[RtoPy]회귀분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 회귀분석"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 다중선형회귀\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>y</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.765092</td>\n",
       "      <td>4.795944</td>\n",
       "      <td>17.832472</td>\n",
       "      <td>15.652137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.009893</td>\n",
       "      <td>0.285189</td>\n",
       "      <td>16.820563</td>\n",
       "      <td>2.893309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-11.617214</td>\n",
       "      <td>6.046287</td>\n",
       "      <td>12.480379</td>\n",
       "      <td>13.415548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.427691</td>\n",
       "      <td>4.788546</td>\n",
       "      <td>16.649359</td>\n",
       "      <td>3.799397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.507688</td>\n",
       "      <td>4.624046</td>\n",
       "      <td>13.028839</td>\n",
       "      <td>4.687639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-12.237411</td>\n",
       "      <td>3.633683</td>\n",
       "      <td>19.638687</td>\n",
       "      <td>18.305614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.428890</td>\n",
       "      <td>2.251135</td>\n",
       "      <td>20.393728</td>\n",
       "      <td>7.554348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.308134</td>\n",
       "      <td>6.811981</td>\n",
       "      <td>16.436605</td>\n",
       "      <td>4.101425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.803563</td>\n",
       "      <td>3.296201</td>\n",
       "      <td>20.312176</td>\n",
       "      <td>7.388880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-6.319101</td>\n",
       "      <td>2.935100</td>\n",
       "      <td>11.876100</td>\n",
       "      <td>11.144962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           y         u          v          w\n",
       "0  -7.765092  4.795944  17.832472  15.652137\n",
       "1  28.009893  0.285189  16.820563   2.893309\n",
       "2 -11.617214  6.046287  12.480379  13.415548\n",
       "3  25.427691  4.788546  16.649359   3.799397\n",
       "4  15.507688  4.624046  13.028839   4.687639\n",
       "5 -12.237411  3.633683  19.638687  18.305614\n",
       "6  21.428890  2.251135  20.393728   7.554348\n",
       "7  24.308134  6.811981  16.436605   4.101425\n",
       "8  21.803563  3.296201  20.312176   7.388880\n",
       "9  -6.319101  2.935100  11.876100  11.144962"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2)\n",
    "u = np.random.rand(10) * 11        # 0 ~ 11\n",
    "v = (np.random.rand(10) * 11) + 11 # 11 ~ 22\n",
    "w = (np.random.rand(10) * 29) + 1  # 1 ~ 30\n",
    "y = 3 + 0.1*u + 2*v - 3*w + np.random.rand(10) * 0.1\n",
    "\n",
    "data = {'y':y, 'u':u, 'v':v, 'w':w}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.780e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>3.10e-18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:09</td>     <th>  Log-Likelihood:    </th> <td>  26.482</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>  -44.96</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     6</td>      <th>  BIC:               </th> <td>  -43.75</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>const</th> <td>    3.0694</td> <td>    0.048</td> <td>   63.601</td> <td> 0.000</td> <td>    2.951</td> <td>    3.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>u</th>     <td>    0.1023</td> <td>    0.004</td> <td>   24.656</td> <td> 0.000</td> <td>    0.092</td> <td>    0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>v</th>     <td>    1.9980</td> <td>    0.002</td> <td>  815.885</td> <td> 0.000</td> <td>    1.992</td> <td>    2.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>w</th>     <td>   -2.9995</td> <td>    0.001</td> <td>-2170.504</td> <td> 0.000</td> <td>   -3.003</td> <td>   -2.996</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.802</td> <th>  Durbin-Watson:     </th> <td>   2.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.020</td> <th>  Jarque-Bera (JB):  </th> <td>   3.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.194</td> <th>  Prob(JB):          </th> <td>   0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.237</td> <th>  Cond. No.          </th> <td>    135.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 1.780e+06\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           3.10e-18\n",
       "Time:                        21:05:09   Log-Likelihood:                 26.482\n",
       "No. Observations:                  10   AIC:                            -44.96\n",
       "Df Residuals:                       6   BIC:                            -43.75\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.0694      0.048     63.601      0.000       2.951       3.187\n",
       "u              0.1023      0.004     24.656      0.000       0.092       0.112\n",
       "v              1.9980      0.002    815.885      0.000       1.992       2.004\n",
       "w             -2.9995      0.001  -2170.504      0.000      -3.003      -2.996\n",
       "==============================================================================\n",
       "Omnibus:                        7.802   Durbin-Watson:                   2.221\n",
       "Prob(Omnibus):                  0.020   Jarque-Bera (JB):                3.012\n",
       "Skew:                           1.194   Prob(JB):                        0.222\n",
       "Kurtosis:                       4.237   Cond. No.                         135.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상수항 추가\n",
    "X = sm.add_constant(df[['u','v','w']])\n",
    "\n",
    "# OLS 검정\n",
    "multi_model = sm.OLS(df[['y']], X)\n",
    "fitted_multi_model = multi_model.fit()\n",
    "fitted_multi_model.summary()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 식이요법을 적용한 닭 데이터\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>weight</th>\n",
       "      <th>Time</th>\n",
       "      <th>Chick</th>\n",
       "      <th>Diet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  Time  Chick  Diet\n",
       "0      42     0      1     1\n",
       "1      51     2      1     1\n",
       "2      59     4      1     1\n",
       "3      64     6      1     1\n",
       "4      76     8      1     1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChickWeight = pd.read_csv('ChickWeight.csv')\n",
    "ChickWeight.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chick = ChickWeight[ChickWeight['Chick']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>weight</td>      <th>  R-squared:         </th> <td>   0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   232.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>2.97e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:10</td>     <th>  Log-Likelihood:    </th> <td> -46.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   96.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    10</td>      <th>  BIC:               </th> <td>   97.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   24.4654</td> <td>    6.728</td> <td>    3.636</td> <td> 0.005</td> <td>    9.475</td> <td>   39.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time</th>      <td>    7.9879</td> <td>    0.524</td> <td>   15.255</td> <td> 0.000</td> <td>    6.821</td> <td>    9.155</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.840</td> <th>  Durbin-Watson:     </th> <td>   0.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.147</td> <th>  Jarque-Bera (JB):  </th> <td>   1.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.211</td> <th>  Prob(JB):          </th> <td>   0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.498</td> <th>  Cond. No.          </th> <td>    24.5</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 weight   R-squared:                       0.959\n",
       "Model:                            OLS   Adj. R-squared:                  0.955\n",
       "Method:                 Least Squares   F-statistic:                     232.7\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           2.97e-08\n",
       "Time:                        21:05:10   Log-Likelihood:                -46.039\n",
       "No. Observations:                  12   AIC:                             96.08\n",
       "Df Residuals:                      10   BIC:                             97.05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     24.4654      6.728      3.636      0.005       9.475      39.456\n",
       "Time           7.9879      0.524     15.255      0.000       6.821       9.155\n",
       "==============================================================================\n",
       "Omnibus:                        3.840   Durbin-Watson:                   0.342\n",
       "Prob(Omnibus):                  0.147   Jarque-Bera (JB):                1.217\n",
       "Skew:                           0.211   Prob(JB):                        0.544\n",
       "Kurtosis:                       1.498   Cond. No.                         24.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "result = smf.ols(formula='weight ~ Time', data=Chick).fit()\n",
    "result.summary()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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qF1HfIiACIiACFU6gwUUZQWakNFYygoXlu27duuCa9mlDTC/q1q2bYQ1jNSPa3gftI6w5/5xzzrFHH300uK+xdBE3F7V8+xFt+rN9XvKOHTuC5XvqqafWqU/5UM6n0XDxxRfX6FP+7rvvwqCunTt3BhYM+qL/+NZbb60qPy74Bx54IPQp0+8NK9z+8QZNhT+bqr4IiIAIVByBBhdlCL711lthkBZuZz4nnnhicCVj6ZIQIER47NixVSO0p0+fHvqK/Q5gYb766qt25plnVrmwGcFMHy2p0H4GTmEtM/obcX/iiSdCP3d8lLRfK+37q6++qtP5NEBwUVNnT0z3YroTo8qpPyPHx48fb+QZTzQ0GH3OMRzLoC+mSymJgAiIgAhUJoGiiDLWHuKCMPJBpOIWoPcpM0La97tgJ28Dg67i+SSPy7cfq5pzKQvXxwKOlyN5reTfdTmfxgFWbzxftuGe9nLz7RZ+8hrx4/jNuUoiIAIiIAKVSaAoolwIJW5d3Nfex1zoeO0XAREQAREQgUog0CiizFziSy65JExzqgTIqqMIiIAIiIAI1IVAo4gyLlrcuT6oqy4F1TEiIAIiIAIi0NQJNIooN3Woqp8IiIAIiIAIHA6BvKLMoCoWvMg1SOlwLtgY5xBHmUFUWUosGIIbn5HofFNGJREQAREQgcomkCrKzLNlLvGDDz4YVqMq51jBhFZkStTChQsz5S5nuVCCUjAdimlbw4cPDyuAVfbjqNqLgAiIQGUTqCXKTO1hUYuTTz45rBV9xhlnhPWmyxET1jHzhVmOk7W0szLdiL50RqBjJTNdim+W9YwH5ChH3iqzCIiACIhA/QjUEmWyY94tc4tZaIMgEeU4dYnGBct4Inas7MU62Mk5zvVDV7+zkw0E1gZnsRNEWkkEREAERKAyCaSKsqOgr5OVt8pRlFlac8SIEWEJTyIxIcosHpLFxPKhNB7wUGSp4ZBFViqTCIiACDRlAnlF2VfeKjdRpk+cdaZZWhOrf+bMmbZ06dJM3kcsY8JQsjb2l19+mckyqlAiIAIiIAKlIdDkRBm39VNPPWX9+/cPwSewmK+66qogzIRSjC+HWRrEua/CiGviPt9+++1GQ0JJBERABESgsgnkFWUCJvTu3busVt7CRX3LLbdY8+bNw6hm4jAfccQRdtRRR2Uq4MPBgwdtwYIFNnv2bLmsK/v/oGovAiIgAlUEUkWZAA70J69evdo6duwYxIMRwuUSWpB51VieuIbpr8U1PGfOnDAXODnAqopECX9greNa79y5s73wwgth+pmPxM5C+UqIQpcSAREQARGIEaglygjGkiVLrH379oaVecwxx1izZs3CfOVyDC3IwCms0ccffzwzrmus+RkzZtjxxx8frHnmKrdt29ZmzZqVqbnUsedEP0VABERABEpAoJYoc00GR2FlMi0Ki5Nv/i7X0IKIYNZGNWPN45FwtvBllS8lERABERCByiWQKsqVi0M1FwEREAEREIHGIyBRbjz2urIIiIAIiIAI1CAgUa6BQ3+IgAiIgAiIQOMRkCg3HntdWQREQAREQARqEEgVZablMACJaTp8iBjF4K9yTY0ZuvGrr74KA7rSpjox+MzDN8JbSQREQAREoLIJpIoyAsx60W3atAmhBVu3bm0ETCjHuMqNEboRfp9++qlt3rzZevXqZVOnTq3VqGFE+GOPPRYYM/WMta+ZU50m3pX9iKr2IiACIlA5BHKK8qZNm8JiIUzVWbNmjXXq1MmWL19eVqLRWKEb33rrLevevXsQ5AEDBtjkyZNrNGgQXliyFOj27dtt37594RiiRLFAi5IIiIAIiEBlEkgVZVDELTYCJQwePNgWLVpUNotbsAhKY4VuJF4yc5D3799vEydODIIbn4PMvptvvjmslOaPHcE/LrnkEtuyZYtv0rcIiIAIiECFEcgpynEOa9eutX79+tkbb7wR35zp31kI3YiLetKkSbVEedeuXSFO9apVq6oYIuBYyitXrqzRIKo6QD9EQAREQASaPIGCovzuu+/auHHj7MEHH8xsPOLkXcpK6Ebc51jEuK/jlvLOnTvt3HPPtbgo48IeOXJkiPuctdXHknz1twiIgAiIQHEI5BVl+kYHDhwYxCNLIQ/zoaCcWQndmEuUGdFO9K14nGosZUQZoY53HeSrq/aJgAiIgAg0LQI5RXn37t0h1OH69evLqsa4jLMSuhFxZeT19OnTazAkbCPb582bV7V927ZtoQG0devWqm36IQIiIAIiUFkEUkX5wIEDIdzh8OHDDbFgLi2fuAs2y5gaO3QjU6KY3sR0LKY6YQHDkWlS7EOsCYvJyOyNGzca/d833HCDjR8/3pjXrCQCIiACIlCZBFJFGfHo27dvjdCCHTp0sA0bNpQdpcYI3YgYn3322WGO93HHHWd8CM/YrVu3MAUKiLjZmRZFyEb2jR071hjlriQCIiACIlC5BFJFGcGgP5SVsJinzIcVp8p1AFKpQzcyJYrBZnCDoXNkG/s8YTF76MZyXJjF66FvERABERCBhiGQKsoNk7VyEQEREAEREAEROBQCEuVDoaVjRUAEREAERKCIBCTKRYSrrEVABERABETgUAhIlA+Flo4VAREQAREQgSISyCnKDPTysIKEbmRubbkkBlAxyMpDT/LNvGuCPcQHWmWlPo0ZWjIrDFQOERABERABs5yi/OKLL9pJJ50UpusQWpB5tOUizIy2njlzZlXoSaYcHXPMMWG96c8//zxT970xQktmCoAKIwIiIAIiUEUgpygz/cmn6xC68fTTT6+xLGRVDhn9wUInPp2LdaXvuOOOsNIXHoCsJMrCuuJHHnlkWN0LC19JBERABESgcgnkFOU4EtZlHjp0aIirXI7CgRv+zjvvzFSsYuaCN1Zoyfi91W8REAEREIHsEMgpyt4vS5jBZcuWhRCE5bgEJEuG3nbbbYY7PkspC6Els8RDZREBERABEcjTp0y/LGLGMpC9evWy119/vWxCN/qNxRrdtGmT/elPfworlPn2xv7OSmjJxuag64uACIiACNQkkNNS5jDvlyVoQvfu3e25554rq7CCe/futYkTJ9qbb75Zs9aN+BcNhayElmxEDLq0CIiACIhACoG8ouzHE9lo9OjRNnnyZCuXNZoZqLZixQq7/fbbMxXdKkuhJf3+6lsEREAERCAbBHKK8vfff181T/mJJ56wzp072+LFizM5zzcNJZGurrnmGtuyZUva7kbd1tihJRu18rq4CIiACIhATgKpooyLdcmSJda+ffswT7ldu3a2cOHCsooShct6zJgxIa5xztpnYEdjhJbMQLVVBBEQAREQgRQCqaLMcbisfZ4vK04h1OWUWLkLi7QcpnCVOrRkOd1HlVUEREAEKolATlGuJAiqqwiIgAiIgAhkgYBEOQt3QWUQAREQAREQAcszT1l0REAEREAEREAESktAlnJpeetqIiACIiACIpCTQEFRZsDUl19+WVarefkSoY0ZupGFVzz0Jd8MlounQvvjx+q3CIiACIhAZRAoKMqPPvqodezYMSyzWS5IshC6cdWqVcZUMsJGtmzZ0oYPH27xsJGF9pcLa5VTBERABESg4QjkFeX33nvPunbtaqecckpZiTJ4fIlQpnWVOnQj3oWdO3caljrX5/uKK64IQT18RbRc+ym3kgiIgAiIQGUSyCnKrOg1derUsHb0gAEDbOvWrWVLqDFCNybnR8+dO9dGjRoVRBqQhfaXLWwVXAREQARE4LAJpIqyB0249tprQ5SlIUOGZHK5yrrUOguhGz/55JNgKT/wwAOpq6IV2l+XeuoYERABERCB8ieQKsrvvvtuWKKS788++8wuv/xye+edd8qutjQuGjt0I+7ru+++22jgMGAumQrtTx6vv0VABERABJougVqijNv6hhtuCK7r999/39avX299+vSxZ555xthXTqmxQzcy4nrKlCkhUhUxlJOp0P7k8fpbBERABESgaROoJcpEV+rbt681b948jBo+/vjj7Wc/+5m1atXKHnnkkVp9oVnF09ihGw8ePGgLFiyw2bNnp7qsC+3PKleVSwREQAREoHgEaokyLt8ffvghzKvFtUrow379+tnatWtDkIriFaVhc27M0I0w9HCXL7zwgn300UdVI7GpJYO8cu1PDgBrWCrKTQREQAREIMsEaolysrD0KY8bN8527NiR3JXpvxszdCPzpGfMmGF4GZijzFzltm3b2qxZs4IgM2Uq1372KYmACIiACFQmgYKijOVGGEesv3JKiFtjhm7k2vTBf/PNN1UhMONzkAvtLyfWKqsIiIAIiEDDECgoyg1zGeUiAiIgAiIgAiJQiIBEuRAh7RcBERABERCBEhGQKJcItC4jAiIgAiIgAoUISJQLEdJ+ERABERABESgRgZyizECpL774IkzlIaAC60fHByqVqHz1ugzl9fCJu3fvtsYY2fzVV1+FAV+5pjoxEMxDTLLYSWOUsV6QdbIIiIAIiECDEcgpynv27LEePXpYs2bNwpSeDh062IYNGxrswsXOiBHj8+bNszZt2oTy8/3UU0+VRPS4NvOkN2/ebL169Qqro7EtmWA8dOjQUD6mTQ0aNMgQZiUREAEREIHKJJBTlHft2hWiGrH+NatPsSQkq2SVS2LN64EDBxrlZ4nLJ598MqxUhsVc7PTWW29Z9+7dgyATYWvy5Mlhelb8usRWJsbyLbfcEthi1bNoS7lNPYvXSb9FQAREQATqRyCvKA8bNsxefvnl1EAK9btscc/GVbx69Wrr3bu3bd++