAysMN2joKCAJk2a4O/vT0FBQYX2gICACu3VbdukSZNq47DZFDk5hVd8Pj5v/xN9QRH2a9qgdfPBsG8vvlYLNjRsHa6n5PP/UBTe7YqPU17fvg9VGXtJic0p5+XpAgN9G8V5egLpa+fLyPDH21th/z1XYbcrjEaNjAykr11AXtN1o2XLgEtvJIQQQghxhVw6hWTdunXMnz8fgOzsbPLz87nrrrvYtWsXAFu3bqVbt26Eh4eze/duzGYzeXl5HD58mA4dOtClSxe+/PJLx7Zdu3bF398fo9HI0aNHUUqxfft2unVzbtKgKubBMWA0ouXmgAKrpmHR6Si58UZUs+Yunz4iCzUK4fmCg+1YrRXbrNbSdiGEEEIIIUTVNKWUctXBLBYLkydP5sSJE2iaRnx8PM2aNePFF1/EarVy7bXXMnv2bPR6PWvWrGH16tUopfjrX/9Knz59KCoqIiEhgdOnT2M0GklKSqJly5bs2bOHuXPnYrPZiIiIYPz48dXGYbU6b6SCLjMDv7kvo9+fzv6DP+Pv7U3r3n0pmDJdqo/UEfkE1XWkr52vbA0MpaCg4Bx+fs3Q6zUWLiwmKqrE3eE1ePKarhsyAsO51xZl5PXqOtLXriH97DrS164h/Vx3qrq2cOkUEi8vL5KSki5qX7FixUVt0dHRREdXrJzg4+PDkiVLLtq2c+fOrFmzxnmBXqayDJB2wfdCCHGhsiRFYqIXP/1konVrxYwZij//WZIXQgghhBBCVMelU0gaGl1mBr7JizmSfxWpv97Lb7Y7+NQ8iCP5V+GbvBhdZoa7QxRCeKCoqBK2bCmkV6/BbNlSyIAB7o5ICCGEEEIIzycJjCtgWpvKsV9K+GhbC04UNmMr3Tljv5qPtrXg2C8lmNamujtEIYQQQgghhBCiQZAExhUwD47hv+neNLHnoNeXtmmanSb2HP6b7u3yRTyFEEIIIYQQdWfj/mz6L9vFbUlb6b9sFxv3Z7s7JCEaFUlgXAF721BmWyfhqyuiiT0HgKYqF19dEbOtk2QRTyGEEEIIIRqIjfuzmfv5QU7lmVHAqTwzcz8/KEkMIVxIEhhXyN62LYu9EvCmiGCO400xi70SsLdt6+7QhBAeTkofCyGEEPVHyrYMiksqlj0vLrGTsi3DPQEJ0QhJAuMKxcdbyDKEsciYQAbBLNS9QJYhjPh4i7tDE0J4uIEDoy+9kRBCCCE8Qnae+bLahRDOJwmMKxQVVUJSkpmS4FBma1M56e1PUpLZUSpRCCGEEEIIUf8FBZguq10I4XySwBBCCCGEEEKISxgTGYq3oeLtk7dBx5jIUPcEJEQjZHB3APVdWpqBuDgTSgEUYTa3IC6uNAsrozCEEEIIIYRoGPp2DAJK18LIzjMTFGBiTGSoo10IUfckgXGFEhO9UAq8vEDTQKcrQanSdklgCCGEEEII0XD07RgkCQsh3EimkFyhrCwdRmPFNqOxtF0IIYQQQgghhBDOIXfZVyg42I7V+sf33t7eWK2l7UIIIYQQQgghhHAOSWBcofh4C5oGFgsoBTqdD5qGlFEVQgghhBBCCCGcSBIYV6isjGrr1gpN86N1ayVlVIUQQggX+fe//01cXJzj+88//5x7772XESNGMGLECL755hsAli5dyqBBg4iJiWHfvn0AnD17lieffJJhw4Yxbtw4ioqKANi8eTMDBw5kyJAhrFmzxvUnJYQQQohKySKeThAVVUJUVAlPPDGUd95Z5e5whBBCiEZh9uzZbN++nY4dOzra0tPTmTBhAn369KnQ9s0337B27VpOnjxJbGws69evJyUlhX79+vHII4+wbNkyVq9ezfDhw5k3bx7r1q3Dx8eHoUOH0rNnT1q2bOmOUxRCCCFEOTICQwghhBD1UpcuXXjppZcqtKWnp7N+/XqGDRvG/PnzKSkpYffu3URERKBpGq1bt8Zms3H27Fl2795NZGQkAD169GDHjh0cPnyYkJAQmjZtipeXF127duW7775zw9kJIYQQ4kIyAkMIIYQQHm3t2rW8++67Fdrmzp3LAw88wK5duyq033XXXdx77720adOGGTNmkJqaSn5+PoGBgY5t/Pz8yMvLIz8/n4CAgCrbytrz8/MvGaNerxEY6HsFZ1nZPnVO36eonPS1a0g/u470tWtIP7ueyxIYVquVKVOmcPz4cSwWC8888wxXX301Tz/9NKGhoQAMHTqUBx54gDVr1pCamorBYOCZZ56hZ8+eFBcXM2HCBM6cOYOfnx8LFiygefPm7Nmzhzlz5qDX64mIiGDs2LGuOiUhhBBCuMDgwYMZPHhwjbYdOHAgTZo0AeCee+7hs88+44YbbqCgoMCxTUFBAQEBAfj7+1NQUIC3tzcFBQU0adLE0Xbhtpdisylycgov88yqFxjo6/R9ispJX7tGQ+3njfuzSdmWQXaemaAAE2MiQ+nbMcitMTXUvvY00s91p2XLyv/2umwKyYcffkhgYCArV67krbfeYtasWfzvf//jL3/5C8uXL2f58uU88MADnD59muXLl5Oamsrbb7/NokWLsPx/9u49Lso67//4axjOJwEZMFM8oEhZqGhqiZTlodpqdy1PtHR3tGyztDTt4Gk1zTWrzdas+1d7Fx3Msu2uddswNUkzKzc13cBDHlNhAFEGhYGZ6/eHt5MIeECYGYb38/HoEXNxcc3n+73G6/rO5/oe7Hbee+89kpKSePfdd/nd737HwoULAZg2bRrz58/nvffeY9OmTWzdutVdRRIREREvYhgGt9xyC4cOHQJg3bp1dO3aldTUVNasWYPT6eTAgQM4nU5iYmJITU1l9erVAOTk5NCzZ08SExPZs2cPJSUl2O12vv/+e3r06OHJYomIF/vsp3xmZ2/nUGkFBnCotILZ2dv57Kd8T4cm4pPc1gPj+uuvrzahltlsZsuWLezatYsVK1bQrl07nnzySTZv3kyPHj0IDAwkMDCQhIQEcnNz2bBhA/feey9wYpzqwoULsdls2O12EhISAEhLS3M1VkRERKR5MZlMzJo1i4ceeojg4GASExMZPnw4AQEB9OrVixEjRuB0Opk6dSoAY8aMYdKkSSxZsoTo6Gjmz59PQEAAkydP5p577sEwDG699Vbi4z37JFVEvNfCr3ZTXuWstq28ysnCr3Z7vBeGiC9yWwIjLCwMAJvNxsMPP8y4ceOw2+0MGzaMyy67jFdeeYW//vWvJCcn1zr2tK5xquHh4dX23bdvn7uKVMOIESM99t4iIiLNUZ8+fejTp4/rdVpaGmlpaTX2Gzt2LGPHjq22LTY2ltdff73Gvtdeey3XXnttwwcrIj4nv7TivLaLyIVx6ySeBw8e5I9//CMZGRncfPPNHD161DVOddCgQcycOZNevXqdcZzqyW11jVM9ebwzaYyJtgD+8IfbcTicZ99RLogmy3Ef1bV7qJ7dR3UtIiINKT4iiEO1JCviI4I8EI2I73NbAqOwsJC7776bqVOncuWVVwJwzz33MGXKFFJSUlxDP1JSUnjxxRepqKjAbrezc+dOkpKSXONUU1JSXONUw8PDCQgIYO/evbRt25Y1a9ac0ySejTHRFmgSF3dRPbuP6to9VM/uo7puHHVNtCUi4use7N+e2dnbqw0jCfb348H+7T0XlIgPc1sCY9GiRRw9epSFCxe6JuCcPHkys2fPJiAggNjYWGbOnEl4eDiZmZlkZGRgGAbjx48nKCiIUaNGMWnSJEaNGkVAQADz588HYMaMGUyYMAGHw0FaWhrdunVzV5FERERERKQZOznPhbetQiLiq0yGYRieDsLdKisd6oHRhKme3Ud17R6qZ/dRXTcO9cBonLaFPq/uo7p2D9Wz+6iu3UP13Hg8voyqiIiIiIiIiEh9KYEhIiIiIiIiIl5PCQwRERERERER8XpKYIiIiIiIiIiI12uWk3iKiIiIiIiISNOiHhgiIiIiIiIi4vWUwBARERERERERr6cEhoiIiIiIiIh4PSUwRERERERERMTrKYEhIiIiIiIiIl5PCQwRERERERER8XpKYFwgp9PJ1KlTGTFiBJmZmezZs8fTITUJmzZtIjMzE4A9e/YwatQoMjIymDZtGk6nE4AlS5YwdOhQhg8fzqpVqwAoLy9n7NixZGRkcN9991FcXAzAxo0bGTZsGCNHjuTll192vc/LL7/MbbfdxsiRI9m8ebObS+lZlZWVTJw4kYyMDG677TZWrFihum4kDoeDJ554gpEjR3L77bezd+9e1XUjKioq4uqrr2bnzp2qZ/FJalvUj9oWjU9tC/dQu8L91LZoQgy5IJ9//rkxadIkwzAM44cffjAeeOABD0fk/V577TXjpptuMoYNG2YYhmHcf//9xjfffGMYhmFMmTLFyM7ONgoKCoybbrrJqKioMI4ePer6+Y033jBeeuklwzAM4x//+Icxc+ZMwzAM45ZbbjH27NljOJ1O49577zW2bNlibNmyxcjMzDScTqfxyy+/GEOHDvVMgT3kww8/NGbNmmUYhmEUFxcbV199teq6kSxfvtyYPHmyYRiG8c033xgPPPCA6rqR2O1248EHHzQGDx5s7NixQ/UsPklti/OntoV7qG3hHmpXuJfaFk2LemBcoA0bNtC/f38AunfvzpYtWzwckfdLSEhgwYIFrtdbt26ld+/eAKSnp/P111+zefNmevToQWBgIBERESQkJJCbm1utvtPT01m3bh02mw273U5CQgImk4m0tDTWrVvHhg0bSEtLw2Qy0bp1axwOhysr2hxcf/31PPLII67XZrNZdd1IBg4cyMyZMwE4cOAAsbGxqutGMnfuXEaOHElcXByg64f4JrUtzp/aFu6htoV7qF3hXmpbNC1KYFwgm81GeHi467XZbKaqqsqDEXm/IUOG4O/v73ptGAYmkwmAsLAwSktLsdlsREREuPYJCwvDZrNV237qvqeeg7Ntby7CwsIIDw/HZrPx8MMPM27cONV1I/L392fSpEnMnDmTIUOGqK4bwUcffURMTIyroQC6fohvUtvi/Klt4R5qW7iP2hXuobZF06MExgUKDw+nrKzM9drpdFa7gcrZ+fn9+jEsKysjMjKyRr2WlZURERFRbfuZ9j3TMZqTgwcPcscdd/Db3/6Wm2++WXXdyObOncvnn3/OlClTqKiocG1XXTeMpUuX8vXXX5OZmclPP/3EpEmTqj25UD2Lr1Db4sLpftd41LZwH7UrGp/aFk2PEhgXKDU1lZycHODEZC1JSUkejqjpufTSS1m/fj0AOTk59OrVi5SUFDZs2EBFRQWlpaXs3LmTpKQkUlNTWb16tWvfnj17Eh4eTkBAAHv37sUwDNasWUOvXr1ITU1lzZo1OJ1ODhw4gNPpJCYmxpNFdavCwkLuvvtuJk6cyG233QaorhvLxx9/zKuvvgpASEgIJpOJyy67THXdwN555x3efvttsrKyuOSSS5g7dy7p6emqZ/E5altcON3vGofaFu6hdoX7qG3R9JgMwzA8HURT5nQ6mT59Otu2bcMwDGbPnk1iYqKnw/J6+/fv59FHH2XJkiXs2rWLKVOmUFlZSceOHZk1axZms5klS5bw/vvvYxgG999/P0OGDOH48eNMmjQJq9VKQEAA8+fPx2KxsHHjRmbPno3D4SAtLY3x48cDsGDBAnJycnA6nTzxxBP06tXLwyV3n1mzZvHZZ5/RsWNH17annnqKWbNmqa4b2LFjx3jiiScoLCykqqqK++67j8TERH2uG1FmZibTp0/Hz89P9Sw+R22L+lHbovGpbeEeald4htoWTYMSGCIiIiIiIiLi9TSERERERERERES8nhIYIiIiIiIiIuL1lMAQEREREREREa+nBIaIiIiIiIiIeD0lMERERERERETE6ymBISJuM3nyZHJycqptO3DgACtXrnRbDOPHj3et7S0iIiJNm9oWIs2LEhgi4lHffPMN//73vz0dhoiIiPgItS1EfJe/pwMQkabto48+YtWqVZSXl2O1WrnjjjtYsWIF27dv5/HHH+eZZ56hY8eOdOzY0fU3mzZtYtasWbz44ou89tprlJeX06NHDw4dOsTHH3+Mn58fqampTJo0icmTJ2MYBgcPHuTYsWPMnTuXxMREsrKy+Mc//oHJZOLGG2/kjjvuYPLkyQQGBvLLL79QUFDAs88+S9euXXnnnXf44IMPsFgsFBUVebC2RERE5GzUthCRuiiBISIXrKysjDfeeINly5bxP//zPyxZsoT169fz1ltvcfDgQT766COio6OZPHkyP/zwA+vWrWPRokW0bNmS0aNH8/PPP3Pddddx6623MmXKFLp37867775LVVUVAG3btmXu3LmsXr2aefPmMWHCBP75z3/y7rvvYjKZuPPOO0lLSwOgdevW/OlPf2LJkiW8//77TJw4kbfeeotPP/0Uk8nE0KFDPVlVIiIicg7UthCR2mgIiYhcsEsuuQSAiIgIEhMTMZlMtGjRgoqKCqKjo4mOjnbtu3btWkpLS/H3r5k/nTNnDosXL+YPf/gDBw