PVxs48aNdvPNN5ekLrigmaO8f//+EPoSUY67/infihUr7IILLrA33njDsJhZpEVJBERABESgsgnkFGVfA7tFixbWqVMnW7duXVlZyizOceONN9opp5wSLFWCarDKVykTK3tNmjQpXD8uyriy77vvPrvyyitDGVlXHPf1448/XhL3eikZ6FoiIAIiIAJ1J5BTlLHmcKcyEAnX73/913+FPtK6Z924RyKIBIM4+uij7ZhjjrGzzjorWM25BlwVo7TwwzpPWso0GK6++uoQ9AOLmTXGly1bZp07d7ZXXnmlGEVRniIgAiIgAmVAIKcox8uOlYfFh3WH2GU94T5evnx5ED4sfuIYI4wMumKkc6lSLlFm+0033WSXXXZZEGTKQxnpLli0aFFZeSRKxVLXEQEREIFKIFAnUUZExo4da9OmTas1YCmLkGhEYKFef/31VcUjNvTZZ59tq1atqtpW7B9Y5VOnTrXp06fXuBQNGxo4Q4YMMaZMkfbt2xdEmahSpbTmaxRMf4iACIiACDQqgZyiTL/nJ598EizLu+++29q3b28rV65s1MLW9eKMEl+4cKH17NkzTOPCOsZy7t+/v23durWu2Rz2cc7unXfesSuuuMJGjhxp27ZtC9OkfGrUa6+9Zl27drWlS5caI92XLFliF198sZVidPhhV0wnioAIiIAIFJVATlFGULAsGYDUrl27IGrlNF0H637KlClVoROpCyOdS1GHOLvjjjvO+MCxW7duVaPBsYZfffVV69KlS9hHnzflk5Vc1OddmYuACIhApgnkFGX6ZZnfyyAk5iiXY8Ji9tCJBw4cKFkVkuzgB0d4si+eKBf7Slm++PX1WwREQAREIDsEcopydoqokoiACIiACIhAZRCQKFfGfVYtRUAEREAEyoCARLkMbpKKKAIiIAIiUBkEJMqVcZ9VSxEQAREQgTIgUFCUWZPZQwsyIKmcRgcz0MtDNxKGsjFSvtCN8fIxIE1JBERABESgsgnkFGVGCa9Zs8Y6duwYpuwQ+nDWrFm1Rg9nFR8LdDz22GMhdCPrd59//vm2c+fOkhSXuciFQjcmy8d8ZuaFl1OjpyQwdREREAERqCACOUWZ2MlnnnlmWDCEFbII38iazeWQmIv87LPPGmETiRLFalksEzpixIjwu9h1KBS6EeH1xUy8fCwDOmrUKGssi77YTJS/CIiACApsAUMAACAASURBVIhAYQKposzCG4gEQsYKU4QWLKe5yjQgWDhkwYIFVQRYZpMVvkqxKhlehnyhG9nHMqDJ8l1yySW2ZcuWqjLrhwiIgAiIQGURSBVlLEuWhiTKEjF/CS3Yo0eP4P4tB/cqjQrW6WZ5UE/vvvtuWD1r/vz5JXPB46JOC93Ispq40+PrcBN7GUuZRkM5MHau+hYBERABEWg4AqmizOAoQjX27t07DPJCpCdOnFgy9299q4eoIXgXXnihvfTSS2GwFw0MlrtElEslejQO0kI30rd97rnn1hBlbwgtXrxYUaLq+wDofBEQAREoUwKposxoa0TjnnvuqarW5s2bg7XM+szlkBjZTCSmli1b2kknnWQzZ860wYMH2+rVqxtdlOFLg+f555+vQomljHeCxkSpGg1VF9cPERABERCBTBBIFWUEgkFRf/rTn6oK+fLLL9ugQYPso48+qtpWDj8YnEYfM2LXp08fe/PNN0tWbMQ1LXQj5WH7vHnzqspCFKmBAweWJIpV1UX1QwREQAREIFMEUkUZK3PRokXWt29fQ4x37NhhEyZMCNYmI7HLITEtiTnWDFJDiGlk3HrrrVaK8hcK3YhYY7EzOnzjxo2B7w033GDjx4+viq9cDoxVRhEQAREQgYYlkCrKXIJBSrh/W7duHeYpX3XVVSGaUcNevni5xcMnUocZM2aURJCpUfzauUI3Mm2LaVFt27YNfMeOHWtffvll8YAoZxEQAREQgcwTyCnKlJypPR76sFzmKDtxyu6hJ6kDjYxSpfi1mUqWK3QjFnO58i0VS11HBERABCqJQF5RriQQqqsIiIAIiIAINDYBiXJj3wFdXwREQAREQAT+SUCirEdBBERABERABDJCoGJE+aefzL79lgFsGSFfwmKUQ53//nez774z++tfSwhGlxIBERCBjBGoGFH+8EOzYcPMtm6N7gAzu9j2wQfRN4Ld1BJCt2uX2dSpZitWmH3+OYP3slnLffvMxo83W7vW7B//qFnGtDJTNwarc//47N5txjYlERABEShnAhUjyqx5MnCgmcd7eOEFs3btzE44waxlS7PhwyPRKuebGS87ArVxo1nHjmZHHGF2zDFmgwaZ7d0bPyo7vxHYESPMVq2qKcoEJlu+3OyTT2qWlfDTY8aYtWoV3cPWrc2eeMLsxx9rHqe/REAERKCcCNQSZayUAwfM6rtGCC9HrBe3ZBADt3j45qXKC5ftHPPFF+nYPvus2hLy8+NH5ttP/ljDfN5+2+zSS81YJZR83n8/yvfrr6PvK64wmzQpf72pExZ10pKLlyf5G+vUGXz6abV7ljJ43dm/Z0+1oJD/Dz+YHTwYMaL8NCq+/75m7jD0vDkmvp+8sTwff9xs5sxIoNkftybznc/95zngHK7B9dMid+7ff/j74Um+5P/ee5HIrl5dky/bu3UzY3G5uOBSLu4lYs49fOyxqAGyeXNNRvpLBERABMqJQC1Rpk8Pi+OBB8x27swvUrkqyssTMWjbNrJCsUTjVhoCcuGFZnPmmF1wQWTt9OwZvZw9T/LAldmpU7Ul9PTT1aJSaD8CMnt2dG779mYTJpj171/tvk4K69y5ZqNGRS94L4N/0yeLcDz7rBkrY9alweLlO+20qAxY5IjL9u1Rrgg05WF7ixbRh/wpF4K9aJHZ5ZebEX3yxBPNjj02ajSQLwmxx7LEQiSPk04ye+SRakFDLKnzM89EnGmEeOIauc7nGK6/eHHUiJkxI7qPXIO6+/UR93feMTv//Oj63OND2U8+y5ZF57ZpY3bllWZ/+IPZmjXVdYA7gb66djUbMKC2tez14Zv7c/bZZnhAlERABESgXAnUEmUqgqDdd5/ZySebIVaIqL+M61JRXpCILMKCRYOI8XErjfw6dzbr3Tt6mdKfePPNNS3Vl182O+us6CWL1fjcc2b9+kXHIyq59pM3+zn+oovMtm2LhHb+fDME8rXXatcA1yiWMg2ReD3JB0v8oYfMfvMbMwQK65HthRLlw3WMqOAFwJpjIBOCR8IKRhjhwr4bb4xEFuuVRIPll780mzbNDD64cP/7vyNLG46INQ0bLEnyIL/4gC7qQX1+/3uzc84xe+WVKF/+ZV++88mfBs3RR0fPAeV78UWzPn3MXn01yoe+6r59o2tw7U2bogYW++GTaz/82c/xlH/Dhqj+NAQRdu6bJ56joUOja992mxnxO/wZ8mP8+9FHo/uN5a0kAiIgAuVKIFWUqQwveKwtBgmdckpk1fDCQyALJURo9Giza66JRANhi79Msdp69IisOM8L0cHqwo3LsVhdvLTpA2YbonD66VGfI9Y8Ype2H0sJ4Zwyxezeez33yF0d71P2PQgOeV17beQKZTv5I9T0b1LO664z+/jjmu5bONAAQDj8Q70RHLfw6KfOt3Im4ojFzPm33BL1a1Me6k/ZsZQRbk9x0cWqptH01FNRWdMGqtEAQOwQVzwV1MkbFPnO5zo0Yrg+DQIS9aA+NLTYj3iecYbZunXR/Xn99ajRhNjn279wYdSA4JvuAq8T9WQgXtzS5Zm4/fao0UEckZtuip5J58E3rBB6rGzO9frFj9FvERABESgXAjlF2SuAMPPipT8WEeB3XRIvW16WXbpEFhAWt/dJIspY0rgqPTE6+NxzI/FDTLAcjzwycm+6ixc35/r10Ysc12yu/ZQZyxcXrDcGEPbLLqse6MV1ETLEmxc/Vqynt96KrEssQ1zoNCqSiUYCLmPKxoeyId6INaI+eXL0iVve8TywLrGGOY/zEc4hQ6JyuCjj0kWk0xLi89VXZtOnR3nQeMBq9vr6OZx/ww1R3a+6qrrvPtf5bOfe0ShgIJVb7jBFlBFd6sd+BpDhencGuNKXLo0s8Vz78TrQaEKQ8QKQF4m6jBtX7b6mEcQz4EG94Iq1TCPEhZzzuD80ONxCj3LTvyIgAiJQngRyijKiQZ8y4vfb30buU17QuUQmXv24MPACRUhxo3r4YCxDRARrzdOdd5r16hVZji5K9CPiBsU9i6Bz7bho5NqPgNA/jLXniUFBHO+jr3nJIzC4aZN1olGASOOCppxjx0biELdGOQceiJ5/yNPLh/ufhgyWcDLBBE8A7nisbUTqjjvMRo6sdo8javydZmlzDT6euD73iYaIH+9ucvKmQYQly8AvOOQ7nzrB3y1lxJLEs4Ab3F383Eu6F8iX+8OHenneNLJy7edYGi2Irj8rNNQuuSQSZYSagVuzZlU35CgD10K4Ycp16J+n4UAjSkkEREAEmgKBWqLMyw5BYrQrljHiwoua7XVJvGQZdY2VjPhi8WBd45rGqiHhBmfQEy9lXsaItvdBu5hwPiJAXyFWLh/cr25t59uPOOBm9XnJO3ZEgnXqqZEoU5cnn4z6tXF5+ghgxC1ZT/rEGWBGnzJCguglj0njgoDQb451yvVhgQC7gNE/zT7fTt5YfFi7iBIudcqfNiqd/VjqHOtlR9QRXRdReGFlMhoZS5lBYIg2g7Pgwz1JO9/vNY2G3/3OjNHQ3CNGcNNdwL31e3jxxZHgUwa/PzTmSNQ113740TXAmIKXXooaXpQRb4EPdqOMfOKJ8+DHM4b3gkYP3GgwcD3E2i3v+Hn6LQIiIALlQqCWKPNSox+SlzgWp4vkoVQIywUryd2ajB7GlewWKUKECGOB+ght3LDxFyovYFySZ55ZnU+HDtHAIMpSaD8Ci7XM4CHEnTrRz411RTnowz3++Gg/5aQcWGZp9UUEEDsECrFCGAolyocg+uhxrhEvP+KGyLH91782u//+aKAUQsO+hx82u/76dPc1nCi/u76pIwJIA4jrkmjAMLq7WTOz//W/ovrRAKEuXv9c57MfLwIDucgDtzT9x1jZnrgOQsjoeb/P8RHg+faTB4LLeAXc33BZsiQag8DAL6+DXyvtm2cMts2bV9/D+Oj2tHO0TQREQASyTqCWKFNgXphp4lTXyvDix8J0t667RP1871NmsBDHsN8F24/xb9yv8XySx+Xbj1XNuZSFMlEvvknsYzvX9vwLiS1iwXl1EQ0vP5aj55+sJ+5u9vl2rk99XDgR31zX8rp53uQVT5xHXrj/sXLffbdmXvnOh5O7z7GAuQZ5pSWvg9cj3rDi+Hz7uQ5158Pzxr31+5N2rfg2joctXQrOID66PX6sfouACIhAuRBIFeViFx53I1aN9zEX+3qVnn9dhc45IZbuPkcwlURABERABEpDoFFEGbcng3roF1XKHgFEGXdyLvd59kqsEomACIhA0yDQKKLsbuD6uMibBv7s1gJXcj73eXZLrpKJgAiIQPkSaBRRLl9cUclpVNAfTT9rrj5fryNWJyO26cOtT8IFTZ9pss/W80REGeiV7Fv2/Y39Xaj8jV2+hr4+95sR4vW97w1dLuUnAiKQbQIS5cO4PwgfC18wranQ4DCWk2SO9sqVh3Gh2Cn5QhvSOHjwQbP/83+qp53FTs3Ez2T5aaz4amaMxk+b+pWJgh9mIRipzhz3u+6qnqZ2mFnpNBEQgQoiIFE+jJuNKLNYB+t1J0eDJ7NjXvB559VfLLG200IbIsisJMbUJdb5LlSeZPlK9beX35fRZA61T3djWhQLxzAAsJDnoVTlre916JrBc8HiJswPJ2qXkgiIgAgUIlBLlHkp8qIvZAEWypj9uFp9cQu3hnwkMPkz4MsXz/BFLzgvXgamvXAcc3eTrlmsK873PAqVGcFiCs2hvPhZk9mvgWWHhUc5fJlID79I+eJ95Li3OY/pX+yLuzHj9SMv54BwxRPldX4s9MELPh7aEJYsbELgDfYnEyOnvexcgzJ5ghX32cvJdeJl9OOYq04eh7M/rfwsrQonWFJmpjPBeOLESLwa2mKmjs6XenA9T9Td91E/ugc8cZ/hRx24xxzH/fHnl+Pg5feHvCl7fD/HUE/uG4vwkKeSCIiACOQjUEuUEVIW2qhP6EYuiJiyGAcLW7C4hK8NzUuShLiwqAj7WOACa5IXGwnBYDGRq6+uXh+a0IUsaIGg8eGFzopQnn98YY4ol+p/eRmSN6tF1XXxD17GrECWDL3IdSkfa2azNjWrkrVqFZXDQ0tSPkYvsyAJi1uwyAXxnD15/ViBC0Zw4DhWPeMlT0K4CoU2RMxYECU5tYzrsy8ttKPn3dihGSljPLGmNfWnAdNQCQEm0Ig/I75AjAsnDAjryTMIfwJweMOIFeNYPIVjWOSF/d27R4uyUD4EGdc0ecaf73jDx+tBhC5Wa/Pn27frWwREQASSBGqJMgfwwqlP6EZEEPFjmUmW0PTFHbAKeRnzUsSq4AWIQLHKFv1vRA4icT5rHCPELJeJwLDiFUtuci7lY3lFRJHBNORBIwAhjSeuxf6GDr1Iw4UGA25X1nimfsSPJoAFi3WQOIbtrLXNmtssu+mJ8iPIRx0V9U1zHC9sltWkntSD1cwQg3yhDVkxjJd9Usg4nwYM53sjAj5uqcEwC6EZnQdCxvSruvTR+zmFvqkvDSbuCV0I/gzyrJBo9LAwinuFeE5pgLH8Jwn+//f/RsLMPSUPyshKaSTuJ3PtWQCH55p8ua/JxgbHcm9xz9d3XEF0Zf0rAiLQlAmkijIV5gWO6/JwQjfi7iMOLqtCpb2kHCgvRF6MCBfLRNIQ4HhebrgzeQm6Zc057h520cFyQbR4UfJi9IQgsq1YoRe5FuVjvWZ/yePexNpPWq3Uj6UzPRAGZfTzqZ+7TKkTfdQMIGMb4pwW2tBFg3w83KVb115/vrMampHyx58J+NGgoXEBw4ZK9E/T0INRvoSLmnvEWuD0cfv9Q5S5nxs3Vp/N/wkvO2Wl24DlThFdGn+5EoPc6Fem0cl9VhIBERCBXARyirKfgDDzwkJY6hq6EcsCywAXdVrixcaLkFCAuH5xDWI1IsokRNkFCoFNS1h7WND01+Eax8rkb/JmXWTWuy5W6EVElfLhYnfrkzojAv5S9zLzwh48uLYoJ+vneSLK9K/jCUgLbegDpcgfKx3OaaIMB/JJhnZkO2WmwQQ7X7GL+1yK0IyUnzKQuM+sHEbjhHvXkAkvAqLqoR+TefP8MDAOl7l3ofzrv9a0lNm3dWvyzOq/KT/Wr68hTrePPw/VR0UxqWHrXpD4Pv0WAREQgTiBnKKM+w9r43BCN/KCxzIgclGaZeCWNO5KLGFEZeDAqB+bwrkoI0xuicYL7S91tvEbqxjLHMuSc7GosTaLFXrRLV0sZbfeiXaEKMeDNlC+fKIcr5+LMiKKqxVBTgttGLeU6ffEwkyKMkzijMjPQzsiwghSY4Vm9PLDDWsejwPly5UQObo6cHHH65TreN9OI4mgJ4SATEs82+xHKHnGEG+CqPgqc1jK7I93O8TzgaEnzkeQcX9zT5KJZwCBjzeoksfobxEQARGAQC1R5sWHoB1u6EYy5YWFJcYAG/o2eSnxQYx5GeP6YxANljEWM9YIVq0LEi85ROSmm9JFmfLhVuTFSn8q/aYM6Ln11kiU47eWl3kxQi8yDQlLDBEm2AN9zIRijI8ipxyIA675+MsdAUZw4/XzbVjQ/GaUcq7Qhl4/3KL0vTNIKp7wLuQK7Ygoc48bKzQjfbAk7l+XLlE0Mp4Hno+00ctYvP/5n5FnIpfXJF53/03jjMF4zBHn/vsz6NegOwHRhR3PIIP6OnaMRJpnhvnl9BkjzsnEM8w4CO4pZed8QoHyDKdZ1vRXX3ZZ7b7/ZL76WwREQARqiTIvPlr99QndCFYsHFyThP3DPYhAY9UhJIg2rlcfuYqVjIgTsxjrmpci33/8Y22RJW+EBTHC9U3eXCMeSzh5W7keYtkQoRexvnnhM1CKRgAvYspBCEPmpSYTjQbcxLjUPdHouP32mvWLb8O7wKdQaEPEFXc5ZYhb6NzDfKEdybuxQjMixogai53QbeHPho/O597HE+JJXGe8EociyuSBtwfviY++9muwnTLQx8uzw/3j+SGa1r/9m9nSpZHg0mUTv2/xcvEs+cht6vCrX0XPV9yC5nimxOE1YjZDmtconqd+i4AIiEAtUQYJgspLq74J0YiH1sNV7S+t+D5/SWIBI07sowx80hL7sSZ9RC3fCGWhxHkcx3ddE2Xz69AY8Bcr33yoE/spT1riWoiJ19uPSatfcht/c00+3A+ul8wHXliENApoMHg5qKeXO1k+8qURNHJkZOWxPz6gzsvId5wz5UgKY779+crPPgSYPL2c5JW8N+ynAcQIes451ASzXNeAqe+DI3/zDPr9gmGSt18/mW9y/jvnMmKbaWk0IJMeFM9H3yIgAiIQJ5AqyvED9Dv7BBAIvBu//W3dVg5D3MolNCPzu+kaSE77yvpdwXOBexxr3BtKWS+zyicCItD4BCTKjX8PGqQEWJhYawh0oYQol0toRqxXrNhyS5QbTwDfSiIgAiJQVwIS5bqSamLHId64aZPu4iZWTVVHBERABMqKQFFEGeuAfsJk/2MxyXDNfJYi1hYLPJSyTLnqixDCh5HASiIgAiIgAiLgBIoiykwDYuBRrsVD/OIN+c3UFBYQSZuSwnKeDBZiHnF8DeqGvP6h5MUgIEZfsyIUU2/q4nI+lPx1rAiIgAiIQHkSKIooM3+YJTBLudavL0ASnw/MLcE6RqyZQ8x80iz08WEpY7kzOItAGixwITdyef4HUqlFQAREoCEJ1BJlxIHpMfUZXMPcTFYwwlJmKggLNyCaTC+KJyzG+MIR8X2IJ4uN+KIPCGrSovTzyQMLOLnGNK5qRhmPHZt7GUeWd/RrEGbP3dv5rh9nRJ24PqODk6NscU973hyTZMp0GxadwKsAMyUREAEREIHKJlBLlBElLLj6hG5EiJgOgsuYubAs3sBCESypiJCSEDP2s3CDB7nnPE8IZP/+0eIS7OfDsow+b5R8OJ+FG1jEgRXAOD6+iAZudFZSSlrPXAOBTwvNyEpNpFzXR5C5NquPNURoSYScBS6YOuMNgqgE+lcEREAERKDSCNQSZQAgOvUJ3YjVSAxhxBgXNoOaWDbS14ZGEFl7meUnsaBZsAGRQ5zc2kS0sWKxLjmfZSlZiIHFHkgs10ifLEEF2E9+8bWHEU9WDRs1qjpGbnRm9C8LbbCsIpY0Fi15UA53b+e6vg8mYwWo+oaWpCSwYHUtVn3SwK/4HdJvERABEag8AqmiDAbmsh5u6EZcsQgwIfkQRxLii/WMS5ulNgkgwTKSbGd5ynvuidYa5m9PCBYWKxY0y0YSaQdRRqgJ5sCqVJ64Jst1eohEzvVVq5KrKfniGeTnQe09n/h3rutj0dYntGT8GvAhvCBscHEriYAIiIAIVC6BnKLsSA4ndCMCichgHXtim/czY0l37Wp29NHV6xLjnqZv1ePSYqnOmVMdFo9jL788WpABIcWyXLy42p1NnzOu6rgoY+1jKSdFGWscUeeD8KaltOsPGRJdH1FOhl5M5oGbHUs/HlqSunkjxY/nb6x+2MQbJL5f3yIgAiIgApVDIKcoI0qHG7rRRTkeWzguygg9rujbbqu2fHGZe38xlixRjPr1i6xHBp4RoIL+acqFq5nfuKw90Zc8YEC1KCN2hAnkuKRbmPwRbAaGYYknU6Hrsx9RjodejOcRF15+J0NLxo/FXY5Ff+WVtRsP8eP0WwREQAREoOkTqCXKiAh9q/UJ3YiYE/aOPl1Pvg2hRtSwci+8MJqni5XLB/FEpHBPE2uZaUy4rnHrIoBEmSJMI5bqokXV85J37Igs51NPrRnPFssT65r+42Ri8FfnztE1ON+vw7XzXZ960DBoqNCSnhdRk+CiJAIiIAIiULkEaokyglff0I2I6CWXVAeMB29yG25jrFUP3+ihHZkGRUJQEW1GV//611GcW9zb5IulzYArrEvOO+ecqMyjR9dcHASRI4g9ru5kfy2ND2L1MiCNa/BhzvCGDdH1cbGnXR8XOQKO5c4HCz+Z6PeuS2hJykfjggFrXE9JBERABESgsgnUEmVwIBY+Cvlw8CB4iFU8j7Rt7PfQebikcVO7C5vr0vfLdo5BxLFgOYa8SFyD/Sz8z3mUO34+x2B9069L3zLTnWh0xJO7w+PX8f1p1+d4vxbXS0uUz88lXz5x8WY/25h2RgxpBnopiYAIiIAIiECqKDc1LAgp/bYMPsvSMpuMFsd17o2MpsZd9REBERABETg0AhUhyiDBKs9KKD1EGKsfa1pJBERABERABJxAxYiyV1jfIiACIiACIpBVAhUjyj/99JN9++239rdcHcFZvUN1LNff//53++677+yvyU7zep4Pt6+//tp+kFlfR5I6TAREQAQOn0DFiPKHH35ow4YNs63/jO345z//2dj2wQcfhG8Eu5zTvn37bPz48bZ27Vr7x2F0Uuc6f9euXdajRw+77777DIFWEgEREAERKB6BihHljz76yAYOHGhb/rnk1wsvvGDt2rWzE044wVq2bGnDhw+3z1mCq0zTl19+aSNGjLBVq1YdlijnOn/v3r02ePBgW7JkySGL8o8//mgHGWWnJAIiIAIiUCcCRRNlXsi7d+8OlijWKC93t7T4/uabb+wvf/lL2M7+L5LLbv2z+J999lnIg7z8/HjN8u0nf6xhPm+//bZdeuml9sYbb4R83n///ZAvrlmuf8UVV9ikSZMMC7quiTJzLh+u4ediqR44cCD8TT3Zfzjlpxz79+8P59OooD7xBGO2k/97771nY8aMsdWrV9dZlPOdj7uaOmEpwypNXL1uXv/vGUkXprL9YDt37rTbbrstMKchxDHwws2uJAIiIAIikE6gKKLMy/7xxx+3tm3bBisUS3TQoEFBgCkGL/sLL7zQ5syZYxdccIG1atXKevbsGV7cXkzywBXbqVOnYM22bt3ann766aqXeqH9CNjs2bPDue3bt7cJEyZY//79q9zXSRfv3LlzbdSoUaH/1MuQ65tzEcHevXuH+mFtd+jQwTb8c+URrj1jxgwbN26c3XjjjaF+MDiU8iNe77zzjp1//vlV1vy8efOMepP4XrZsWdjXpk0bu/LKK+0Pf/iDrVmzpk6inOt8mJPWrVtnJ510krVo0SKU37ezj/rjVcAy575Qf4597LHHwj7OPfHEE+0Xv/iF/cu//Is1a9bMKON1111nLtzhIvpHBERABESgBoGiiDJWESK7aNGi8BLGguTjVhKi3Llz5yBqHEt/5s0331zDUn355ZftrLPOMtzMWG3PPfec9evXLwg3opBrP3mzn+Mvuugi27ZtWxDa+fPn22mnnWavvfZaDQD88cknnwRL+YEHHqgSvVoHxTZQl8mTJwchxFLnbyxJF0xE+aqrrrLjjz/eVqxYEa5PI6VPnz4Fyw8PEhZq3759jTKR96ZNm0IDhvJTP/6mYUNDAGv/iSeeCA0E6l2X9Morr+Q9n7pgCb/77rs2ZMiQGm5x9i1YsCCcT+PE6++D6NiPhX/nnXeGc6mLDxaj7EoiIAIiIALpBIoiyrgpR48ebddcc02wKBEuF2SKgTuUwUPPPPNMVamWL18erMI9e/aEY7EKER1cn2x78cUX7fTTTw/iwAjju+++O3U/Io7reMqUKXZvLLYj14z3KfuFEQvyuvbaa41+1bokF6Xu3bsHUUTU46OT+T1x4kS74YYbgmCRJ40F6ox7OV/5n3/++SC6zz77rJ1xxhnBYqX+r7/+emiULFy4MIg037jbXQixXBnIVpc+Za7PwK26nP/VV18Ftzhc44JK+U4++WR76qmnQqMmbaAcZcSCZ1S4kgiIgAiIQGECRRFlLotYYNV16dIlWHC4h71PFIHEksbV6gmL8txzz7WPP/449Pni9j3yyCODaxT3KG5UXKDr168PeeOOzrUfK40+4sWLF1c1BhC2yy67rGqgF9dFSBDv22+//ZCFg0YGQkg/LuVCEGl8kBBlLP/p06dXiSbWIiKOaCKKucqP6xfxo0FxxBFHhHpTfz64ih966KHQ6EBQp02bVjUFCvHEWwW7rQAAIABJREFUXZ4UT+cb//ZGQ13OZywAwprMlzJyTepI/WlwYDV744v7j3di5MiRdW7sxMuo3yIgAiJQiQSKIsr+YgYoL2eE9Pe//71hBZJw0fISx9ryhKuzV69e9umnn4YXO6I0YMCA4MbFPYqgY6EiBuSZbz+iTP8wouCJAV7k56OvESZcsPQ7k++hpLjFyG8s5aFDhwbLk8FoiC6WMg0LH5yGmxkG9M1SfizVXPUjTxopuO+xkKk/H85jH79xn5O/s6ahc8kll9TJUj6U89NEmTLw8YS3gUYGDSH3NngduQ94LpREQAREQAQKE2hwUUYkGGmMlYz4Yvli/TFgyQcLMWK4W7duQVQQE0Tb+6BdxDj/nHPOsUcffTS4r7F0ET+3tvPtRxDoz/Z5yTt27AiCceqppwZRRlCefPLJ0K+NBegjmBGXuNjkwoc7duPGjWGEMXXFQsT9feuttwaB5/oI5nnnnReuR7/s1VdfbWPHjg3WJfnmKz/7cXdffPHFhoeB8nn96V+mjFjcDDR76aWXQsMFV/nRRx9do6GTq/yHcj7eAMTW3erkSaOD7gTq7ezuuOOOME8a65nEc4Crnqlm/izER+DnKpu2i4AIiEAlE2hwUQbmW2+9Faw8d7syEhdXslukiDUijEj5CG3coLzsPSEcr776qp155plVLuz4COdC+xFYrDRGPSPuDISin3v79u2hHLfccksYiMV+ykk5Zs2aVWXZejnSvhkAhauYUePuVmbhDhckLFHcy/Rh0xjB7cwoc4TVU6Hysx9h5jznyAjnRx55JGSB8E+dOjW4t+HCPGL68LHIObdQonFTl/PxOtDAoOHk+XKf4IfbmrLBkAYEDTA/hutzD3Dve/njI/ALlU/7RUAERKASCRRFlLGSmPrCS5kPIuZuViB7nzIjhX2/C3byJuD6jOeTPC7ffoSHcykL10fIvBzsYztl8/wR07okhAf3t5/Ht1vwnO99yggzg97Yz7a0lK/8npdfh7LGGy7Uh2188DDAxuuXdq3ktrqcT125pnswPA9n62XLVb/4cdT1UMrn19K3CIiACFQKgaKIciF4LCyB+9r7mAsdX277ESisS/qVk42IcquLyisCIiACIlA6Ao0iyrhlGZREv2RTTFjcjOj+4x//WMOCbop1VZ1EQAREQAQajkCjiDIuUdyaSZdow1Wr8XPCNcxHSQREQAREQATqSqBRRLmuhdNxIiACIiACIlBJBPKKMv2hLIgRH8RUbnAYEc2Arvio4HgdGCTFaHA+5TplhwFUfHLVMV7fUv3GS8Ccc2fLgDclERABERCB/ARSRZl5uMw/ffDBB8O0l5UrV+bPJWN7GS2MIGzevDksSMLUn/ioZS8uU5RY9KOcp+xQT6Ya5aqj17XU38St9ulsrMbGwjAM8MtSw6HUTHQ9ERABEShEoJYoM2WFIAisa8w8W9ZfZqGKckrMk2ZJS4SAVbNY/Spp7bMoBgtbMN+W5TYZnMWo6XKasoMng7nVv/zlL8PiJVnpw4YhC72wuAhTpmDNSHQWIZHFXE7/k1RWERCBUhOoJcoUAKsSty4xiFn8otymLjGADJc1C18gBohyfA4y1hrLWLIwB8tvYjH7utWlvgH1uR6rihEjmpCILL4Sr2N98m2Ic5MWMYEreJZYAU1JBERABEQgnUCqKPuhTF1i5a1yE2UvP5YjC3gkRZlGB2tPE2iB+cS+MhfhFctlRDiWKOEhWYCFaEzEb86SKPs94JsG0vXXX5/pMsbLq98iIAIi0FgE8oqyr7xVrqKMO5poTUlRxpXNWtTNmzevine8bNmysBY2cYaznhBfhPiuu+4KXg0iR7HudhYbFJSVxg793jTylERABERABHITqEhRRqxvuummEMqRPk8S0Y0IYEEgi6yvwkUQClzBLL5CwwlBxmrGNZwlYabxQ6xqrGT6lZVEQAREQATyE8gryvS1EomoXFfeol+TUcn0t8YTbm3c10OGDKkKIrFv374gysm4wfHzsvCbQVQE92BEswfTIK70L37xizAwj2ldWUgwJjQnffre8MlCuVQGERABEcgygVRRpg8QVyOh9zp27BjiDjPflJGz5TA6mT5j+lzfeeedMOJ35MiRtm3btjBNyqdGEU6wa9eutnTp0hD6kChLRDoqh4FICB73iMF4iDCxqBnslaX7Q7SqLl26hKhWPEvl9Pxk+T+syiYCItC0CdQSZUQXgWrfvn2wxo455hhr1qxZmK/Mix8xyHpCjM8+++ww//i4444zPsxFJggGoRtJWNGEhkQ42HfWWWeFkdjJUcNZryvle+yxx2zOnDmZcbu7NU+fPWyx6AnzWC7PTzncc5VRBESgaRKoJcpUE2sSlyOWGAuJ8M3f9MWWg2jRr0q5KTNzkPnwm23JPlcPnch3uSb6wLGes5Ti1jzsy+n5yRJHlUUERKCyCKSKcmUhUG1FQAREQAREIBsEJMrZuA8qhQiIgAiIgAiYRFkPgQiIgAiIgAhkhIBEOSM3QsUQAREQAREQgVRRZjAXg7s87B4Ro3wqUbkhU+jG4t4xH0iXNlWO58jDN7IOuZIIiIAIiEB+AqmijABPmzYtTGNhSkvr1q1t7ty5tSIt5c+68fZSfoVuLB5/RnuzQhcBS4i0xRrijK6OJ45Zt26dderUKUyLImIXc8XTxDt+nn6LgAiIQCUTyCnKmzZtCotR8LJds2ZNeLkuX768LKZEKXRjcR9pFmYhrCfxki+//PKwQAseiXji+bnwwguNtcRZ1OSee+6xfv36af3rOCT9FgEREIEEgVRR5pj4fGTWhR48eHBYFzo5zzeRXyb+pIwscqLQjcW5HVi7zOtGiBFbVkzjGfHEmtfz58+3KVOmVHV7sPIYxxGbO/5s+Tn6FgEREAERsLqNvl67dm2wcog9XE6JBSwUurF4dwxxZQ3xpCjTGGIbkay8EcfCM4j0ggULMrfQSfEIKWcREAERODQCOS1lz+bdd9+1cePG2YMPPlh2L1OFbvS7WJxvGj333ntvLVHGKibi1v3331+19Cf3ghCajFXIatzn4lBSriIgAiJQdwJ5RZm+WfoOcTmW4wCdXKLMdoVurPtDkuvIXKKMpTx69OhgFSctZYSa85REQAREQARqE8gpykRLIoDA+vXra59VJltwryp0Y3FvFvGnx4wZE/qY/UqMfsd1PWHChKo+ZQaHDR06NAwaVJ+yk9K3CIiACNQkkCrKDOK59tprw3QXprEQeo9PubgdEQWFbqx5oxvyL58SRdcGjR5GWRMKM/6MvP766zZgwAB7/vnnbefOnXbHHXeE0Jgff/xxQxZFeYmACIhAkyKQKsrM8e3bt68df/zxIewec5U7dOhgxMgth6TQjcW9S5999lkQWJ4LnpFjjz02hPmMPyNYwwj1GWecEeYpI9wsQiMrubj3RrmLgAiUN4FUUab/mH5XD3nIXGVW+MJCKodEP6ZCNxbvTvmUKJ4LnhFnnfaMMDWN43ielERABERABPITSBXl/KdorwiIgAiIgAiIQDEISJSLQVV5ioAIiIAIiMBhEJAoHwY0nSICIiACIiACxSAgUS4GVeUpAiIgAiIgAodBIKcoMzCHKS6Eb2TU7MGDBw8j+2ycwmCktIFGDFzzOhI0oZwSA6i87H6P9u3bl8mFOZhix0cjr8vpCVNZRUAEGoNATlF+8cUX7aSTTgrTWVq0aGE33HBDWQoz06POOeecGuswA5pVpR577LEQnpL6nX/++WE+bWPchEO9JqOflyxZYu3btw/3h6lJxx13nP3ud78z6pulxPS6QYMGhfnMzB9XEgEREAERyE0gpyhjRTLFxUM3nn766WEhiNxZZW8P1jHrdh955JE2b968KksNUXv22WfD4hbbt283LEwCV4wYMSL8zl5NapcIgePe8CFC09KlS+2qq64KYRJrH904W3iGZs2aZb/85S/DYjRaXrNx7oOuKgIiUD4EcopyvAqsZVxuSyS68F5xxRUhOMLixYur5lnjiveIRV7P999/33r27GkrV670TWXzzf357//+b2Np1CyljRs32qWXXhqWa50+fXrZrAiXJYYqiwiIQGURyCnK9P9hhe3atcuWLVsWLMlkIPsso9qxY0ewfFm7m8hEiLJbaljQbLv77rurqsCSkV26dAlxgD2IQtXODP/AYmb96Ycffriq0ZGF4rLMKZb7c889F7oOZsyYIVHOwo1RGURABDJNIKcoI2C33XabtW3b1nr16mWsZeyilukamYUVpm699VZ74oknQkCEmTNnBveul5sGB5GvWPrxpZdeCgOmZs+eHfpl58+fX+Xm9uOz/P3ee+/ZH//4x0y5rVkjnYAUd911V+D/0EMPGfejnBo7Wb7nKpsIiEDTJZBTlKkyL1esZdyQ3bt3D1ZP1kfQ4rZ+6qmnrH///mHtZSxmLDaE+fPPP68KQUl/53333RfW9mZAG/sHDx5sq1evLhtRZgQ2FujatWszVWYaOgycY7Ag3QIIMvcA97qEuem+TFQzERCB+hPIK8qePS5S4uMSpP4vf/mLb87kN9b8LbfcYs2bNw+Cy8jqI444wo466qjQt4mQxRP1oY8Zy7lPnz725ptvxndn+vemTZtCtwIDvbKSaBTRVQD3li1bhtHhDLT7xS9+EWJz7927NytFVTlEQAREIHMEcopyfB4sbuDOnTuHl205WDoIrQdJoG+TMJRz5swJwRPc0qehQbSjPXv2BCFm5DUWXbmEp2TuNSPGaUwghFlKNIx4fhi9jwjfeeedoUHEXPCslTVL3FQWERABEUgVZV6c8Xmw7dq1C32EuHzLLVFm+osff/zxGoIQD+/YunXr4AYuF0HmHhCXeMyYMfb2229n/pYwH5xGUTk+P5mHqwKKgAg0KQKpokwN4/NgscrK2cLBcksKAha/W9NYdOUyiM2fPu4HHoFyuC+wLze+zlnfIiACIlBKAjlFuZSF0LVEQAREQAREQATMJMp6CkRABERABEQgIwQkyhm5ESqGCIiACIiACEiU9QyIgAiIgAiIQEYIFBRlBkQxD7acB+oodGPDP208D0SAImwkn2Toy0L7G75EylEEREAEyp9AQVF+9NFHrWPHjmGZzXKsrkI3Fueubd261c4888yq0J4sxbpz586qlcUK7S9OqZSrCIiACJQ3gbyizLrKXbt2tVNOOaUsRVmhG4vzcDINi0VZeD5YhpXlSydOnGhE5PLVxXLtT1rUxSmhchUBERCB8iSQU5RZkWnq1KnhZTtgwADD8imnhHAQM1mhG4tz13xlNM+d9cZZ79rDRxba7+fpWwREQAREoJpAqigjaLxkWZ6S9ZWHDBliW7ZsqT6rDH4pdGPpbhINuOuvvz7nqmiF9peupLqSCIiACGSbQKooE1uYJRz5Zn3oyy+/3OibLZfESl0K3Viau8XSpCxhOmjQoBACM3nVQvuTx+tvERABEahkArVEGavmhhtuCK5rwu6tX78+RE965plnQpCBrMNyK1+hG4t/p1jm8+677w5WMv3KyVRof/J4/S0CIiAClU6gligzzaVv375VoQ+PP/54+9nPfmatWrWyRx55pGp0bVbBMRVHoRuLf3fgTJ89A7wY7JVMhfYnj9ffIiACIiACKctsYmkyapm5vbxs6Uvu16+frV27NgSpKAdoCt1Y/Lu0YcMG69KlS2ioffjhh1VzlXl+SIX2F7+EuoIIiIAIlB+BWpZysgr0KY8bN84YOFWOSaEbG/6uIbyLFy8O3pQTTjjBWrZsaW3atAkxkw8ePBgumGs/3SNKIiACIiAC6QQKijJTWwjj6BZQejbZ3oorVaEbG/YewRSBJewlHhU+eFh8KlSh/Q1bGuUmAiIgAk2DQEFRbhrVVC1EQAREQAREIPsEJMrZv0cqoQiIgAiIQIUQkChXyI1WNUVABERABLJPQKKc/XukEoqACIiACFQIgZyiTMhGggd4aD6mvbA6U7klBiJ5Hfbu3WvUK5lyhXZMHpe1vxm89tFHH4X68c1UsFIn2PHJNRCQwWDOnznwDBpUEgEREAERSCeQU5T37NljPXr0sGbNmoXwfB06dAhzT9OzyeZW6jB06NBQfqbusBQkwhxPuUI7xo/J4m9EkPXJmYpE3Vq3bm3z5s0riejRGGAFr7ffftuGDx9uV155ZeoCIozIZnU4ysenW7dutn379iziVJlEQAREIBMEcoryrl27bNSoUWH9a+aeYg0lpxVlogY5CoFoIBis7kXZsfKZshO36HKFdsyRZaY2swQqjYyXX37ZWOub5VCJaVyKaF6EZRw4cGCIp8y66ETi+uqrr2rwwUK+6aab7Kqrrgrrp8Of5yjNU1HjRP0hAiIgAhVMIK8oDxs2LLz0PUZuuXBiruyKFSvsggsusDfeeMOwmFkEJZ4Q51yhHePHZfU3K6316dMnrLRGGbdt22bjx4+3jz/+uOhFht2BAweCEN9zzz02cuTIqjjKfvFXX301LNf64osvhjLhuo43iPw4fYuACIiACFQTyCnKvgZ2ixYtrFOnTrZu3bqysZTpt7zvvvuCW/XGG28M63bjPiWakVtq+UI7VuPJ7i/qcdddd9mRRx4ZLFJiGbMUaikTjR84J0UZ8aVR9Ic//CGEc2zXrp3xHM2dO7ck7vVSMtC1REAERKAhCeQUZV64uHcZKPXkk0/af/3Xf9nmzZsb8tpFy4sBT1dffXVYBhJxoG9z2bJl1rlzZ8OCw91722232RNPPBFEYubMmbZ06dKilacYGSN81Omoo46yY445xv793//dsEq90VGMaybzZNWue++9N1WUEWDKhWjDn7LBH++Er/qVzE9/i4AIiEClE8gpynEw9AdOmjQpvGB5EWc90ZigP/Oyyy6rGoCEC54+5oULFwaL+eKLL7bXXnstrOlNvyfCTD90ObhYEbVXXnkl1Gfnzp2h4TR//vwQIGLTpk0lE71coszYA9zaZ599dhgdzvPCsRMmTAghQctxFH/Wn3mVTwREoGkQqJMoI3Jjx461adOmNcq0m0NFjQBgoQ0ZMqRqANK+fftsxIgRtnz5cps+fXpwpxJIAbfqEUccESzO6667rixiRiN61G/w4MGhbxc+DLRi4Nf9999fUmt50aJFNmbMmKpyUBYaDXgozj33XGPAIAnvBaLM8eXQsAuF1j8iIAIiUGICOUWZfllG2TLHlED27du3t5UrV5a4eId/Oazgrl27Brc0wrBkyZLQx7l79+4gELiwcatSx2uvvdbmzJkTRmmXi2v1+eefD+5gpkVxj+jz79+/v61Zs+bwodXxTJ8S9e677wbL98ILLwxeh/hcdn5TnjvvvNMYKU45+bsUo8PrWA0dJgIiIAKZI5BTlJm/i/uRAVIM1MHCLAfXrhNGXOk/JuYvdTjrrLPCSGzf798IzOzZs4NLu5zq59ayz1P+1a9+ZS+88EJJ7hEj2XH/w/X444+3Y489Nngc4nPZ4Y8wMwKe42jUrVq1qmSudb+/+hYBERCBciKQU5QZMOTWJPN8yzUxdQeLmO9cCXcqIlduiUaEh07ku1RWPtd1rjwb/pxQhiRHuj7gz3GlKl+53UeVVwREQAScQE5R9gP0LQIiIAIiIAIiUBoCEuXScNZVREAEREAERKAgAYlyQUQ6QAREQAREQARKQ0CiXBrOuooIiIAIiIAIFCRQUJQZaeuh9xiwU06DdVikghHAlJ+pUGmrXTG/l+AJ5VQvv6uUmeVQqV8y+pUfU+xvBnDxSRu5Hi/f/v37i10U5S8CIiACZU8gpygjYMx57dixY5jSwtSbWbNmpQpbFikwz5pQhj5liG/mylIv9iFmLBtKZKWpU6eW3ZrMjHJmbjLrkjPl6PTTT7eXXnqpJI0Lrl0odGOyfHAmaEaaeGfx+VGZREAERKAxCOQU5Q0bNoTQfCwY4mH3WJWpXBLLTRJekAUumLLD+t39+vULEaOYg81qUwjFgAEDbPLkyWWxUpmzxwIlZONFF10Ultv84osvwgIvffv2DQt1+HHF+q5L6Eb4s6gIy4FSPpbdhD+eCyUREAEREIF0AqmizNxShIr1rnH7EvoQF2W5JERr9erV1rt3b9u+fXso9saNG+3mm28OIQaxlnFZ41KdOHFiqGs5rceMpb9gwYJQbl+ykroQ15jtybnCDX3ffJ4yrv+00I003liLe8qUKVUeCNzrRJPSAiINfTeUnwiIQFMikCrKrBPNC5SVrliRqVWrVtajRw8j+AGCVw4JYSBs4ymnnBLEi9jDb775Zo2iI2g0PGiAlJsos8Y1QTdclBE9lrFkW6k8GjwLaaEbaSDw/BD8w/vxaegh0jQavMw1bob+EAEREAERsFRRxsVIqEYsTQYRIdJYlAR04Hc5JF78NCqOPvroEEKQZTaxmuONCoQC67ncRBn+rO2N+/qZZ54J9+jRRx8NoSppiBTbUvb7D+O00I00EIYNGxaCY3hZ3PtCUJNyagB5XfUtAiIgAqUgkCrKCDF9rrgmPTEoCmv5jTfe8E2Z/cY6Y61uYiozoIuwjQgvfcjUzVM5izKNCyIxtW7d2tq2bRssfkJQLl68uNFFGUt59OjRwSpOWspY+LKU/QnUtwiIgAjUJJAqyrxUsYr/9Kc/VR3NwCJCA3700UdV27L6A0sMC/j666+vKiKRigiwQZ+mJ4SNkdeEcizXhKuaxsWWLVuCZ6MUUaLirNJCN9LnjeuaUI38JjE4bOjQoWFEf9xbEc9Lv0VABESg0gmkijIuR162jOZFjHfs2BFesDNnziwL1yPlRxR69uxpjCLHOsZy9tCBCAUiwShsBkfR/8l0HaxqF5GsPxjUka4EBuHR148Ajhs3LngFil12rs2UqHyhG19//fUwsp0Qk5TvjjvuCJGlPv7442IXT/mLgAiIQNkSSBVlaoOLkUE8uEeZB4trlMVDyiVhPTKwqGXLlqH8WMnE8mXkcDws5XHHHWd8qGO3bt2qRmtnvZ7x8InUcezYsSURZLjEr50vdCP93meccUZgy/QovCyykrP+ZKl8IiACjUkgpyhTKPoDPTRgqUb0NiQMLDovfzx0I/XycIO+IhUNDrZ5H2hDlqMYefm0JMrNp5T3J35t+DnLtNCNTD2jfDSSlERABERABPITyCvK+U/VXhEQAREQAREQgYYkIFFuSJrKSwREQAREQATqQUCiXA94OlUEREAEREAEGpJAUUT5xx/NPv/cjCnBzKD66iuzv/+9fsX+858ZYGT2zxk2tTL76SczVgLl2vH0t7+ZffmlWRkt2x0vfoP9hv933+Xm12AXKlJGLCRHt/SBA2aNsahcY1+/SFgPO1v+n+3da/b994edhU4UARFIIVAUUUY8L77Y7IQTzI491qxHD7NPP025eh03IaqzZ0f5vP12+knEORg2zGzr1pr7N20y+/3vzVaurLm90v5iIbbx483Wrm0cUasvbxpjDzxgdtttUQMLkeS5oOHHZ/fu+jf88pUxfn0aiJWemNk2eLDZxIlRA7zSeaj+ItBQBIoiylhlWDRYyA8/bHbuuWaHGxwIgUdsr7rKbM8eRoSnVx2LfOBAs9dfr7n/nXfMzjsvEqOaewr/hXXdVCxsBGzECDPWTmkMS7Mw7cJHLFhgNnlyZO1joY0ZY9aqVdT4a93a7IknantKCuda9yP8+ociyngnmmLi/zjP1JQpZn36mO3c2RRrqTqJQOkJ1BJlXtgI6qG8ePIVGxE4//yaoozV4aK9f39k4SDg/CePu7k57u67zcaOTW+NI5iIPR8s6EsvNfNVQHlpY0G9/35kRaWJ6xdfVFta5OF15lyCS40bZ3b11dFv8mKa9qEIWq7844y/+aba0ktrcMCHa9PoSKtDvv24GDmP8997LxKx1aur60A5qDOftHp52TgfPnFXJedwD53z4ZSP5yZf+eP7scwQRRZf49q4srnXPDPcl8ceM+vY0Wzz5nxP46Hv8/Ilr09O1B0ufn9cgLmPdN/QSOje3eyZZ6Jj8BbxTHvKdb7vr8s3+fk9phw8c/5/CE5ePr75P+aJMnJ/eUZgyH7c0fHuH37jgSBfPuxPPqPUGWGmsXTwoOeubxEQgcMlUEuU+U/OywRXIa1f/mMfbuJFv3x5TVFm25o1ketr1Cizs84yW7bMbPRos759oxeMX2/XLrPLLqtt/bIfgcKljYu8fXuzCRPM+vc3IxAUL6WlS83atjVr3tysU6dItD1fyoBI9e5t1rJllEeHDmYbN0bi9NBDZu3amf3859GnRYsor1mzar+UPM/4d678N2yIjqLsM2ZEon/jjZG1Rzmefrr6hUodsPJp0FBH9s+bV/3SLLSfFypcObdNG7MrrzT7wx8i9pSPRDnghDcjLhbsR1SwrLFAyeOkk8weeSQ6jxfz4sVRI4h6wJljDqV8hcpPGd59N+qyoAxYYzfcEHFLeyYRjbPPNnvhhaiM9f033/UZp0CCAc8edec5u/zyqIGAeNF9c/TRZv/zf5odc0x0TLduUQOPc8k/7XxEta4JEeSZ9GeY+3zddVFjiTxogJ14YnTtZs0ijxHiS+L7wgvN5s+Png3q8P/+X3X3D8/P449H95b8+QwaFAlzlEP1vzR8cWW/9lr1Nv0SARE4PAK1RJlseFnfd5/ZySebzZ0b/QeOt6DreilePElR5lxEn//kWDfXXBMJM9YEIoBge1qxwgzhpiUfT+T73HNmF11ktm1b9CLk5XLaadUCjshgQW3ZYjZgQPV28uGlTsseocI9zt+84LyOnMv2a6+NXnL8Jq80MYiXy3/DL1/+7Mcdf/zxZtSRvHkBIjyIC4kGCY0UGkeUjb7xCy4we/XV6IWeaz8vRvhwPC9dGgLk78zhFk9LlkSiHN8GB6xSzqfx4nxcjBBUGkSIDs8J+b/4YlR+ykfKVb5C5ffz6QOnoUb+WHg7dkSNKPow4w0IL/ejj0bPA1ZjQySENdf1nQPdKe5wJEVZAAAgAElEQVRVWr8+ev6efz5qWLEdL9E550RdJzDCqnRLk+9c51P+uBXtlirfiCn3gzLQCOrc2Yxrkz8fPAjcf+4RAs//HY7H88PYjvvvj+hg+dNYxbtAlw9588z6mA2u07On2aJFUVnIgw/5JhPXGDkyulf+fyh5jP4WARGoG4FUUeZU/tPjups61eyUUyKrixfeoSzMlCbKbGPQFQLMSwlR4EXBb9zUHi+C/9z33hv9Z4+73SgbLzxeIOz3RGudPmVEOJ7IF7d2fLuLDq5FROuTT2rXi2Nwy02bVv0ijeeLaPAig4m/aNnPixcRRdRy5Q9DxAXLjxcdiZcgL00sPRg9+6zZGWeYrVsXseHF2a9flC/3BnFN279wYXR9vidNiu4j+WP50jdP/pSP6/Givv12sz/+MRI9f+FzPNenUfbUUxEfRrZ74vo0grAMPZInL+bhw6OXeL7ywaXQfnjSuMCyp0wkthG0jOfRmbEdkfBjnV10Rv3+xQ1el+vjAuYZ4z6deWbNAYXcs169Iu9NrtIkz3dRp5Hxb/8WWbpYu/757W+jBhfcuZ/8H+B5yZX4v0L5eP6x3mnkcLw/b9xfTzzzLrrcTxolNJppmNEw9X1+vH/zPN98c/S8xe+N79e3CIhA3QnkFGXPAmHmhYOw8ZLmd10T//mTljLbePHQX0sfG+5T3KIIIwN33P3IC4IXCJZyUpR5IV1xReT+8xcFLx5c3XHxpZwIJ6615HbOQ6i4Jm4/XnC8ePwF58KJ+MdfNOxHzLBqcfnh5uYFynV4CXM8+8kfayuZP2Xylxj9owgUCcsSEafBggDxsj3iCDNc51yHD25ceMEm135c77yIEWQaFG5VwpDR17g0GYH9q19FeR95pBkfroML313s1JNzKCN8aDDwcmY7Zeb61A1RIfGcIMqILtfMVb5C5Wc/7PAgIGj0aZLYRgPO+5SjrVFdcKu6Be7b6/NNHWn05Ls+5cFL490LuIf/9V+jZ9uv/corETeei2TKdz7XRxR5ZmlwxD/0o2NF87xQPu5nWiIP/k9wTxgMh3v9qKOi/1McjyhjCec6n2O4z1y7S5fIs4XXDC9PMtHIo5FJIzb+fyV5nP4WAREoTCCnKPMfjT5l+mppnSOuPjCkcLbREbwYcokyVjFCzIsWIeEFEhdlcsBqxi2W7GdDABBrrDVPvKxwUyfFN02UKZcnflOOoUMjIfOXDsKJsGGZxRPb77orajzgLkSAcfO6QOPuc6HlvGT+CC6ixUuM/mT+JiGGTN3Cfc85iDP97bzQedHxIV/28UG0cu3nWBoH5M/Ln4Qn4ZJLIqbkw72kUUJ0TgbT0YDw++vXiM6M3KI8BzSEOIY83VL2BhPPCq5a3O00Gmh45SpfofKzn/ozah43K4nncebM6MXPPeIY2PPMvPWWl7T2N3Xl+UHIOKcuKd/1eR7Ik+cKUcPLA2/GMlBfGjyesLY5xl3Cvp1veKWd741eGqq//nXUp0ufvX/+9/82Q+ypE96mO+6o7naJ58/95Jmmz59GGh4dPEncH5KLMvcpLflzwz7qi4s819RCysKzwbPPvVcSARE4fAK1RJkXEi5YXtZYxlirvIjr+kKLF4VzcINiAfISI7HN+4oRYga7PPhg9PJFgHlJ+LV4keAiffnleK7RS4IXANYt85Lpb+SlcOqpUcs+fjQWBW67uLVC/RjUxYsRSwwLkP7jW2+ttgQQTl5gDJzBIqL8vHx4Wblw+3UQDAQaTogsApArf15avOQQTESHRgQDmhjlTUPFRY6XJuXGOoEDrGg8cC1Svv3wo0HDQLaXXoqsKlzl9AFzPzxRF+pIw8iZs4+600cMF65N3Xn5Y2n7s0B/5u9+F1laCD6CSePELVvOO9zyUwauSR8695nfjD/AmsedSrnxluC9ofHBfYRHcnQz+SCM//mfkXuVetU15bo+zwn3GBFGVHH/cm8QY/pn6Tbg/pNgQBk5hvwoIwJO4r7nOp/nk7J6P3H8G/48QzDAG8HYDLwT5M8HMaZ8XIsxCfz/pXz8P8G7g0iTH/8vGHjG/8VkIm/uI1YyedIworGAVyDe6PDzaBzhjUquEeD79S0CIlB3ArVEmZcBg4JoqWORuiVX9yyrj+RF7yOt3eJhGy98LEXcxQxwevLJ6PdNN0UtchcIxAtLBMHlJRNPiBfWMi8lLDTKTB9Y0irh5ZK0pnix4T6Pz3FFcFwQ/Tq8zBAGrGCORaD9hevHpH0Xyp8XM1Y4lgsvOtzSXIfreYIBdfbrUwYfAc2+fPvJA3ZYde6WRngRNCxy58u9xvXOKG2O98T2W26J3NZcF8YILPeQcxEFhICXPiPeKT/92z4djXzylY99+fb7+VjL5I37HFc8fcpz5kSCRUOJgUq4ZSkf5YyPbva6IH40HmiUUK+6JsqXdn0GuMEK4UKAKR/PBs8PDRMasrAmkQfPr49Oj3cP8P8q1/m48OuSKAdeDsrg9wlXPg0Wyofg+rV51hDx3/wmGkNAQ4ruHv4vpiW8D1j+5MuHPm0a0ElLmHcE/y94XrzBkZaftomACNSNQC1R5jT+s9dHjOOXJh93N/p2tvGC5KXFf3L+5jfbktfFOkVUEWBa5PEXK/nS6kcoeQn5y9Kvw7fny35PbMMNHbdAktavHxs/jt+cWygVyp98fGAM9aMcbEtL8esj9vH6c3y+/fDgHD5wdQsrfh22JV+07He2zihePvLlBY9ng4YEx+AiTUv5ysfx+fbDkQFm5O/PkJeV+4nXwPdzDBZm8vmh7ggpXSSU+1BSvuuTD9cif66NIPG3W7l+nfgxHOvlr+v5nk+u73gZ/T74sx7fBysvH2XlGJiyLS2xn/9X5MmHsnu+HE89aPDi8aHbyD0kaXlpmwiIQN0JpIpy3U8vzZG8UBCBXP1zpSlFw10FIeJlhrcg/pJuuCsUNyfEDQuN7gNe1llOeE7oGpBoNOxdwmuCyxp3OA0RJREQgYYhUBaiTFVp0dNyz9WybxgcpckFS8WnIuWy0EtTksO7CqKMi/b66yMr6vByKc1ZPC9yqzY8a6xmvCOH6n1o+JIoRxFoWgTKRpSbFvboZVbOLzQsfO+CaGr3RvURAREQgcYiIFFuLPK6rgiIgAiIgAgkCEiUE0D0pwiIgAiIgAg0FgGJcmOR13VFQAREQAREIEFAopwAoj9FQAREQAREoLEISJQbi7yuKwIiIAIiIAIJAhLlBBD9KQIiIAIiIAKNRUCi3FjkdV0REAEREAERSBCQKCeA6E8REAEREAERaCwCEuXGIq/rioAIiIAIiECCgEQ5AUR/ioAIiIAIiEBjEZAoNxZ5XVcEREAEREAEEgQkygkg+lMEREAEREAEGouARLmxyOu6IiACIiACIpAgIFFOANGfIiACIiACItBYBCTKjUVe1xUBERABERCBBIGiiPKPP/5on3/+uX3wwQf20Ucf2VdffWV/Jyr6P1Oh/X6cvkVABERABESgkggURZQ/++wzu/jii+2EE06wY4891nr06GGffvppFddC+6sO1A8REAEREAERqCACeUV5//79wdrF4v3www/twIEDdUKDVcyxWMgPP/ywnXvuueF8P7nQfj9O3yIgAiIgAiJQSQRSRfmnn36yrVu3Ws+ePYO1i8V74okn2tKlS+0f//hH4PPnP/85CK0LNgKcllatWmXnn39+DVGOH5dvP9f6/vvv7YsvvrC//e1v8dP0WwREQAREQASaHIFUUX7ppZesS5cudu+99wZB/Prrr+3LL7+0PXv2VIny6tWrg1Aj2M2aNbPzzjsvWNVxQojq8uXLc4pyof1/+ctf7Prrr7f/+I//sNdffz2etX6LgAiIgAiIQJMjUEuUcTtPmDDBJk2aZD/88ENqhXE/Y70i1FjM27dvD/3GCxcuNKxsT4VEt9D+v/71rzZlyhQ744wz7M033/Rs9S0CIiACIiACTZJALVF+//33g9v6hRdeKFhhBBzrecuWLWFg13333WeMrPZUSHQL7Scf3NYIf3z0tuevbxEQAREQARFoSgRSRRlX9LPPPpuznogpYjx8+HBr1aqVNW/e3I466ihDlNnnqZDoFtrv+ehbBERABERABCqBQC1RxiU9YsQIGzRoUI1pTHEYe/futaFDh9qMGTPCKGvmIg8cONAeeOCB+GFBoOvTp4xoa6BXDaT6QwREQAREoAkTqCXK1HXz5s1hoNf48eNtx44dVdOiGPBFYnpU3759g2WMxcxI7T59+tj06dNDX7Nby3xjcXfv3r3WIDDyKbRfA70Cbv0jAiIgAiJQIQRSRZm679q1Kwgto6v5tG3b1mbNmhUGctG/u2LFirCNfVjJjNT+zW9+YzNnzqwa7IXorlmzxgYPHmwff/xxLaSF9mugVy1k2iACIiACItCECeQUZerM6GusY/8w4MoTgvrtt9+GfQcPHgxC/N1334VBWX4M34zGxuLl+LRUaL8GeqVR0zYREAEREIGmSCCvKDfFCqtOIiACIiACIpBVAhLlrN4ZlUsEREAERKDiCEiUK+6Wq8IiIAIiIAJZJZAqysUIrehLdcYXAeE6DABj/Ww+rBIW399Y0Oj/pry+rvcnn3xiDDrLcoIbffrJctJnT11yrc6W5TplqWw8EzBkwZxc4yOyVN5ilIVni2essf6P8iwzjiW+QFEx6lkJefIMcy8JsauULQKpotyQoRX5D8y0qpEjR9qNN95o33zzTSDA9qefftpatGgRRne3adPGrrvuujAvubERMbiMUeSUiXW9jzvuOGOt7yynffv2GVPY1q5dW0M0GEVP6EwWduGlpnR4BBAk5uHfdttttQYzHl6O5XUW/1/5P3D11VeH5XUpPf+XvUHNWgXJBmFD15CpmMOGDQtTMBs670rLj3fBkiVLrFu3bkZQIAbjKmWDQKoo8x8wX+jFuhad1ti6devszDPPtAULFgTBdSuDFu+oUaPCVCquxchu/lP7/rpeo1jHUR4szNdee826du1qK1euLNalGiRfX/SF/2Bxhiz0wpQ0/gMeqihjkTCyXikiwDM8efLkihRlCDz//POhcU1EOJ6ladOmhYYr0yJbt25tc+fOLerL3RcpOpTgNIiNBCf9fzDv2/Xr14f321133aVIfOmYSr41VZTjpUgLrcjNdNEm5jIizn9UhIHfnnCNYCHzMvNWNPtxUzN/mUATixcvDkt24iJOulhZzctb4ljvnhAdF3JEgxb07t27q87nhYHocz6Lm2D5cpz/Jh/O5+MCRvnSXHPk26tXr/BC8uvzzTUQPC8feZfarcb1eFFRhvfee8/GjBkTrBnqBEu4YCmznnmauMYtHY6FF4lzd/5/9u47yL6i2hf437dKX1lqXZ8kQTEgiHrr3VuW5b3e8sEDBAFRVEDJBkDMIBmUoKCCgiioBEVEclIURVEQEFCRjIgiopgFQcHcrz49dw09+7dPmF+aMzOrq87sM6d3p293r+9aq3v3vuuuahXuvvvu9WxzZazs5QXCNNrXLV+fqj/lydgRH16Ytp/aMWRMdMOo+HinuGUW49gBOe2jgd38uv+rpzLaedG9Z9D/+tf40zYf4y0UK1fthVGMQ/3TDeL1beA32/hYxjG+L7300rL33nvXea7866+/vo4J95jPm2yySX0rXMypbllL83/UXxtuvfXWEuNRXsPmIMy9KGe//far1r3vMFDXqN+w9EtT16VNQ05GH993331LyMqIMwbhEcE8Na8DI3NFu9swaI6391x99dVlxx13rIdGtb/n97lBYCgpG7zdYzL9FgeCsHS32GKLcuGFF9bJ6pQvA0OItPvuu2+duNE8gvHAAw+s52U/5jGPKauuump1X6+33nrlvPPOm54wCNLbqtZee+0av/nmmxevlDSRDELCkSvtxBNPrNr66quvXoWmcgzePffcsxx22GFl/fXXL8ccc0w54YQT6neHnhjMjgj1CWWh65qL+sYLOlgJbTB5dt5551o3Lngfp5ctjfBt8x33O4ENd1YKN/tee+1VXv/611f3tTx4KGCqXs4n59aOoG8IAsepsnDk4d4LLrig4i+t92c/8YlPLI997GOrC39lLy/oY9q7Q2uijZY3KFICa8kpcqwzY8M4cRZ7S0wE8Hve857pPppNPIzuvPPO6vqHkbLkZczMhpRvvPHGal1TWGcT9O9FF11U269tPo6+RcACkiJIjf8ddtih9rH3nxPgEQhs417/GweUy9nEU4Qdpys9bxcvQZCyMmAUQft4ZD73uc9NKw4Rt7RXY0D99f+zn/3sKg/MOZgK6tedg+QVGXHOOeeUddZZpzzucY+rH+1vD0CSfhLmsHm54YYb1jZqJ3cyBUIgKy1JxRzVhpNPPrm2TxthTc5RFs1XMtDb/YwdoW+OtzK23vQ/L/1x+JP+JRszzC0CsyZl1b344ourkCDEESxi/tKXvlSFPMIWCASvXfQ6xxgkfjeRxRGqW221VSUWwpNGFwTpapAgGsJHPKIgfExI8QarQWhgGnynn356Of/88yspSmNwK1+8yfipT32qTnBrxQTIEUccUT9RJvc0q74V6uo7iJS1Qbnh5rZejuR4DEYFZYZ1F1pwXFtNflg+1113XRXKtFxpok9YMwLMYYpY3vzmN9d1oxCi4kxkQp2FrQ3aw6qLtCzEj3/84zUta1sZJmzkUW9cgX8sG+hD7VGuOhLSUT4r7YUvfGFdAtEP8NN/1s4pRu7Xx8aJthDg++yzz8h4uBB40iAg+elT+yK22267cvjhh0+P03Gaz7qTpmvBjEqrPUjW+JVWe3xC6TPGX/rSl9Y6udeeAu0llOHkvlNOOaW+vY2iTMmlyI4Tb3zKg5DmojYnWMrwtabbN8aRy/bbbz9NmKPaNypeP+v7nXbaqdx+++11/J166qmVwIwNwbjozkF1VD9t0OcHHXRQ3aviuzEMwwhzPYevvfbasvHGG1eihbH66SfjT1A/Spg6GwOUVGOC7BAoZE9+8pNrH+l/CsnHPvax2nb9P2yO1wyaP+RfV2lrovPrSkRg1qRssuhABGSiWqtEiL4TevHKR4Ps7W9/e41rSTnaFm7hIPH43dVANPnlG0F+tHYkb+ASLocccsi05eS+GMyEFK3dGd7O5Xaf+p155pnl6KOPrqSMsAmdIGWWMCGsnDYMImX3aBdtW3nyZYmNYxERMjZf0W7bj2NKTbRoR1uP9rs6IwsYBJESTjDrrikTUNza+iUITV6s+uc///nli1/8Yp3kfa5dWFOM4D3boFy4IA8CPkIsO8T/g67SEchwlQ+h2gak7G1mlJMIhDbvTbjskDrlLnb4629jaFh8kA6l0fGx6iHoEyR31FFHzRDsUXZ7NS5CUYCvuYJItIPgbfuhTdd+Nw6NR0ovxUn7g5DdZ1waQ5ThCOHVIrSNQ1aU+iJlGKg/TMyFYfHu1W7tDwJUBpI0HrpzhOLHTWx+xXiMOi3t1TjRX/ovgjark1fFRhg2B8WZ5xSLQXNqWPooo++6rHMYTqzeUTLDWNH3+sR8NOZ9NxZgo4+NtQgt/uPM8UhnPhlPs1mvj7R5Xb4ILBUpIzCTECGdffbZ1e1MEMSapiqauIQRTd/A74ZhZMcy22abbWZsrpKfAUzbJ9hisgWptvkTftaeWNU+7kX06sKVTSB1SZlyMBtSpsWG65zbiRueRUrgjwo0fJYXgdd+CBsTbJTQlp71RdhE+5GgPumSr3YTpN3flSENPLmmTUjCPwS/yY3kWB7jKBrRZvkaF/o+3M40fH1CkHH/shCinEjXd0XmFED1kxdvQAgdQsQYufnmm2tSQpeQMka0y+8veMEL6s55rl/pXS2nsDooa33xMKSghOVAeRTUlwIKr9ba6qs3IbrrrrvWMnlzHv/4x9flmq77tC9t+5u2Gh+bbrpprXu7kSrmT6vUWpohtNVZH/BgGZfRfu5P3iZW1aB43hNj8Lvf/W4dE3CKcOWVV9Y53pLyLbfcUomSMjhOn0Zeo67qSKm35yTypUzsscce06RsHgybgzFPkHtfn831HFYvnz75CB9zm/cv3NerrbZaefnLXz6toBnv5jZ52BdGzfE2DeXW2GkVnjY+v688BJaalFnFiJigsn5jwoRFpvomBAFm4PQNuhAq3bVaabkOCXWkEIEgMCE9lsJyiwHdN9mClGl9BhmLkqBsSdlvSE0weM8999xqZbUCR1xfPQnLk046qbrrWBS0+o9+9KOVwMaxKpHT1ltvXd3qBHV8rH9bQxuk1QcW2qz9XOYhsNTzTW960xKWch8pa69PBJPa+j0hGASsjaxxlmes48b9w64EibVQypq8aPXGBVIkXFi+reU8KK9ol3jtRcjW3kKT5wWBoaugXHsMKFZIlXVIyLDeCF95uCfypfgNiocNUpa/+gvysPRBmRtVf2XADK7culzo+sf/feO1FtD5E/X0s76wS3aDDTaY3nBojFOkWEMRLDdwsSNcion+9AgXRVG56h35mmPD4oOUA2/pYo7EGEH+1vnVbVAwltVFn0TZg+5tf1c/Y6+VARTsXXbZpSpUZApLk8t80Bwkg8xz3oJumOs5HPOL8aC/usEY9MgohcwY13/w5742pwWydZDSPM4cb8uk0Nk3EXuC2rj8vnIRGEnKJn376kWdrQNNGERMkyX4CC8DpCVZ1hlNLgZR2zS7e7nS5NUNBizyRcxIlQDiOjMhrdEZoEjERq4+IUfg2vREsBAq7jXwrbEgYkKCxcwq4nojODfaaKNKauLa0FdPZbL4WF3qRiggSRtxbNLoU0LaPAkq5E1Idz997WnT+q4PWCYEsM1v2msTEquoFdLuZfUQvvpFOgE5sXpYxiahNlAqkEe0nwC1+Y3lyVpzj34cpTDIX/+1Alibop3iRgVlwFHfwdY4+8IXvlCFRljGLOWXvOQldaOX9iNta6zhvkdAFADjz5iRh496qBuBPSgeTtrLqqTI+W7/BEuTO3mcNkQbWZrt8/nx+7Cr+iG8wN3csqeCZ8BYFfSb+SNvhI8YYw1aeh91Z/nCKtpP6YSvMTosHilSvL0ZTv4s8pgjxq4+tV5rfFAy9ZNPd/z6TT2NQdiPG2CsfpYTYMizJI8XvehF1fsV/TtoDmqjcU6OUBzUUT9qP2zUcy7nMByMb2NWG7RP/QJD/aPuluGMef1HCYGlcWH8+h8+XUNC3uPM8egL9/JmmQ/d/ot78rryEBhJyiajgREWA4FFoHOfsj5ZRQSm70iScAjhHxOb9de1tgw+lp28+oLBgUDt/GRlcdvQlOVtQh577LHluOOO67VaCDQChWvNx70IhStIXaQ30D3CwXpDzrRQbQpSijoNqiehSOCp23Of+9y6/k2II2ZYrOigDSwARMHC5rFAGDZ+Bf7qoA8I7rZfTEITMNzC3Jvcrfq4TUuIhpWrne3u3xXdPgqBHbfK9XnOc55TlQQCVSCYKIsUN+3Qj/YgRLx7CC4YhfvPfe0BNYPijVU4sJallc5YtCYb42fc9ht/FMDuuBqVXjru52i/vQcU4FD4CHAkbJzztOhDnil9GwGx8XaIl4972j4cFU8hRhrdOcLqNZ88bfGUpzxl2j1uHBp/bTCmKBMswtmQsjxgRvlXb5tCKV48IbfddlstwjwfNAdjnRWZmZfaT5a0/T/Xc9gYYwWTQ9HPLYbar3/FeTqCq57ho9/JQh5KyngfruPOcXVgGMFInhnmHoGhpKx6NE4E0Arr0EL9Rkj433cDwfc20NJNKOth1olCaLpfvt3727SEhgHn0yV1cT59IeqiLB/3RV3bNFyS8iacxcd9bZ7D6imd9NyDcKBIqGe0sc1nRXxXX2X7wFEdumUHFl2cYR/YBgZ9dWzvW5lt0xbtijoiAm2JYFwRTtanow+6bXdvYBT5RF9HPsPiladcaWMOqNdsQjv+ZpvO2nfUGxZt+1iv2s+DFO3vq5t+b3Hs9uGoePfL39iGR8wR32EZ+Ayqg3QUXq5/32cbYvzBIrBscRhnDsY96tjt/4iDEfxW9hyGR8ihPgyj/eoHezIWFjHf/d/OixbfSCvfaHvES8PjYfw4HMkmsq6MiHvzunIRGEnKy6M6tFUumtm68ZZH2ZnHwkQAGXPlLdaNKbGs0i4XTWJPm/s8OO2GsUms52KrEwLmXbOvwiavVtFZbFhMWntXCilrNK2tq6VOGhhZn/mDADK2/MHNuxjDoGWVScOCsA8re9Lqttjrw8rmCckwWQisNFKerGZnbeY7AoQ9RW+xavjcj9qfLsf5PpKz/onATASSlGfikf8lAolAIpAIJAJzhkCS8pxBnwUnAolAIpAIJAIzEUhSnolH/pcIJAKJQCKQCMwZAknKcwZ9FpwIJAKJQCKQCMxEIEl5Jh75XyKQCCQCiUAiMGcIJCnPGfRZcCKQCCQCiUAiMBOBJOWZeOR/iUAikAgkAonAnCGQpDxn0GfBiUAikAgkAonATASSlGfikf8lAolAIpAIJAJzhkCS8pxBnwUnAolAIpAIJAIzEUhSnolH/pcIJAKJQCKQCMwZAknKcwZ9FpwIJAKJQCKQCMxEIEl5Jh75XyKQCCQCiUAiMGcIJCnPGfRZcCKQCCQCiUAiMBOBJOWZeOR/iUAikAgkAonAnCEwp6T8t7/9rdx7773l7rvvrp/f/va3M96P652xv/vd76bj77vvvuLF3G3461//Wu/xbtmlCcuafmnKzDSJQCKQCCQCiUAfAnNGyl5Of8kll5SnPvWp5WlPe1p55jOfWQ4++ODy0EMPTdcTWb/qVa8qa621Vr3nFa94Rbnjjjum4325/vrrywYbbFAuu+yyGb+P+8+yph+3nLwvEUgEEoFEIBEYhcBQUv7Nb34zbaX+5Cc/KX/84x9H5Td2/B/+8Ifyrne9q3z605+u+T7yyCPVCmYdRzj99NPrPSxk8T7IvA233XZbJe4rrrii/Xns78uaflhBrPelteCH5ZtxiV6iA1UAACAASURBVEAikAgkAgsTgV5S/vvf/15uuumm8prXvKZaqCzZddddt5x99tmlJc2WtO+///4lEEJIyJzFyzUtSO/7N7/5zbLZZpuVM844o/zsZz8rP//5z8vDDz9c72Ets4j32muv8u53v7vcdddd9Z4HHnhgunz3yPdHP/pR+elPfzqQ/H75y19OKxbInbtaGDf9L37xi5peGXARtIGCQklQ52gjV7u4qP9+++1XDjjggNoWdYVRix8c/O4jD/l1w5/+9Kfyq1/9amD7uvfn/4lAIpAIJALzF4FeUr7mmmvKpptuWq1YxIFMEA7yZKkiljvvvLNsu+22lbTXXnvtSqBIL0gHmZxwwgnl6U9/enVRu/eee+6pZHbggQdWl/RjHvOYsuqqq9Y81ltvvXLBBRdU4jvnnHPKOuusUx7/+MeXJzzhCTV9uLflK5x11lnlWc96Vs1nk002KbfeeuuMXrBezXrecMMNpxUL7m/1VkcKxjjp5U0pecYznlHd7dJSNj74wQ+W/fffvxx//PFVYeFi32677SrBnnvuubX+j3vc44oPF72y3IvY5fHDH/6w3g87+a+//vrl6quvntEGWJ944onlSU96UvnKV74yIy7/SQQSgUQgEVh4CCxByizAQw89tLzvfe+btly7zWZ9cj2feuqp5fe//321fH3fe++96/+I55RTTim77rprJeIHH3ywkpg8karPDTfcULbaaqty4YUXVtJnBccmLlcW6kEHHVTXmX2nGLBKg/Td47fvf//7ZZdddqn5tfW89tpry8Ybb1xOPvnkWj/3qkdYu+Ok32KLLcrll19ey7300kvL9ttvX61yhM8CXmWVVcoHPvCBWg/W7lvf+tZy2mmnVdJm3XbrH5aw6/vf//6qyGib/2Ei3zYgZbhSCL7+9a+3Ufk9EUgEEoFEYAEisAQpcwdzWyOjQeG6664rr371q8uNN944fYvfXvnKV1aS5Nbec889y1FHHVVJ2Q5rJI2EuYEFv7GeubH7AoI68sgjK+kFkfbdx3rffffda7kRz0WNjN/2trdVCz9+77sOSn/SSSeVHXfcsebrniuvvLJstNFG0yRNwTjkkEMq0ctXfY844ohKtuFNGFR/9yJveLGOW9d9t47uRdjDMOimyf8TgUQgEUgE5icCvaRsx/OXv/zl3haxVL/61a9WUrYOGuE73/lO3QWNzBHulltuWV3T4Z7lwt1hhx0KK1v48Y9/XMn/a1/7WmQx48oqDpILC3PGDf/zDwt1t912m0HK0rJEfZDasNCXnhX93ve+t7qNuZZ91J8L/aqrrqqWrbxZye4Vor7IWpl+P/zww2sd+uqPuGFhzVy+rGxWc3gChtU54xKBRCARSAQWJgJLkLK143e84x3ljW98Y7FG3BcQMEuZCzoCd/HWW29dbr755uoufvvb314+/OEPF25ppGQdFhFFCIt8RZAyS/kzn/lMtaAHtSHq0UfK0tsVzi1OeYj6I1ukGQSMmINw29+ClBE0b0E3tMTrO0v5LW95S10y6O7WZiXnRq8ugvl/IpAIJAILE4ElSFkzka6NXjYy/eAHP5jeIWxdVuCeFvfxj3+8kpbd0TYkWUNFwsj3c5/7XHX/eg6Y+9fHprFww0pj4xWruy8gOdbqYYcdNk18ffchTWvXrYLgPv+/9KUvLfvuu+90G/p2OA9K/73vfa+6288///zp+iNPpNlXt/Y3pK6dn//85+ua+O23314x1H4kbG3729/+dt1Vzp1v0xfsjjnmmJp/tBOOudEr0MhrIpAIJAILH4FeUtZsZPXa1752eudyu3tYvJ3U3NFcu3ZYI2mbviKEtSqde7ixWd+svkj/pje9qa7VRpr2ygI99thjy3HHHTeDqNp7fEdqXMC33HLLjCjkR7mI3dPq0LfDeVj67373u+VlL3vZNAbS8wggZvVq69bWN1zaFJEWI2vQyNsjUx6Xgpt62cjVxU9jkHJu9JrRrflPIpAIJAILGoGBpKzVCIR1HJ9w1QYibXzX7eoe1iLLOdLb2R0ubKQpTVjOkWd7Rew+w4J8kGDk272X+zfKV5fuGvOo9Orcl76vbn2/tRj5rjyf9nf59+GnLeqbG726vZr/JwKJQCKwMBEYSsoLs8nZqkQgEUgEEoFEYDIRSFKezH7JWiUCiUAikAgsQgSSlBdhp2eTE4FEIBFIBCYTgbFJ2bqnx6UGrd1OZvPmplbWjOFlt3WG+YFArPPbQ+B7hpWPgL0hnkxIGbPysc8SJweBkaRsgngs6p3vfGd9RMlmqQzDEbBpy87xnXbaqb5asru5bHjqjJ0LBBCCR9g8W9/d0DgX9VlsZZIz3/jGN+rxtZR/QT/Ey15cvVkuQyKw0BEYSsoshm9961v1sSDHQnqUx29IJt6+5PQuj0c5hIN1OBsrg1Y8nwPy7ds1DQMC5eKLL66PYXnRxmxwmc+YzOe6G+PtgTDzuS3zse4OEqL8Byk7792LaeKRSsfmxomA87F9WedEYBwEhpKyCWCSEFbx7K1MHQfpwA5vRvrXf/3X+mKG9jnmeBTK1bPLCBtxe7QHOXHrOkva2c+sE/F9B3tEWeI9TxyPT8mDmxHxyVNa8R4z6oZQHuThdK+2HePkzzPQLX/cVzPS/r/whS/UZ5WdYJZhJgKBbfQ/XCN4vEy8PnZoi3v6vDTSiPPps6RGxcfrRymXxrm3fylzMQTKtXkT+DlDIOZYzGFKp9/d07ccIz6s2aWJp8jL2zP9XvripTZByg4YEhf3OCXQKXmLpX8WwxjMNi6JwEBSRiheF+hErO5kE4cUnUTlABCvaCTcYrKYpA7N+OQnP1lJ3cEhCJwVYoIffPDBZc011yz/8i//Up785CdXTbh7sAeB4dWLcfhH36sTvanJiVfOjl599dWrUA2LNNJ3X93oPc1CxA/K/0Mf+lA94MOpYg750AbngRNW8WrJQa9mbGGmKBAkn/3sZ5dQCNr7FtN3fURZcpyrfmUJeXXneeedNw2DE9UcXvOJT3yibL755hV/llI7Fgnr97znPTW9s8lnE68OXuPpuFh1UJa89HuM4+nKLMAvxv9FF11UXylqbHcP9zGHvZDF/DKXzQEvqkGSESjE3dezziaecu94WXk7pId8aEk55nKUZyx4O51+z5AILFQEBpIy0vWWI2RiAvcFk8MkOeOMM2ZE076DDC+77LI6ibwNigAkbBGVVy52X93YluPkrGGvTnQCFiJ2nCcBf/rppxdHYkYeo17dOCp/J2495SlPqceAaicBRnA76Yy1TaAMejVjC4b6sMBo+S2htPcstu+BCaFPsUOCBHx7UIzjWV/4whfW8RXeDl4bZ5pTCqXxwhLjgEKoP/bZZ5+R8fqCYiUNApAfb459E96H7SUiXW/KQuwf5IlkzR/eBHj6wFZAyo6pjXeE//rXv654UzBZx+4b9HrWUfHwdQ8S9lIX84KlrH+9mKU9GTCw5y0xh3jWYo5HXF4TgYWEwEBSNglNAmQ3aBK4h7XTJW6uWgSMyELb5b72W7yAwiQc9OpGwnncVydSHiKE6036Ya9uHDd/lhNBJRBS6h/vNVbWoFczRn1ctZ/HgQCUR4YpBHgdnv/855cvfvGL1T3ddT0jZW8r80rQCI4cpQSG29vZ6V4cwvWMZAh5ltew+BD6zkZ/wxveMN0n+hPJeIFI9HmUuxCviJBScuCBB1bFiFIThKy9MYe/9KUvTTffON5mm21qf3Ex972eVZ+Y28Pi9Ze5AH9KegTu67322msJ5ZVSbD5TgsO1HWnymggsNAQGkjJLAuHSpJeGlJFQ+67k7luhCNGWpFtgadKzfXVim54lTkD79NV93PytL4b1xkK2Bk4wCQT3sFczRn2QMmFDmFFMMkwhABcWEYwtPxgLrOYgBqQMM28dE5AmAuails7vL3jBC8oaa6xRXa+xGchyC8vvpptu6o3nAaEA8OBQCnl1BOWeddZZi2pN2dhGil4+w33NPcyCFWK+tnPYy2MoSjCzP4Mna9VVV53GP17PSlkfFM87wvPhXHl9rp8i2GfiHPvWo6SvKL+eZpjvG0OjnXlNBIYhMJCUuRMJTIKwj9hkasLQts8888wZZcSEDqtYZPc3mjKhayJ2A2Ex21cntnlIP+zVjePkj3ApBmF9X3311dPvi1YWUh70asa2LkEmLIA+t1x772L5jpB9IrCEDj300OqZCYy8TMSrQF0FipQ9BMabcUfBQRDGnrGqP9wTpE6JGhSvbKQsf1abII+PfOQjlQCCmGrEAv0TOGme+eA94RtssMG0JyuU5va96t4Kx52NcPUTT9qg17NS6ofFBynH293U59xzz62ekLCG9YnlBuvWg2TQAu2ebNYiRmAgKZskl19+eXUnxZudAicThLZLYNoEgrztlCRchb7XMnZ/ow1bA2SJE6BImoUTgQZvzXncVydGuriOenXjsPwJeIRMqFv7tiEIIahvCAwE0PdqxlbYqQvBgnCQB+GXYYpgKWMsY+SKAD760Y/W9eEYQyzll7zkJdV6Mz48XmaN0/IBUkWcRx99dB2ft9566/TrNaXXB7wlg+KlV6axa/z57rE1lh537kLvJ/iwds0BbaeYePSRkmxzpaBfuKLNAwo10o41aOl9YMfy1Vdc1j7mNUWUjBgWj7TNp+OPP77mzyLfaKON6sbR2GUffU4OxTjRv61Cl/MpEVhoCAwkZQ2NiUNTbddurT95JIoQs9nKhqh4JMqEMcm7r2VEuu1v7mNJx3OIsfs2JpzrbF6d2O0Y6Ye9unFU/lxm1rwIKrtzCXBCpw3+9zvXqR2kdpW3igXhTjA5RCTcpG36xfqdQoMwua3D7Ww8GTfR/wjDcoElFPfpA/sbkEEExGsNOHZwu6/tg0HxxrJyWMvSSmfDkTVlY30xWMpedcr9DH+fddddt27YDIsUWSNhxGluc29TvvVdBOObR2rQ61lHxVN4KVr6wLICS5mHiicEsdsJT7YoWx1DxoT3KuqR10RgISEwlJQ1lBbMYrXeZK0otGSCjdZqncck8p2FKRB4BFs7efp+k1ekpR23Ez5AjnLk754QGia8z6jAUpW2mz7S9eVPmNvZyz1N85fWb33B75G/79rp4zeW9POe97zpdei+9Iv1N+MjcOvD17hDCnbJi9f3LSEHbsaAuMgr+mCceP0U4099/B/jK9Iv1CssKZCBWxffWG6yHyLw78PGHG/xN5/afhoVH/OP7IC//nQV9Ik6tvmHjFmo/ZLtSgRGkjKIWIQ20HBlmSALPRDs2kpr7xNEo9pPmNiYwtL2qE0ImVHpMv5RBJAx9ylrKsPKRyCWm9p9ISu/FlliIrD4EBiLlMGCaLpWyEKFizaOVI877rilcmUiYXnAK8PSIYCMLXdws2ZY+Qh0l5tWfg2yxERgcSIwNikvNnjGdY8vNlxWVnu5QCmCrSt0ZZWd5fQvQSUuiUAisOIRSFJe8RhnCYlAIpAIJAKJwFgITCwpc/9yodkF6mpDztIGaVtXMveyzSvyjo/d0fE4R5RjPTnqMGgt3fOaNqMMWzfulh/5z/erNntmFYZ26s82tOm7j91FXsaBfunb1Af36B/9124UZGF7fK3t3z6rO8aBR27a9FH+fL/CCAZw4nmYbRiVvo23qbIbljVefuZe9KNx0m4g7ZaX/ycC8x2BiSXl5fXatttuu63uHncUaExmAt5BEfFIjsctVltttfr4k+evBfd4dtU9Hv1yEAICQiSEt+8euXIqlMdyBgn0vvLn+6BRfwqLZ1vjhR5wuP3228d2N3fTe0b1mmuumYaGsmMHsAMrlNHd8IVgncD17Gc/e/qxKi9AiX4gyJ0O5VE1/euxG8+9xsY9/WgDo0euxPt4+UIf+U9Xap59oXAcdthhtW1w8AQFJWfcMCp9G2+OOJa2JeZljVdPfeTo1OijN77xjfWlNuO2Ie9LBOYbAhNJysjT7k/asYntihQ9ojQbocI69vzjk570pHqWNkEcQT7y9vGol8MrPDsb+TtOc+eddy7eKiXekZ1O5WJ9IR/P0CKiXXbZpcb1WSHDyo96zNerR5YcHOFsapasZ3y33377apGNapN+sLva89uR3tnGr3vd6+ohEfr/mGOOqY+TOV/55S9/+RKkrAwEHH144YUX1sMnPN8seNTmxhtvrP3lHgrWxhtvXJ99F8/icjqYZ5/1qcNMvKnICVbtOKmZzcM/yNGY9Wyx/nHAiv4adkJf28xh6eFjbMvfs8SRv/43DihMyxIfihEF2bGq5iVvk7kp3z6PR1v3/J4IzGcEJpKUAdoVjLN9bZuJS8Aicy8h8CarsJK6Hca1xyIjXAQuN88pO+IvAqvNbmAWG9JwD5ftoPOvZ1N+lDFfrhQQL4dwwEpYpkjOW37ixK1hbZEGtoR6CGBY6qt4uQkBzNqNk6TiOMZB+eqf17/+9TPOUm7vpdgh92984xv1Z+TtkbU41EV/eQkKyzlObWvTz7fvlFrta3Eb9MKHvrYNS8+Lwd3Pam3z9/IKh+3AdGnjPZsureCsbYfzUK5YzA4typAILHQEJpaUW+CX5rVtng8mYAl1pzUh5SCANm8WlfN72zO4HetIuMQbodyPNLyhyClQEeTHekcuYWFH3Ljlx/3z6QoLBNwuCbBgkDSy7cO5bR9SZqFyrca9SJ1nwm+t18GpbgR1K/wjL0SqLo5BZaG1L1SIe+LquNb2ZDX5IS1kLVACP/axj1UPSJBCpJ1vV21BaMZw25ZQRKLNg9o1LD0iRro+lNQ4m1xevBX//d//XRVX8bvtttus41/84hfX9BRoY4l3ypkBsQzhzXOxDDWo/vl7IjCfEZh4UuZ6nO1r25wyxv1pDREBWD8+++yzl+gnQp0b9lOf+tSMjWCsBC8raEmZCxsRtRY3ImJRd0l53PKXqNA8+QGB8j60r/UMdyUFqKug9DULQSBJ1hWSQJprrbVWFcCtRyNO9uojZS5NyxOOauXmdJZ2V2DrY2Wxop2hHB4YZO48c8dqUsJ4QJC/0+viJRV99Z4Pv2kjL1Frdap37IFgeQ4Lo9J7dlwfm2OUKLhTQvXBf/3Xf9Vny5c13hjQP8YEBYMcQPqO5Wxf5zmsHRmXCMxHBCaalAnd2b62jRD2jl5WF2FMWDiNDDFboxIfAblwP8frAeN3JOFtOO1pRmEdtu7ZPlKeTflR3ny7wsJ6LEsmSDAsZUQd1u+wdoU1ZgOWM415HPRTq/RIP4yU5WHtk5vbHgAngLm/DV6wwLpjcbu/DaxI69jqYG8A5azvfb5tmvnwXTuNXXseWkuZYsO6tVwzLIxKH3ly88OLFUuZoTzDOpSapY23iZIyjfD32GOPSsjqKz/KoPPkW8VtWFsyLhGYbwhMLCkTtoT+oNe2ERzWda0DtyTgu40hNGwH2dsV+oQnPKGsssoqM15WgExo4E7u6lp2yraj2hpjBJu7uDvb97+qg/tspolAWIxTftw/H68EJte1t1/5LlhisEvW237gIsCR4tO6o2tE80ccQmepUoTa9/e6jVXnd7vYhwWeDBuNwqWuDjbp2YE97FQwSpT+l/6ggw6qG5e642FYuZMaR7FEkK1VydKER6yZmwPWhym/rbKqTeOkj7bDiyXrjU9eYOF7G2YbH/Xxsos3v/nN06881UdIufV4tOXk90RgISAwkaRMQIx6bRthfsghhxRrUF3XpjguZMIBWRC2Hncx2YMwaONe09d91EanuseGINbTt7/97Wpte9xj//33rwICEckXUdicxK2NtEPDH6f8+T54YA4fFhl3v93r8aYnbdOHMLfznRXbDZQXQtYGHukRPFc0woC/vkMMsWvaePB/K/DjHr9bU/Z4VHg3KAO77757XVqQP+sw+kddogxWnTqcc845dSNYdyx16z1f/keE9krwQHAv21vBK+AlKeHdgInNb5SpFldtHCc9pTjwu+SSS8qWW245gzCXNj4w5umSp6UnSwwegTPGrFdnSAQWKgITScqs3VGvbUOMXNubbbbZEu7ntrMIf9a2DSKtNcBlzWpArn3BvcgkXkvHAmBVCMiYMPPs5BprrFE/vnOfss7aMKj89p75+B2pEZrw13aP23Br+l2Anx3aXMPt2ny0NV7/KS2PBnzDgtP/8Rw5j4dnyF3j1X3KQCxxjzwQsv4KwmEde745PCbd/tEv0qufOK8xRMhR/6jnfL7yVFjj1z7P27d7ALQLub32ta+t88ASQDcMS69/2+fEvfrRJsjAb1nj1UVelh023XTT6T7iOYkyuvXN/xOBhYDARJIyYFmbNG3CghbvQ3tvA+HtNwJgWHAfIdwGwlsZwya4uCjfvRGkDUuc9e2jfn4LUoh7XfvKb+Pn83d9pO1c0N0Ac4K9DxN9Fq/tk77FVz761e/wh2v0QzsG4h736YM2KFPZ0Tfu6fZPm15dFmKgvAaO3XlifMPdPYPmwbD0EdeHPyyXNT76I8bJQu2jaGdeEwEITCwpZ/ckAolAIpAIJAKLDYEk5cXW49neRCARSAQSgYlFIEl5YrsmK5YIJAKJQCKw2BBIUl5sPZ7tTQQSgUQgEZhYBJKUJ7ZrsmKJQCKQCCQCiw2BJOXF1uPZ3kQgEUgEEoGJRSBJeWK7JiuWCCQCiUAisNgQSFJebD2e7U0EEoFEIBGYWASSlCe2a7JiiUAikAgkAosNgSTlxdbj2d5EIBFIBBKBiUUgSXliuyYrlggkAolAIrDYEEhSXmw9nu1NBBKBRCARmFgEkpQntmuyYolAIpAIJAKLDYEk5cXW49neRCARSAQSgYlFIEl5YrsmK5YIJAKJQCKw2BBIUl5sPZ7tTQQSgUQgEZhYBJKUJ7ZrsmKJQCKQCCQCiw2BJOXF1uPZ3kQgEUgEEoGJRSBJeTl1zSOPlPKLX5Tyl7+U8s9/lnL//aXcfXcpP/lJKT//+dTvy1LU3/5Wyr33lvLww/25/OMfpTz44JLl/P3vU3UZlK4/t4X3qz6BwR//ONU/87GFxpY+1tdzEea6/Llo86AyY47/9rf9d4yK70+VvyYCpSQpL4dR8LvflXLCCaW8+tWl3H57KYjw2GNLeeYzS1lzzVLWWKOUb3xj6QtCJGeeWcr/+T+lXHFFfz6//nUp++8/FU8gRPjxj6fq9ZnPTNUrfl9sV4Ty+c+X8uEPl0KBmm8BERtDBxxQivEmPPDAlOJH+bvnniUVsuXZxr7yl2f+8y2vP/95ao7vtFMp119fCqW5DaPi23vzeyLQIpCk3KKxFN9Zx299ayn77lvKz372KPER/Kzl732vlC23LOWyy5Yi8zJl2R18cCk77zxF+N3JH7kS1O94Rylf//pMS/BXvyplt91KOeusR+sWaUZdlfWnP426a/7En3ZaKe9///wkZSh/7WulvPOdpfz+91N9+YEPTCl+T3taKc94Rimf+EQpyGBFhSh/kHXYVy7LfiEGiq85fvHFpay/fikXXDBz3o2KX4iYZJuWDwJLkLLBxDJbVmuCQP/pTx/V5JEDC1IIlyrrRYgyw8Uq/g9/KOWhh6aI7q9/nSIHpOe7eJN9UHyUoUxWhI+0QWhRnjYiHS5mdVV+xPUJE+mRX+Sj/iefXMo++5Tyy19OtaX7V77bbjslUCNuVPviPtbJJZeUQhv/4Q/j10ev6sFC0j7x7373lDWlDdqiXSzlH/2on1xbS8u98BSkveuuKaty991L+f73p8ogjGfjOh2UvzJgb5wpMyy9PkKJePcYE90wKv43v5nKn+sfKX/wgzPHtvEkj752qU/gq/y2/dJpn3ZYnhDv/24YVb9R8bEMYvxeemkpe+89NQaNIRaaOrnnm98sZZNNSvnKV2aSQ7c+s/2/r/wgZW03brTdlbIgwNI9V15ZyitfOeWhiHukiTAofcSPezX35O9z332PegxgtDQyoC1XOyJvbWzr7z5t/cIXStlhh6l51qYdJ757f/6fCCxByoiG9sfVRzB3B+E4kCGLiy4q5VnPKmXttac+b3zj1ASR3iA3iMOliwSOOWbKmjPICdA99yzlsMOmtFBx3MPPfe6U0CEo3/KWJeNprF/+8pRQYsGyLlkRT33q1CfiCFvCmSvwxBOnrI3VVy/l9NOnJvQ555TyrndNCZZor3ohyNe/fkoI+x3h7bFHKTfcEHcteUWIr3nNTFJW/772tfWXE2FDCLNQugHGF1441T5u8r32mqpbuLe/9a1S1ltvqt1Pf/pMtzfSljfLmoUFI/eGti/tuuuW8sQnlvLYx0654JXBYkcio8Kg/M87byolYXnGGaUg/A99aGqcqMNJJz2q8LgTIbznPY/24dveNrNPhsWrw513TrnutfG1r53KS3ntmL7xxinrOVzC0TZj5JOffLRu0X6KhKDP5ck63XzzqTE+m/rJY1j9xRvDxrn+e9nLpuoZpCxeGyOoP4/I5z73qPIbcUt7HVR+kK/5a5zoO8s0r3rVlHILowMPnPrtX/6llCc/eeoe4/vqqx+tTV96smHcYA4Y7xtuOJW/erziFaXcccdUDgh6tjKA4gZXH4rudttN9a28u/WPepJf73tfKZ/97KMKQcS5jopv783vicASpAwSAska5POfPyV0aIgmwLjBxEJEBAQhTgj6IDYB2W+99aMuXYOWW9Ggdo/yTK4jj5zKA7l/6lNTBPqRj0y5cQmAbjziPvroqUmgPMTjSvi9971TJMTa0hbrr4hYHd2HkM8/f6p8ZGsyIvEI1mzf/vYpXAKLr351iry7Aj3SuPaRsvz72qf+SAP+wne+UwplhrXdDdddV8qOO04JOe2jSFGAWFOCOrLcENOb3zzTrS2O8JGe4IERjwHrL9KyMD/+8am06qsM/dQSwdTdS/7Vh/KnePXlL15bV111Ck95s6qQ3He/O5Wfso44Yqqf1AVB8EgYl9Krc1+8cpG+NAjM/UjkBz+Y6tPDD3+0nUq69dZS/NZVNiw76CN4qovy9Eu0xRBQ9wAAIABJREFUn5X6whdO9b/xY8xzLY+q32zizQkuatYaS1n+lkqCFFvkkdP225dCyVgewXgYVL7+EtTL2IcNIrSnwhw2vqSnuGy11ZTyKI3xGHNHHw5Kr/8YB+GBgG37kY9+MAc23njKWyUvZfBwSS+ow9LKAH2t/ZRdYy/mSNR/qoSpvzGfyAf16IZR8d378//FjUAvKYOEgCbYjjqqlBe8YGpisfAIqFHBwCQQacuEskFtEkboEpU8lYMg3accWj9SuummUg45ZEooWRdFWrfdNkUW3XiboeRjAgkmA23ZhEbWLBkT2oQniOUblo/7YzJLR3i2ls+3vz1VZhAkfD796UfX+KZKXPJvt63uUJ++9kX9g5S5IrfZZgqPNmf1Vz/aeRAp4UNgd9eUCXBu7csvf5RQ5EXhoHR98YtTwq/PNUzAEkp9rnz5agcFKuorX3jCX/7/9m/9+avzqadOeQsoOwLhDm/KEYErb6QIY54T/xOSLEd9OCg+SAshvOENU/WTv7495ZQpkkPAQaTaz2OAhOVJsCtfuywbGDd+N4bbgJQphoghgjbxsAyr36j6i1cH5au/ekWgIOiPruCneO2339RmwBgPkWZprxSxccrX3xQGSxy77jo1LmMexdIN1/qg0E1PqSIDKFFIniXefp73vClDgcwwNoyZYUrxssgAdeF+Z91TEAbJPuPFXGWI6LduGBXfvT//X9wIDCTlgAUxc2dyNRLivo8TCAcCZdNNpyy4dhNKl6iQDOHXkrLyaP0+LGJrQ1ye7kPK3L/deOlZFkgCMYRrmuuJVcZiJJCVx8pyr+99gVBieSA5aRACl75JLmgfYiSE+yyXyLPbVr8T8n3ta+vvPpa49WhKShsIB0pFW391IJi75As3grz7O0EhDTc+1ywBSIEK5SmIk3XWCj3pKDqIDK7ScvNqk53nlKZrr50iNuTUzV96eROolAX3CMYZARuWJGWMMmjnOg+AslxtqNMfg+JhQMGwsQ52oURpF6XO+AnCkSdvyeMfX8paa025qo8//lHlzDiSjza6lzdC3QWkTGG6+eap/xFRkARc/b409Y/28RjoE+2MwJsAs5aUb7llijyN0+i7uH9ZrpSNYeXrR2Ssz7jX4bfKKlP9J04w9uXRt/wyKr0xjpjJkPaD/ClUYcmblzEnu+11z7LIAHgqC+bGAIWPchbti/L8T2HqU6DdMyo+8slrIgCBgaRsQHMzH3poKf/xH1OaIAE6aAK0cLbCgRC76qpSNtjg0clJKLIyCBmB0OdOZiVJi4SQFmvHJGQRmgxdUu7GIzVCl1BmtSBVmitt/KMfnbJqtUudkLIJHVZ1W3/f3cNSdB9CI3xCwItXT4IQabVCspvPMFLuq38oFfIhjLivu6SszurOJR9YK+dNb1rSUu4jZUKiFSwsM/3M/RYErP2hdMAvgt/tFzj77Kl7WbGEFtKydgt/9RuUvzGkzmEph0JjrHF16mNBm40R3gN9Jk8KVLQ3xlBfvLKRqSUS9RPkYemDJ4XA1ybt5vY19uDn/6h7lCOt3xCytUt9JvDSyN9VUDd7FHiIjL9lqb/8gpSjPPU599yZyyXGo3V+82tQoCzAWJ3aNg26P34fVj6cjHlWPSUMltrLsqa4RoApoop5Hr+7Gpd96fWnwB0OX0tX7ce6LqLVh+YnOUFJ7Abj1B6FpZUB7fj1naWsvmRR6xlSbihklN8Yz219RsW39+b3RGAJUjYAuSut4bKMCWaCtB2kw2Az8QkLhMJ6IhRZ1yZnbEISb/0QCZu4rGgWkTIFRGpDFcFAKCEME88kJFStAyKgbjx3E1IzMTyPyupQB/khMQRH+BPQ8rSRjMAdFFgpiMFkREImVxsIIhY7y3BQUB43LKs3AkLpa5/6m/RRJ65dlh8Xaxv0BYXAuvc110xZfjZE8Qa06+DS0PSRLWsl+hCBEJQsY22AEaUFOYVQ0Y824lBGoi8JUkoZgdcKePUlqH3E+QzKP8YSgfl//+9UGcYAwrTGrT6CPBE8Qae/WWU+ylA2oTwoXju1yZo2Rc13m9hs+LOk0iqW+phyo14R9DNSMPaMHeXaYWvMhmXMUn7JS6bGrv5E2i996aNKEcE9qH7j1J/nwBo6yx0+XMAbbTQ17s1P+Bx00FT/8FCop0+MnWiL34w/YwB244ZR5ZvXr3vdlHyADxzhwzMSSmrkoQ9gpC68HILvw9LrA+2McdVeo436B+bmOas65rp4HwrD0soAZVuyMn/JK3MF3jaddkk55AmFwtjvhlHx3fvz/8WNwBKkTGATMHbKmlRdIhoHLi61LbaYsp5YUNaEWEAhDOWJgCLOWp/JTHASWCY5gSQfHwdxIAQbsWjJ1tAQSF+8zUkmhjwIeWXYtU0BIKTtlg4SQkTdCda2T5x159biauOVI18Cj5DpC36nQLTWgkk+qH0f+9ijdUIuyJSw627gUTcKCqJhPXDNIhzrX0G+6qMPkQ5rKn7Xxwgj3LLcwtYDCdq4R1qCMKxgOLY76PvaGr/BZVj+xoH+J5TtjmVhb7bZkm1EvNoYO8TVt90BPiie5aYdrGVppaOsGWc2mLV9bgwRtKGMRBsoJM9+9qNj+DnPmVIgQhmhqFhv5MaXv3LC0xN5DKpfENOg+PBM8BIhHXlTzljKli1YveYD/J7ylEfd+327g/UphZhFORtS1oZB5Ye3g6LJijU2WMnc99Z8KVjmuD4wftdZZ+oeO/xjBz4cB6WnUAfOgWXfVf48FR4FUwefFgPzbGllgDbCnGtevvqgVVqjPsY6pcP+A+V1w6j47v35fyKwBCmDxEBaGjIOOE0ogie025jEEe8qf7/7ENL+jzJNNsQhHx/18Zv7fPfbsPgoh9BThyiD9kxrjTzlNSy4n0sSMbSCvE3DKkBc1pZZV+rVBvWWNtomblT72vTqYE2dQsEi16YI6h8Yyh8+2taGKKstX7w6Rf+4tvm26dv7kEU3//be9nubrpu/ehPgXP+UJ/FBRG0evkcbo67qqU0RhsW7D4FJqz7+h1EbYiy0eYp3H2yjXPm097CUbezRJ+5xbx82w+qnnFHxcJG/caB897v6wCLaF3Xotk86ZI4ofZ9tGFS+fNQhyjevjLGw4qMcmMQ9MGrnxzjpI59hV2VHPymjxQBGLTYwGEcGBL6Rr6sxFEG837jrKSI2erVhVHx7b35PBFoEekm5vWExfjeRWdNc9yx+FsOwYJIjGUKaq3V5B0KG98LafiwBLO8yVmZ+iMWhKzbOEKLzMSBjbuFRY2Ou20bp4UFpN4zNdZ0WQvkImgePh4DrHAm3YVR8e29+TwRaBJKUWzT+5ztXc7gk27XYnlunf2Il8A50LdLpG5bxi0nP4mitgGXMcs6SI2XudksDrI35GJCxZQnu70kOLNWwsie5nvOtbuYjXCnwfWFUfF+a/C0RgECScs84QHzIAglmWDEIwJgrs2thrJjSln+uyI411OeyXv6lZY6JQCKwWBBIUl4sPZ3tTAQSgUQgEZh4BJKUJ76LsoKJQCKQCCQCiwWBJOXF0tPZzkQgEUgEEoGJRyBJeeK7KCuYCCQCiUAisFgQSFJeLD2d7UwEEoFEIBGYeASSlCe+i7KCiUAikAgkAosFgSTlxdLT2c5EIBFIBBKBiUcgSXniuygrmAgkAolAIrBYEEhSXiw9ne1MBBKBRCARmHgEkpQnvouygolAIpAIJAKLBYEk5cXS09nORCARSAQSgYlHIEl54rsoK5gIJAKJQCKwWBBIUl4sPZ3tTAQSgUQgEZh4BJKUJ76LsoKJQCKQCCQCiwWBJOXF0tPZzkQgEUgEEoGJR2CFkPLf/va38stf/rLcfffd5Z577im///3vyz+aF8+Oip941LKCiUAikAgkAonACkBghZDyL37xi7LrrruWpz3taWX11Vcvr371q8t99903Xf1R8dM35pdEIBFIBBKBRGARITCUlH/zm99Ua5fF+5Of/KT88Y9/HAsaVrF7Wcjnnntu2XrrrWv6SDwqPu7LayKQCCQCiUAisJgQ6CXlv//97+Wmm24qr3nNa6q1y+Jdd911y9lnn13++c9/VnweeeSRSrRB2Ai4L3z9618v22yzzQxSbu8bFq+shx56qPz2t78tf/3rX9tk+T0RSAQSgUQgEVhwCPSS8jXXXFM23XTT8ulPf7oS4v33319+97vflZ/97GfTpPyNb3yjEjXCXnPNNcurXvWqalW3CCHVr3zlKwNJeVT8n//853LIIYeUF7/4xeWGG25os87viUAikAgkAonAgkNgCVLmdj700EPL+973vvLwww/3Npj7mfWKqFnMd9xxR103/uxnP1tY2RFGke6o+L/85S/lyCOPLJtttlm5+eabI9u8JgKJQCKQCCQCCxKBJUj5Rz/6UXVbX3755SMbjMBZz9///vfrxq7PfOYzxc7qCKNId1S8fLitEX+7ezvyz2sikAgkAolAIrCQEOglZa7oL3/5ywPbiUyR8dve9rby9Kc/vay11lpllVVWKUhZXIRRpDsqPvLJayKQCCQCiUAisBgQWIKUuaTf8Y53lDe+8Y0zHmNqwfjVr35V3vKWt5QPfehDdZe1Z5Hf8IY3lM9//vPtbZWgl2VNGWnnRq8ZkOY/iUAikAgkAgsYgSVIWVu/853v1I1e+++/f/nBD34w/ViUDV+Cx6Ne97rXVcuYxWyn9mtf+9rywQ9+sK41h7XsyuJ+5StfucQmMPmMis+NXhXu/JMIJAKJQCKwSBDoJWVt//GPf1yJ1u5qn2c961nl+OOPrxu5rO9+9atfrb+JYyXbqf285z2vfOQjH5ne7IV0v/nNb5bddtut3HvvvUtAOio+N3otAVn+kAgkAolAIrCAERhIytps9zXrOD42XEVAqH/4wx9q3J/+9KdKxA8++GDdlBX3uNqNzeJ1f18YFZ8bvfpQy98SgUQgEUgEFiICQ0l5ITY425QIJAKJQCKQCEwqAknKk9ozWa9EIBFIBBKBRYdAkvKi6/JscCKQCCQCicCkItBLyivi1YpxVGd7CIhybABzfraPU8La+LkCzfq3+sa53j//+c+LTWeTHOBmTb9bT2v22jLodLZJbtMk1c2YgKEDcwbtj5ik+q6IuhhbxthczVFj2T6W9oCiFdHOxZCnMawvvWI3w2Qh0EvKy/PViiawx6re+c53lve+973lgQceqAj4/ZJLLilPfepT6+7uZz7zmeXggw+uzyXPNUQ2l9lFrk7O9V5jjTWKs74nOfz6178uHmG74oorZpCGXfRenelgF0Itw9IhgJA8h//hD394ic2MS5fj/EplvpoDBxxwQD1eV+3N5VConVXQVQiXdws9ivnWt761PoK5vPNebPmRBWeddVZ5xSteUbwUyGbcDJOBQC8pm4DDXr04btVpY9/61rfKy172snLaaadVwg0rg8b7rne9qz5KpSw7u03qiB+3jBV1n/qwML/3ve+VLbfcslx22WUrqqjlkm8c+mKCtRg66MUjaSbgbEmZRWJnfYYpBIzh97///YuSlCHwta99rSrX3ghnLH3gAx+oiqvHIp/xjGeUT3ziEytUuMchRbN5OQ2yScLpn8Hk7VVXXVXl2yc/+cl8E18/TCv9115SbmvR92pFnRmk7Z3LSNxERQy+R+AaYSETZqFFi+em9vyyF02cccYZ9chOLuKui9VpXqGJs94jIJ0gcqRBg/7pT386nZ7AQPrSO9yE5eu++C4f6X2CwNSvzzUn32233bYKpCjfVRkIL+on75XtVlMeQaUOP/zhD8u73/3uas1oEyzhwlJ2nnkfubaWjnvhJUh71113Vatw9913r2ebK2NlLy8QptG+bvn6VP0pT8aO+PDCtP3UjiFjohtGxcc7xS2zGMcOyGkfDezm1/1fPZXRzovuPYP+17/Gn7b5GG+hWLlqL4xiHOqfbhCvbwO/2cbHMo7xfemll5a99967znPlX3/99XVMuMd83mSTTepb4WJOdctamv+j/tpw6623lhiP8ho2B2HuRTn77bdfte59h4G6Rv2GpV+aui5tGnIy+vi+++5bQlZGnDEIjwjmqXkdGJkr2t2GQXO8vefqq68uO+64Yz00qv09v88NAkNJ2eDtHpPptzgQhKW7xRZblAsvvLBOVqd8GRhCpN13333rxI3mEYwHHnhgPS/7MY95TFl11VWr+3q99dYr55133vSEQZDeVrX22mvX+M0337x4paSJZBASjlxpJ554YtXWV1999So0lWPw7rnnnuWwww4r66+/fjnmmGPKCSecUL879MRgdkSoTygLXddc1Dde0MFKaIPJs/POO9e6ccH7OL1saYRvm++43wlsuLNSuNn32muv8vrXv766r+XBQwFT9XI+Obd2BH1DEDhOlYUjD/decMEFFX9pvT/7iU98YnnsYx9bXfgre3lBH9PeHVoTbbS8QZESWEtOkWOdGRvGibPYW2IigN/znvdM99Fs4mF05513Vtc/jJQlL2NmNqR84403VuuawjqboH8vuuii2n5t83H0LQIWkBRBavzvsMMOtY+9/5wAj0BgG/f63zigXM4mniLsOF3pebt4CYKUlQGjCNrHI/O5z31uWnGIuKW9GgPqr/+f/exnV3lgzsFUUL/uHCSvyIhzzjmnrLPOOuVxj3tc/Wh/ewCS9JMwh83LDTfcsLZRO7mTKRACWWlJKuaoNpx88sm1fdoIa3KOsmi+koHe7mfsCH1zvJWx9ab/eemPw5/0L9mYYW4RmDUpq+7FF19chQQhjmAR85e+9KUq5BG2QCB47aLXOcYg8buJLI5Q3WqrrSqxEJ40uiBIV4ME0RA+4hEF4WNCijdYDUID0+A7/fTTy/nnn19JURqDW/niTcZPfepTdYJbKyZAjjjiiPqJMrmnWfWtUFffQaSsDcoNN7f1ciTHYzAqKDOsu9CC49pq8sPyue6666pQpuVKE33CmhFgDlPE8uY3v7muG4UQFWciE+osbG3QHlZdpGUhfvzjH69pWdvKMGEjj3rjCvxj2UAfao9y1ZGQjvJZaS984QvrEoh+gJ/+s3ZOMXK/PjZOtIUA32effUbGw4XAkwYByU+f2hex3XbblcMPP3x6nI7TfNadNF0LZlRa7UGyxq+02uMTSp8x/tKXvrTWyb32FGgvoQwn951yyin17W0UZUouRXaceONTHoQ0F7U5wVKGrzXdvjGOXLbffvtpwhzVvlHx+lnf77TTTuX222+v4+/UU0+tBGZsCMZFdw6qo/ppgz4/6KCD6l4V341hGEaY6zl87bXXlo033rgSLYzVTz8Zf4L6UcLU2RigpBoTZIdAIXvyk59c+0j/U0g+9rGP1bbr/2FzvGbQ/CH/ukpbE51fVyICsyZlk0UHIiAT1VolQvSd0ItXPhpkb3/722tcS8rRtnALB4nH764Goskv3wjyo7UjeQOXcDnkkEOmLSf3xWAmpGjtzvB2Lrf71O/MM88sRx99dCVlhE3oBCmzhAlh5bRhECm7R7to28qTL0tsHIuIkLH5inbbfhxTaqJFO9p6tN/VGVnAIIiUcIJZd02ZgOLW1i9BaPJi1T//+c8vX/ziF+sk73PtwppiBO/ZBuXCBXkQ8BFi2SH+H3SVjkCGq3wI1TYgZW8zo5xEILR5b8Jlh9Qpd7HDX38bQ8Pig3QojY6PVQ9BnyC5o446aoZgj7Lbq3ERigJ8zRVEoh0Eb9sPbbr2u3FoPFJ6KU7aH4TsPuPSGKIMRwivFqFtHLKi1Bcpw0D9YWIuDIt3r3ZrfxCgMpCk8dCdIxQ/bmLzK8Zj1Glpr8aJ/tJ/EbRZnbwqNsKwOSjOPKdYDJpTw9JHGX3XZZ3DcGL1jpIZxoq+1yfmozHvu7EAG31srEVo8R9njkc688l4ms16faTN6/JFYKlIGYGZhAjp7LPPrm5ngiDWNFXRxCWMaPoGfjcMIzuW2TbbbDNjc5X8DGDaPsEWky1Itc2f8LP2xKr2cS+iVxeubAKpS8qUg9mQMi02XOfcTtzwLFICf1Sg4bO8CLz2Q9iYYKOEtvSsL8Im2o8E9UmXfLWbIO3+rgxp4Mk1bUIS/iH4TW4kx/IYR9GINsvXuND34Xam4esTgoz7l4UQ5US6visypwCqn7x4A0LoECLGyM0331yTErqElDGiXX5/wQteUHfOc/1K72o5hdVBWeuLhyEFJSwHyqOgvhRQeLXWVl+9CdFdd921lsmb8/jHP74u13Tdp31p29+01fjYdNNNa93bjVQxf1ql1tIMoa3O+oAHy7iM9nN/8jaxqgbF854Yg9/97nfrmIBThCuvvLLO8ZaUb7nllkqUlMFx+jTyGnVVR0q9PSeRL2Vijz32mCZl82DYHIx5gtz7+myu57B6+fTJR/iY27x/4b5ebbXVystf/vJpBc14N7fJw74wao63aSi3xk6r8LTx+X3lIbDUpMwqRsQElfUbEyYsMtU3IQgwA6dv0IVQ6a7VSst1SKgjhQgEgQnpsRSWWwzovskWpEzrM8hYlARlS8p+Q2qCwXvuuedWK6sVOOL66klYnnTSSdVdx6Kg1X/0ox+tBDaOVYmctt566+pWJ6jjY/3bGtogrT6w0Gbt5zIPgaWeb3rTm5awlPtIWXt9IpjU1u8JwSBgbWSNszxjHTfuH3YlSKyFUtbkRas3LpAi4cLybS3nQXlFu8RrL0K29haaPC8IDF0F5dpjQLFCqqxDQob1RvjKwz2RL8VvUDxskLL81V+Qh6UPytyo+isDZnDl1uVC1z/+7xuvtYDOn6inn/WFXbIbbLDB9IZDY5wixRqKYLmBix3hUkz0p0e4KIrKVe/I1xwbFh+kHHhLF3Mkxgjyt86vboOCsawu+iTKHnRv+7v6GXutDKBg77LLLlWhIlNYmlzmg+YgGWSe8xZ0w1zP4ZhfjAf91Q3GoEdGKWTGuP6DP/e1OS2QrYOU5nHmeFsmhc6+idgT1Mbl95WLwEhSNunbVy/qbB1owiBimizBR3gZIC3Jss5ocjGI2qbZ3cuVJq9uMGCRL2JGqgQQ15kJaY3OAEUiNnL1CTkC16YngoVQca+Bb40FERMSLGZWEdcbwbnRRhtVUhPXhr56KpPFx+pSN0IBSdqIY5NGnxLS5klQIW9Cuvvpa0+b1nd9wDIhgG1+016bkFhFrZB2L6uH8NUv0gnIidXDMjYJtYFSgTyi/QSozW8sT9aae/TjKIVB/vqvFcDaFO0UNyooA476DrbG2Re+8IUqNMIyZim/5CUvqRu9tB9pW2MN9z0CogAYf8aMPHzUQ90I7EHxcNJeViVFznf7J1ia3MnjtCHayNJsn8+P34dd1Q/hBe7mlj0VPAPGqqDfzB95I3zEGGvQ0vuoO8sXVtF+Sid8jdFh8UiR4u3NcPJnkcccMXb1qfVa44OSqZ98uuPXb+ppDMJ+3ABj9bOcAEOeJXm86EUvqt6v6N9Bc1AbjXNyhOKgjvpR+2GjnnM5h+FgfBuz2qB96hcY6h91twxnzOs/SggsjQvj1//w6RoS8h5njkdfuJc3y3zo9l/ck9eVh8BIUjYZDYywGAgsAp37lPXJKiIwfUeShEMI/5jYrL+utWXwsezk1RcMDgRq5ycri9uGpixvE/LYY48txx13XK/VQqARKFxrPu5FKFxB6iK9ge4RDtYbcqaFalOQUtRpUD0JRQJP3Z773OfW9W9CHDHDYkUHbWABIAoWNo8FwrDxK/BXB31AcLf9YhKagOEW5t7kbtXHbVpCNKxc7Wx3/67o9lEI7LhVrs9znvOcqiQQqALBRFmkuGmHfrQHIeLdQ3DBKNx/7msPqBkUb6zCgbUsrXTGojXZGD/jtt/4owB2x9Wo9NJxP0f77T2gAIfCR4AjYeOcp0Uf8kzp2wiIjbdDvHzc0/bhqHgKMdLozhFWr/nkaYunPOUp0+5x49D4a4MxRZlgEc6GlOUBM8q/etsUSvHiCbnttttqEeb5oDkY66zIzLzUfrKk7f+5nsPGGCuYHIp+bjHUfv0rztMRXPUMH/1OFvJQUsb7cB13jqsDwwhG8sww9wgMJWXVo3EigFZYhxbqN0LC/74bCL63gZZuQlkPs04UQtP98u3e36YlNAw4ny6pi/PpC1EXZfm4L+rapuGSlDfhLD7ua/McVk/ppOcehANFQj2jjW0+K+K7+irbB47q0C07sOjiDPvANjDoq2N738psm7ZoV9QREWhLBOOKcLI+HX3Qbbt7A6PIJ/o68hkWrzzlShtzQL1mE9rxN9t01r6j3rBo28d61X4epGh/X930e4tjtw9Hxbtf/sY2PGKO+A7LwGdQHaSj8HL9+z7bEOMPFoFli8M4czDuUcdu/0ccjOC3sucwPEIO9WEY7Vc/2JOxsIj57v92XrT4Rlr5RtsjXhoeD+PH4Ug2kXVlRNyb15WLwEhSXh7Voa1y0czWjbc8ys48FiYCyJgrb7FuTIlllXa5aBJ72tznwWk3jE1iPRdbnRAw75p9FTZ5tYrOYsNi0tq7UkhZo2ltXS110sDI+swfBJCx5Q9u3sUYBi2rTBoWhH1Y2ZNWt8VeH1Y2T0iGyUJgpZHyZDU7azPfESDsKXqLVcPnftT+dDnO95Gc9U8EZiKQpDwTj/wvEUgEEoFEIBGYMwSSlOcM+iw4EUgEEoFEIBGYiUCS8kw88r9EIBFIBBKBRGDOEEhSnjPos+BEIBFIBBKBRGAmAknKM/HI/xKBRCARSAQSgTlDIEl5zqDPghOBRCARSAQSgZkIJCnPxCP/SwQSgUQgEUgE5gyBJOU5gz4LTgQSgUQgEUgEZiKQpDwTj/wvEUgEEoFEIBGYMwSSlOcM+iw4EUgEEoFEIBGYiUCS8kw88r9EIBFIBBKBRGDOEEhSnjPos+BEIBFIBBKBRGAmAknKM/HI/xKBRCARSAQSgTlDIEl5zqDPghOBRCARSAQSgZkIJCnPxCP/SwQSgUQgEUgE5gyBOSfd0ifAAAAgAElEQVTlhx9+uNx///0j3wvrvbF/+MMfyt/+9rdlBsu7aJV59913Fy+L//nPf1688DtDIpAIJAKJQCIwlwjMKSkj2FNOOaW85jWvKffcc89QHJDnW9/61nLTTTcNvW+cyL/+9a/lIx/5SHnmM59Z1lxzzbLGGmuUb3zjG+MkzXsSgUQgEUgEEoEVhkAvKf/jH/8ov/vd7ypRhjX54IMPrpBKnHvuueWNb3xj+dnPfjY0f6T9hje8odxwww1D7xs38pFHHqnW8ve+972y5ZZblssuu2zcpHlfIpAIJAKJQCKwQhDoJWUE+cpXvrKsvvrq5WlPe1r9vOUtbym//vWvZ1Tiz3/+c3X/Iu7f/va3M+JYwT/96U+ri1j8r371q2kXNdJ3/49//OPyox/9qMb5rRsif1byrbfeWnbfffdy4403Fu7nP/7xj/Xju8D9THGIfLi7lalsH23qc32r47bbblu+9rWvdYuvdYz06oDI26Dshx56qN7H+s6QCCQCiUAikAgsCwK9pIwokfIFF1xQrUmE+OpXv7qcdtpp08T6pz/9qZxwwgnl6U9/ennqU59aiQ2BCQjxoosuKs961rPK2muvXT+sYSQpWBs+8MADK9lzH++9996VUGvk//xByPKnFDz72c8uhx56aNl5553LLbfcUsnxgx/8YPnQhz40vRbM/XzAAQdUC18W9913X71fevXz+fKXvzxN2lGWtnKft6SMbH/4wx+W7bbbrtZdHuuvv365+uqrI1m9quMhhxxSXvziFy83C35GAflPIpAIJAKJwKJCYCApb7PNNuWaa66pYPzmN7+pruPTTz+9WqlI11rwrrvuWl3cLFQk+f73v7+SJOsT0X3uc5+rliQL0yesWKSH1LnIEf873vGOGZa2+EsvvbTstNNO5fbbb6+Kwamnnlo23HDDainL67DDDitHHHHENClzP7/zne+czkf+v/zlL2u5NnW9973vreX8/ve/n9HBfaQsf23Za6+9yi9+8Yuah/y6ljbr/MgjjyybbbZZufnmm2fkm/8kAolAIpAIJAKzRaCXlLmVX/WqV5Uzzjijupc/8YlPlFe84hWVEBWApPfcc89y1FFHVVK+9957K0m7h5sYCb773e+u1jCLE7EFIXcr+M1vfrNayq37m2saKX7605+evh15WlNGfnZsv+9975tWAtzE0mVxt/kgURYzC/7oo48ub3vb26Yt6ci4j5Sl4xXgLWAd252tzL7Abd0qHH335G+JQCKQCCQCicA4CPSSMkv3ZS97WVlllVXKaqutVi1B5BUBCW+++eZl1VVXnXbvcg/vsMMOlYDdh6xsotp0003rPYidu7cNiLqPTK1dv/3tb69KQZA5st9jjz0GknKX3Fm2J554Yt1hzf2srm9+85vLAw880FahKh1d97UblMvSplzYpW3nN+WCFZ8hEUgEEoFEIBFYEQj0knJYjxdffHF9VAixXnjhhdPls5SR5oc//OFKcixFhIvIkFYQqQTI+aqrriobbLDBEjuc3ddHyvJ/17veVbisI9jgtcsuu0yT8uGHH14+8IEP1Ghl2sUtDStdvieddFLZfvvt60Y0lvdHP/rR6t7u7iKPtnbXlKNcebOUbXRjnbeKhbjc6BVI5TURSAQSgURgWRHoJeW77rqruqvj2d2vfOUrZYsttiiXXHJJdeNaU7ZevOOOO5brr7++uqxZslzHXL8sbVYytzGr+lvf+laxRn3FFVfMqC/y/OpXv1rXehFxBEQu/3gu+Qc/+EFVAl70ohdVFzpitIa93377lTvvvLPmu9FGG5U3velNdcOY9DaB7bvvvrUOdk5zh9tsdscdd8xYG462qkcExP3tb3+7iNMWLviDDjqoHHPMMTNIWT1yo1egltdEIBFIBBKBZUWgl5SRGIK78sorp/NnNf/7v//7NLEi38985jN1hzX3sF3W0nA933bbbZXE/e6z7rrrVle0NG1Aysqws7q7Acv/LF/5brXVVkX51ozlLbCcN9lkk/KMZzyjkjNLmfXMUhY810xpUP5zn/vcYpMa9zpi5oaO0NdWLm6Eb2e59MrYf//9l6hjbvQKFPOaCCQCiUAisDwQ6CVlbllWIIs4gt9YkC2xikdgiNCHm9h9yJZbN353j9/6gjyQm3TdoA7ykJf0LOA2H+vG4m3Ckl58m08c4al89eZmV8c2D/f3tTXSRhvc0xeUmRu9+pDJ3xKBRCARSARmi0AvKc82k7w/EUgEEoFEIBFIBJYdgSTlZccwc0gEEoFEIBFIBJYLAknKywXGzCQRSAQSgUQgEVh2BOY9KVvTdTLYoDXfZYcoc1gZCFjnt2fB/oI2WPO3p8DRrO1egPae/J4IJAKJwEJBYN6Tskey+p6BXigdNKodyGohEJZd+3a4e2yuu1nPrnrPxVO+MjyKgM2Inh7w6KGnDWx8zJAIJALzG4GJI+WwjDwSFbu//TaIfDwi5UjQ7jPQ87tbhtfeTnKnjXlRiKNDndFtl3g32HVOYPu0x49275vt/4S/l4vwUkSwq73ts/h93CvCdQb617/+9RmkLP3xxx9fDj744CUeSRs374V6n8cJ11tvvfrYnhP13vOe98wgZvMHWQdpzxdv0mxlwELt32zX4kRg4kiZ+9Jzy//5n/9Zvvvd79ZeQShO1GrJh0uTsHEilwM+WoFjUiMJjyq11sRCsbScMOYccEehOoOcFdl9zts9Dl+JZ8Vf+9rX1oNTltUFLP2nPvWpegTrl770pdo/sPfCD2U4RKYNDoUZRAqUiyANB7Q40tSBNfoPoVA84vWe+s7vbTAGWkuxPa2NwkCRUwYrXB2646TNaz5+17Z4JNExsw7QiZPpxJ133nn1GXtjwBvbPvnJT9Y5MeltHVcGGCODXs9qnBoz7ZjwG1nSXSKZdDyyfosLgYkjZQLewR3/+3//73pUpon0/e9/v7zuda+rQpugFc4666wqaNZaa616iAirMQIyduIXdygry+El7nOqGBKY7wEmYZl6W5e3Y7UKBzJD1o4FJYQQtmNHvYoSiS1LULYzxZ/4xCdW65VgRMT6x/uuKQOC+3gxYI4UHAKjDshCcHV0qzhni1O4Xv/61097PLTBW8jE6zsntEXayN8LU7zWM+7R5sDBiXLq9NnPfrYeGuMen5NPPnnaA1MrskD+UH4orqxnffLFL36xvPSlL62n6fGieJELPLzkpcVxEps/SgZQRIRhr2fVZvMfJsaS4OUyDg9qZUWNyD+JwAQhMHGkzDWKUL2BisVM2CBgvznrOty0tF3fEbYzsW+44YZpWE1qE9ILNSINS4nlSEhPulCabsiILyxHp6p1Sdl7o//f//t/9ajTyIJHwTux2+NEI242V7ifeeaZtW+cuIaQLR3AmxIQApCFiwQ+//nPV5eqtX8nqiFL9fa/E9cISv3oxDbE7ZWdQigeLCHv1XZMKmUrQigD4RFxvrpXe3J/C8r5j//4j7LxxhtXj4syvCZ02223XWbFJOow11c4ahesKTjwp7RSTJCRV5vGWHcvBc5yx/JcylgRGIySAUHK7jPejAs4tK9n1V5H8Opvbn2vgKXse8VrYLIi6p55JgLLisDEkTIh60UX559/fhX8rs6cdhb1scceu4SbFimw0JBzBC5rm4OcSx3uKxORkOoK90gzH69ctCyflpT95kUe1mdblzblxm/LqpRQeChJyqAoIV0v++A+/chHPjJ9hCnXtvdMO/dcH1GavCBE+YSpKxKJdWnCldLUXVMmXFnElKuWlKO/CGj5K4c7P9y3lj7sNUD6ERA1xaRV4CJuPl5hZ65wTSMf7TLOkbR2hoISbaOQeSPasnpLIr8VdZ2NDNDeYa9nNQ4oqF4ra3ym63pF9Vrmu7wQmEhSRsKXX3558brHf/3Xf60Ey9I97rjjpt2TAYDfd9tttyVIGQET5DEJEbXfEEGfcI/85tN1ECkjapZSuHK1CSkvD08BUmYpI2bW7ZOf/OTqKmeJ6594vSXSfsITnlBsQArXsTPEzznnnOp61w9t/1AgWDL6HRFH0H8IvEvKLGnWT7jH11xzzbrkEUREGIu76aabIqtqnSOlhULKGhZWIqXV+79ZgvpC27teES+W8ft8IOVxZADlbtTrWZG2pQ8vs6G4ZUgEJh2BiSTlo48+uq6NcXFuueWWdfOPTTos5ZZogDuMlFurOEi5/W3SO2ec+nmblg1SrIsIhLHHxAjqCFx5CCne/BW/z/aKlFnHyNXmrJ122qmSNEtX/7BaBHXwZjEEiDh8KBEI13f9wN2IXAVE4oUmXUt5ECl7g5f2eNGI/KyZKi9eooKUuwRsPHV/q4UvgD9w8sIW3gt9YI3em83CEwF3ywDGSutBmcSmG8vDZADviOWLcV7PalnDe9SNLZ6iSXfdT2J/ZJ1WLgITScpHHnlktZhMPCRAcNugRePtTiquOhuCWusHARP4hx12WBXYIO37beVCvfxKo/0jQURr7d3aLNcsCwhBsYoJX2tpiBNGNjhZo7f2tixBf7BcKQO+IwP9ZIez/uFKFvSXfuHt8N3vNoGxbhAE8rXx7Jprrqn1U9dVV121WA9vg/xZ/l0Ph+UKBGtDk7yta1s/RtJ2ZSNgLkvkHOHaa69d4reIm4/Xdvc5r4WNXRQm4wO+3Pnwobhy41vmQVKTHpDyMBlgDBsXxlvf61nNC/HeJEdppIhS4jwdwAKPNelJxyHrtzgRmDhSJrRp9IR168Yk0D3S0SUVFjQCuuWWW6Z7EDGx2rhTEYfQ99t0gnn2hYs4diY/5SlPKauvvnp1E6+//vrTa6g8CiwnrmMuZLtOuXuXNRB21vltLAorTJ7K0z/xWkx9R0mwuSvc156p9ZiOIC2FQt3Umzv8wAMPrPVv+919Xsv5sY99bLovpUc8lAMuca/YtNHMmuG//du/lbPPPrsqaUioHReInMXU/lYrMw//UFRhFrvP11lnnYoHXAQY2jTn98DfkkLssZjkJo+SAUGqlL2+17Pusccedee/fSX2I4Q3xj4Hr3G1/NKOsUnGIuu2+BCYOFLWBQQx66sNJpHfu5PJ/4giJl6kca9PG/p+a+Pny3dtZU1QUOy2JWh9J6xCKGsLhcTvyJE7c/PNN68WFLyWJSijLUdeg/qHh0Idon5t2fpDnX30tzy7/Shvv3f70u/SSCtvSpf/YRHjQfvb/Hzv/rYsOMx1Wu0MbI2Dtq1RN7+7hxKFkGx+5G3qzqO4f1Ku+nscGRDjK8a+cYDUpfW9xUSbY3xMSjuzHolAF4GJJOVuJfP/ZUeAAL/kkkvqRp+FYCkuOyKLLwfKmc10vA6tcrT4kMgWJwKTi0CS8uT2zQqpGQu7a4GskIIy04lEABmzIDMkAonAZCKQpDyZ/ZK1SgQSgUQgEViECCQpL8JOzyYnAolAIpAITCYCS0XKNkzYWOFRCx+7IPvWqGwwiXu6jzINg0P+bVp52GUtj3C9csFZIxPnakNLN9ggFPeo78oObfmzaX9bT+0atIlH/rCPPoid5m1637msfVbm5h4bbLQ5+t8jU0tTvn6WT99GL23z+FeU0TcGRsV3sVoR/5sbxnOM3dmWYUd7tFE+EVbkHIwyxrlql9333T7yezsGzMWlcZ17rtrjX33jx8atmAMwavEZR0aM0768JxFYmQgsFSkTMk5Y8iIBj1t4LMXzqC0pOLPYw/rOM/bYi2dSPSvYN7G6DTa5Pd4S+StjtdVWq5uU5Ct4DjMe91CGM309uxtBHs46lofyvUnJY1XjlB95LMu1W76TlLR/nIBstWXYqxmRnmdQAyN94DCFrnLkIAmPQ3n8qBs3Tl2W9h4E6RGn6H9HHV533XUzdsMOylsfEcQOFPn4xz9eXzjSHqMqnXvgaVzFIz/6OMaHe0bFDyp/ef5uHHgpinree++9s8oaqTm+1PPX2qiv5aXt4lbkHJxNRT0ip47tWQHSe37ccZ9OW1P/9pG9Ufkbq8bud77znXqEaN/4dY/n172wRP6OG4UPbIRRMmJUHTI+EZgLBJaalB3OQAummRIcm2yySXGMn4BUHPjg1CbxtGjPDDptaFxN2X3S+rCynK/slJ9IT+CGZuxKIJugQTzqsvPOO9fXFUqvLl6goM4rOiBMh2B4UcYdd9xR669ulBR1GRUoD6NezYiwkK0DMVgLDoVw/nF7rKR+IKQchdme7jSq/OURjzg8mqL/KBgOctFHxsKogMgc8vC85z2vHuH58pe/fMYxqtKz/PWpcab9PCnyd3iGsj0qMygeLisrOLTjxS9+cT0HfLakLK0DQBydadzDMxRfGK3oOTgORg6ncereC17wgiVI2aE15pzDPNSdojYu9p4QcGyoMW0e6ctoe9TLGEDW5rr8jYOQD4h5kIyIeyKfvCYCk4TAUpGyBhB8EQha50875cnviBGBOkHKxCFAvM3I0YxBmpF2nCu3F4upJdS2fHmw1AkAE5NAds71aaedNp19HOPYtbimb1iOXwgIJxJ1y3cCFQE7KiB1uLEW+17NKL12OKHISVaCg0EcoNEKfsdsOkDj4IMPnpVCNKp+SxP/hS98oZ6+NA4py5/g5J6lbPQdjWksOM3Ju52NL+5NlmOcbwy7vnjHcMJ3ZQSWnoNtjAUKVNs3o8oPpYIyR+FgdXbd8+0cWNFzsK++MEeKFG7E2SqE7kfKzlunOI7b71EOUpW/5Qf5I+UumYZi5hAV+LR4yKf7f8gIimKGRGBSEVhqUm4bhBi8AcixdhEQaWi6SOGAAw6oQjbix72aeN6EE2ca96VjWYaVROBaY+Iu5r6KYHIjbaTYnaxxz/K6EqgIwtGWEVgLm266aX27UrjXIm7QVT37Xs3ofnk4QetJT3pStUK1NwhaPEyQklOdnHzlSMKuUBtU7vL6nQKmHo66ZKl3T2kbp5y+M6wjnbj//u//Lk5w0rfdtwCNio98VsSVVUehUieKgvOXjctxA4+Kt3853c6paE4t4wpm/fWN3xU5B/vqbJ5ZPtGvLHbt6yq8lBKv77R8xJMGh3Et5SiTwkUx6SNl48u45r6GD9IdNMZbGTHbOkRd8poIrAwEeknZpKdNxuYSVyRLc+0GZONAAkfXmUARuCwJS65T68HuYcXOJpj4JjxrCNH1BfVEfoRDaOOs4q233noGKYeQc+ze8piU6gOTFiPWmzrDj0LgCEBE5D7CdY011qik3CdU+9oGT+7Z9tWMcZ9ynNLkndHw/a//+q/pF9wjBESMtAkuHgru4HGVgShj2DUIt22/9dy2jNtuu626X631af/SbLbT/32WMgyNPedbOzMbBpY4QiiPih/WtmWNUzYvBaXIuIPDnnvuOWPPw6gyjBknsFmLhrF8WIx9SyArcg4OqqcyeQFcbUTTPu1sAxzME/3OU6I91ohnE6Qf9spV41D+XkhivlFiu/O7T0bMpg55byKwMhHoJWVk0N1oFecWm2gRrPtY+0RASCIC8uays77H5WZdlbZvXXEQuUba9krIE0QmXF+Qt3Kcc90SPiFGmMW7daVlKSM3dW3b0JfvOL+x3GFig4mPTTg8AqG4EAwEhI1O7oMnF7/D8cctfxApS2/TlM1tLCdCybnG1h+RGEXAWpw6UlAQMoLgBm1Jc5x2DrqHALbWG+3XTi7adqOVsvQLBU0dHPWJXGYTBpFyuIb1pzKsvxL6rFNjUZlIoy++VR5nU5dx71U3CimFigsXIVlqUMdQGkblZQxTLLUnAkJjLbceqRU9B6Ps9mqMe4EI17XxZYlB+5wtHeO/vd937WbxmhOzwX8UKbflWFumtJjrEQbJiIjPayIwaQj0krJKmkQ0zPgQ/LTSCAQ8Eup76wyB4gXzsfFLGoKJQJFOILCt+5k0LaFH/kjN6/8Qbp8gs25rzZYF1tWMxREYdiNHsOZKgeiue0X8bK/KhEng40qAdAmX1ao+yIHgCgXDfQQY63qYkOp7NWMQPpLn3hdg6X9Cj5WMJH2QJhf3E5/4xNr+ljRn2+b2/iDctv3q0teX0nGvIvGWUOCiPt0NPG057qdgda0wrmlWcusyNRYIZf3i9754yxywWpEBUXLXctvqA1b8//pf/6u+PAIxR9Dv+t846I4bxKItvEQRrM22a9PWqJdlDka+s72GW5rbWPu8FEX7uJC9cKTbFvmbG/vss09d1on+HiUDpJOXuWyT6KjAc0QZjbV7ZQ6SEaPyyvhEYK4QGEjKwypE+HIXs9SQHVebT5AnQeMNRR6JiVcHWu/xfwhE9xPSb3nLWyqxdcsz8d3fCt24h+CPV9Vdfvnl088pIgiT2IdFavMJN+IPfvCDqtnbCBXlR14r6kqB4dazAQURE7CsxcCIYPJyADtzu4+SIF2WHtcggdR9NaM68wJ4VV+8ms96nd3mdsIT9gQ9ckJ6NskR3uFeX1FtbvONNlDQeEpgrx0hMPWhF9RTGFrlTR76T19K67E2j9vob//7XTB+eF+8CczvsOJVocRpP+WvL57HIvqgre/y/I5sKByhtFHI7LkwDuASgeX7n//5n3VDZKvwincfhcyaLDI2hm2cVH9xCGdZ52DUY7ZXfad8CrX+MEe1j+LVtsN3a7n6xxKTN1q1Gx2HyYBISxmjSPFykTXkQpShHsa0/HmFKP3WmPW/MTRMRsy2zXl/IrCyEFgqUg5NmYYc1lj7DKIJYVOLicpSi1frtYRIaLIcuRgJr24gwMSZ1N1g0pl8bfndZxRNWMLe7+pAS481525+K+L/1r3rGeLuRiuChet9s802m7aeox7IfNSrGQl+VnE8p/yc5zxniWWEyA+xIcCWECJuRV3bNsAfIRsTxoagf7jcYYO02qB/Y/mENcbSdG37WD7eIQ0/+cOhVXpGxbflrejvxoI9FRStNlDGKFY2BQbRtPFw0Mcw0kZLEKGULI852Ja1LN/1tfZRHNrQzgFnCpiP+j3CMBnQprUXwwcGvB+UPMGyBcXd72QMHEPhGkdGRD3ymghMEgJLRcomVqspExSItSv03SPOJ1xW0XhC02+EUQjqiHNFOuL74sSLC2swyogJGflIG9ZKt/y4Z0Vd1Z/QCGwIiW7wmzq3gso9/ueNkJY10ubTYuy+aJ/rIKyk6Su/W5/l+X/bBu3o9o2y1ItFCatucH9gp/3Rzm4+gZN7+9o4Kr5b7or4HxZ949zv2tNX76gHbKLt7RiWdlnnYJSxrFfjTvvUqQ3tHDCOu0E6berDpps2rHJjIcaL9MZPjJN2bihrHBnRrVP+nwjMNQJLRcpzXeksPxFIBBKBRCARWIgIJCkvxF7NNiUCiUAikAjMSwSSlOdlt2WlE4FEIBFIBBYiAknKC7FXs02JQCKQCCQC8xKBJOV52W1Z6UQgEUgEEoGFiECS8kLs1WxTIpAIJAKJwLxEIEl5XnZbVjoRSAQSgURgISKQpLwQezXblAgkAolAIjAvEUhSnpfdlpVOBBKBRCARWIgIJCkvxF7NNiUCiUAikAjMSwSSlOdlt2WlE4FEIBFIBBYiAknKC7FXs02JQCKQCCQC8xKBJOV52W1Z6UQgEUgEEoGFiECS8kLs1WxTIpAIJAKJwLxEIEl5XnZbVjoRSAQSgURgISKQpLwQezXblAgkAolAIjAvEUhSnpfdlpVOBBKBRCARWIgIJCkvxF7NNiUCiUAikAjMSwTmlJT/9re/lXvvvbfcfffd9fPb3/62/OMf/5gG8p///Gf53e9+Nx1/3333lb/85S/T8b789a9/rff8+c9/nvH7uP8sa/pxy8n7EoFEIBFIBBKBUQjMGSkj30suuaQ89alPLU972tPKM5/5zHLwwQeXhx56aLrOyPpVr3pVWWutteo9r3jFK8odd9wxHe/L9ddfXzbYYINy2WWXzfh93H+WNf245eR9iUAikAgkAonAKASGkvJvfvObaSv1Jz/5SfnjH/84Kr+x4//whz+Ud73rXeXTn/50zfeRRx6pVjDrOMLpp59e72Ehi/dpLWn33XbbbZW4r7jiikg2q+uyph9WGOt9aS34YflmXCKQCCQCicDCRKCXlP/+97+Xm266qbzmNa+pFipLdt111y1nn312aUmzJe37779/CYQQEjJn8XJNC9L7/s1vfrNsttlm5Ywzzig/+9nPys9//vPy8MMP13tYyyzivfbaq7z73e8ud911V73ngQcemC7fPfL90Y9+VH76058OJL9f/vKX04oFcueuFsZN/4tf/KKmVwZcBG2goFAS1DnayNUuLuq/3377lQMOOKC2RV1h1OIHB7/7yEN+3fCnP/2p/OpXvxrYvu79+X8ikAgkAonA/EWgl5Svueaasummm1YrFnEgE4SDPFmqiOXOO+8s2267bSXttddeuxIo0gvSQSYnnHBCefrTn15d1O695557KpkdeOCB1SX9mMc8pqy66qo1j/XWW69ccMEFlfjOOeecss4665THP/7x5QlPeEJNH+5t+QpnnXVWedaznlXz2WSTTcqtt946oxesV7OeN9xww2nFgvtbvdWRgjFOenlTSp7xjGdUd7u0lI0PfvCDZf/99y/HH398VVi42LfbbrtKsOeee26t/+Me97jiw0WvLPcidnn88Ic/rPfDTv7rr79+ufrqq2e0AdYnnnhiedKTnlS+8pWvzIjLfxKBRCARSAQWHgJLkDIL8NBDDy3ve9/7pi3XbrNZn1zPp556avn9739fLV/f99577/o/4jnllFPKrrvuWon4wQcfrCQmT6Tqc8MNN5StttqqXHjhhZX0WcGxicuVhXrQQQfVdWbfKQas0iB99/jt+9//ftlll11qfm09r7322rLxxhuXk08+udbPveoR1u446bfYYoty+eWX13IvvfTSsv3221erHOGzgFdZZZXygQ98oNaDtfvWt761nHbaaZW0Wbfd+ocl7Pr+97+/KjLa5n+YyLcNSBmuFIKvf/3rbVR+TwQSgUQgEViACCxBytzB3NbIaFC47q+slqAAACAASURBVLrryqtf/epy4403Tt/it1e+8pWVJLm199xzz3LUUUdVUrbDGkkjYW5gwW+sZ27svoCgjjzyyEp6QaR997Hed99991puxHNRI+O3ve1t1cKP3/uug9KfdNJJZccdd6z5uufKK68sG2200TRJUzAOOeSQSvTyVd8jjjiikm14EwbV373IG16s49Z1362jexH2MAy6afL/RCARSAQSgfmJQC8p2/H85S9/ubdFLNWvfvWrlZStg0b4zne+U3dBI3OEu+WWW1bXdLhnuXB32GGHwsoWfvzjH1fy/9rXvhZZzLiyioPkwsKcccP//MNC3W233WaQsrQsUR+kNiz0pWdFv/e9761uY65lH/XnQr/qqquqZStvVrJ7hagvslam3w8//PBah776I25YWDOXLyub1RyegGF1zrhEIBFIBBKBhYnAEqRs7fgd73jH/2/vvoOtP+r6gf/NHzKZSQZMICF2Q/mZOINkBBIyQIIYIiioKERBKZqAAhawIRq7QCAhKGCjJmBAWiLYUFEQBQsIYu8Fe2/jnN+8vvq+s88++y3n3HvuPc99dmfO/Z57dvezu5/99O+W1ZOe9KSVd8StRAHzlIWgk4SLH/WoR63e//73D+Hipz3taavrr79+JSxNKXkPSxElxSPfhlLmKb/85S8fPOixMaQfLaWsvlXhwuKMh/SfsqU0o4Ap5ijc8rcoZQpatKBOpeL1naf81Kc+dXhlUK/W5iX3hV41Bvv/HQMdAx0DxxMDJyllw6R0LfSykOnDH/7w3gph72Ul4Wl5L37xiwelZXW0BUneoVLClO+rX/3qIfxrH7Dwr49FYwnDqmPhFa+7lSg53up11123p/ha5ShN765LA0E5/z/0oQ9dPetZz9obQ2uF81j9973vfUO4/Q1veMNe/ylPSrPVt/I3St04b7755uGd+Ic+9KEBh8ZPCXu3/e53v3tYVS6cb9EX3D3/+c8f4Gec8NgXegUb/dkx0DHQMXD8MdBUyoZNWT3mMY/ZW7lcrh6WbyW1cLTQrhXWlLRFX0nxVtVTRhib983rS/0nP/nJw7va1CmfPNAXvvCFqxtuuOEERVWW8Z1SEwL+wAc+cEIW5ce4yOppfWitcJ6q/973vnf1sIc9bA8H6osIUMz6Vfat7G9C2gyREkfeQVPetkzZLgVv+mUhV40/g6GU+0KvE6a1/9Mx0DHQMXCsMTCqlI2aAuEd55NQbTBS5tdhV2V4izzn1LeyOyFsSlOdeM6BWT4pdp+pBA4lGLh1WeHftK8v9Tvmufr63Krf6lvrtxJHvmvPp/wd/Bb+jEV/+0Kvelb7/x0DHQMdA8cTA5NK+XgOuY+qY6BjoGOgY6BjYDcx0JXybs5L71XHQMdAx0DHwGmIga6UT8NJ70PuGOgY6BjoGNhNDHSlvJvzcmi98k7fO23vuE/nlPf81hD43tPRYsAcoEtbFu2asPMhCyiPtmebt25NjrMI1h0HHi3X46zbg1Z9+LULJOdGrAuzl98eBrpS3h5uTwnIVtnbc25f99Siu1NiMPvoJEFpC5u99fWCxn2A7VU3xIBFky94wQuGg3XOP//81Xnnnbd6xzvesSG0o6/m/Ad3AeC1+pz+ud45jMm5+eXulrk6ZX6rPl53f4BtqY7wHVtoWsLp3w8HA10pHw6e127FimtbqngK+dhOto7izMrtqcbBdCIaBl0H9hTM5C1pP2U3ebL2eR7Bj2f2ggceb2BpcvRpeSDM0nqtcsZuPz8vL/1r7ZNv1d2l39bB30H3m3HEW3ZmgBMCN70zfdN+2bpIoWX+bJ8c2+Ux1QYadWKfMxPw9Lp8Zvvp8573vOaRwUt4bKw+Q9QJhXD70pe+dHany9QYe97BYaAr5YPD5YFBwvg813PPPXe4Bau1z1tjtnhFYBD4hIgkFO1wFl5fzgWPwopQUUYdnrLT1RgBScoQxkJmwlvKYmACJV6kMhRg2ie8EpZb0n7aorjAIDjWsdYJNgewlPvIc5MYvOj3W97yluF88dtvv31ow+lu6WPdvv5Tym7/yhhTZpOnc+Evu+yy1d3udrcT9rnnJjCeYLboMYzMBW8q82N8yfe7fOUI4TIFf3BIgSUtrZ/y5TNz67x357OLIICvD0tww1hCOy2F7vfMM1hgBvaYJ0gZOie/PP1PG+gdPN8lc6vN4DBjiuEGzlKFCJZjfu9617vuzR/6evOb3zwor4zRGPCOcYCP9ssEjnP4n/nMZ54QKva7O+WXJHDxMjpIWofHWvUDxxNNOuffuQ49HT0GulI++jk4qQcEtgs8eLCUJmHrQwAROAQCpeM4VAePUNq5+lLeO9/5zuE6ybPOOmv1UR/1USvhvygsMCQCVx1nejvExDWXSQS9w0x4jVddddVwspqrLi+88MKVizr0gfBTJu2Do8+UxlT7MRzA+OAHP7h69KMfPfTf4TJg10onfaqfBLnDZxzmQjDqDxwRVpSvU95cC3qHO9xhT7AK1bmnW4In13gKJxqDg3Ke85znrJ773OcuUjx1f+r/XdDiAhYXrmT+omSV5f099rGPHe4TJxBd/UkB5lhYQt7vDo9xr7g5puTdcy6l/63rU+XP1R+AjPyhLFyvim7g7+yzzx7abx2+0wJhDkVe9BstJZkjp9aZM/2HG/e0G5u2nLlvXuvUOpLXPDt73nxRcJLwttvborz0A13nACHzfNttt52ktOv2/A++2/LggTFkDl0t6+Y5c8Cw0Lb2nLqHvxjRDLvSKMC/11xzzUknDqJ93q9Q9pgxkn61lCr+hbsWj4fHpuonz5O8cawwfjfuno4WA10pHy3+m62HSYS7InDKgoQN5ie0HdEZa109ST5h6BjUa6+9dk+xYzjCMGUoCYpJGe+VkqcuQeIqTt7m5ZdfPpzh7Z5rFj8PmVAirLRNCAh/uV2MAFnSPkFLKfHCeBqOY3X6mVPUliTCm0J6zWteMywCKgWhcTA+jIliJJgJVUohnpK+G5+IBKHoOFn3YbtEpIXzJX0qyxiP/sFvK/FK7nOf+ww4ILgJaSe+WdAkiRxQJpSAI2ONl9DM+0j9n7o+lec/Vb/Vp/wGf+ZEu/X1quZ2SUKX6KE8RpchwvuMoqY8fdAQY4mB9MpXvnJvjtLOmFKmNMHLfAlvP+MZz9hTypR/6/pV+J5LeAVsRkSSe+bBo5TxGqOUInakMCP5Va961cqxvOFDNGn85imGQmChQzTCUGS8uDMAfbZSSykv4bHAatVPXp5wh15bRlHK9OfhYKAr5cPB81qtYGoe0hOe8IQ9gczzIbyS3OJ10UUXrd761rcOgrwVCiPgMPwYs4NFITmm1O1eUcoR+JQaJpVPyFJu4FHKkvJCgxSAtng6+pk01r56QspXXnnl4FULi1MA7qumJJcIfkKNYcLLcpQrL5vwKXEEJkFD4NWJUoTf9Bc8nr4LREoYdb2l/4PP8zU/cOjDMEgikOFLCD7JvGcOKCJKSv0keInxMXd9qvYoxbH6gTn1hE/4G7tedaouHLq6NEqT4kQPPLyMQX3Gk/l3L7roRmv+x5Qy2AyVKGXh7dzpro2x61eXvJumlBloof2cT//4xz9+bwW19hlSiT4ZT4w+380nD5Sh0PKGzTWDFi9ffPHFw1iMFZ2EDsCZUqpjPKZe0lT9lEGP6A3P9HS0GOhK+Wjx32ydQPEe6s53vvPeO+U6dIhpMbp3oEJnGIrgiMCLYi89h1ZjFDBFWytlvwlDE+5CdBhbGUKP1U8Q8goSvvbu7RGPeMQgYLUz1b4+UoBnnnnmED6nWH3AuuWWW04QSK0+5zc4YIx85CMfGW7YEs6Dt+AgiqsWNOq97W1vGxSOcUnqCLke1DtlXuGnf/qnD55UDAcraCO0CUGh+4SjM6Y8o4haK471f+76VPUp1Fb9tDH3BANdle9y5+qU+aIeXsEw2uDZpSsUsGQMvrvz3OsT4ftzzjlnUMryyhRclP2IJ1sqZcYD+qTU4Hns+lWGwVxC315n4EGvZtCmyFUWEqb98vrWGiYeYGTwlFtKOeW1ZQ2C98b43Krz0IkyY0p1iscCe6p+WQavMBIZRz0dLQa6Uj5a/Ddbx2wsbALLO0ZeRxbHqEBolYKLEBLKc11mwmSlQCgt+brBKaXMMxbqE