4cwDAMAPr27QtAjx492LVrF9u2bePAgQPceeed/Nd//RclJSXs3bu3WiytWrXCbrfz888/06lTJwIDAwkICCAlJaVR60JEREQunNoWIlIbJTBE5IKZTKY6f+fnV/0y89BDD3HnnXcyffp01++dTicAS5YsYcaMGbz99tv89NNP/PDDDwBs3boVgH//+9907tyZjh070qlTJ9566y2ysrIYOnQoSUlJtcbStm1bduzYQXl5OQ6Hg59++qlByiwiIiKNR20LEamNhpCIiNsNGzaMf/3rX3z66ackJSXxyiuv0LVrV7p06cJtt91GdHQ08fHxdOvWjY8++oicnBxWrFiB0+lkzpw5tG3bliuvvJJRo0Zht9tJSUkhPj6+1veKiYnhkUceYeTIkcTExBASEuLm0oqIiEhjU9tCpHkwGSf7UYmIeKHJkydz4403kp6e7ulQRERExAeobSHSdGkIiYiIiIiIiIh4PfXAEBERERERERGvpx4YIiIiIiIiIuL1lMAQEREREREREa+nBIaIiIiIiIiIeD0lMERERERERETE6ymBISIiIiIiIiJeTwkMEREREREREfF6SmCIiIiIiIiIiNdTAkNEREREREREvJ4SGCIiIiIiIiLi9fw9HYCInL/9+/czaNAgkpKSXNsMw+COO+7gtttuO69jffnll2zatIlHHnmEFStWsG7dOp5++ukzvvfNN9/MDz/8UO/4PeW3v/0tWVlZREZGNtp7nGv9fPDBB9jtdm6//fZGi0VERMTbORwO3nrrLT799FMcDgeVlZUMGDCARx55hMDAQADy8/N54YUX2Lp1KyaTiaCgIO6//34GDhwIwLXXXktAQADBwcGYTCYqKyvp168fkydPxs/Pr8bv7XY7fn5+PP7446Snp9eI6UzHO3DggO7zIh6kBIZIExUcHMz//u//ul7n5+dz0003cdlll5GcnHzOx/nxxx85cuQIANdddx3XXXddg8fqLU6tL0/bsGEDnTt39nQYIiIiHjV9+nSOHDnCm2++SUREBMeOHWPChAk89dRTzJs3j+LiYkaOHMkjjzzCnDlzMJlM5ObmctdddxESEkK/fv0AeO6557j88ssBsNvtZGZm8u677/KHP/yhxu8B/vWvf/Hkk0+yZs2aWuOq63jXXHPNOZVL93mRxqEEhoiPiI+Pp127duzevZuEhASmT5/Onj17KCkpISwsjOeee46OHTuSmZlJixYt+Pnnn7nxxhtZvHgxDoeDiIgI2rVrx+eff86rr77Kxo0bmTdvHna7HavVylVXXcXs2bPrfP/9+/dz++23k5iYyC+//EJWVhb79+/nueee4/jx4/j5+fHQQw8xYMAAHA4Hf/7zn1m5ciURERGkpKSwc+dOsrKyqsU3atQofve73/HMM8+wbds2KisrufLKK3n88cfx9/fnpZdeYvny5QQEBBAdHc2cOXOIi4urc3uXLl1Yt24dMTEx/PWvf2XZsmWYzWY6dOjAlClTsFgsZGZm0r17d/79739z8OBBrrzySmbOnImfX/URd5mZmVx66aVs2LCBw4cP89vf/paHH3642j6VlZU8++yzrFu3DrPZTEpKCk888QTr1q1j5cqVrF27luDgYD2dERGRZmn//v18+umnrFmzhvDwcABCQ0OZMWMG//73vwF49913SU1N5Xe/+53r75KTk3nppZfq7FEZGBhIz549+fnnn2v9vWEY7N+/nxYtWpxTnKce79QEhu7zIu6nBIaIj/jhhx/Yu3cv3bp1Iycnh8jISN5//30Apk6dyjvvvMOUKVMAiIyM5J///Cdw4iZ++PBhxo8fz0cffeQ63ltvvcXDDz9Mnz59KCsr47rrrmPLli1ERUXVGcOhQ4eYP38+vXr14siRIzzxxBO8/vrrtGnThvz8fIYPH06XLl3Iyclh69at/OMf/8BkMjFmzJhqxzk1vieeeIKuXbvy7LPP4nA4mDx5Mn/729+46aabePPNN1m3bh2BgYG88cYbbN68ma5du9a6/WQ3U4ClS5fy1Vdf8eGHHxIaGsqCBQuYPHkyr7/+OgB79+4lKyuLY8eOccMNN/Dtt9/St2/fGuXdtWsX7733HsePH2f48OFcfvnl1Z62vPLKKxQUFPC///u/mM1mnnrqKf785z/zpz/9iRUrVtC5c2c1akREpNnaunUrnTp1ciUvTrJYLAwZMgSALVu20L9//xp/e8UVV9R53Pz8fFatWsW4ceNc2yZMmEBQUBAlJSUApKWlsWjRonOKs7bjge7zIp6gBIZIE1VeXs5vf/tb4MT40ejoaObNm8dFF13ERRddRNu2bcnKymLPnj18++239OjRw/W3vXr1Ouvxn332WXJycli0aBE///wzFRUVHDt27IwJDH9/f7p37w7Axo0bsVqt/PGPf3T93mQykZeXx+rVq/ntb39LUFAQACNGjCArK6vW+L788kt+/PFHPvzwQ1e54USPk+TkZH7/+9+Tnp5Oeno6V155JU6ns9btp8rJyWHo0KGEhoYCcMcdd7Bo0SLsdjsAAwYMwM/Pj/DwcNq1a+caYnO6ESNGEBAQQEBAANdffz1r1qyplsDIyclh/PjxBAQEACd6bZxaHyIiIs2Zn58fTqfzjPuYTCYMwzjrsSZMmEBwcDBOp5OAgACGDRvmSoLAr0NC9u3bx1133cUll1xC27Ztz/t4+/fvd+2j+7yI+ymBIdJEnT4HxqneffddlixZwu23387NN99MVFRUtRvuyS/uZ/KHP/yBLl260L9/f2644QY2bdp01gZEYGAg/v4nLisOh4PExEQ++OAD1+/z8/OJiYmp1tMDqDE849T4nE4nf/nLX0hMTATg6NGjmEwm/Pz8ePvtt/nxxx9Zt24ds2fPpn///jz++ON1bj/1mCaTqdrrqqoq1+vg4GDXz2dqOJ0sK5zoyXJ6OWp7n8rKylqPJSIi0tykpKTw888/Y7PZqvXCyM/PZ8qUKbz00kt0796djRs3uuayOGnx4sUcP36cu+66C6g5x0Vd2rZty5///GfuuOMOunXrRkpKSq37ncvxdJ8XcT8toyrig9asWcPvf/97hg0bRocOHVi5ciUOh6PWfc1mc7Uv73AiSfDjjz8yYcIEBg8ezKFDh9i7d+9Zn5Kcqnv37uzZs4fvvvsOgJ9++okhQ4aQn5/P1VdfzSeffILdbqeqqoq///3vdR4nLS2N//mf/8EwDOx2O2PGjOHtt98mNzeXm266icTERO6//37uvPNOfvzxxzq3n6p///4sXbqUY8eOAZCVlcUVV1zhmu38XH3yySc4nU6OHDnCZ599xrXXXlvjfd577z0qKytxOp288847rsnGaqt3ERGR5iQ+Pp6bb76ZJ598EpvNBoDNZmP69OlERUURHBzMiBEj+Pbbb/nkk09cDxS2bNnCSy+9VG01tvNxck6N6dOnn1fb5nS6z4u4n3pgiPigu+++m6lTp7qGXXTv3p1t27bVum/fvn2ZMGECM2fOpGvXrsCJOShGjx7N73//e0JDQ4mPjyc1NZU9e/acsbvlqWJiYnjppZf485//TEVFBYZh8Oc//5k2bdowdOhQdu3axe9+9ztCQ0Np06YNISEhtR7nqaee4plnnuHmm2+msrKSq666invvvZeAgABuuOEGbr31VkJDQwkODubpp58mOTm51u2nuu222zh48CDDhg3D6XTSrl07nnvuuXOtXpfy8nJuu+02ysrKyMjI4Morr6zW02XMmDHMnTuX3/3ud1RVVZGSkuKahyQ9PZ1nn30WgPvvv/+831tERMQXTJs2jYULFzJy5EjMZjN2u52BAwcyduxYAKKiosjKymLevHm8+uqr+Pn5ERISwjPPPONKFtTHo48+yg033MCSJUsYOXJkvY6h+7yI+5mMcxlUJiLSgNasWUNRUZFrDo9Zs2YRFBTExIkTPRzZucvMzOT222/n+uuv93QoIiIiIiLNgoaQiIjbde7cmY8//pibb76Z3/zmNxw+fJgHHnjA02GJiIiIiIgXUw8MEREREREREfF66oEhIiIiIiIiIl5PCQwRERERERER8XpKYIiIiIiIiIiI13P7MqqbNm3iueeeIysri61bt/LAAw/Qvn17AEaNGsWNN97IkiVLWLx4Mf7+/owZM4YBAwZQXl7OxIkTKSoqIiwsjLlz5xITE8PGjRt55plnMJvNpKWl8dBDD501BqfTicPhvVN/mM0mr47vQql8TZsvl8+XywYqX1PnzeULCDB7OgSPa4y2hTefc1+junYP1bP7qK7dQ/XceOpqW7g1gfHf//3ffPLJJ4SEhADwn//8h7vuuou7777btY/VaiUrK4ulS5dSUVFBRkYG/fr147333iMpKYmxY8eybNkyFi5cyNNPP820adNYsGABbdu2ZfTo0WzdupWuXbueMQ6Hw6Ck5FijlvVCREWFenV8F0rla9p8uXy+XDZQ+Zo6by6fxRLh6RA8rjHaFt58zn2N6to9VM/uo7p2D9Vz46mrbeHWISQJCQksWLDA9XrLli18+eWX3H777Tz55JPYbDY2b95Mjx49CAwMJCIigoSEBHJzc9mwYQP9+/cHID09nXXr1mGz2bDb7SQkJGAymUhLS2PdunXuLJKIiIiIiIiIuIFbe2AMGTKE/fv3u16npKQwbNgwLrvsMl555RX++te/kpycTETEr9mWsLAwbDYbNpvNtT0sLIzS0lJsNhvh4eHV9t23b99Z4zCbTURFhTZgyRqW2ezn1fFdKJWvafPl8vly2UDla+p8vXwiIiIiZ+P2OTBONWjQICIjI10/z5w5k169elFWVubap6ysjIiICMLDw13by8rKiIyMrLbt1O1noyEknqXyNW2+XD5fLhuofE2dN5dPQ0hERETEHTy6Csk999zD5s2bAVi3bh1du3YlJSWFDRs2UFFRQWlpKTt37iQpKYnU1FRWr14NQE5ODj179iQ8PJyAgAD27t2LYRisWbOGXr16ebJIIiIiIiIiItIIPNoDY/r06cycOZOAgABiY2OZOXMm4eHhZGZmkpGRgWEYjB8/nqCgIEaNGsWkSZMYNWoUAQEBzJ8/H4AZM2YwYcIEHA4HaWlpdOvWzZNFEhGRBvbZT/ks/Go3+aUVxEcE8WD/9txwSbynwxIRERGpQe2WxmUyDKPZrftSWenw2m644N3dhBuCyte0+XL5fLls0DTL99lP+czO3k55ldO1LdjfjycHd67RGGiK5Tsf3lw+DSFpnLaFN59zX6O6dg/Vs/uors+uIRINp9fz+bRb5My8YhUSERGR87Hwq93VGgEA5VVOFn612zMBiYiISJN3MtFwqLQCAzhUWsHs7O189lP+BR1X7ZbGpwSGiIh4rfzSivPaLiIiInI2jZVoULul8SmBISIiXis+Iui8touIiIicTWMlGtRuaXxKYIiIiNd6sH97gv2r36qC/f14sH97zwQkIiIiTV5jJRrUbml8SmCIiIjXuuGSeJ4c3JlWEUGYgFYRQZoIS2ooKiri6quvZufOnezZs4dRo0aRkZHBtGnTcDpPdBFesmQJQ4cOZfjw4axatQqA8vJyxo4dS0ZGBvfddx/FxcUAbNy4kWHDhjFy5Ehefvllj5VLREQaR2MlGtRuaXweXUZVRKQp0zJZ7nHDJfGqV6lTZWUlU6dOJTg4GIA5c+Ywbtw4+vTpw9SpU1mxYgXdu3cnKyuLpUuXUlFRQUZGBv369eO9994jKSmJsWPHsmzZMhYuXMjTTz/NtGnTWLBgAW3btmX06NFs3bqVrl27erikIiLSUE62KxqjHad2S+NSAkNEpB5OXybr5OzVgG5aIm40d+5cRo4cyWuvvQbA1q1b6d27NwDp6emsXbsWPz8/evToQWBgIIGBgSQkJJCbm8uGDRu49957XfsuXLgQm82G3W4nISEBgLS0NNatW6cEhoiIj1GiwfvYKqr4ckchG385yl9u71nrPkpgiIjUw5lmr9bNUMQ9PvroI2JiYujfv78rgWEYBiaTCYCwsDBKS0ux2WxERPy6nnxYWBg2m63a9lP3DQ8Pr7bvvn37zhqL2WwiKiq0IYuH2ezX4MeU2qmu3UP17D6qa/fw1nr+ZNMB5i/fxsEj5VzUIpjHBiVxS7fWng6rVsftDlbmFbDsx4N8uc1KpcOgbXRInfsrgSEiUg9aJkvE85YuXYrJZGLdunX89NNPTJo0yTWPBUBZWRmRkZGEh4dTVlZWbXtERES17WfaNzIy8qyxOBwGJSXHGrB0EBUV2uDHlNqprt1D9ew+qmv38MZ6Pr2X8IEj5Tz18RbKjlV4zUM2e5WTdbsPszyvgJydRRyvdBIbFsht3VozONlC11YRdf6tEhgiIvUQHxHEoVqSFVomS8R93nnnHdfPmZmZTJ8+nXnz5rF+/Xr69OlDTk4Offv2JSUlhRdffJGKigrsdjs7d+4kKSmJ1NRUVq9eTUpKCjk5OfTs2ZPw8HACAgLYu3cvbdu2Zc2aNTz00EMeLKWIiMi589ZewlVOgw17S8jOK2DV9iJKK6poEezPDZfEMzjZQveLW2D2M531OEpgiIjUw4P921fLbkPzWiZLE5iKt5o0aRJTpkzh+eefp2PHjgwZMgSz2UxmZiYZGRkYhsH48eMJCgpi1KhRTJo0iVGjRhEQEMD8+fMBmDFjBhMmTMDhcJCWlka3bt08XCoREZFz4029hJ2GweZfjvJ5bgErthVy+HglYYFmrunUksHJcfROiMLffH4Lo5oMwzAaKV6vVVnp8LquPqfyxq5IDUnla9p8uXznW7am9iW+oc7d6V0T4UTyxtPLhPnyZxO8u3wWS91dPZuLxmhbePM59zWqa/dQPbuP6to9vLGeb35tfa29hFtFBPHp6D6N/v6GYfBTvo3sXCvL8woosNkJ8vejf8eWDE62cFWHGIL8z560qKttoR4YIiL11Fxnr/bWrokiIiIizZ2negnvLCwjO8/K8twC9pWU4+9n4sr20YxNj6N/YgxhgQ2TelACQ0REzos3dU0UERERkV+dfJjkjl7C+0uOszzPyue5BewsPIafCXq1jeK/erdlQOdYIoMDGvw9lcAQEZHzoglMRURERLxXY/YSLiitYHmelew8K/85VApAt9aRTLy2E9clxdIyLLBR3vckJTBEROS8NPcJTEVERESak8PH7KzYVkh2npWN+49gAJfEh/NwegcGdbHQKjLYbbEogSEiIufFnV0TRURERMT9Ssur+HJHIdm5Vr7bexiHAR1iQhl9VTsGdbHQLibUI3EpgSEiIuetuU5gKiIiIuKrjlc6+GpnEdm5Vr7eXUylw6B1i2Ayr2jLkOQ4EmNDMZlMHo1RCQwRERERERGRZshe5WTd7mKyc63k7CyivMqJJTyQYd1bM7iLhUtbRXg8aXEqJTBEREREREREmokqp8H3ew+TnWtl1Y5CbBUOWgT785uu8QzqYqFHmxb4eVHS4lRKYMgF+eynfI2DFxERERER8WJOw2DTL0f5PLeAldsKOXy8krBAM9d0jmVIsoUr2kbhb/bzdJhnpQSG1NtnP+VXW4ngUGkFs7O3AyiJISIiIiIi4kGGYfCffBvZuQV8kWelwGYnyN+P9MSWDO5i4coOMQT5e3/S4lRKYEi9Lfxqd7VlFAHKq5ws/Gq3EhgiIiIiIiIesKOwjOW5BWTnWdlfUo6/n4mrOsTwcLqF/oktCQ00ezrEelMCQ+otv7TivLaLiIiIiIhIw9t3+DjL86x8nlvAz0XH8DPBFQlR3NU7gWs6tyQyOMDTITYItycwNm3axHPPPUdWVhZ79uxh8uTJmEwmOnfuzLRp0/Dz82PJkiUsXrwYf39/xowZw4ABAygvL2fixIkUFRURFhbG3LlziYmJYePGjTzzzDOYzWbS0tJ46KGH3F2kZis+IohDtSQr4iOCPBCNiIiIiIhI85FfWsHyPCvZuQX8lG8DoPvFkTx+XSeu7RxLy7DARntvT82F6NYExn//93/zySefEBISAsCcOXMYN24cffr0YerUqaxYsYLu3buTlZXF0qVLqaioICMjg379+vHee++RlJTE2LFjWbZsGQsXLuTpp59m2rRpLFiwgLZt2zJ69Gi2bt1K165d3VmsZuvB/u2rzYEBEOzvx4P923suKBERERERER9VfMzOim2FLM8t4IdfjgJwSXw4j1zdkYFJsbSKDG70GDw5F6JbExgJCQksWLCAxx9/HICtW7fSu3dvANLT01m7di1+fn706NGDwMBAAgMDSUhIIDc3lw0bNnDvvfe69l24cCE2mw273U5CQgIAaWlprFu3TgkMNzn54dQqJOIrPtl0gHmf5zXI51kr9IiIiIhIQygtr2LVjkKycwv4bm8JTgM6tAzlgX7tGNQljoToELfG48m5EN2awBgyZAj79+93vTYMA9P/rS8bFhZGaWkpNpuNiIgI1z5hYWHYbLZq20/dNzw8vNq++/btc1NpBE4kMfSlTHzBZz/lM3v5dsorLzyTrBV6RERERORCHK908NXOIj7PtbJudzGVDoOLWwTzX73bMjg5jk6xYR6LzZNzIXp0Ek8/v1+XbCkrKyMyMpLw8HDKysqqbY+IiKi2/Uz7RkZGnvV9zWYTUVGhDViShmU2+3l1fBdK5WvafLV8i9bucSUvTiqvcrJo7R5GXdnh/I9VS1a6PsdqSL567k5S+URERKQpq6hysm5XMdl5Vr7aWUR5lZO48ECGdW/N4OQ4Lo0Pd3UA8CRPzoXo0QTGpZdeyvr16+nTpw85OTn07duXlJQUXnzxRSoqKrDb7ezcuZOkpCRSU1NZvXo1KSkp5OTk0LNnT8LDwwkICGDv3r20bduWNWvWnNMkng6HQUnJMTeUsH6iokK9Or4LpfJ5h/oOcWgq5TtfB4+U17n9fMvbkMdqSL567k5S+TzHYok4+04iIiJSQ5XDyXf7SsjOtbJqeyFldgdRIQH8pms8g5MtdL+4BX5ekLQ4lSfnQvRoAmPSpElMmTKF559/no4dOzJkyBDMZjOZmZlkZGRgGAbjx48nKCiIUaNGMWnSJEaNGkVAQADz588HYMaMGUyYMAGHw0FaWhrdunXzZJFEmgQNcaipITPJWqFHREREROriNAw2/nKE7FwrK7YVUnK8krBAMwM6xzI42cIVCdH4+3lX0uJUnpwL0WQYhtHo7+JlKisdXvsUC7z7KVtDUPk87+bX1tf6BbtVRBCfju5zxr9tCuWrj9PnwIATmeQnB3e+4DkwLuRYDel8zl1TnITUVz+bJ3lz+dQDo3HaFt58zn2N6to9mlI9N8X74KmaUl03ZedTz4Zh8J9DpWTnWfkiz0qBzU6wvx/piS0ZnGyhb/sYgvz9zn6gZqKutoVHe2CIiGd4cuIdb3XDJfGEhQY1yCokTX2FHvXQERGR5kz3QWlIO6xlZOcVkJ1r5Zcj5QSYTVzVPoZHrrbQP7ElIQFmT4fYpCiBIdIMaYhD7W7p1pr0dlENcqymvEKPJ5fGEt925MgRdu/eTUpKCn//+9/ZsmULnTp1Yvjw4fj7q0kiIt5B90G5UHsPH2f5/yUtfi46htkEVyREc3ffBAZ0iiUiWPe8+lLNiTRDnpx4R7yfeuhIY3n00UcZOXIkzz33HCUlJQwYMIDvvvuOSZMmuea2EhHxNN0HpT4OHS1neZ6V5XlWfsq3AdDj4kgmXdeJa5NiiQkN9HCEvkEJDJFmqKkPcZDGpR460ljsdjuDBg3irbfeIisrC4CBAwcycuRID0cmIvIr3QflXBXaKvjohwMszytg4y9HAbi0VQTjru7IwC4WfWYagRIYIs1UUx7iII1LPXSksfj7+7N582ZSU1P57rvvuOKKK9iwYQN+fpq0TES8h+6DciZHyyv5cnsR2XkFfLe3BKcBibGhjOnXnkFdLLSNDvF0iD5NCQwRkXpq6jOU10U9dKSxzJgxgylTplBcXMyrr75KeHg47du3Z9asWZ4OTUTERfdBOd0xu4OvdhbxeW4B63Yfpspp0CYqmPvTO5LePppOsWGeDrHZUAJDRKQefH2GcvXQkcaQkJDAm2++SUVFBSUlJURFRREU9Gv32sWLF2s4iYh4hfrcB331wUZzVVHl5OtdxWTnWvnq5yIqqpzEhQcyosfFDE62cEl8ONHRYVqu1s2UwBCRenP3jdqbGgaaoVyk/oKCgoiPr/nv5J///KcSGCLSJPn6g43mosrh5Nu9JWTnWflyeyFldgfRIQHc3DWewclxdLs4Ej+TydNhNmtKYIhIvbj7Ru1tDQPNUC7S8AzD8HQIIiL1ogcbTZfDabDxlyNk51pZsc3KkfIqwoPMXNs5lsHJFnolROPvp6SFt1ACQ0Tqxd03am9rGGiGcpGGZ9JTLRFpovRgo2kxDIOth0rJzrXyxTYrVpudYH8/ru7UkkFd4riyfTSB/ppg2hspgSEi9eLuG7W3NQx8ZYZybxqWI3K+HA4HTz/9NLt27cJsNjNnzhwMw2Dy5MmYTCY6d+7MtGnT8PPzY8mSJSxevBh/f3/GjBnDgAEDKC8vZ+LEiRQVFREWFsbcuXOJiYlh48aNPPPMM5jNZtLS0njooYc8XVQR8XJ6sOH9DMNgR2EZ2blWsvOsHDhSToDZRL8OMQzqYqF/YktCAsyeDlPOQgkMEakXd9+ova1h4AszlHvbsByR8x1CsmrVKuDE5J/r1693JTDGjRtHnz59mDp1KitWrKB79+5kZWWxdOlSKioqyMjIoF+/frz33nskJSUxduxYli1bxsKFC3n66aeZNm0aCxYsoG3btowePZqtW7fStWvXxiiyiPgIX3mw4Yv2FB9jeZ6V7Fwru4qPYTbBFe2iubdvAtd0iiUiWF+JmxKdLRGpF3ffqL2xYdDUV+rwtmE50rwUFBRQVVWFYRgUFBTQo0cPJk6ceF7HGDhwINdccw0ABw4cIDY2li+//JLevXsDkJ6eztq1a/Hz86NHjx4EBgYSGBhIQkICubm5bNiwgXvvvde178KFC7HZbNjtdhISEgBIS0tj3bp1SmCIyBk1tQcbvt4D89DRclfSIrfAhgno3qYFk1M7cW3nWKJDAz0dotSTEhgiUi/uvlE3tYZBU+Btw3Kk+XjiiSfYtGkTx48f5/jx4yQkJLBkyRJSUlLO+1j+/v5MmjSJ5cuX89JLL7Fq1SrXXBphYWGUlpZis9mIiIhw/U1YWBg2m63a9lP3DQ8Pr7bvvn37LrDEItIcNJUHG77aA7OozM6KbSeSFpsOHAWga6sIxl/TkeuSLBrO4yOUwBCRenP3jbqpNAyaCm8bliPNx65du1i2bBlTp05l/PjxPPLIIxd0vLlz5zJhwgSGDx9ORcWvn+mysjIiIyMJDw+nrKys2vaIiIhq28+0b2Rk5FljMJtNREWFXlA5ah7Tr8GPKbVTXbuH6tl9zlTXi9buqbUH5qK1exh1ZQd3hNdgjhyvJPs/+fzjx4N883MRTgO6xIfz6MDO3Hj5RbSLadzPmz7T7qcEhohIM+WNw3KkeQgLC8NkMnHs2DFiYmKorKys13E+/vhj8vPzuf/++wkJCcFkMnHZZZexfv16+vTpQ05ODn379iUlJYUXX3yRiooK7HY7O3fuJCkpidTUVFavXk1KSgo5OTn07NmT8PBwAgIC2Lt3L23btmXNmjXnNImnw2FQUnKsXuWoS1RUaIMfU2qnum58n/2Uz6K1ezh4pFy9KN3gTJ/pg0fK69zeFP4dHLM7WL2zkOxcK9/sPkyV06BtVDB39klgcBcLibFhrn0buzy6djQeiyWi1u1KYIiINFMaliOe0rVrV15//XXi4uIYP348VVVV9TrO4MGDeeKJJ7j99tupqqriySefJDExkSlTpvD888/TsWNHhgwZgtlsJjMzk4yMDAzDYPz48QQFBTFq1CgmTZrEqFGjCAgIYP78+QDMmDGDCRMm4HA4SEtLo1u3bg1ZfJFmx1eHLDRVTbEHZkWVk7W7ilmeW8BXPxdTUeUkLjyQkakXMzjZQnJcuJbibiZMxvlO+e0DKisdXp0p8/VMnsrXtPly+Xy5bKDyNXXeXL66npLUpaqqivLycoKDg8nJyeHyyy/HYrE0UnTu0RhtC28+575Gdd24bn5tfa1fmFtFBPHp6D4eiOjsTp3kMiLIjMlk4mh5VZNJ9p/pM316QglO9MB8cnBnrypXlcPJ+r0lLM8t4MsdRZTZHcSEBnBdkoXBXSykXByJn4eTFrp2NB71wBARERGPslqt2Gw2Jk2axJ///GcMw6Bdu3aMGTOGDz/80NPhiUgjceek0Q2xusbpX/CPVjhcv/OF3iPe3APT4TTY+MsRPs8tYOW2Qo6UVxER5M91SbEM7hJHz4Qo/P3U06I5UwJDRERE3GLTpk28+eab7Nq1iylTpgDg5+dHWlqahyMTkcbkriELDTVUpbZlxk/lC0uOe9PE6IZhsOVgKdl5Vr7Is1JYZickwI/0xJYMTo6jb7toAv39PB2meAklMERERMQtBg4cyMCBA1m9ejVXX321p8MRETdx16TRtSUe6pNsOJeeIVpy/MIYhsF2axnZeVaW5xZw4GgFgWYTV3WIYXByHGkdYwgJMHs6TPFCSmCIiIiIW1100UVkZGRQWlrKzTffTOfOnRkwYICnwxKRRnIyedDYq5A01FCVunqMnL6PnL89xcfIzrOSnVvA7uLjmE3Qu100913Vjms6xRIepK+ncmb6hIiIiIhbPfPMM8yZM4enn36a2267jXvvvVcJDBEfd8Ml8Yy6skOjTnjYUENVausxciotOX5+Dh4tZ3mulew8K3kFNkxAjzYtGJV6Mdd2thAVGuDpEKUJUQJDRERE3K5du3aYTCZiYmIICwvzdDgi4gMaaqjK6ZNcNsVVSDytsMzOirwTSYvNB44CcNlFEYy/piMDkyzEqQeL1JMSGCIiIuJWLVq0YPHixRw/fpxly5YRGRnp6ZBExAc05Ooa3jTJZVNx5Hglq7YXkp1nZcO+EpwGdLaE8WBaewZ1sdAmKsTTIYoP8IoExu9+9zsiIk6s89qmTRseeOABJk+ejMlkonPnzkybNg0/Pz+WLFnC4sWL8ff3Z8yYMQwYMIDy8nImTpxIUVERYWFhzJ07l5iYGA+XSEREROoye/ZsFi1aRHR0NFu2bOGZZ57xdEgi4iOUeHCvMnsVOTuLyM61sm73YRxOg4ToEO7uk8CgZAsdW6qHnTQsjycwKipOjFPLyspybXvggQcYN24cffr0YerUqaxYsYLu3buTlZXF0qVLqaioICMjg379+vHee++RlJTE2LFjWbZsGQsXLuTpp592axkaYr1pERGR5iI8PJzU1FSio6NJSkoiKirK0yGJyDlSu1fKKx18vauY7Dwra34upqLKSXxEEBmpFzM42UKXuHBMJpOnwxQf5fEERm5uLsePH+fuu++mqqqKRx99lK1bt9K7d28A0tPTWbt2LX5+fvTo0YPAwEACAwNJSEggNzeXDRs2cO+997r2XbhwoVvjb6j1pkVEpHGose19ZsyYQUlJCd27d+eDDz7g66+/ZtKkSZ4OS0TOQu3e5umzn/L5a84u8m12gv39MICKKicxoQH89rJWDE62cHnrSPyUtBA38HgCIzg4mHvuuYdhw4axe/du7rvvPgzDcGXtwsLCKC0txWazuYaZnNxus9mqbT+579mYzSaiokIbJP5Fa/fUut70orV7GHVlh3od02z2a7D4vJHK17Q1Zvk+2XSA+cu3cfBIORe1COaxQUnc0q11o7xXbXTumrbayvfJpgPMXr6d8spTGtvLtxMWGuTWz1ZD8KXzl5uby3vvvQfAf/3XfzFy5EgPRyQi52LhV7trbfcu/Gq3Ehhe4NSE/UUtgnmgX7sLOi8Op8Gir3eT9e0+HMaJbeVVTswm+K/ebXigXwf8/ZS0EPfyeAKjQ4cOrpnIO3ToQFRUFFu3bnX9vqysjMjISMLDwykrK6u2PSIiotr2k/uejcNhNNgSTgePlNe5vb7vERUV2qhLTHmayte0NVb5Tn+qc+BIOU99vIWyYxVuaxQ1p3Pni70Sajt/8z7PcyUvTiqvdDLv8zzS20W5MboL582fT4sl4uw7naJ169YcOnSIVq1aUVhYSKtWrRopMhFpSPm1LFF6pu3iPrW1o+rTO8YwDH48WEp2bgFfbCukqMxeYx+HAZ//ZOWh/h0bJniR8+DxBMaHH37Itm3bmD59Ovn5+dhsNvr168f69evp06cPOTk59O3bl5SUFF588UUqKiqw2+3s3LmTpKQkUlNTWb16NSkpKeTk5NCzZ0+3xt9Q602LNHd6quM+zakLsBrb3iUtLQ0Au93O8uXLXYmM6OhoD0cmIueisdu9vphcd5cLaUcZhsE2axnZuVaW5xVw8GgFgWYT/Tq2ZNX2wlr/RvdR8RSPJzBuu+02nnjiCUaNGoXJZGL27NlER0czZcoUnn/+eTp27MiQIUMwm81kZmaSkZGBYRiMHz+eoKAgRo0axaRJkxg1ahQBAQHMnz/frfE31HrTIs2dvmi6T3NKFinJ7F3WrFnj6RBE5AI0Zru3OSXXG0N92lG7i4+xPNdKdl4Bu4uPY/Yz0addFPdf1Z6rO7UkPMifm19b79X3USW9mh+PJzACAwNrTTq8/fbbNbYNHz6c4cOHV9sWEhLCSy+91GjxnU1Drjct0pzpi2bDOdvNvDkli5Rk9i6//PILn3zyCW3atOHmm2/2dDgicp4as93bnJLrjeFc21EHj5aTnWslO7eAbdYyTEBq2xaM6tmGazvFEhUaUG1/b76PKunVPHk8geELtN60yIXz5htkU3Kmm/nJiYWbU7JISWbv8vLLL7Ny5Urmzp3r6VBEpJ4aq93bnJLrjeFM7ahCWwVfbCskO9fKjwePAnD5RRE8OiCRgUmxWMLrvv97831USa/mSQkMEfEK3nyDbErOdDM/mcBobskiJZm9x0MPPURERAS7d+/2dCgi4mWaU3K9MdTWjurdLopPtuQz/bM8nAZ0toTxx7T2DEq2cHGLkPM6tjfeR5X0ap6UwBARwDvGEHrrDbIpOZebuZJF4ikXX3wx48ePJyTk3BvOItI8NLfkemNIT2yJYUB2rpX1ew7zyZZ8EqJDuKdvAoO6xNGhpW8sxX2Skl7NkxIYIqIxhD7kXG/mShaJJ2RmZmIymWr93VtvveXmaETEmyi5Xj/llQ7W7iomO9fK2l3FVFQ5aRURxF1Xtefq9tEkxYXVed1tqk4+dKutvaOkl+9TAkNENIbQh+gJlnizGTNmAPDXv/6V6667jp49e7J582ZWrVrl4chExBsouX5uKh1O1u85THauldU7ijhW6SAmNIDfXd6KQV0sXN46kpjoMEpKjnk61AZ3+kO3U7VS0qtZUAJDRDSG0IfoCZZ4s44dOwJQWFjIjTfeCMCgQYPIysryZFgiIl7P4TT49/4SPs+1smp7IUfLq4gM9mdQsoUhyRZS20Rh9vOtnha1qe2hG5xIXnw6uo8HIhJ3UwJDRDSG0MfoCZY0BR988AEpKSn88MMPmhNDRKQWhmHw48FSsnML+GJbIUVldkIDzKR3asmQZAt92kUTYPbzdJhupYduogSGiGjYgYick4qKCqzWAgoK8ikoOPn/fObOfea8jvPcc8/xxhtvkJ2dTWJiIi+88EIjRSwinnT6BOETh3QhvV2Up8PyaoZhsK2gjOy8ApbnWTl4tIJAs4m0ji0ZnGyhX4cYggPM53w8b5ikvSHpoZsogSEiGnYg0ow5nU4OHz5Mfv4hV0Li1OTEyYRFfv4hSkpKaj3G+SYwLBYL9957LxUVJxqhJSUlhIb61uz4Is1dbROEP/W/W3hyUGe1L2qxu+gY2XkFZOda2XP4OGY/E33bRfNAv/akJ7YkPOj8v7b54iTteugm5/QvwWazceTIEWJiYtTNU8RHadiBiG8pKys7LRlx6LTXBa4ERVVVVY2/DwkJIS4unri4eDp37kK/fv1dr+Pi4lw/x8Zazju26dOnk5OTQ1xcHIZhYDKZWLx4cUMUW0S8RK0ThFdqgvBTHThSzvI8K5/nFrDdWoYJ6Nm2Bbf3asOAzrFEhQRc0PF9cZJ2PXSTMyYwPv74Y959911KSkqIiYmhtLSUyMhIMjIyuPnmm90Vo4iIiABVVVUUFlpr9JQ40XuioNr2sjJbjb/38/PDYolzJSEuvbQr8fGtqiUkTv4cFhbeaEvvbd68mS+++AI/v+Y1dlukOdFcBbWz2ir4Ylshy3ML+PFgKQCXXxTJYwMSGZgUS2x4ww2F8NVzoIduzVudCYzJkyeTmprK//t//4/IyEjX9tLSUj799FMmTpzIvHnz3BKkiIiIrzIMgyNHSk5LQNRMThQWFlBYWIhhGDWO0aJFlCvx0L17D+Li4rFYTiQjTiQoTiQnYmJiMJvPfex0Y2nXrh0VFRXq1SniwzRXwa9KjleycvuJpMWGfUcwgCRLGA/178CgLhZatwhulPfVORBfVGcCY8aMGQQF1fxwR0REkJGRwa233tqogYmIiDRl5eXlWK0FtfaOODF049efT84FcarAwEBX74h27dqTltaPFi1iah3GERzcOI3fxnLw4EEGDBhAu3btADSERMQH1TpXQUDzmavAVlFFzs4isnOtfLPnMA6nQUJ0CPdemcDgLnG0b9nw8/6cOmHnRS2C6dcxmmVbCzRfhPiUOhMYr7zyCo888ki17qNFRUU8/vjjvP7667UmN0RERHyZ0+mkqKioWk+J/PyTyYjq80scOVJS4+9NJhMtW7Z09Y5ITOxcIxlx8nWLFlHV7sFRUaGUlBxzY2kbz/z58z0dgog0strmKvD1VUjKKx2s+bmY7Dwra38uwu4wuCgyiNt7tmFwsoUkS1ijDc07fcLOA0fKWba1gN90jWPtz4c1X4T4jDPOgXHXXXfx/PPPExMTw5o1a5g6dSr33HOPu2ITERFpdIZhUFZmq7H6RkFBwSkrc5zYXlhoxeFw1DhGWFi4KwmRnHwp6enXEBcXX2N+iZYtYwkIuLBJ2XyBv78/8+bN4/DhwwwZMoQuXbpw8cUXezosEWlgp89V4EuJ2JMqHU6+2X2Y7DwrOTuKOFbpoGVYIL9PuYjByXFcflHEeSUt6rvsaV0Tdq79+TCfju5z3uUS8VZ1JjDGjRvHqlWruPPOO+nevTubN29m0aJFJCUluTM+ERGReqmsrHQtAXqm5ITVWsCxYzUb1P7+/q4JL1u1akVKSrfTekqcSE5YLHGEh4d7oIRN15QpU7jrrrtYuHAhvXr1YvLkySxZssTTYYmInBOH02DDvhKyc62s2lHI0fIqIoP9GZxsYXCyhdQ2UZj9zr+nxYUse1rXxJyHSiu4Yn4OAJFBZiZc10k9MKRJO2MPjE6dOhESEsLXX3/NDTfcQKdOndwVl4iISA2GYVBScrjW3hElJUXs33/ANZyjqKio1mNERUW5ekf07HlFrXNKnJzwUqtkNI6KigquvPJKXnnlFTp27FjvYamVlZU8+eST/PLLL9jtdsaMGUOnTp2YPHkyJpOJzp07M23aNPz8/FiyZAmLFy/G39+fMWPGMGDAAMrLy5k4cSJFRUWEhYUxd+5cYmJi2LhxI8888wxms5m0tDQeeuihBq4BEWlqnIbBjweOkp1r5YttVoqPVRIaYObqTi0ZkhxH73ZRBJgv7J5xIcue1jVh56mOVjj402d5wNkTIiLeqs4Exv/+7//y0ksv8eSTT3L11Vfz3HPPkZmZyfPPP098vD7wIiLScI4fP15n74jTe1BUVlbW+PugoCAuuugiWra00L59R3r3vpL4+PgayQmLJU5zOHmBwMBAvvrqK5xOJxs3biQwMLBex/nkk0+IiopyDUf5/e9/T3JyMuPGjaNPnz5MnTqVFStW0L17d7Kysli6dCkVFRVkZGTQr18/3nvvPZKSkhg7dizLli1j4cKFPP3000ybNo0FCxbQtm1bRo8ezdatW+natWsD14JI81bfoRLuZBgGeQU2snOtLM+zcqi0giB/P9I6xjC4i4WrOsQQHNBwKztdyLKntU2aWpsqg3NKiIh4qzoTGB9++CHvvvuuK1kxefJkli9fzu23384XX3zhtgBFRKRpcjgcFBYW1lhx4+TEl6cmJkpLj9b4e5PJRGysxZWASEpKdv186tKgcXFxREREEh0d5nNjq33VzJkzmTt3LocPH+aNN95g+vTp9TrO9ddfz5AhQ1yvzWYzW7dupXfv3gCkp6ezdu1a/Pz86NGjB4GBgQQGBpKQkEBubi4bNmzg3nvvde27cOFCbDYbdrudhIQEANLS0li3bp0SGCIN6EKGSrjDrqJjZOcWkJ1nZe/h45j9TFzZPpoxae25ulNLwgLP2Im93i5k2dPTJ02tueD2r84lIeIpTSGxJZ5V57++t956q8aEM4MGDSI5ObnRgxIR36UbU9NmGAalpUdPm0+i9vkliooKcTprPgkKD49w9Y647LKU04ZuxLnml2jZsiX+/g3TSNTnzru0atWK++67j127dtGpUyfatm1br+OEhYUBYLPZePjhhxk3bhxz5851tV/CwsIoLS3FZrMRERFR7e9sNlu17afue+qcJmFhYezbt++ssZjNJqKiGnZZRLPZr8GPKbVTXbvHyXpetHZPrUMlFq3dw6grO3gktn2Hj7Fs80GW/XiI3PxSTCbo2yGG+9M7MvjSeKJC69dT7HxMHNKFp/53C+WV1ZeenTikyzl9Pkdd2cFVf1fP/5IDJeW17ndRi2Cv/Lx/sukAs5dvd5X/UGkFs5dvJyw0iFu6tfZwdLXTtcP96mwZ1jVbbn0bGSIi3v7EpTmz2+1YrSeHbtSVnCjAas3n+PHjNf7e39/flYC4+OKL6dEj1TUB5qnJCYslzvWl0130ufM+L7zwAuvXryclJYWsrCwGDhzo6glxvg4ePMgf//hHMjIyuPnmm5k3b57rd2VlZURGRhIeHk5ZWVm17REREdW2n2nfyMjIs8bhcBgN3gPIF1ds8Faqa/c4Wc8Hj9T+xfrgkXK3ngerrYLleSeGh2w5WApASutIJgxI5LqkWGLD/6/ng72KEntVo8eT3i6KJwd1rpFwT28XVWu9nCk5/9jAJCZ/9COVzup9MfxN8EC/dl75eZ/3eV615A1AeaWTeZ/nee3yu7p2NB6LJaLW7Y3T/0lEpBYXMjmVt/PGJ/xOp5PDhw+7EhE2Wwm7du11vT51fonDhw/XeoyYmJj/mzsiniuu6F3r0qBxcXFERUV77YSXvvy5a6q++uorPvzwQ/z8/HA4HIwYMaJeCYzCwkLuvvtupk6dypVXXgnApZdeyvr16+nTpw85OTn07duXlJQUXnzxRSoqKrDb7ezcuZOkpCRSU1NZvXo1KSkp5OTk0LNnT8LDwwkICGDv3r20bduWNWvWaBJPkQZ2IUMlLlTJsUpWbreSnWfl3/uOYABd4sIZ278DA7tYaN0iuNFjOJPTl56ty9mS87d0a03ZsQrmr9zJkfITyRdvX4XkQuYAkeZDCQwRcRtfvTG5+wl/WVlZjWEbJ+eYOLUHhdVaQFVVzSdGISEhruRDp05JXHVVWrWeEieHd8TGWuo9uaI38dXPXVPWqlUrVy+IqqoqYmNj63WcRYsWcfToURYuXMjChQsBeOqpp5g1axbPP/88HTt2ZMiQIZjNZjIzM8nIyMAwDMaPH09QUBCjRo1i0qRJjBo1ioCAAObPnw/AjBkzmDBhAg6Hg7S0NLp169ZgZReR2iecDPb348H+7Rvl/WwVVazeUcTnuQV8u+cwDgPaRYdw35XtGJRsoX1M0xsCcC7J+XNNhngLTya2pOmoM4GRmZlZ5zASwzAwmUy89dZbjRaYiPgeX70xNcQT/qqqKgoLrXXOKXFi4ssTyYmyMluNv/fz8yM21uLqHXHppV1rLA2amNiOkJBIwsMj6ry++yJf/dw1ZQUFBQwZMoTk5GR27NhBQEAAI0eOBGDx4sXnfJynn36ap59+usb2t99+u8a24cOHM3z48GrbQkJCeOmll2rs2717d5YsWXLOcYjI+Tl9wsnG6LlYXungq5+Lyc4t4OtdxdgdBq0jg/jDFW0Z3MVCZ0tYk74X+mJy3t2JLWma6kxgZGVluTOOC+J0Opk+fTp5eXkEBgYya9Ys2rVr5+mwROQ0vnpjqquxcOhoOUeOlFRbFrSu5ERRUSGGUXPO8MjIFq4kRLdu3YmPb4XFEn/aEI54WrZsidl85qXcmus4TV/93DVlf/nLXzwdgoh4WGP0Dqh0OFm3+zDZuQXk7CzieKWT2LBAhnZrzeAuFi67yHcS+L6YnHdHYkuaPp8YQvLFF19gt9t5//332bhxI88++yyvvPKKp8MSkdP4yo2pvLz8lPkjCuCnbygpKsBRdhhHWQkO22EcZYdxHjtM5z9X1vj7wMBAV++IhIQEeva8wpWQOHV+CYsljpCQEA+U0D3cNW+Ir3zufMmxY8ew2Wz4+fnx/PPP88ADD7jmsBAROR9VToMN+0pYnmtl5fZCSiuqaBHsz/WXxDG4Sxw92rTA7OcbSYtT+WpyvqkNexH384kExoYNG+jfvz9wotvnli1bPByRiNTFW29MTqeTgoICtm/fXaN3hNWaT37+r9uOHCmp9Rh+oS0wh0VjDosmOPZi0rp2pM8lHWskJ1q0iPKZJ0D15e55Q7z1c9dcTZs2jaeeeooFCxYwfvx45s2bpwSGiJwzp2Gw+ZejZOdZWbHNSvGxSsICzVzdqSWDk+PokxCFv9k7J5ZuKErOS3N11gTGjz/+yOWXX+56/e2339K7d+9GDep8nb5mu9lspqqqCn9/n8jPiMgFsNlK6xy2cfLn/PxDFBZacTgcNf4+NDTUNUwjOflS0tOvqbb6xsmfvy908tq6X9SIOEdaGaR58/f3p3PnzlRWVtK9e/da/+2JiJzKMAxyC2xk555Y9jS/tIIgfz/6d4xhUHIcV7WPJjjgzEMpfY2S89Ic1fkN//vvv2fHjh38z//8D3fddRcADoeDd999l3/84x9uC/BcnL5mu9PpPGPywmw2ERXlvbMNm81+Xh3fhVL5mjZvKF9lZaUr8XDw4EHy8/M5dOjQKf//9edTrw0nmc1m4uNP9Ii4+OKLSE3tQatWrWjd+iIslnhatTrxu1atWlVLjp7JJUBm/6QGLmnDashz98mmA8xfvo2DR8q5qEUwjw1K4pZurc/57880+Vh9Y/SGz2ZjutDyXeg5a0gmk4nHHnuM9PR0/vnPf/r0UCkRuTA/F5W5khZ7Dx/H389E3/bR/LF/e9ITWxIWqAeWIs1Jnf/iIyMjKSwsxG63Y7VagRMNjokTJ7otuHOVmprKqlWruPHGG9m4cSNJSWf+EuFwGF49kZ2vT7Sn8jVtjVU+wzAoKTlca++Ikz+fWCo0n6Kiojpii3L1iOjWrQcDB1ZfFvTkfzExMfj51exaenrZqqrwqXPZUOfu9OEfB46U89THWyg7VnHOT4LONPlYfWPUv726NcQ5OxOLJeK89n/hhRf48ccfSU9PZ/369bz44osXHIOI+I79JcdZnmclO9fKjsIy/EzQs20Umb3aMKBzLC1CAjwdooh4SJ0JjKSkJJKSkhg+fDhxcXHujOm8DRo0iLVr1zJy5EgMw2D27NmeDklE/s/x48fPsPrGoWrbKytrTngZFBT0fytvxNG+fUd6977ytBU44oiPb0VsrIXg4GAPlLD5aYjhH746+Zi38pYhOw6HA4fDwdSpU3nhhRdcQ0hGjx6tpdlFmrmC0gq+2HYiabH1UCkA3VpHMvHaRK5NshAbFujhCEXEG9SZwHj44Yd56aWXGDp0aI3frVmzplGDOl9+fn786U9/8nQYIs2Gw+GgqKiI/PxD/9cr4tTExK+v8/PzKS09WuPvTSYTLVvGuhIQSUnJNeaUOPk6MrJFs5/w0pt89lN+rT0n4PzWnj/5pXn+yp0cKa8CINCs89xYzjRkx52WLl3KokWLKCws5Prrr8cwDMxmMz179nRrHCLiHQ4fs7NyeyHZuVZ+2H8EA0iOC+fh9A4M7GLhokg9mBCR6upMYJycqPPll1+me/fu7opHRDzEMIxqE16eOnTj1MREYWEBBQUFOJ3OGscID49wJSEuvfQyrrnm2v9beaN6cqJly1hNstsEnRyGUJf6rD1fcUqvgKMVjkZdiaQ5O9OQHXcaPnw4w4cP58MPP+S2225zba+t95WI+CZbRRVf7ijk81wr3+05jMOA9jEh3HdVOwZ1sdA+xnfnMhKRC1fnN4j333+fNm3a8MILL/D4449jGIbrd2lpaW4JTkQunN1uZ8m6//D6is1YrfmEVdno0dJJpNN2SnLixPwSx48fr/H3/v7+rgRE69at6d37CqKiYrBYTs4vcWJpUIsljrCwMA+UUNyltmEIJ9Vn+Ie3DGtoDrxtyE5JSYnr523btjFp0iT+/ve/eyQWEWl85ZUOcnYW8eXPxXy5zUqlw6B1i2Ayr2jL4GQLnWLD1NtSRM5JnQmMRx55hC+++IKioqIaq44ogSHiWU6nk8OHD9c6bOP0/w4fPlzj73cBYZFRtLmoFXFxrbjiit61Lg0aFxdPdHR0tQkvfX2iRKnbmYYbPDm483knHbxlWENzcPLcLPxqt1cs9btt2zbee+89jh07xscff8yMGTM8EoeINB57lZN1uw+zPK+AnJ1FHK90EhcRxG3dWjM42ULXVhFKWojIeaszgTFw4EAGDhzIypUrufbaaykuLiYqKqrWmftFpGEcO3bMNXfEyQREzTkmCrBaC2rtch0cHExc3IkeEYmJnbnqqjSW763kmH8E5vBozGH/919oFBdFh/Pp6D4eKKU0VXUNQ2gVEVSvL8LeMqyhubjhkniv6dny7LPPMmHCBIqLi1m6dCmBgZqcT8QXVDkNNuwtITuvgFXbiyitqKJFsD83XBLP4GQL13S9iNKjNXt7ioicq7MOQg8NDeW6664jIiKCo0ePMnPmTPr16+eO2ER8QlVVFUVFha4ExKnJidMTEzZbaY2/9/PzIzbW4uodkZx8abXVN07tNREeXvNpxqr5OdS2wKGecsv5auhhCN42rEEa34gRI1zXqMrKSvLy8rjjjjsAWLx4sSdDE5F6choGm345SnZuASu2FXL4eCVhgWau6dSSwclx9E6Iwt984gGo2U89LhrDZz/l1+hhN+rKDp4OS6RRnDWB8Ze//IV3332X+Ph48vPzeeihh5TAkGbPMAyOHj1SY9hGfn4+JSVF7N//i+t3RUWF1eaQOSkysoUr8ZCS0q3G6hsWy4n5JVq2bInZbK53rHrKLQ2loYcheNuwBml8zz//PADl5eVa9likCTMMg5/ybWTnWlmeV0CBzU6Qvx/9O7ZkcLKFqzrEEOSvXtvucHKC7ZMPAw6VVjA7ezthoUGkt4vybHAijeCsCQyz2Ux8/InGZHx8PEFB+tIjvqu8vByrtaDW3hEn/n/I9XNFRc2kQEBAAK1atSI21kJCQgI9e15x2pwSJ362WOIIDXXPLNt6yi0NqaGHIXjTsAZpfBdffDEAo0aN4r333vNwNCJyvnYWlpGdZ2V5bgH7Ssrx9zNxZftoxqbHkZ7YktDA+j9wkfqpa0Ls+cu3kX5vbw9FJdJ4zprACA8PJysriyuuuILvvvuOFi1auCMukQbjdDopLi4+ZVnQXxMSp88vcerM+Kdq2bLl/yUe4unTJ7Ha6hunJieioqKJjg7zqkku9ZRbRLxNaGgos2fPpkOHDq65tUaMGOHhqEQ8p7YhAN5yn95fcpzsXCvZeQXsLDyGnwl6tY3izt4JXNO5JZHBAZ4OsVmra0jwwSPlbo5ExD3OmsCYN28eCxcu5IUXXiAxMZHZs2e7Iy6Rs7LZbNWWAD2RoKjZa8JqLcDhcNT4+9DQUFfyISkpmbS09GrJiJPzS8TGWggI0M1ZRKSh9OjRA4CioiIPRyLieXUNAQA8lsTIL63gizwr2XlW/nPoxPxc3S+OZOK1nbguKZaWYZp411vUNVT4ohYapie+6awJjOnTpzN//nx3xCJCZWUlhYXWWie5PH3yy2PHymr8vdlsJjbW4uodcdlll9eyNGgccXGtCA8P90AJ3c8bG0Yi0ryNGTOG7du3Y7fbPR2KiMfVNQRg4Ve73XqfPnzMzopthWTnWdm4/wgGcEl8OA+nd2BQFwutIvWF2BvVNVT4sUFJHoxKpPGcNYFht9vJzc2lQ4cOrpnDtdyZnA/DMCgpOexKRthsJezeva/a5Jcn/6vraVyLFlGuJESPHqn/t1To6YmJeGJiYi5owktf5C0Nowvlzd1rReT8jB49GrvdTmRkJAAmk4mXX37Zw1F5n082HWDe53m67vm4uoYAuGO1sNLyKr7cUUh2rpXv9h7GYUCHmFBGX9WOwclxJESHNHoMcmHqGip8S7fWXjWkWaShnDWBsWvXLh588EFMJhOGYWAymVixYoU7YhMvd/z4cazWglqHbpyYW+LXHhS1PWULCgpyJSHatevAFVf0rXVpUIslTrPVXwB3N4waI9GgXiQivqWiooK3337b02F4tc9+ymf28u2UV+q65+vcvVrY8UoHX+0sIjvXyte7i6l0GESHBhAcYKbM7uCYvYqLo4I9nrzQg4tzpwmxpTk5awLjH//4hzviEC/hcDgoKiqqtXdE9SEdBRw9eqTG35tMJlq2jHUlIDp1SqrRU6JTp/aEhEQSGdnC1atHGo87G0aNlWjwlV4kInJCr169+Oqrr0hMTHRta926tQcj8j4Lv9rtSl6cpOueb3LHamH2KifrdheTnWslZ2cR5VVOLOGBDOvemvAgM2+u30eF48SS7/k2u8eTZXpwISJ1qTOB8dlnn/Hss88SHBzMvHnzSElJcWdc0oAMw8BmK611adBfV+Y4sb2w0IrT6axxjLCwcFfviEsvvYxrrqm5NGhcXDwtW8aedcLLqKhQdWlzI3cuo9pYiQZPdq+VunnL0zFviUPOXVFREbNnz642hGTx4sUejsq76LrXfDTWamFVToPv9x4mO9fKqh2F2CoctAj25zdd4xnUxUKPNi3wM5m4+bX1ruTFSZ5OlunBhYjUpc4Exptvvsknn3zC0aNHeeaZZ1i0aJE745JzYLfbsVoLaklM/Po6P//EUqHHjx+v8ff+/v5YLCeSDxdddBHdu/cgLi4OiyW+WnLCYolrNhNe+iJ3LqPaWA1ud3evlbPzlqdj3hKHnJ9du3bx2WefeToMr6brXvPSUEMAnIbBpl+O8nluASu3FXL4eCVhgWYGdI5lcLKFK9pG4W/2q/Y33pgs88aYRMQ71JnACAwMpEWLFrRo0aLWL7/SOAzDoKioiO3bd9foHXH6/BLFxcW1HiM6OtqVgOjV6wrXz/Hx8dUmvIyOjsbPz6/WY4hvcdfYyMZqcLuzF4mvObV3wkUtgnmgX7sG+Sx4y9Mxb4lDzk9SUhIbN27k0ksvdW3TBOHVPdi/fbU5MEDXPamdYRj8J99Gdm4BX+RZKbDZCfL3Iz2xJYO7WLiyQwxB/nW397wxWeaNMYmIdzjrHBhw4sIoF+bYsWO1JCNqm/yygMrKyhp/Hxwc/H8rb8TRsWMn+va9qloy4uTwjthYC0FBuriLZ5wt0VDfrv7u7EXiS07vnXDgSHmD9U7wlqdj3hKHnJ/vvvuOL7/8UhOEn8ENl8QTFhqkVUikTjsKy1ieW0B2npX9JeX4+5m4qkMMD6db6J/YktDAX1dlO9P91xsfEnhjTCLiHepMYOzbt4/nn38ewzBcP5/06KOPuiU4b1dVVUVRUWEtE1zWnF/CZiut8fcmk4nYWIurd0Ry8qXExcXTrl0bIiKiqyUnIiIiNeGleL0zJRoutKu/Ztg+f43ZO8Fbno55Sxxyfj799FNPh9Ak3NKtNentojwdhniRfYePszzPyue5BfxcdAw/E1yREMVdfRK4plNLIoNrzkN2tvuvNz4k8MaYRMQ71JnAePjhh2v92dcZhsHRo0fqnFPi1OREUVFhrb1TIiIiXRNbXn55txpLg56cY6Jly5b4+9c8BZrkUpqyuhIN