6bjUVLKwteUIKXmJjEeof7deuutg2eWd6JT7es7pSIUSGGq76NOOa66r+X/pdAKToTXCcB4pJQCbzEh39TXBm+JEKIwJOFawpB3F1zrDyHMm1nar7QRzyPvvI2vTPL1rTYYUqaliJLnCe+1Z6OtXJ/KmKL0S0VW1l/yXR/AWKLEWvCEqdEOT9B8m594tejOdaXey6JP4fonPvGJw+uMGlYLF5SiG+QoRcn8oMEoQEYWHhq7frVuo/4/SpcnLBqEpkIXyiafUVDPbWDpg2gTwzjRpeR5omH88vrXv35173vfe8CFcjW8KaUcpT/F42P1y76YH+sqzENPR4uBrpSPFv/N1ikDHh+BIvTKo/DBXBiWYCMoecaYiAC+8cYbBwEYi5xA4CVR7Lw2ZQjCWpl5F0aZE95RPPlNfQJRCE99ZQg9QsrCL15Q+qe/3o9RNvoH1lT7lLxw5Ute8pJhDMbnfSrlOJeMwXWYBJ5V5vrm3a1wI28jwhNurD5PCFmbEXjqeIftfaDvFvHwiCzsSfic4rv//e8/GDxRJnN9Sz7Pg9EhTF/On/f4knz4GnuHPne1aRRe6/pU0QNjAp+w3TRpgxEGR4wz+KsXEU3BRoO8V9uBwMkiO3XAsqaAUoEfEQNKQaQi3mhgt3BhHilkt62ZewbkFVdcMSz+i1GG7r0Tb12/GthjT0qXp+1O9Jpn1En+3NWyaPApT3nKsLitbCs8rH/GgLfHaGxMqS7l8bH66Y92rQmx8Cz8kbz+PHwMdKV8+DifbRGzWcBkRTJFkfBuro6ktC1AEbaWpxwFx+uLYtUI4UShpn55dWY6QfASPvYrpm5+876TUOENgK0Mj5xiAZugBdsqbitQeQS8T4uWJOXG2tcWwZyrLQOH15B+pI+tJ2PAyvCMLeHFGCXqEKYW3+T60PIVgDZ4y+rBIwEvpO6AhwhHXqw7uadClK2++U3/1BWWTR/146abbhr6Jd/q8frK0cCDm6mrTZUzN8FffX2qvLn6aWvsCUcMsQsuuGBvnpfOT2BSCIxLAj94lYfGGQyZG14yz9YK/zp8O4YLIV+L2cwhxcZTZpiFBvSf0VNumytpIH1sPSmnqatj5/IDE6+iQYavcSThEeOEnzlFOKVUl/D4VH04Mg/oCD57OnoMdKV89HPQ7AFvzQItTJcPJRcvjjeY3z1Z7q1UlhPiIgzLhCkJy9IbKH/Ld09llPVdCmz9Inzk8aRKWCmjj6329TvjAKcU3GU/6+/Goa3U9dRWnfQFXPkl/pQzDl5l6vo/+JWvDQLT2NZN6hpb2k4/I4Dl669nK+mL/BKXrXIl/srxL63fgln+pn/B0TrzExj6MYZDeYEtQmKsaD44KmGM4UI9uIUH8MyVZ5nQXfBf00BZrv4O1tTcz+UHHs+fcSrKJFoQfivnK2VbTwZWIgitfHAyvhaPterDEVx7DeXwEAvG5mit1Xb/7eAx0JXyweO0Q+wY6BjoGDgBA4wHkYDWGocTCjb+8VpHeJni3SS16lPAFjaKbHmVMmYcbtJer7M/DHSlvD/89dodAx0DHQOLMEAR1pGkJRXHIgBL6iozVp/HzrPuabcw0JXybs1H703HQMdAx0DHwGmMgcVKWejEdpse5pinFpYpfLW2QczX7iU6BjoGOgY6Bk5XDMwqZUrYEYRW1lqla6FET9MYsPDCys3HPe5xwxahcvHQdM3luRaZ2ONo64vPfgwAISwfxkSSMJvVooFvsYj9yeXCF+XTByu2W8lCHp8pY67VfgvWNn6zqAjuynHV7QjzMbI2WQgDL8EhPNRprH00Y2ta6nraOlTDUM4WtalFQ/IDxzjKea77c9D/oyNto6WpPk61azU1OGP9JpMyvnLbn7Hmd0+rkM31JvM41b/95Jm/8NkmslV9vGl8ni0cZw6Uwa/lYsol9fczvl53fQxMKmVM4FQnWwocaRjGMJERGLbKhCjWZfip4x/XH8rh18AALSaAN8LWjTK2YNgDOyZQNu21fZ3Z6mHblNOb7Odctx1MaquU4yXDrBSoRSB3v/vd97bzuM/2gQ984N52JzSANnLYv/atLNU+oYc+HNphn7TTwdBGK7Xab5U7yN/0kaC3B9s+X2MYO8mIsnYSl3Oxc9DIkr5ow3yoly1RtsUwbOTBx1T7FKltbqlrjl0M4bANuMc7+M4JaLZ0tY6ONA/2CTs/GxzlylPFloxjP2WM0T5ebduyZU96vbJ6DD5aRBv2iqOtkj7LOgwVh5AET2g5iteWo2wblO/UOYehoM1dSGiLbNBH84s+LMpCH0sSPrUHP2O0NczRouFjMMyBk8mCH3vXs198Sf0l/ehlDhYDk0oZ8fKQKeRyoiMw7MG8053uNByPZ79hGJ4wYPV5En6x4qxARHCYxuEXS66Fi5XPygVPAoN3hcHBZGnKty2iTjEeWlaisnPwY4WX7TNOELa9kY6g9B18DFAyFKJ3oYM9gATwQSVwMa/DQ7RpjPZnYmq4XZoId3NG2DsCsfQWzTfYPl5bOCzEMYM5MYywdCGGlZvatMfXOOFJf+w7ZTQ4OEG/sne07NtU+2W5g/5unPZ52xOrf44HHVPKrrW79NJLh3O611HK6NP+bituKVB4gQd4RKcUxlT75hiMzIFolUM4HJIBb4wmZ5/DszPEHaRSJ303BxR2eKVlRNb1DuJ/fGn82crDQEMv8KH/c8n+bfKBQrbPGay67+iM0WcPtAiCMWZrFPj+D/4YQw7YUbYlJ+b6c9D55MTb3/72lbO0yQ/9M0bbppbyMJnCCHGSGxpzjgB85ehW9MMowrfkXGggcnSu/kGPucNbhoFRpUwoIJqc91qCi8CgFBxQ4MAFYTqTLlGSBLRl/JS6wy0ocEQnvPTsZz979lo4jEsAxRNjBbp2DTFjTszeujYtSjH1H/zgBzetxOSPweeRULpC9qx8Y3BcIIJ2XKADFc4444zhw8otjZISVwSAU4HsAywNm7LMJt8zztRlMfMCCP+lyeEgTrwyH/CZ+avrm1uHXkQpGYcLMxxJmTGZV3PtKE44IhAoYsra71HmJeyl7Zd1Duq7sTK4CLKx4y55avaXGifhl/Ev6QPBinde9rKXDcYOQ84hJIxR/LOk/bIdp6M5mSr49tR/yq51nCa6w29oD020Qt8l/IP+LkrAYCiPEbUntrzQZKpNNARnaI/BaSwlfaL/8tAL46N4xhKZJCqyVOGNwTmo343NhRYcniRKkjwdMxBTLk/lnIJGTkpOuXOsKVxITjRzahqaQ7voGe0ljdVfh84Dqz8PDgOjSplQJYwokzHLlhXKsnP6VJkIgShDVrpywmjO6kUoBAaCmLoWbuzaNbD1Z+zatPRVfWE7xz9iRH1gTcZKnINPIbugHuOr+6Y3vWlgAMcNEogEAO+SQvNdmVJoBB/6g/HW9WJTf8kTgzujlyHR6kMLBk+b0iAozfFYXWN1zKLTkoI7gpKiLQ8cMKfoxbGJ8bgJTv+3lPLS9lt9P8jfcj52qTzAZ/gxKHi0wvSutxQuXieB/YAHPGC4BhOfgBWlGjhj7Sffk4Izvy1hSdkwKuozrnle8M5gZiAzLPHfJq84yr4s+R6FyVgocYb3KWpRpaUJLTEsaqUMj2iLko/hLESLT0OnaYMsc/wq5bQriRyBnzLCga/QCZlZG92tfhsnx8elGTxi8KKglSe73ISGtzkRnAevEEKDc/Vbbfbfto+BUaWMqSkSR8RF0dXdUebpT3/6SYqbxUcAYJAQF+b0W4QHa06ohbKuE0Ycu3aNJxbvc+zaNPUpY6Gtloe2FL53MVFyhJ/+h4kQNCXE+6mFQDke4xdxIDjBOOikf/DMk1sKXx0KFUNjUJ6/cG5rHKIhN9xwwwkeBq9YKLWkjXhm8BGcwbNwZa2U12n/oPFVw2tdDGHOePGMFjTuSERhbuHSpQkMZzJ7h3eXu9xleJ8pfBrcBE6r/eR5wiujiEJpzQ9eaylltOAYUu+0KUHj4HHiV9+3mYxdVEm/SqWc98PrHOdo/LmiscQdo0mkTAQuhvMb3/jG4WhTr1T0QeIZwrGIBVi7khhHLg+JPNEv84JXODljMrfsv7EZs9u1vC+/5JJL9ujE+ClgvzNeOA1oyNGv5kb+VP2ynf79cDEwqpRZbRiYQBgjEEQ0ppQxZKlwa+FBUJRKuhw2RcH6ZQFmgQIrz4IG4UbMyXIeO5M4CkKZVt+XwhfSpVgklq13XBSspA+tsNqQWfxB/LzR2msoimz8lWBifDBO1lEYLmvXH0xqXihkCkgUohT8PHBWNus7Qk5n0YYr8kQAUh7OGSkUdXA2ppSXtr8xYtao2FKKwnzXXHPNYFCYd+sChAm9oy0Vw1QzCX0TuiI06lKSvO/gR/1W+yVc+ei4ZVwqV/NV6uIvQl97SZQinltHKabuOk+0wvhmdJdKWTRCeHap8ajNMaXsd96heaJwJDhiLJJZoUsGJD511vguJfPDYIqTom+JQKGZkt9a/ZbP+OB4UPBeZXil5OY20RcK1//WS2QO0J2z68lNskO5Vn00N9d+q0/9t4PBwKhStlCDUppamGFxBeFsBWiZWoKi/g1jRjGUdX2PMB+7dm2MUQNHfdah96WEY52WwMfIDIMwN6H6oAc9aHhnCh7hLKxmVehUUh8Ohdlai52m6k7lGQOLVz8jlOry5pBQwoBJmJUlzsjxnpzRw/g566yzhtCi8kmY0xhrhcCo4WljcN8l4WirYEtDzO8EpPeyQohJS9tP+W0+KSjCMZdoaMsiI2sNgiPexh3veMdhNTo6SDIHXo0wXmohRuDxksv3g0LJ9aK3VvuBT5lbmASn5q2VRJz0vw7NxqjmISZ5ZSOiEiGd37fxpHS0RXEk8erQQugJb+AJcmRsfPCKx8iiMoXHvVYIX3ESKGXRNAlMXrTtiUuNqbKNbX7Hm8YlIpjknbDwfhZq+b3Fw37nbJBxbmoLb8EDnMeRovAZZgxLiRzAszfffPNg7ODhVn2GdeTeULH/OVQMjCplBI24KZNSUOsdguCZsby9r8IwrLUoB9/ra+Pq3wizqWvhvH8au3aNUqYwWcpjzJYbfniAVq4SEgyBlJ+Crwz4ufRbGFKoTH8jUCgjxO2dMmYC35hq4YKpMALDhSA5qEQ5sIrd2mNcdfv64eYmCjfefdrWD4qEdW1uLYAxjrL/BCWFzGqvxwQO/DKaML65FZrNTVWhD3gjeKy6hW/9NHdL2k9ft/Ek6NEqnNmSYu2B7Wv+9zuBZN7gx/9wcPXVVw/eVhl5Qf9jVzsaK94Q+gcXLhhQ3iujr6n2M2ZKNPOS3/I0f9qwAEz/RS1KGtBPwtlCH3DwADpM+4Gzracxeo+LhrwCEeHSFzwTga//PDnGHHyUCX8x9BhLDBlhXXzGyI4hiKZcpmBFO8VjRXpoECxrPVzFWRpGZRtH+R0Nmjs85FWJ+fG6zFqZGBlTPKzveE84OleTWvtgNXcMY5Ev/+NvTpFy/k/UAF1P1T9K/JzObY8qZUhhbVNELPxYY35H7IifJ3HuuecOC6Ky+hixWZBSXxuHAcvflENUY9fCyedtZC8ujy5XF7L4CDuf0gssJ1J9QjOrq1M/ns4cfKFYVitv3spvAjarGtOO//0OtoU0BChhmUT5EIwOEcEgB5UwK28z1wLyeIX2y/aVEb7Sd8w3lSgmCrxUOOaQV2N1byvBH6FoO47xU7zG6PfQh98tlkMjaGXs2rxW+602D+o380I5wRkc8oQ9Q8O1EUIxWPhXvyKI4dd6jVLjR1teE2ibUpprXxkGl1XvNY3rX/aRw6v+n3/++SfRgLZ4U2jAXDBQa+V3UDhtwWHYwI22jb9cg6A8evFaAJ0xgMoE5xS2uvbI+/he7rMNDzNO5bm/WuTB7xLlAzblvovJPDKY0Z3+l0a//s7xMH41v3Cr/r3uda/BkQr9wkOMQ/nOHSALgp+5+ruIs9OhT5NKGQKEMHmsFg0ID5lwH0oagwux8ap8jxdq0gmSWMTgtH4DJ3UxZSzgEvFpB3xlojgIHJ+5RDCoW9dPvRb8hMdZ+bxHdf3WSn4PfN+N08dvvAJ7UWtPtQVn3d+MPd5u3X5gwZXxl/OQvPKpXI1Lc2MOPaeSPtT4KenD/KIRZcr5K2G22i/zt/E93qo+6Z+nPoaGyzaNB22a1zL5Xfkad2WZkr7KckvaV76sU8LVn+C07H9oMGXNfcZWK/eU2eaz7GdNS5EJLdzqd+gGDUVO+K2m5+DYs0zKGXM9b2WZo/6ub1PzM8fDcJr6nq2xRkbBYZ2/pP5R4+h0a39WKUMIj5CVLaRr4o97QsTGKtyIKdZNBIH3WDxtYamaEdaF18t3DHQMdAx0DJweGFiklKGCoqmt8OOKIl4MpToVHp8aOyUMBnz11DHQMdAx0DHQMbAUA4uV8lKAx6XcVOjwuIyxj6NjoGOgY6BjYLcwcMooZe+HvBPZJJy8WyjvvekY6BjoGOgY6BhoY+CUUcpWEdqDWO7haw/p9PlVmNwWEVthrJRfN5X1621vgZVFNvUiHf9bBKdtH4sAy3fnIg3pW7bqBGaeS9pP2cN+Gp/tbxmflcI1Dpb0yWIsMFr1s60p+RY8tRKDVF/KRV9wZ31H+mfvcVl/Lr/VzjZ+M0Z9xL/7WWiGDqdeB43lM+K1rQ/odddS2b9N1+sEx8ZYbhkzVgs90V7opNz2GFxM1U+Z/jw8DJwySpnQsXDKNpSe/neveH11on2cSxUHYVDWv+KKK1ZO2iLMKQHbf6auXiQE7QG1Hcu2nMsvv3w4KCLtz10tOdX+LswvAWk7ja1utpPYVmQvs34voyOTQwAAIABJREFUSfBogaQT71Lf4RlRrPCUbU3y4THHntbwXURhL3JJ+xSw7UbZDqN/dkhE8c3l121s438GibMEjA8e9c9ai3WT7VF2gJRnrZcwxvLhurwa0fZGe+p3JdX9sx/b9i20szTB8dTVjLZM4U9zgFbKbZPamKq/tA+93MFiYFQpEz6lhcWTIqwlT0KLAPD7mBUqf8pKXZoPBgXhhK5dPAjgYKdkGTRb1eqrEx1wAVdzCdM7UML+6Vy96LjO1LfPmAE0dfUiGNluRoETvuVpVYRL62rJHL6i3bp9h0s45GAXEu/Bnlf9JbhyyIi970sSvnDanb25YDhxyzGb5fY4ihNsHwqbYWTvd5ng0AEZF1988UlKGQ1ky54DI+zJB9/cEPhT+WUb2/iONhwP6mAhfcS/61zdmD7xju0RdwiO069qhTWWz+hx4p3DOXI1oi2Oh3H2d/o+9TQOc7WfqxvRKL6zMwbPMnjKLZDkK/qjmBnRysNXcDhXf6r/PW97GGgqZQrZJQc2tbPgfRzflhAnwY/BHDjh8AxWsLOuKeckxOHQEXksNefgrpOPoNRn4dn07jQiBExQnu4JbsauTiwPBxjDE2XgBChCM56b8Del6nf59nw6WWjq6sUSvvOhCYco3TB+yuRqScqaUTfV/lJvNLAP44l2HWaRIxzn2mR0OCwnR1oasznj3ba8RcaIG33K1zOEphPRbM2jXMo87Zc4hndHJjqsJsbzXP7cGPaTzyOtI1vrXN2o7ShWdOnVVX1Rw1Q++eMAIHSWlKN+3cJ01Mnc7vfqRocrTV3NSE4YvzvT0S++LtNc/bJs/354GGgqZRNIyWJwxEOI+GACiVJ2PFt5Aw0CY4kiBOUIc6d+EUo2/LOYl+RHYGFgnpSQLE+CQHMdZO1JHB6qdqclCtSxg2U4jwUcIRRFO9ZjzMmCZmWnLIOL0eO3MC+hzsqub3kKXOUoWcztGsuEv5OfJxrK1ZLqUBpT7aOhXUtCyOgxSnaqf3AKb7yyHJkIlwwmv+X9Pz7x3RGc+KUMP8tjyMArj9cZz1NRIpeGiHSMGa1z+VPjWTfPWJ05LVxc4gvvUtSlcT4F2x5/+HJEJ2OGUg69qjeVjx/UEQFKgmenf5ElMVySd9hPx4LCT3naHlpYenUj+pi7mlEZOOPYcIzwNo9ZWlL/sHHS2/tfDDSVsnCT0Jt3hsJnQiMmMYnF6baZ22+/PT8NoRhERkiz2l11x8rHlI5sJHQckec921w+xcyLc5FDkjYx9JRgStnj/qRAeQ4UW7xKQgjOxjyxGicEJCVjDglJSsdRkw5NCUwCsHX1YmB5lyfELaIiqtFaqGIuW1dLLmk/7RzlE93rKy+Wl1x6n2P9gjf0TsCWSpmH5lhJr4UkAlJo1lGzbuvBa1EWFIh32p74Dz/BdyspA87Y+epz+S2Y+/kNjvZ7dSND3rGk3uMz5BxL6ozrpLn8GEEieoxFjgQadVwnpbxkHtPWNp4iCfu5uhFdMuKmrmbUb7yMzhgBZDrDPYbNkvrbGHuHOY2BplJWxcQRRixL4evSik8YKAefK89qc4EDgWMFoHNo3SOrbiw1oW6rdOfyKW0hK1ZejAHK3DVtXSkvvzpxauoJJXNmgRClKooh/FyGCOeUMgVCOHqnTIC6vMT8JvF4x66WXNJ+4Bzlk4fp1Y1owFJBDm+iGC1PGY4YvRJ4wqyMGe8XGa14TmSBccSoxWs8RcqcASWvTG60YqzyuMIr6+SXZQ/qu3E5137TqxuNI5cnwAePGG1SzOgL3d12221DZKfOR4vBA4UkYkEG3fOe9xzqC/G7CGLpXB4UTmo4DOH9XN1obAy/1tWM6CYRx7JdERfGJRkNR+vWL2H179vDQFMph6g1S8AQCq4txGgSguIps4aT3ESCyChc3gGl6pYYAgeBJKytvDDNXD4vg0WbJCznvVpXyqvBcyD0vWdPqFmEIlcnRuAQ+LzqqXCwPF42vJq/0tCCe68weGz1ucKZlzwpLwIi4VNCY+5qSXXn2g/8w37CoQVCxk7xtRLeoGApyuA85ShxyjLX5lEkPDX3U7fmg7IRfmYUiSwxYEUuKBSXerg60voMl1SkLcLValr82Upz+a06B/UbGbHp1Y3w6srKjF/o9cwzz1ydc845g7FCvpAtrfx6dbHxwDdeYLgwbnJL0kGNdRM4+kN5bnp1IxpgVLeuZsSzcFgnuy04Thm/yM069Wt4/f/tYOAkpUwhY2YWKMYiIEym0DTBKwlJs+pZ87Hk8w5afR+EIXTEOuPl+hBghBOBPZWPoORnXzJLmRK/3/3uN/RrO6g4taBOXZ1oJOZg7OpG+eaAIjAvQmkUvBCoKIU54nEIexIc5hE9CAHGAk8ZNEJ5uXJOOfCkuaslp9ofABzxHyFjq/29EoAfY6/3gE5d3YjWvf4hdPGIhXCiR7w0iVC1VgL+fHgtFjQyfM0dQ0nYURkGE4WN/2KEMZK8bxb2tu5C/8r5mcvfNnrRyX6ubqRIRWGMn8FprOgZTuAOHqbyjU8Z84gmKSKRCxGd0PC2cTAF3xj2e3Wj+bYOpL6aMQsCSxrDv+hZxAFOpbn6U/3vedvDwElKWVM8AwJE2NnnHve4xwlhTUKEEnbVWFZoW8gVgQEGxSp0JB8MFn+5gnsunzXMW1bPHkXvlrwTGbtKcHso2k3IGA6jlVcnMpb8LhHsIg3C0+VikoyGsLIQL3NTXhtX5o1dvUhppz4YFHLa1zaPjycT+PUeybINc1y2nz4e5RMP2GIUb8w4ymsD9W3q6kb5IkLGpa73xjwTuJHQf65vlE8hC2EzduoEVwwmxmkSA8HKW/MDf2CUV2OqM5UfONt88gatcdA381+ugdAu43/s6sayXww4UQZrE4K/Jfnewef6R3wgSrELCjl9NxZzHhlZ84D8KR7G6xRrro+tr2YMjYmwmANRH0Z30lz9lOvPw8VAUykjBiE5FpWPEHTJDHmnbIV08jFOnQgYdQOH9V7CmctnLaurL+ohsrJ+3d7p+H/miWdVJ3NCMLYEPTyaj8xNGVIt83gm8UjMZea5LANGLezMlb6V86+PpdEw1n49jqP4H87gLt6qMcJDiUs4MG5jHUvy1QWnTskby095OGPwai/J99KbBqOen6n8wNn2U7/Tt7L/2jUudKdM6GKsP3Ac2muVaeWbq5J2p+apBfMwfjPu8EjJg2l7iodTxjyHhmo8ljRWOk2p6zlVvyzXvx8OBppKea5p4TxeQ94xz5Xv+R0DHQMdAx0DHQMdA/MY2EgpC5k4GMEpRT11DHQMdAx0DHQMdAwcDAY2UsoJO5WhvIPpTofSMdAx0DHQMdAxcPpiYCOlfBToYgB4L7eL74WOAh+9zY6BjoGOgY6B44eBU0YpC5lni5RpsIDBb1aCe7YW0hy/6TpxRCIWVuHCgZW+66a5+mV+zj0v29hvPlgWg+m/T73lqGzrqL5bQKNvVgrXC5WW9GmuPmPTqnVtoGP4SJJna1XwIx/dlyn4k2dhWpIFQlbIp66nrUGHzSf6YSV4axFT+jr1tFAr+DEG+ER3ZbJTAx7q35VZUr+Eddjf4cfcGZsFX5skc+pT06f/S/qxBbKFo9CQPuwiD26Ck1O5zimjlDFmecymIw9tM7HU35YQ+zUJodMlYeby6kWnJ+3n6sa6fg2/vNoRjvebDwYBO3bt3FHPI+FFidnbisZsqSmvXpzr35L6oj633nrrAFsbTp0qDwfRvkN6zj///KEP5Zan4M95xuo6YAMuo5jLLWfJP/vss4dtQeZu2ynK0NGftkNtcgmE1cJOmsvWOluHXK7AWJFHgdgrjnbtp69XF0/V3/b4l8A3/+XVks5isCe7pThreOaQvNvP9apg7jIP1mM+Xf4fVcomnXfAevLhKWEGyZNVx/r1u3wWWZ3kxwrcTz4YubrRiVHaty1Lu/FEEDQGrj2Juk/H5X+HstgbnKsXHT7hgAm4WpLG6mev8djVik6oIjQ2zTdvku1QFIrDDCgQ8za2fWvJeA66DLqeu3pxqs25+nDoKEnnAcCl8cNJ+XoGru3Vd4hLtmdFoSrrYBN7b/EW/jD/6AAMXlK55cweZ5Gmped3T41tLk/bN9988+qiiy4aDGl76Vt75efg6D9lay+v8VP04W/7yB/5yEcOCtlJf3BRe+NT9efa3na++TeuTa9upLw5KZtcr5q9yvCzyzy47TnYVfhNpYzxd/XqxvK0mhKpzuYmwCjp454IH4cKOFw+3gEl4DYnwm/O0p6q75hN+Y7xJOiiJHI0qgMgCEjPTfLBRV9OqZq6du6o55CiXOfqxbq/Y/Xj0RGM6JUS5RETsvaLlolSpkjdfR1BmnwGqcN4HGCS5Gzs+grV5Dk9yqUjteJK/kE/0SXDnbHgNMBNtk9SGmjsta997YCjkq4Z5sKu6NLVlspFYWcsU/VT5qie+r6fqxtjdAndo6Gxm9zK8eV6VXUkhw/tMg+WfT+dvjeVMobPsZmIB7H7IASJN7ZLVzcSaDxl1nk8ieM8iQTRfq5unKpP2RJmTuRiRUcpU/qsetEINAHXm+QTRJQP48Hh+Dw9ryGEXxlWMTKOcv6MecnVi2N9nKtPwYpIGL8xX3XVVcMrGGeXw3OS8CyhCTdOF/O6IvStPqNB+DZJeP3SSy8dLs/Ib57OFXB1piNzDzuRFWTJJkoZLaAP4WunUsFVrXjhGk22lPKS+oeNj7TH4NrP1Y2Bw1BBq2NKGQ7Ix/J61dSdu/ox5frzcDHQVMrCYafK1Y08YzcRORu39iYOF5WH1xrBzYMqjy2k6AimJVc3TtXPcaleE7SudnRGNsWwaT5FTrA6E3ru2rnDw+iJLRH0vA+ebLwKwi9XL1KIU2mqPiVLOfJw73vf+w5teDdIeXl/zeuLx6xN33mcvBzKO0oYDl3aAJ+ufBSetq7ikksuOeHSFvWdI+9Mgbx+mur7Qefl9L9NlLK+UCrG7+xqr2sooBgm8o2PoddSykvqH/R4l8JjKO3n6sa0g9Y2uV6Vg8XI2VUezPhOx2dTKUOEyRbe2OWrG604FMJ94QtfOLxvOl0mkKfLaHrFK16xJ2gJJ7igqM3dVJqrT3hTCGNXO+4n38IfCoUgWefauanxHHQe/Amzz129ONbuVH3zxnjEW5dddtnKUbVJ3jFatGQtR53gjEdYKiVwXAXJi3Q+PONUSLusb+0AhXVUBut+lXKJB/gxJ+g3aU4pp5xnq36Zf5jfRSP3c3Vj+orWppQyXvUunuGX61XRAv5meO4qD2Z8p+OzqZQTpoYQk76LVzdiRkrJQfWl5Xw6TCLvgdLY9OrGpfXh0jtIuB672nHdfDcdxevc5Wvjlly9iDdElYTzjalMY/V5t+rxtoV1vQdO8r28bzm/e5oDFxaIhNTvhSlsESMrk5XJugrCWHvWCZQ8XcLd9nfvyymf1ul/FIZIRGs7T6tfwvO10QLv3tOL8MylVv25OtvKty5Dv0WMkuyesHgr62b8rpzIVj3nqeMpErLO9aqB369uLLG4O99PUsqYl6XNkmfN7drVjZQDRhTO817balICTl8Jo1o47g6qD7Yn+726ca4+Q2fsakcj2TQ/4WDh2qlr5w4WW+tDo2ynrl4EcT9XNxKyFKa1EEKz5sN7wde97nV7RmbeB6JtXrBbgMqtRYwB/Enx3XbbbauHP/zhe6ur8YEFYu4XNpbDTvpmji0we8hDHjIY0MahLzEQ5PPUvEuPIVH2Uznl1aPUbQ/zjhnughs3QcEh3FFq5T7bqfplO0fx3fzs5+pG/Mf73c/1quTmLvPgUczLLrR5klLWqV2+utHdvQiSQCuvrSv3MO4CYrfdB0zNcNr06sa5+uU+19bVivvN1z6hPHbt3LbxtwS+MOnY1YvqU6QMQ94rJVGnufq8X96SfcRC0BROCae8etBiOOHXKDRPq5IpavVdr0phw6vEG7fn+aabbpr0suo+H8T/Zd8sUvPe0l7r+vpOxv/U1Y08aIZR8FNGCUrcnHfeeSsf5crrNUUK6vqiCruS4MmcbnJ1Y8l/m1yvCgenAg/uylwdZj+aShmxsHRZrz4WWvgtKe+JjvLqRtayPupb+rlLDBdcbfuZeSLg68R4Ef4SJhxLY/XNt1XYwW0dPttvfvqj39oggKNQkrcLz4SG9a9OcCCfAhxLU/XVUTc8VsPJ+8Dgp26DAs/8tPoHXg2zhrGt/9M3/Ek5hk/Nd+bZM15vfiv74zf0G/yg56QaN8avnLZC71P1A+eon/oY3NQ8pm9jPFzyn7Ebd42nsoy8Mfm46zx41HN02O03lfJcJ/rVjXMY6vkdAx0DHQMdAx0D62NgI6Us7Nivblwf2b1Gx0DHQMdAx0DHwBQGNlLKQi5CLQkTTTXQ8zoGOgY6BjoGOgY6BpZhYCOlvAx0L9Ux0DHQMdAx0DHQMbAOBrpSXgdbvWzHQMdAx0DHQMfAFjHQlfIWkdtBdwx0DHQMdAx0DKyDga6U18FWL9sx0DHQMdAx0DGwRQx0pbxF5HbQHQMdAx0DHQMdA+tgoCvldbDVy3YMdAx0DHQMdAxsEQNdKW8RuR10x0DHQMdAx0DHwDoY6Ep5HWz1sh0DHQMdAx0DHQNbxEBXyltEbgfdMdAx0DHQMdAxsA4GulJeB1u9bMdAx0DHQMdAx8AWMdCV8haR20F3DHQMdAx0DHQMrIOBrpTXwVYv2zHQMdAx0DHQMbBFDHSlvEXkdtAdAx0DHQMdAx0D62CgK+V1sNXLdgx0DHQMdAx0DGwRA10pbxG5HXTHQMdAx0DHQMfAOhg4cqX8b//2b6u///u/n72b2d3N//iP/7j67//+73XG1yzrPmht/sEf/MHqD//wD1d/9md/tvrP//zPZtlT5cf/+Z//Wf3TP/3T1saxbfinCp57P3cXAwcpI3Z3lOv3jNwk5yLv/v3f/30tIPutv1ZjE4XJbfriX/7lX1a+H9d0pEqZgv2hH/qh1ed//uev/uiP/mgSx4jqK77iK1a/8Ru/MVluSeZ//dd/rV7wghesPumTPml1/vnnr84777zVO97xjiVVd7bMRz7ykdXXf/3Xr37u535uKwS7bfg7i9iZjhFwf/M3f7NCU3Xy21/91V8NwpBA/PM///O1jSY88hd/8Rer//iP/6jBD//HuARfP3YxUZZ/+7d/ewKOjOsv//Iv93Cj/3/6p386GN6bjuEgZcSmfdi1en/913+9uvbaa1cf+7Efu/q4j/u41f/7f/9v9Yu/+IuLu7nf+osbWlCQ43TzzTevrr/++tW6hsUC8DtTpKmUeUWYiKKMdcUL20a69dZbV0960pMGhpyCry9PfOITV7/6q786VWxxnkkl0N73vvetHv7wh69+8id/cnHdpQUJ0jFhuhTG0nLm6+lPf/rqp3/