6urvfo3ZO8Fbno55SxwiIo0lv7SC5XlWsnML+CnfBkCPiyN5/LpOXJcUS0zomYdfncv91xsfEnhjTCLieXUmMLp06VJtbOrptm7dSteuXRslqMZ29OgR3nwzq0Zi4uSEmOXl5TX+JiAgwJWAaNOmDampPV0TYJ46v4TFEkdoaKgHSiXi3dTV3/0as3eCtzwd85Y4REQaUvExOyu2FbI8t4AffjkKwCXx4TxydUcGJsXSKjL4nI+l+6+I+JI6ExhfffUVr7/+Otdffz1dunShZcuWlJaWsmnTJv75z3+SnJzcZBMYu3b9zMSJ4wCIiYkhPr4VFks8HTp0rHVp0Pj4eKKiojWEQ+QCqKu/+zV27wRveTp2Mg71Xmtatm/fTufOnT0dhojXKC2vYtWOQrJzC/hubwlOAzq0DOWBfu0Y3CWOttEh9Tqu7r8i4kvqTGDcf//9HDp0iPfee4/XX3+dw4cPExsbS+/evZk0aRKtW7d2Z5wNKikpmY0bfyI21qJZz0XcRF393e/03gkNuQqJyIWaOXMmb731lqfDEPGo45UOvtpZxOe5VtbtLqbSYXBxi2Du7N2WQclxdIoNu+D30P1XRHzJGVchadWqFePHjwegpKSEFi1a+EQvhJCQEFq3vtjTYYg0K+rq7xmn9pJQDwXxFl999RVHjhxhzZo1AKSlpXk4IhH3qahysm5XMdl5Vr7aWUR5lZO48ECGdW/N4OQ4Lo0Pb9D2tu6/IuJLzrqM6nfffceMGTNwOBxcf/31tG7dmmHDhrkjNhHxMXUNOajv8qoi0jT985//pKioiGXLlgEXnsDYtGkTzz33HFlZWezZs4fJkydjMpno3Lkz06ZNw8/PjyVLlrB48WL8/f0ZM2YMAwYMoLy8nIkTJ1JUVERYWBhz584lJiaGjRs38swzz2A2m0lLS+Ohhx5qiGJLM1blcPLdvhKyc62s2l5Imd1BVEgAv+kaz+BkC90vboFfIz4k9JYhfyIiF+qsCYwXX3yRt99+m7Fjx/LAAw8watQoJTBEpMFc6PKqIkqANT1z5swhMzOTOXPmXPCx/vu//5tPPvmEkJAQ17HHjRtHnz59mDp1KitWrKB79+5kZWWxdOlSKioqyMjIoF+/frz33nskJSUxduxYli1bxsKFC3n66aeZNm0aCxYsoG3btowePbpJT1wujetM1x+nYbDxlyNk51pZsa2QkuOVhAeZubZzLIOTLfRKiMbfr+n3bBYRcaezJjD8/PyIiorCZDIRFBREWNiFj8U7yTAM0tPTad++PQDdu3fnscceq/PJx8svv8yXX36Jv78/Tz75JCkpKRQXFzNhwgTKy8uJi4tjzpw5rkaMiHg/La8qF0IJsKZr0KBBDXKchIQEFixYwOOPPw6cWCWtd+/eAKSnp7N27Vr8/Pzo0aMHgYGBBAYGkpCQQG5uLhs2bODee+917btw4UJsNht2u52EhATgRO+QdevWKYEhNdR2/Xnm823sLzmOrcLBF3lWCmx2gv39SE9syeBkC1e2jyHQ38/DkYuINF1nTWAkJCQwf/58SkpKeO211xp08s69e/fStWtXFi1aVG17bU8+AL799ls++OADDh48yNixY1m6dCkLFy7kpptuYujQobz22mu8//773HnnnQ0Wo4g0rgtZ3k1P3r2DJ8+DEmBN1x133NEgxxkyZAj79+93vTYMwzV/QFhYGKWlpdhsNiIiIlz7hIWFYbPZqm0/dd/w8PBq++7bt++scZjNJqKiGnYZdbPZr8GPKbWrT10vWrunxvWnwmHw2td7CTCbuLqzhd9cfhHXJlsIDTxrk7tZ0GfafVTX7qF6dr+zXk1nzJjBBx98QM+ePQkJCWHmzJkN9uZbt24lPz+fzMxMgoODeeKJJ4iLi6v1yUdgYCBpaWmYTCZat26Nw+GguLiYDRs2cP/99wMnnp48//zzSmCInMZTXzDP5X3ru7ybnrx7B0+fhwtJgIlv8vP79el2WVkZkZGRhIeHU1ZWVm17REREte1n2jcyMvKs7+twGA0+Sa4m3nWf+tT1gSPldf7u8weuJCL4RDPbfsyO/Zj9guLzFfpMu4/q2j1Uz43HYomodftZExj+/v6MGjXqggP44IMPePPNN6ttmzp1KqNHj+aGG27g+++/Z+LEifz1r3+t9clHUFAQUVFR1baf/lTl5LazaYynJA3J1zN5Kp97fbLpALOXb6e88pQvmMu3ExYaxC3dzr9H1bmW71zfd+KQLjz1v1tc+wEEB/gxcUiXM75PbU++yqucLFq7h1FXdjjfYgHed+4aWmOUrzHOw/m4qEVwrV8iLmoR7HPn0tc/nw3l0ksvZf369fTp04ecnBz69u1LSkoKL774IhUVFdjtdnbu3ElSUhKpqamsXr2alJQUcnJy6NmzJ+Hh4QQEBLB3717atm3LmjVrNImnuBw6Ws7yPCvL86x17tMqIsiVvAD1FhQRaUhu6882bNiwGpN/Hj9+HLPZDECvXr3Iz88nLCys1icfAQEBZ3x6Ehwc7NGnJA3J1zN5Kp97zfs8r1pyAKC80sm8z/NIbxd13sc71/Kd6/umt4viyUGdazTu0ttFnfF9Dtbx5OvgkfJ617+3nbuG1hjla4zzcD4e6NeuWg8QgGB/Px7o187nzqU3fz7rekpyuvfff7/O340YMaJBYpk0aRJTpkzh+eefp2PHjgwZMgSz2UxmZiYZGRkYhsH48eMJCgpi1KhRTJo0iVGjRhEQEMD8+fOBE71PJ0yYgMPhIC0tjW7dujVIbNI0FZXZWbGtkOV5BWz85SgAl7aKYEiyhS+3F1LhMFz7Bvv78WD/9q7Xnu6lJiLiazw6IO/ll18mKiqK++67j9zcXFq3bk1EREStTz7MZjPz5s3jnnvu4dChQzidTmJiYlxPT4YOHep6eiIiv/JUF/vzed/6LO9W36En0rA8fR5Ofm70dLNpsFrrfmp9Idq0acOSJUsA6NChA2+//XaNfYYPH87w4cOrbQsJCeGll16qsW/37t1dx/MEPbH3vKPllXy5vYjPcwv4fl8JTgMSY0MZ0689g5MttIk6MWH82c6V5ukREWlYHk1gjB49mokTJ7J69WrMZrNrObW6nnz06tWLESNG4HQ6mTp1KgBjxoxh0qRJLFmyhOjoaNfTExE5wVNfMBv7fR/s377WJ++nPvmSxucN5+FkAsybeyjICacOxSgoKKCqqgrDMCgoKPBgVN7FG57YN9cEyjG7g692nkharNt9mCqnQZuoYO7sk8CgLhY6xdZcie9sCfimNk9Pcz33ItJ0mAzDMM6+m2+prHR4dSPX1xvhKp97nd4YhhNfMJ8c3LlejZJzLV9Dv29d79GQDS1vO3cNrbHK5y0NXp0/zznXISQnPfnkk2zcuJHjx49TXl5O27ZtPdrjoSE0VNvi5tfW15r8bRURxKej+1zw8c/GHddub1JR5eTrXcV8+XMxK3ILqKhyEhceyKAucQxOtnBJfLhrVZv68PT5PB/uOPfefB3zNapr91A9N556T+IpIk2bp7rYu+N96zP0RBqezoOcr59//plly5YxdepUxo8fzyOPPOLpkLyGp5/YN4chD1UOJ9/uLSE7z8qX2wspszuICQvk5q7xDE6Oo9vFkfhdQNLiVN7QS+1cNYdz3xC8JWkv0lwpgSHSSLzpBuepL5j6YisitQkLC8NkMnHs2DFiYmKorKz0dEhew9Pzyng6gdJYHE6Djb8cITvXyoptVo6UVxEeZOa6pFgGd4njussvwlZa97Ko9dWQyfzGblf46rlvSN4wxEukuVMCQ6QR6AYnIlK3rl278vrrrxMXF8f48eOpqqrydEhew9NP7D2dQGlIhmGw9VAp2blWvthmxWqzE+zvx9WdWjKoSxxXto8m0N8PAH+zX6PF0RDJfHe0K3zp3DcW9VIR8TwlMEQagW5wIiJ1e/TRR7HZbAQHB5OTk6NlSk/h6ZV1PJ1AuVCGYbCjsIzsXCvZeVYOHCknwGyiX4cYBnWx0D+xJSEBZk+Hed7c0a5o6ufeHdRLRcTzlMAQaQS6wYmI1O3ll1+u9vo///lPtRVKmjtPrqzj6QRKfe0pPsbyPCvZuVZ2FR/DbIIr2kVz35UJXNMplvCg82vyetMwUHBPu6Kpnnt3Ui8VEc9TAkOkEegGJyJSt9jYWODE0/L//Oc/OJ3Os/yFuFNTmb/o0NFyV9Iit8CGCejepgWTUztxbedYokMD63VcbxwG6q52RVM5956iXioinqcEhkgj0A1ORKRuI0eOrPb63nvv9VAk0tQUldlZse1E0mLTgaMAdG0VwfhrOnJdkqVBvtB74zBQtSu8g3qpiHieEhgijUA3OBGRuu3atcv1s9Vq5eDBgx6MRrzd0fJKVm0vJDvXyvf7SnAa0Ck2jAfT2jOoi4U2USEN+n7eOAxU7QrvUVsvFW8bciTiy5TAEGkk59sNUzc/EWkupk6d6vo5KCiIxx9/3IPRiDc6ZneweueJpMU3uw9T5TRoGxXMXX0SGNTFQmJsWKO9t7cOA9XwDu/kjUOORHyZEhgiXkA3PxFpTrKysjwdgnihiiona3cVszy3gK9+LqaiyklceCAjUy9mcLKF5LhwTCbTWY9zoQ8ENFxDzoc3DjkS8WVKYIh4Ad38xFepZ5Gc6tprr632BdTf35+qqioCAwP57LPPPBiZeEqVw8n6vSUszy3gyx1FlNkdxIQGcMtlrRjcxULKxZH4nUPS4qSGeCCg4RpyPrxxyJGIL1MCQ8QL6OYnvkg9i+R0//rXvzAMgxkzZjBy5EhSUlL4z3/+w7vvvuvp0MSNHE6Djb8c4fPcAlZuK+RIeRURQf5clxTL4OQ4eraNwt/v3JMWp2qoBwIariHnyluHHIn4KiUwRLyAbn7ii9SzSE4XGHhiWct9+/aRkpICwKWXXlptUk/xTYZhsOVgKdl5Vr7Is1JYZickwI/0xJYMTo6jb7toAv39Lvh9mtIDAfVQ8w0aciTiXkpgiHgB3fzEFzWlLxLiXhEREbz44oukpKTwww8/cPHFF3s6JGkEhmGw3VpGdp6V5bkFHDhaQaDZxFUdYhicHEdaxxhCAswN+p5N5YGAeqj5Dg05EnEvJTBEvIBufuKLmsoXCXG/5557jr///e/k5OSQmJjIuHHjPB2SNKA9xcfIzrOSnVvA7uLjmE3Qu100913Vjms6xRIe1HjNz6byQEA91HyLhhyJuI8SGCJeQjc/8TVN5YuEuM+PP/7I5Zdfzr///W/atWtHu3btAFi3bh1paWkejk4uxMGj5SzPtZKdZyWvwIYJ6NGmBaNSL+bazhaiQgPcEkdTeSCgHmoiIvWjBIaIiDSKpvJFQtxn3bp1XH755SxbtqzG75TAaHoKy+ysyDuRtNh84CgAl10UwfhrOjIwyUKch3pbNYUHAuqhJiJSP0pgiIhIo2kKXyTEfUaPHg3AnDlzcDgcGIbBxo0bXRN6ivc7crySVdsLyc6zsmFfCU4DOlvCeDCtPYO6WGgTFeLpEKvx1oky1UNNRKR+lMAQERERt5o3bx5t27blwIEDbN26FYvFwrPPPuvpsKQOZfYqVu8oYnmelXW7D+NwGiREh3B3nwQGJVvo2DLM0yHWypsnylQPNRGR+lECQ0TkDLz16Z1IU7ZhwwYmTpxIZmYmWVlZ/Nd//ZenQ5LTlFc6+HpXMdl5Vtb8XExFlZP4iCAyUi9mcLKFLnHhmEwmT4d5Rt4+UaZ6qImInD8lMERE6uDNT+9EmjKn08nmzZtp06YNdrud4uJiT4ckQJXDyfo9JWTnFbB6RxFldgcxoQH89rJWDE62cHnrSPy8PGlxqqY4UaaS5iIiZ6YEhohIHbz96Z1IU/Xb3/6WmTNnMnv2bObNm8cdd9zh6ZCaLYfT4If9R/g8t4BV2ws5Ul5FRJA/A5MsDE62kNo2Cn+/ppO0OFVTmyhTSXMRkbNTAkPOiZ4ISH009c9NU3x6J9IU3H777dxyyy0cOHCA8ePHExoa6umQmhXDMPjxYCnZuQV8sa2QojI7IQF+XN0plsFdLPRtH02A2c/TYV6wpjZRppLmIiJnpwSGnJWeCEh9+MLnpqk9vRNpKj7//HNeeeUVHA4H119/PSaTiQcffNDTYfk0wzDYZi0jO9fK8rwCDh6tINBsol/HlgzuYiGtYwzBAWZPh9mgmtpEmUqai4icndsTGMuXL+df//oX8+fPB2Djxo0888wzmM1m0tLSeOihhwB4+eWX+fLLL/H39+fJJ58kJSWF4uJiJkyYQHl5OXFxccyZM4eQkBBWrlzJX//6V/z9/bn11lsZPny4u4vl0/REQOrDFz43Te3pnUhT8be//Y0lS5Zwzz338OCDD3LrrbcqgdFIdhcfY3muley8AnYXH8fsZ6JPuyjuv6o9V3dqSXiQbz/LakoTZSppLiJydm69a82aNYs1a9ZwySWXuLZNmzaNBQsW0LZtW0aPHs3WrVsB+Pbbb/nggw84ePAgY8eOZenSpSxcuJCbbrqJoUOH8tprr/H+++9z++23M2fOHD788ENCQkIYNWoUAwYMwGKxuLNoPk1PBKQ+fOFz09Se3ok0FX5+fgQGBmIymTCZTISEhHg6JJ9y8Gg52blWsnML2GYtwwT0bNuCUT3bcG2nWKJCA4CmP8zP1yhpLiJydm5NYKSmpjJw4EDef/99AGw2G3a7nYSEBADS0tJYt24dgYGBpKWlYTKZaN26NQ6Hg+LiYjZs2MD9998PQHp6Os8//zx9+/YlISGBFi1aANCzZ0++//57brjhBncWzafpiYDUh698bprS0zuRpqJXr1489thj5OfnM3XqVC6//HJPh9TkFdoq+GJbIdm5Vn48eBSAyy+K4NEBiQxMisUSXv3a6wvD/HyNkuYiImfXKAmMDz74gDfffLPattmzZ3PjjTeyfv161zabzUZ4eLjrdVhYGPv27SMoKIioqKhq20tLS7HZbERERNS57eR2m83WGMVqtvREQOpDnxsRqcujjz5KTk4Ol1xyCR07duTaa6/1dEhNUsnxSlZtLyQ7z8q/95XgNKCzJYw/prVnULKFi1vU3bPFF4b5+SIlzUVEzqxREhjDhg1j2LBhZ90vPDycsrIy1+uysjIiIyMJCAiosT0iIsK1f3BwsGvf2o5xakKjNmaziago753x3Gz286r4Rl3ZgbDQIOYv38bBI+Vc1CKYxwYlcUu31vU6nreVr6GpfCc09OfGHXTumjaVz/tVVVWxcuVKIiMjSU9PJz09HavVyrhx43jxxRc9HV41TqeT6dOnk5eXR2BgILNmzaJdu3aeDosyexWrdxSRnWvlmz2HcTgNEqJDuKdvAoO6xNGh5bl9RnxhmJ+IiDQ/Hp25KTw8nICAAPbu3Uvbtm1Zs2YNDz30EGazmXnz5nHPPfdw6NAhnE4nMTExpKamsnr1aoYOHUpOTg49e/YkMTGRPXv2UFJSQmhoKN9//z333HPPGd/X4TAoKTnmplKev6ioUK+LL71dFOn39q62rb4xemP5GpLK96uG/Ny4g85d06byeY7FcuYHBydNmDABs9mM1Wplx44dtGnThqeeeoo77rijkSM8f1988QV2u53333+fjRs38uyzz/LKK694JJbySgdrdxWTnWtl7a5iKqqctIoI4vaeFzO4SxxJcWGYTKbzOqavDPMTEZHmxeNTT8+YMYMJEybgcDhIS0ujW7duwInxsSNGjMDpdDJ16lQAxowZw6RJk1iyZAnR0dHMnz+fgIAAJk+ezD333INhGNx6663Ex6vrnYiIiLfZu3cvH330EXa7nVtvvZWAgADeeustEhMTPR1aDRs2bKB///4AdO/enS1btrj1/SsdTlblFfD3DftZvaOIY5UOYkID+N3lrRjUxcLlrSPxO8+kxak0zE9ERJoitycw+vTpQ58+fVyvu3fvzpIlS2rsN3bsWMaOHVttW2xsLK+//nqNfa+99lqNnxUREfFyJ+e9CgwMxOl08sYbb1Sb88qbnD5Pl9lspqqqCn//xms6OZwG/95fwue5VlZtL+RoeRWRwf4MSrYwJNlCapsozH71T1qcShNGiohIU+TxHhgiIiLS/LRs2dJrkxdQc54up9N5xuRFfefXMgyDjftK+PTHg/xrSz5WWwVhgWYGXhLPzd1ac2WHGAL9/epVhrMZdWUHRl3ZoVGO3dT4whwzTYHq2X1U1+6henY/JTBERETELXbs2MFjjz2GYRiun0+aP3++ByOrKTU1lVWrVnHjjTeyceNGkpKSzrj/+cyvZRgG2wrKyM4rYHmelYNHKwg0m0jr2JLByR3p1yGG4ACza94T75z55MRSrL7Sg8Ob55jxJapn91Fdu4fqufHUNb+WEhgiIiLiFqeuNDJy5EjPBXIOBg0axNq1axk5ciSGYTB79uwLPubuomNk5xWQnWtlz+HjmP1M9G0XzQP92pOe2JLwoKbTLPvsp/xqc2gcKq1gdvZ2gCabxBAREe/XdO6UIiIi0qT17t377Dt5CT8/P/70pz9d8HEOHCknO7eA7Dwr261lmICeCVHc3qsNAzrHEhUScOHBesDCr3ZXmwAUoLzKycKvdiuBISIijUYJDBEREZEGZLVV8MW2QpbnFvDjwVIALr8okscGJDIwKZbY8Ka/VGl+LUuwnmm7iIhIQ1ACw0ucOo70ohbBPNCvnZ5giIiINBEOp8FHmw+yPLeADfuOYABJljAe6t+BQV0stG4R7OkQG1R8RBCHaklWxEc0/eSMiIh4LyUwvMDp40gPHCnXOFIREZEmZLu1jDnLt9MuOoT7rmzHoC4W2rf03ZnpH+zfvlrbBSDY348H+7f3XFAiIuLzlMDwAhpHKiIi0rTFhAXwdmYqSZYwTCaTp8NpdCfbJ76yComIiDQNSmB4AY0jFRERadriwoMIjAv3dBhudcMl8UpYiIiIW/l5OgCpe7yoxpGKiIiIiIiInKAEhhd4sH97gv2rnwqNIxURERERERH5lYaQeIHTx5FqFRIRERERERGR6pTA8BKnjiONigqlpOSYhyMSERERERER8R4aQiIiIiIiIiIiXk8JDBERERERERHxekpgiIiIiIiIiIjX0xwYItKkfPZTvmvC2/iIIB7s314T3oqIiIiINANKYIhIk/HZT/nMzt5OeZUTgEOlFczO3g6gJIaIiIiIiI/TEBIRaTIWfrXblbw4qbzKycKvdnsmIBERERERcRslMESkycgvrTiv7SIiIiIi4juUwBCRJiM+Iui8touIiIiIiO9QAkNEmowH+7cn2L/6ZSvY348H+7f3TEAiIiIiIuI2msRTRJqMkxN1ahUSEREREZHmRwkMEWlSbrgkXgkLEfFpWi5aRESkdkpgiIiIiHgJLRctIiJSN7fPgbF8+XIee+wx1+vs7GwGDhxIZmYmmZmZfPvttwC8/PLL3HbbbYwcOZLNmzcDUFxczN13301GRgbjxo3j+PHjAKxcuZJbb72VESNGsGTJEncXSURERKRBaLloERGRurm1B8asWbNYs2YNl1xyiWvb1q1bmThxIkOGDKm27dtvv+WDDz7g4MGDjB07lqVLl7Jw4UJuuukmhg4dymuvvcb777/P7bffzpw5c/jwww8JCQlh1KhRDBgwAIvF4s6iiYiIiFwwLRctIiJSN7f2wEhNTWX69OnVtm3dupWlS5eSkZHBs88+S1VVFRs2bCAtLQ2TyUTr1q1xOBwUFxezYcMG+vfvD0B6ejpff/01O3fuJCEhgRYtWhAYGEjPnj35/vvv3VksERERkQah5aJFRETq1ig9MD744APefPPNattmz57NjTfeyPr166tt79evHwMHDqRNmzZMmzaNxYsXY7PZiIqKcu0TFhZGaWkpNpuNiIiIOred3G6z2c4Yn9lsIioq9AJL2XjMZj+vju9CqXxNmy+Xz5fLBipfU+fr5ZMTHuzfvtocGKDlokVERE5qlATGsGHDGDZs2Dnte+uttxIZGQnAddddx+eff05ycjJlZWWufcrKyoiIiCA8PJyysjKCg4MpKysjMjLSte30fc/E4TAoKTlWj5K5R1RUqFfHd6FUvqbNl8vny2UDla+p8+byWSxnvu/KudNy0SIiInVz+ySepzIMg1tuuYVDhw4BsG7dOrp27Upqaipr1qzB6XRy4MABnE4nMTExpKamsnr1agBycnLo2bMniYmJ7Nmzh5KSEux2O99//z09evTwZLFExIM++ymfm19bT+/5Odz82no++ynf0yGJiIiIiEgD8OgyqiaTiVmzZvHQQw8RHBxMYmIiw4cPJyAggF69ejFixAicTidTp04FYMyYMUyaNIklS5YQHR3N/PnzCQgIYPLkydxzzz0YhsGtt95KfLyeUog0R1p+UESaOl3HRERE6mYyDMPwdBDuVlnp8NpuuODd3YQbgsrXtHlz+W5+bT2Hapmpv1VEEJ+O7nPWv/fmsjUEla9p8+byaQhJw7UtLvQ6JvXjzf++fInq2X1U1+6hem48dbUtPDqERESkIWn5QRFp6nQdExERqZsSGCLiM7T8oIg0dbqOiYiI1E0JDBHxGQ/2b0+wf/XLmpYfFPFty5cv57HHHnO93rhxI8OGDWPkyJG8/PLLru0vv/wyt912GyNHjmTz5s0AFBcXc/fdd5ORkcG4ceM4fvw4ACtXruTWW29lxIgRLFmyxK3l0XVMRESkbh6dxFNEpCFp+UGR5mXWrFmsWbOGSy65xLVt2rRpLFiwgLZt2zJ69Gi2bt0KwLfffssHH3zAwYMHGTt2LEuXLmXhwoXcdNNNDB06lNdee43333+f22+/nTlz5vDhhx8SEhLCqFGjGDBgABaLxS1l0nVMRESkbkpgiIhPueGSeDX0RZqJ1NRUBg4cyPvvvw+AzWbDbreTkJAAQFpaGuvWrSMwMJC0tDRMJhOtW7fG4XBQXFzMhg0buP/++wFIT0/n+eefp2/fviQkJNCiRQsAevbsyffff88NN9zgtnKdvI5pcjgREZHqlMAQERERr/bBBx/w5ptvVts2e/ZsbrzxRtavX+/aZrPZCA8Pd70OCwtj3759BAUFERUVVW17aWkpNpuNiIiIOred3G6z2c4ao9lsIioqtL5FrOOYfg1+TKmd6to9VM/uo7p2D9Wz+ymBISIiIl5t2LBhDBs27Kz7hYeHU1ZW5npdVlZGZGQkAQEBNbZHRES49g8ODnbtW9sxTk1o1MXhMBq8t4R6YLiP6to9VM/uo7p2D9Vz49EyqiIiIuLTwsPDCQgIYO/evRiGwZo1a+jVqxepqamsWbMGp9PJgQMHcDqdxMTEkJqayurVqwHIycmhZ8+eJCYmsmfPHkpKSrDb7Xz//ff06NHDwyUTERERUA8MERER8SEzZsxgwoQJOBwO0tLS6NatGwC9evVixIgROJ1Opk6dCsCYMWOYNGkSS5YsITo6mvnz5xMQEMDkyZO55557MAyDW2+9lfh4zasjIiLiDUyGYRieDsLdKisdXt3Vx9e7Iql8TZsvl8+XywYqX1PnzeWrq5tnc9IYbQtvPue+RnXtHqpn91Fdu4fqufHU1bZolgkMEREREREREWlaNAeGiIiIiIiIiHg9JTBERERERERExOspgSEiIiIiIiIiXk8JDBERERERERHxekpgiIiIiIiIiIjXUwJDRERERERERLyeEhhutnPnTnr27ElFRQUAGzduZNiwYYwcOZKXX37Ztd/LL7/MbbfdxsiRI9m8eTMAxcXF3H333WRkZDBu3DiOHz8OwMqVK7n11lsZMWIES5YscX+hgGPHjjFmzBgyMjK45557KC4uBnyjfKWlpTzwwAP84Q9/YMSIEfzwww8+U7ZTLV++nMcee8z12tfKdyZOp5OpU6cyYsQIMjMz2bNnj6dDOiebNm0iMzMTgD179jBq1CgyMjKYNm0aTqcTgCVLljB06FCGDx/OqlWrACgvL2fs2LFkZGRw3333nfXfq7tVVlYyceJEMjIyuO2221ixYoVPlc/hcPDEE08wcuRIbr/9dvbu3etT5ZML11SvSZ7mrmtibffB5sLd1+fmWtfuvk8013o+VVFREVdffTU7d+5UXXs7Q9ymtLTUuO+++4y+ffsa5eXlhmEYxi233GLs2bPHcDqdxr333mts2bLF2LJli5GZCl4rJwAADulJREFUmWk4nU7jl19+MYYOHWoYhmHMnDnTWLp0qWEYhvHqq68af/vb3wy73W4MHDjQKCkpMSoqKoyhQ4caBQUFbi/b3/72N2PBggWGYRjG0qVLjZkzZ/pM+f7yl78Yf/vb3wzDMIydO3cav/vd73ymbCfNnDnTGDJkiDFu3DjXNl8q39l8/vnnxqRJkwzDMIwffvjBeOCBBzwc0dm99tprxk033WQMGzbMMAzDuP/++41vvvnGMAzDmDJlipGdnW0UFBQYN910k1FRUWEcPXrU9fMbb7xhvPTSS4ZhGMY//vGPM/579YQPP/zQmDVrlmEYhlFcXGxcffXVPlW+5cuXG5MnTzYMwzC++eYb44EHHvCp8smFa4rXJE9z1zWxrvtgc+HO63Nzrmt33ieacz2fZLfbjQcffNAYPHiwsWPHDtW1l1MPDDcxDIMpU6bw6KOPEhISAoDNZsNut5OQkIDJZCItLY1169axYcMG0tLSMJlMtG7dGofDQXFxMRs2bKB///4ApKen8/XXX7Nz504SEhJo0aIFgYGB9OzZk++//97t5bvzzjsZM2YMAAcOHCA2NtZnynfnnXcycuRI4ERGPCgoyGfKdlJqairTp093vfa18p3NqfF3796dLVu2eDiis0tISGDBggWu11u3bqV3797Ar+dg8+bN9OjRg8DAQCIiIkhISCA3N7fG+Vq3bl2d59wTrr/+eh555BHXa7PZ7FPlGzhwIDNnzgR+vV76UvnkwjXFa5KnueuaWNd9sLlw5/W5Ode1O+8TzbmeT5o7dy4jR44kLi4O0PXD2ymB0Qg++OADbrrppmr/3X///Vx99dUkJye79rPZbISHh7teh4WFUVpaesbtERERdW47ud1ms7m9fJs3b8ZsNnPHHXfw9ttvc/XVVzfJ8tVWtt27dxMcHIzVamXixIk8+uijTbJsdZVv8+bN3HjjjZhMJtd+TbV89XV6ucxmM1VVVR6M6OyGDBmCv7+/67VhGK5zeLZzUNf5qu3cekJYWBjh4eHYbDYefvhhxo0b51PlA/D392fSpEnMnDmTIUOG+Fz55MI0xWuSp7nrmtjc/6258/rc3OvaXfeJ5l7PH330ETExMa4kBOj64e38z76LnK9hw4YxbNiwatsGDRrE0qVLWbp0KVarlbvvvptXX32VsrIy1z5lZWVERkYSEBBQY3tERATh4eGUlZURHBzs2vfkttP3dXf5TnrrrbfYuXMn999/Px9//HGTK19dZcvLy+PRRx/l8ccfp3fv3thstiZXtjOV73S1xdYUyldfp8fqdDqrNYSbAj+/X/PRZzsHp24/076RkZHuK8BpDh48yB//+EcyMjK4+eabmTdvXo3YmnL54MQTnwkTJjB8+HDXvEjgO+WT+vOFa5KnNdY1sa77YHPiruuz6to994nmXs9Lly7FZDKxbt06fvrpJyZNmlStV4Tq2vuoB4abLF++nKysLLKysrBYLLzxxhuEh4cTEBDA3r17MQyDNWvW0KtXL1JTU1mzZg1Op5MDBw7gdDqJiYkhNTWV1atXA5CTk0PPnj1JTExkz549lJSUYLfb+f777+nRo4fby/fqq6/y8ccfAxAaGorZbPaZ8u3YsYNHHnmE+fPnc/XVVwP4TNnq4uvlO11qaio5OTnAiUmXkpKSPBzR+bv00ktZv349cOIc9OrVi5SUFDZs2EBFRQWlpaXs3LmTpKSkWs9XXefcEwoLC7n77ruZOHEit912m8+V7+OPP+bVV18FICQkBJPJxGWXXeYz5ZML5wvXJE9rrGtGXffB5sKd1+fmXNfuvE8053oGeOedd3j77bfJysrikksuYe7cuaSnp6uuvZjJMAzD00E0N9deey2fffYZQUFBbNy4kdmzZ+NwOEhLS2P8+PEALFiwgJycHJxOJ0888QS9evWisLCQSZMmUVZWRnR0NPPnzyc0NJSVK1fy17/+FcMwuPXWW7n99tvdXqaTsdntdhwOB4899hg9e/b0ifKNGTOGvLw8Lr74YuDEl/tXXnnFJ8p2qvXr17N48WJeeOEFAJ8r35k4nU6mT5/Otm3bMAyD2bNnk5iY6Omwzmr//v08+uijLFmyhF27djFlyhQqKyvp2LEjs2bNwmw2s2TJEt5//30Mw+D+++9nyJAhHD9+nEmTJmG1WgkICGD+/PlYLJY6z7m7zZo1i88++4yOHTu6tj311FPMmjXLJ8p37NgxnnjiCQoLC6mqquK+++4jMTHRZ86fXLimek3yNHddE2u7DzYX7r4+N9e6dvd9ornW8+kyMzOZPn06fn5+qmsvpgSGiIiIiIiIiHg9DSEREREREREREa+nBIaIiIiIiIiIeD0lMERERERERETE6ymBISIiIiIiIiJeTwkMEREREREREfF6SmCIyFktWrSILVu2nPffZWZmsnPnzmrb8vLy+O677xoqtLMaPnw4+/fvr7ZtwYIFrvW9T/rggw+4++67ObkwU15eHr///e+x2Wz85S9/YceOHW6LWURERM7N5MmTycnJqbbtwIEDrFy50m0xjB8/vka7QkQahxIYInJGBw8eZNu2bVx22WUNcrzs7GyvTAYMGzaMyMhI/t//+3+UlpYyceJE5s6dS3h4OHfddRd//vOfPR2iiIiInINvvvmGf//7354OQ0Qagb+nAxAR71BZWcm0adPYs2cPTqeTcePG0adPH9577z2GDBkCnOi5sGfPHg4fPsyRI0fIyMggOzubXbt2MXfuXGJjYxkzZgxRUVGkp6e7jr1y5Ur+9re/8dxzz/H3v/+dgIAAunbtyooVK/jmm29wOp385je/4c477yQzM5MOHTqwa9cuDMPghRdewGKxMH/+fL777jsMw+DOO+/khhtuIDMzk+TkZLZv3+7qKXHxxRfzwgsv8NVXX9GqVSsOHz58znUwc+ZMbrvtNr7++mvuuusukpKSAIiMjCQoKIjc3FySk5MbtuJFRESkmo8++ohVq1ZRXl6O1WrljjvuYMWKFWzfvp3HH3+cZ555ho4dO9KxY0fX32zatIlZs2bx4osv8tprr1FeXk6PHj04dOgQH3/8MX5+fqSmpjJp0iQmT56MYRgcPHiQY8eOMXfuXBITE8nKyuIf//gHJpOJG2+8kTvuuIPJkycTGBjIL7/8QkFBAc8++yxdu3blnXfe4YMPPsBisVBUVOTB2hJpXtQDQ0SAE0MooqOjeeedd1i4cCF/+tOfAPj222/p0qWLa7/g4GBef/11Bg8ezOrVq1m0aBGjR49m2bJlAFitVl5//XXuu+8+AJYvX84777zDq6++Snx8PL///e+58847SUlJ4eOPP+a5557jnXfeITg42PUeqampZGVlccMNN/Dqq6+yevVq9u/fz+LFi3nrrbdYtGgRR48eBSAlJYX/+Z//oV+/fixbtoxt27bx3Xff8eGHH/LnP/+ZsrKyc66DiIgIrr/+ev7zn/8waNCgar/r0qUL3377bf0qV0RERM5LWVkZ//3f/819993He++9x8svv8yf/vQnPvroIw4ePMhzzz3HU089BcAPP/zAnDlzWLRoERdffDGjR4/mpptu4rrrruOjjz7iqaee4v3336dt27ZUVVUB0LZtW9566y3Gjh3LvHnz2LFjB//85z959913effdd/niiy/4+eefAWjdujWvv/46mZmZvP/++5SWlvLWW2+xZMkSFi5cSGVlpcfqSaS5UQ8MEQFg27ZtbNiwgc2bNwNQVVXF4cOHOXz4MLGxsa79Lr30UuDEl/1OnToB0KJFCyoqKgBo06YNgYGBrv3XrVuHzWbD37/m5eb555/n+eefp7CwkP79+7u29+3bFziRyFi5ciXx8fFs3bqVzMxMV2wHDhyoFk+rVq0oLCxkx44dXHbZZfj5+REeHu7qRXEufvzxR1atWsXIkSOZNm0a8+fPd/3OYrGQn59/zscSERGR+rvkkkuAE+2NxMRETCaTq70RHR1NdHS0a9+1a9dSVlZWa1tjzpw5vPHGGzz33HN0797dNdfVybZGjx49mD17Ntu2bePAgQPceeedABw5coS9e/dWi6VVq1b8+9//5ueff6ZTp06u9k5KSkrjVIKI1KAeGCICQMeOHfnNb35DVtb/b+eOQRrZojCOf6YLCKMjGFBTmIkGURSbBNHCwiLaCJLAGBXsFYJdCpFYiREstFCwSYyViCls0thYWihGJI1tCjsFCRJw3GJ5A4r4ZHdd83j/X3tn7rl3iuFy5szJa29vT9FoVIZhyDRNt9pBkhoaGj6cx+N5/VpZWVnRyMiItra23Psdx1GtVlOxWNTm5qZyuZwKhYIqlYokuQ1DLy4uFAwGFQgEFIlElM/nlcvlND4+ro6Ojnfjd3Z2qlQqyXEcVavVT/fbeHh4cPteJJNJ3d3d6ejo6NV4S0vLp+YCAAC/56PzxtuzxuLioubn55VOp91xx3EkSYeHh1pdXdXBwYHK5bIuLy8lSTc3N5J+njW6uroUCAQUDAa1v7+vfD6vqakp9yPI27X4/X7d3t7q6elJz8/PKpfLf2TPAP4dFRgAJEm2bWt5eVmzs7N6fHxUIpGQx+NROBzW1dWV2trafnnuhYUFxeNxjY6Oqq+vT5lMRpZlyTAMTU5OyjAMDQ8PuzEKhYKy2ay8Xq8ymYyampp0fn6uRCKharWqsbExNTY2vhurp6dH0WhUsVhMra2tn0o6vLy8KJVKaWZmxv3KsrGxIdu2NTg4KMuyVCqVtLS09MvPAAAAfJ14PK5isaiTkxN1d3drZ2dHvb29CoVCisViam5uls/n08DAgI6Pj3V2dqbT01M5jqO1tTX5/X4NDQ1penpatVpN/f398vl878YyTVPJZFK2bcs0TXm93r+8W+D/q+HlnzoqAHhHpVLR+vq6W0Hx1ebm5pROp2VZ1pfF2N7eVjgcViQS+dT19/f3SqVS2t3d/bI1AQCAvyOVSmliYuJVw3EA/w38QgLgQ+3t7QqFQrq+vv7upXybbDZL9QUAAADwzajAAAAAAAAAdY8KDAAAAAAAUPdIYAAAAAAAgLpHAgMAAAAAANQ9EhgAAAAAAKDukcAAAAAAAAB1jwQGAAAAAACoez8AQc0vUd7pqkAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(Final_model, \"mrktspend\", fig=fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=Final_model.predict(startup1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.3119083806339"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.mean(startup1.Profit-Final_model.predict(startup1))**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Profit using final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    195497.939734\n",
       "1    188292.353735\n",
       "2    182544.350319\n",
       "3    172607.868574\n",
       "4    171004.561290\n",
       "dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_model.predict(startup1.iloc[0:5,])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:-We will consider the last model (final_model) for profit prediction With using the cubic transformation\n",
    "R-squared value of Final model is 0.965 & Adj_R-square value is 0.960 both are greater than comparison of other model\n",
    "\n",
    "Probability value is less than 0.05 of all attributes means all attributes are significant\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>R_square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Base Model</td>\n",
       "      <td>0.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Model1</td>\n",
       "      <td>0.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Final_Model</td>\n",
       "      <td>0.965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Model  R_square\n",
       "0   Base Model     0.951\n",
       "1       Model1     0.961\n",
       "2  Final_Model     0.965"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = {'Model':['Base Model','Model1','Final_Model'],'R_square':[0.951,0.961,0.965]}\n",
    "R_square = pd.DataFrame(d1)  \n",
    "R_square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note:- I have tried different type of transformation with model but i have gotten better result apply cubic transformation with adminastration & marketing attributes \n",
    "# And apply of other transformation the prbability value was insignificant but i have gotten better result with it  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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