6p7eilLcNf+k4d6EcK/3v/u7vVr/3e7+3evGLX7z6jM/4jNWv/dqvndS1X//1X189+MEPXn3Mx3zMIBA/53M+Z/Vbv/VbJ5Vr/YDf0Pwtt9wyGI8t+qTwzfnHf/zHD0L3C7/wC1e/+7u/u5X5b/Vx6W9veMMbVg95yENO4F2Gxpd+6ZcOeKEsKI2zzz579dznPnfjaNhBy4il49vlcj/zMz+zeuxjH7v67d/+7UGR/eu//utsVLIcz37rl7AO4vsrXvGK1Xd+53eefkqZxfrIRz5yde655+4xzVOf+tQVb6lMFE7CIrWVzhL+4z/+4z1LmABhMUuUvvK///u/Pwg2eX6rU+Br4zd/8zdXX/7lX74i6AhFIYwyjMGKIsgCR1ull2JM+lQnffyCL/iC1U/91E/VWbP/GwOjJYZLrLd//ud/HoTv133d162+4Ru+YfiuDCMgYZep/ikjTIOBggP1azwZTwyn3/md31l97dd+7eDxqx8cp39/8id/coKBUMMHR7/LNAW/LDf1naWtD+CXBspc+7zMf/iHfxjmzLjRAMMg86tN4yTcM8ay/0vqg6GceVQ3czM1ntR5/vOfv7rwwgtXT3nKU1aPeMQjmkqZwfdN3/RNg8FpLrURHphqwxh5BBdddNFgiF555ZWDsVXWQe8/8iM/MggodAU33/Zt37b63u/93rUEVuYHDsFJ0k/492SAZA6NY52ELhm9F1988QlK2RjxrzZ9PvzhDw+RMEpg6TzoR/ijlBG1gRQawes1/lPf+OAwr8j0Qf/wtDGDrz66rdMUfGWn8sdkSN3G2P9l//FYxqefDDT08CVf8iWr97///QMdmsuSh8bgLqk/x8OBHRqrZcC6+WQYpVzS+JQMMHd4zrxm3PpijtehsfTzsJ5NT5kHQCn/2I/92MAwFOLnfd7nDQjJpCPU7/u+71t9wid8wmDlUmwIW1LmTW960+qTP/mTByueJc8bJlwlSPnGb/zGQeELH3/N13zNoFCHzP/7g9jAZ0Xf/e53X33rt37r6vGPf/zqAx/4wMAoJoZVnXfBws8UoAmQhAqVjxXOEv/xH//xkwjSWIXP11HKJpSw4ZkYW8JC7373u4fJ/tEf/dHVBRdcsDrjjDOGj7bh4kUvetEe04z1D2z4e/WrXz0IfOH9e97znkMbV1xxxeDZG58yb3zjG4ffheG/+qu/evWEJzxhCF/Lh2Ph7E/8xE/cw8EP/uAPDvVK+Ij8Hve4x2CAfcd3fMee4UIht+D/7M/+7CKCxvgf/OAHV49+9KOH9uHpB37gB4b25WV8Y+1TaCz817zmNasv/uIvHrxNNOn9vwSGMBwvFf6NkwUdxSyiMlV/ALJarX7lV35ldf/733+gr9BS8qaeGJ7S+oVf+IWBfloRnPe+972DoeSVi/lYJ+kL+HgPDmv61P7znve8od+UB+Pi5S9/+QrtLRkHOuM94Vv4Mz9oCF1KlNAXfdEXrV760peunvGMZwz5PH6KH28uSebiu7/7u4c6FMPUqyf8y6NeClv7YzIi7aBhr3NKGrntttv2ZID63//93z+MDf2QUaJxH/rQhwYZQ76QKcrgMU4Keo1An4M/lq++T0uGrBNaTv/D4+Sk8aGFd77znQNf3+lOdxpkkLmr5fDUHP78z//8aH2KbYqHtS+NyQB4WZIfGqV7jPExj3nM6jnPec6e3B+TAfhBnijTV33VV604lGQI+Y8ORZeiJ4aO7NifUaVMELzrXe8aumsSEOurXvWqgZgIdcpC+In1w0OlJAlFAoFFSdERvBiTAPGBKAmyKXWIofghicWYJP9tb3vb6nGPe9zAICzpH/7hHx5CgTxlsK677rrVt3/7t+8JIOE9wiNwwPfOSln1v+VbvmVoh8VUpk2UMpjGSoixgv2vvRAbHPidl/TsZz97+K4PyiWN9Y8gll7ykpesMFQEJYMGQX7Xd33XIIwIfISGicF+85vfPDAdvEnaUsfTHBCu5iRKjaARLvye7/meIQLy9re/fXXTTTftCcX3vOc9k/CHRib+wCulyOMz1l/+5V8ehDzlhQ6Mb6p9yvI+97nPAENEhYL/5m/+5r3+g88oIoSM31ybE0qAsNJOq76ISZmUe+hDHzrgYYkyK+v6bh7gtaWUGZBgM8ooP8I+PFDDGfufchwzGuUxVMBGZxRI6GcMXn6HLwILX+EJfOM7A9n/8CT0TmHjLThmkBGQrbEGbp7G+da3vnXgAXPvvWbtwaYsj87c8oSWpikZkf6h4c/6rM9a8b55dXjj6quvHgyO1EejaMuYGY2XXXbZEI2jWBi1FDE5Bl/kH8MhfP5Lv/RLTficE/DH8slHtIaXx2TIHB7Ap2T0n+FmfhgjDKmf+Imf2IsAeb0C93hImSisOfjGSO636mvb/I7JkMi5MRmAZ8DQp5aMkC+JzKJHxqb5EU3B8wxD/YPnlgygi+gkTgiDzLyLslLIaPnLvuzLhjmew8FR5TeVMmR97ud+7uClQCwB6n0YhSiZLGE7ApBSxkyUtDKY2eQLpfKGWYMU1JgwwugQH2UKPsIhYIXnkvSDYSAMg8F4dTEClOFJ1HBMHMvf5PEqvvIrv/IkCwncMaGXtusnuCxmApFSpOj0qUzKYDpKjxHTSq3+wR1cGTtmiueivt8IUvh85StfOeAgVimhYSFc+U4Z4StrfpQ3p74HvjlULymwCAyMAMf5rQU/9eqndm+//faVsCuLHU0QlASifmBa+BtrHzyCXH9FH5L0BWwfAokyKgW53ygN883THqsfeJ5woT8ZZ5m35Lt+jillsBkkaJuAIDAp0nXSFH2ak2uuuWYwbu5617uuvC4hjJYkCguuwtPq+A1NU55waFwiXvAt4XV1aq+91R4vnAzwRIPmmvKrE76h9HhmY3xS1/H/lIygnPAWnDNcjQcNakO0iZJWH337pF1yr5QxjH7GgrJJKYteKPEWfDyIh8bal69/eGFKhqTN1hNdcUzIQLAkbXI+9Dv0rA2Kfyld1G2N1QefPBrj4SkZgPfVpyxbMkI+PONh0b/wjN/oGREMdMP4aMkAdGvtBiOK/KWvvG5Ct+aYgo7hVo93F/5vKmWW3MMe9rDVOeecs8LsEEc4JBGEV1111eoud7nL4J2xpnkDhA4GlCAdUj/zMz9zKEOx82DKRGi1lCkL6WlPe9pgFCgjYSoCaEwp18od0SbspH/6SsnVnsSU0Cv7Wn/XL0KR4BHaohCNPQIM0bDoME0sxxLGWP8wD9gIHjOlv/BJUTI84B9cFiFGlFiShDKBI/mdVZ/Qlnn07lMfS/iMgDql7wi6BT9jrOvlf/Axz5lnnjnQBfz76MvrXve6gQ4wnvG12geHshOtSSgysD3hQn04L8NQmPRBD3rQwHCsbQzbql/C2u/3KaVcwkbTIkIs9Tn8lfXG6FP0g9EnEiE0TgjhP4La/E0l7cMVBRuBp7zoBPyhIe3CH75KGutL8vPUN1EdRrs6QvxCjww1eWWCP7RczmOZP/Z9TkagEa+87nznOw+0FxmFV/WHoCZjGAToVWrJmJIHyr6RQXrFAAAgAElEQVREAY7Bn2qfcSBpd0qGlO3V3/UfPUWBySdnGBnGhSbC5yKI6+IXPGMgh1r1kzfGw3MygCEB9piMUD9Klz6S/Pba1752cLCMz9y1ZMDll18+GPOUsl02jGJPDlSU8ljUZmjoiP80lXKYT0jUux6K1fvFpBC0pemUBmKgcCENw3smmTxMgNkJpDIp11LK4Ce0lvIs+ixYKJWGfG1axa1OPE1WLM+M0GHp3njjjQNx1RZjxrrE+k9fSqHqO0/ZewsMEcNDH/1PMNUJTsb6p69g1swQJqCUCQ/CgtAJro3jyU9+8jBffiMAzRtBa37ghwWZ9/o1/LKPyhOUrO4afumJl3XK7/qPoYQOWaTg+RiDvIylxeyBM6XsAp9SCcOqJ1xKkfuNQTjmwaYNT33BtJQFuOsmdCmE1vICS1iMIWEz78jj2Sxp21yDH0EemDxZkQCvHZLe8pa3DIq2xEny6ie6gL/SYxBufdSjHjUYvuDX77KX8orojrBk3mPe7W53W93xjncc1p+8/vWv38MzXhTBovhDZ3U/x/6fkhHxlBmxZAZBjP7wJtybZ/XRHz5MUq+UMfh3zKgODY/Bn8svac33lgxJv1pP8kXfyIDSe/fKjLGmfTid4vMW3PK3jKHFp1N5YBjTlAxYkk9fJLoHJkeGciX7zCdZ1JIB+EWEltLmIZtrukrUDg3zlE85pewdj1B09u5ifALW+zvEgAgMWOiG8CQ4fAg3RB+hiLF5dZCBwS26KBOiMXEsPohLMuGxgjCKdwmsv/vd735DuA1z8RJ5hsJj4ApLUUoYXX0hjmc961lDGI5iRsAWcvAoIhS1l7Hqx9KkDWFVdY0VAWAGBBClzJLmxXjX510iXMCPMSOosf6Bpa7QFytQHcmY/CYE7zeCDPF570/o8ExEA4zD+LRNCWTVpbrmlLIyhzX8cuwYBsG34AsRL0lwbs2BCImwJ/ogeDBWxlKOr4ZJQehv3i/V+fBpzQHP23fzykt84QtfOOCP0pmqH3jKrbvQC34Yf9q1JsJ2Hwas/0vPH57hwZyiDdGlUhhMtc1IUBcPgs8jAj805MlAyysiNADX/hc1mUv4zTtT7wzVRcsiS+iYF6It+Cv5YimvoHFjBwc+jJmBjE8TeYFDc4w/QuNzfS7zp2REDA3Pz/7szx4iRpFRaDDKWWiWUMdD+ARveafsOx6l8NCU762El8bgKz+Vv0SGtNrMb/AnogGvnB7zxXCzuDVylhyY4vPAGntO8elUXuBNyQBl5vKNSfSHLvAdr4nIonE0Rva2ZAAHDH7NLwUuqkJfwAsaFhIPjaSvu/RsesqQRcGV1jmhc+973/uECWeJWlUsNGRlnzoQwGugxBO2tLq39BCCAIjVBuKvBYn/eb7gInztE0LxSHgoXuILiVLOPEHh4ghFioDRoA+f8imfMrxfMMEUc0LsIYx6rOnf2FN0QJtWnoOvDwRcPQaCQJvKKEsAJXw31j9WHGIzHu+zMh5MIHTjN8IOw/HCEakDAeQh1izO05dnPvOZQ9tWbxO4LF7eI9xZpUuRB349VoKrBV8IiECYS8qgo4wfDvQDYxHM2i/HV8MjyM2LxVKtBD7G8poFbGHJ0iiaqx+YYKy70Mtc5PAZ3qBXA571Cnu0jfb1j0LWVom7sbbxhfm0mtb8gm+XgjGGhsBBQ4Ry6KtFgxln66l+5gd9lvXl1XzRkgstuPVv+A2/MK6T4JDXXC4uTN7S55iMsPBJgiNGXWgEnsrDM9A+GWPu/M5rJrCjlBl4N9xww56hXfdrDv5YPmMED8P3nAyp2yz/JwMYa5HB+IsxjX6kUmaM8XkJr/4+VX8qL3CMvyUDEi2ZygdDPm+ZfEX7FKzXYnblkCHyWzJAHseRDGWoCt1b6GrNhP6Qi2NyJX0/ymdTKRssoZywSBDE+kAISfIpKBPuk9AroqB88rsyIZTUzROMIDi/5akPYIClPkIo4fC65LPK9Vm+Z5Lf5Wtfv1m8+ljCaI019cee6gR2xqivrVSWSz9TLnmt/ulvjZf6N+NV1wce5ZdzFvzJVxY8uEzZGn76lWcLfom7lJt6ZoyZB21K9VhqGNrR/7n2zGfmQH+TltZXDl2UdQNj6qlOxoQv4Nj/pVdljPkdrdZpqm14asEfoyFlx2iwbrf8v5yfsn6LL1q/lbDGvqtnPPVcwvm6eK/bCI2PyQjlSxoxH+alTGCYNx4nozXrEJb2bw5+K38dGVL2tf6Ol0NjnuCWaY7PyrKt71P1p/JKWCWN6WNkQMpM5RtPIi7myf/1/JX4LelJOf+r44n+fG/RYvqyC8+mUt6FjvU+dAx0DHQMbBMDlIHXayJawpoiTV55bbIoapv97LBPLwx0pXx6zXcfbcdAx8D/YcDrhRzMI7Tt/SQl3VPHwFFioCvlo8R+b7tjoGPgyDAgvJnQ76bh/yPrfG/42GJgK0oZsdt/Z8WcBSMWZNTvk5Zi1LsA4aTyfddcXe8NMJn2vdi34rJ+jzEHo+evhwHz691qx/N6eDtOpc09Gqh53f/eC3rvizd3OWUR5S73cVf71mXAwczMVpSy1Za5AcYxdbYdlCdTrdN1W65ae5ynYFDkWR17XK5mnBrvLuRZdW81qW0Hhyl4Y3xZsV4rgyV4sVCG4RgDjuIo01g+BcTYUy91s+Ur9cFiFMrXv+NssMC97Vvl+fPBg+1XthuV+/iTt0tPc2WbXhZ67VLfToW+HEcZQJc42yF8To+VfDzF43iCQ5m6S2XUSUqZEOLZllYtz9deQgsjliSdsSIOHMvSHUiA4OvEqp5LtkDZQJ69d3Plk5/VsfYKbuNqRvjx/ikIR5CHqYwyzl15Ij77zZccLnIQfYZrC3S06X2gbRMOuMFES5Oy6FNdMLxfzHYNMFr5TkmT7D13Qpp6PrYu2Sduf3hStjXJt7XPtoySoVNul594meHTWj1e99sBPLbd1fuO1bX9LnvI63r7+V8EbZ0o2lRbjDPHbNpmsyR1GXAilo6TDMjIbB8du36VDJrica9GnPhYbnuztZc+nUonKWWESYk60hIzSvZ3OcDAZmxK20deBIzOUcItpU1IOzgkShmTY1qLLJz7SohRbPLL7SQI3u9OYGFhlIyX9pQ3cOWU0a86+d1xgWMndvHqp+rX8Pxv3M6XtX80AtnhJfBkfEsTPGjbpx4/GMYcLw6+4ZiiMH64LkOFfhd6Cw6C58BnQJQ4VJ+w9Ju2tQPndeLlgCG/rK8c4kr/HJCBAHlL+pekDZZmXTf5mz7BtG/dUXqEAXqyF7g84WoKNvw4Acx+evOGloJf9eby4Rn+zYuPg0DsgS8PB0EnyWcwlLd8TfVtl/LMPXngdLeSP9NH41OGgeQsY3MSpYye0JbDSXzQZ9JSGTLGI2A7MMb+Z9657/qhPyX9aW+Kx0P/+ml/89LTnroM+N+ZPM4ywAg5dQ7UQd9kGbqLjJU/xePkCaVOPqHLHDREVkylk5Qy4nSYglOOhHEg3e0xzq61UV0iiB06kBO/CHgHN7AaKIckzEFIUsrqSBjT1gNh5Tvc4Q7DYfoUW7mpXz2wbIq3sd8hITkQAAzCwYlYGJKwYInwRMpr2dKHsaMBjYv3PXatW+q3nsbrwBPjoLR8/M9rMwFzyfgoMSdm6Xc9fvUJCwcrGL+N8zwQhpLDO+DYKTe2b5hsiXUv9JbVo4wV4eR4gTw5p/sgKB/1HSZvTh1w4TWD8GLmj1ISpTB38fQcrgBvkufc1Y5gOLTE+cBLleUAfMEfitThFqErY0KnOYJvDoQ5c3CEwwgwnNCzeU0S+ZjKTzlPuHCymH2uwU+Z7zs6dDDFqRQaRadO9GLsiArUfafsHC+L/xzQ4dQ8Sjk0GS8C7aFjBjiYEiU9JkPgcIxH1rkeFZwpHsdjrethCdK51GXA8ZcBaMDhM5wNtF8alS36mONxRiM+yv0ELRh+O0kp8yzz/oegIZwoHEeXOYWJoI31nLOsESiG5ElHqAOOsaKUKfv8xuKgRJzURbBjYkqkFGixQHgezpctQ0qYiUJypi6hob7bbBgOmL1MY0rZqTq8JAjS/1zrVtcvYeW78jxlhkgS/FDKQvZziVEBXw5zJ9j8Dyfl+J1dzTiihMF0Mg3PPMdoUkDmKe2xvoTeoqT0kTcJNutu6dWNykvw1rpWjeVoXr3rd2Ka/sG/sAwDAx6T0Ip+MgxETA4qoTGnyZX41idt+I3CnUvmmZKEV0pH3ykYOJPm8kv4cO/0s8xF8oxfXxwFS/lrC+2eKkkkAI2jRfTqHGV8KaFXvzGCeLMMG4YjwzB4CA8zQB35as7iZdgXzANvyRBzqe4Uj8jHO7wYp5z5jg5Dv/o4xuNkkTbQaut6WDQ+l7oMOP4yAA3MXb+6Do979YXeIqPHaOwkpawChSe05z0QQLwdytV3TIWonXyTkDACJZR4X3NKOR1JWLm8hSZ55ROz1yEl7TlSU//ChPpkQVkt/FtKWR95fa1r1yIkyj7U39O+49oIXLhhYDhKNEJHGX1iwORDeJlEypeHOnZtG4FnfBR/FDXB5ixiRgSBRMARghGALDqCr5xwgoew4j0zmHK4uz4QsJtcuwaO/nmWnrXV9gQy/Gs3Sf+VD17y+36e5o+S48lm/No0dwyzEgdj7VCk973vfQcY+m6uKHQ4pzgZga1882Juk1jPzo92NGM5bvnyRHMuuOCCwYAyhweJh/RhW084EJEyf4yvq//vghftMVoYgaUCo+QYmglfp1+UpblCMxl/zZdwGhmCPuFyikfARltj16NO8TgeElpUFx8k6ZMxla8gklc/uww4vjKg5HG0iP7RtEij6A5ZkbSEx8HAJ5wAtFfLicDK8ySlrEFhT0LGOzLXN7qEQGjOohhMpUyplCkJt72so5QxACUaxZ4O1U8KjbIpGQVDEJ4OGY8RQEhQcnWYtGZ+8PVX2Lt17Zp3k3NJfQbBR3/0Rw9heDgq751Vvz6YoDy3WL6Jal3bJk90ghdMySsnMWIonDGlLOTGeIlC0kdGVMLXvOylVzdqk9JrXasmWkKgUci8JO1IlCMFtITohgr7+GPOCfiWp9xSCq2mePouHyg9e7TjlQJDkJHTyrc+ITiGJ5EL5yPHOCzbwnwYWhQIbEaVdueYsoRxVN+Nh+HKU8ZDwsDWlRivxKjBv2VIG80L9dVKGT2bqymljI5qGTLGI8FfFCOPusY/eGM8TpaJYIxdD1vKmjH8g99lwPGUAXicvK1T6JjxHxoc4/GyLt6hS8mU1Cvz6+8nKWVKkFL2pGS525QwS5lAppTl8bqiwLxHVceim3h2GtIBwqhc6JUOYHS/B0Z+r59jSpk1g+lieetf7oItYbSUMqHOQh67dq2s3/oeo0BY3wITHqu+UFZJ8EAY8xLyUQ9Oyonx3fvMXP2oHgUHJqGYxEhKuJ1AEBbmfYApMW5Y+Xmn7P36plc36hPhqz3eEoHnA2/yfCcIjTlGAzx7x1t7ypTSNhZ6IXDjZYxJ6MD7QZ5dQsT6S0EI35c4V149hqXQbJLveSdK8bbyKf2sG2BUocNSMQVW/cTQPM1SMSkz1ccaxmH+j44YEV4RWVPgY3W5OddniotSzmsldMBoR5NLlLLw9ZgMCZ1lvDWPZH7RPuOQh10nMKZ4PGsK8FESw5ZMWKKUuww4vjJgzLAXdeQgti5XQkM1j6Nb+oGhus4FGCcpZcIKYxFKBB0FgMCFmW+55ZbhNwzLa6OECWPvys4777zVy172sj0hrZM6xcvmwVKuZcIUwr8UvzYpfsKzTvLseQ7zy6cUCAdMjYGEkK3ABC8CM3Awf30FnTzhhKlr11K/9cSQ2qeYjBGuXJkmjKl9v00l7+rGrn6EawJO6M74lIMDNyrxdllpFDfCQCAEiQ8Dx7tRCl795FPmPD+hF3gwbv2fu9LNfIxdvWh8lO/c1Y76sa2FXgS/hXYMFzToLmFGRBYfwj9vbuxaRjjgmfGW4Ah9MYSsozA+dD+Wb1zKoG3RGrDqJJ8xhu59RB7c+lRHhvTxAQ94wBD50eYuJDTo9ZWxMaiMg4HJgEaT3tXiX6+5XvSiFw34Jx9yfWpeKWQs8TBKgwR9jckQuGPMjfFIlDJ8WTzmnXJ9Paq2p3jcGMme7Esur4dVby51GXC8ZUCcS/OMVr16EjUiY2O0zfE43hG9pCfoIXDqfc4tOjtJKXPbWf+EexLljBGzx0pnKA3Ws5W7BA4GtuQ7g1FXOcxKecSDC0x5BJT3beDUe0RTTn9qSwOiWMg8JcpIiFasn/KpE0TUV9Apo33e1ti1bjWc8n9GQa51iyA1aZQYIYXhpxIBJ9Rb7l8TaSiFme+MIytXXT1J+TBuhIclYyXULFDSrvA5oyTzxjjwf3C77tWN8AN38ApG4NjHKxkjD0X/yqsjRSzUlSivbSz0GoCvVoNiyBjREYNFm0kU7dS1jOjIGIzNXPCyM59gTOXLY4TBR0nzaRt+coAN+BSyqFEiOymHwR/4wAcOodCy7eQfxZPBQwa4B71ModvQOGMGfvEfeuYpq1fSsfqUMoOHzAiu4GFMhqAfhuscj4CND0Kj5jBXW8qb43HGBh7DQ+X1sOVOj3L85fcuA04PGcAQpePwMIVMpkS+zfE479juHjsP0BgYHCPe81Q6SSlrkHBIw6mMicJQfvM/JvXxu/996uQ3lm0NTzkC1IvyWOItoZT+lMKWQPROmWImQNT3Wyupr/1W35QXclY/fSjH2IKX30yIT5n0AbPOJX1SNu16xvov68JH+kXp1/utA8MYgifPJDBTX1/Byz4742zNc+rmmTYCp5wjMEMD8AtmOU9g+I3XM4b/tLPpE771DR3VSV/k1/NUlpOXsbXKjeUH31PjSt/G+qcf8Cfs7tVQq/2yr4f13djQTj2X2jf/Ja2Gf4xVPWMoaTBj9HqGp1zyF9yFfvzu/+ATjJL24LBst8RFWc73uv30MfNc9iE8gi+MV/9b4y7by3dl6znTPlzMpaXjg+/0u8uANlbDZ9uQAWgFjZoDcqxOaVt+3T5aVic6ThnGZmi8hpX/T1LKydjlJ8IXPmaVlwy2y33eb9+EaFth+P3C7fWPFgO8Mq9eWgtLjrZn+2sdX3rnLnQvWiaiZcFLT5tjoMuAzXF3KtU8JZUy6yTh4zHr+VSahCV9HQvDL6nby+wuBljNSzyr3R1Bu2cWxXitImQnvF2/GmjX6r9OYaDLgCnsHJ+8U1IpQ38rdHR8puXkkQh3MUDmQh8n1+y/dAwcPgaEgBM2Fv47XSJa28R0lwHbxO7uwD5llfJSFFLeFj1ty6PeNvyl4+zldhsDeYeY9/+73dveO/PlHaDwOw/VAspyPUXHUMfAtjBw7JXyJlc/roPsbcNf0heCvhb2Fq4QJtmSY9uWVbD1wpQ5+BYp+Ewtfmm1Pwd32/n6yxjL+L2znRrDtvtDoNu+c/31168drhYdMZZy7sp3thmjlcj1YpNtj+u4wofrrJ7f5etfLSRC26EBC1/XoXN0RDao71k7L1Pw4cgWn7LtbdCD1zvGVdK/dnaNxw9q7E2lnMFmskqkrzPhc520Em0/CQHVRFTD2/TqR3C2Db/u6yb/YwqnxdjaE0veHOUyAO/0fOwjt/UGPuZSBL5FSE4Ws5me19BKrfZb5Q77NyFTW+nKbWfZ0nfYfUl7tgC1Tp9K/tjTyWxO0yr36pfvbM2vrWlnn3328O52Sai4xeNwlhRDYNsyIO3t4jMra+1b3sb1r/sdszl03K65RwP1qYFz8NW3H1099b37t/WylCMt+IxwyaE52VKqD3aH2I8ryrDfBIatdRa32Xpoa1H2Bwf2LvJ4+rafZ1Mps7jtiXVzkMnaZMIJhtKCs5EaoyMEVo/9X9oYu7pRWXViEOhThA0v0F4v+xjra9uCDGXUNan60VLe+hH4vMosuDko+PqSFajaqTeOIzxttazAjGPqCR/2jBLGDuYvLUmMRZH68LIciuGwB23NJaE6e8AxnPOxHbBR7z0FY6r9uTbkm5N484S/OU8yFl6fNnj48Dc2j6lTPuHVoSrGDgdLr02rYZT9K43Ipf1zyIa+26dPKdvPHzor2xr7bhsMhXDxxRefoJTxEeGYOXb4hYMwlh5zWvM4gcwAg2N0WedvIgPGxrTkd30wPrgifDP/JY1MwUH/aBaeyoSezCP4UzKmrAMn9XbE8K4++i5pE+y6TQbUWP8DZxMZgH8c4ERx4hU039oSVo6l/E42MuidQaHfjh81zpxQZzxj8I2RnECfaDCn25EVtYwJD7Q88bI/5Xf85bCOCy+8cJBBjgiulfJB8HjZ5q58byplk0VhEmRhes+lE47wHWbh6kWbpn1MvslBxHNXN0IOBeaULMKAFeaTwxfs6XRYxBlnnDF85GmLggpDTF39iBEQU3114pJr4ZbA1384sAXEBdkxbOqN45jKSV2XXnrpCQJ3KXHorxNjHJgwJezh3TWQhMuSZIzmiVBzMIyDHyi3Oi1tv67nf2N3oAllAD8O12C1x7Dgnbilyt7WHA6hXK6fbMGc+o1QXHJtWmDAgVPR9Eu7DgBgoAQPc/1DY06acxSlMTroxVnJViEvVcqEjgiIrX+Of4ywTB/Lp5PMeNTwuiTVPM7w0Q6hzICAL7c4bSoDlvRhqgwcwRXD2/ZHEQ9ypHU9awsOg4z8cGJaEp50fKx94eiMIqlljFPawuOpB1flWf9+Jwud/V6uKjcHnITQSGTA1PWw+5EB6jL0yD3zRYmukyg5dJmtak5Fc4hRDnoCbwp+jJG0iX9tfYucgcep619Tb+wZg4yxAP9lpKhVZ10eb8HYhd9GlTLkvutd79qoj5ADiY6xI1gg18ck+nhPAcFTVzcqg2nUYxBgTMqBokAsrM+pa9uUUQ/h1Vc/gimMKCwLjv+1h4mk/cIHg/Up5EiJsBz1hTVaWvracVPNlVdeORz1uA6yWak8XxcqUFxjwl4b5sFpSxnf0nbMVW6jiqBJ3aXtp3z5BJfwo3SFyOGGwqF8ncolUXqf9mmfNuDQyWvKUBCUBu913URhLbk2LXDNE28x3hrBwMDKLWRT/SPUGELO0YY/NMuTZQRSfOZkLhFohBwat27h2muvPclTCAwhQ8ZdhGl+n3pG0dhDnMRQxbe8bTBzjGryD/NJ4aDvJdeztvpl3q677roB3/hbMifwH4+LYq1lTHnJSeAGV+URqeo6+94hRplPtFsasGRAzqtXHq9effXVe+e1g78fGRDDkdHIMTHeddYUoHFXurqYR916L/k68Ml5NFjKIXhrXf+69GKG4F/5JUp5XR4P/F17NpWys5adcSt0QcH6lKFXQpWQTJ4nQWliJEqIQOIRY3SKzwSXKSGhUiiU+b5TItoF3znEwmtRDvLGrm0r4RCs9dWP6rIARQMcC0nBYJoy7Qc+K5wyLvtbwi6/K0to1Pgpy9TflaeIMRSmFjkQ6ikVfurAv1uM6pBS8qee+uZQ/1LQKL9O+y34DCBCgGEEz5JxMLyc0kYgU3po0Pwk+a28BCG/Tz3hVb2l16bVsIRO0ZAjJ4XzI5gJiqn+yfcKIAaEuRF14PlGSdRtlf/zsr0T98Q/vPTWegB0y+jyOqg1/yXM8ntL0aARSgk87RLSYzKghLWN78ZFgU5dz6pMXi9EFhlDeMlcMSyETSVes3P6S/yjvzEZk3G1cKVtChkNRylrj9xL2Nz72SXXw24iA9I3/aeIyWxtk4nl+FKu9YQn99m75c65+pdccslJdLQEvvZERkVDQ+90hAteOBx4Bw9xxBglDFW0Cm9kb+bOM685y/4ySqeU8n55vGxrF743lTKFSQCZLFaYTxl6NVFZmZj8+uxqhEYYuqlI2Km+4B2hE7ARcjUyCG7hzSxCcEMNbyGLUcK0cwtnTHR99aO2TCQrmeDThvdxhB9ikvYDX139KpVOPb79/C+CQWASxPBIIfMqzFspmBlJLFfhqYxrnXbHlPLS9sfa4rEQ/gyj9BdjU8h+Z/ChHWMsQ7ZzzNlqz9gJi3Wtc/QhnKcPaNwKXFd1xlMGr9U/v8nzqoVCKEN5XqlMvWZI/80bZUSBm19eujAjIRfDN2XhBJ3FWM3vc8+WogEDHwjbE/K8vDEZMAd/v/lRevCFDiV9Ykjn+kjCntyJDKoXOhHwomE8f56zle/qhBfmZEzG0MJV+gf3UcocDIoR/aJrRuam18Om7XWeaIHxSebNJTh4z3veMzgOoiLkqnPqyWtwgqMSTgs+A5oDwksmT5PwDyO0df0rPpDwl1dKmb+85jRvZdLulFLelMfLNnbpe1MpI0IKkxWFwHzq0CshmjxPkxriNCFJGIpQca1iQpPytEGAUSx1UoeVyapieWGoG2+8cfDY9EPCFIQ4wTWVWkq5JDjfWWu5OhGR7Re+/rMGeeis8LGkbUKWdR/BM1Y2v8MtoSlchYgRNMY/66yzBs+sJGjEDEdjAptQUj5jThvlk9fEcDEHSUvbT/n6GaNF+C9K2fiFanNHM6VcM+Icc5btwO0m16YFBkGlfZ4iWs/VmaFXirfVPzSt74xNPJSQMlwzZHkyU/jWPpoR9stB9kK4d7zjHYf3qi7ACP3iBREkyqDkuYxh6hlFk/EoywAyJr9RgMYyJQPUMX88Q97aun2Y6h8a4SlTbKGR+npWzgG5U8oh9YIfNGUhKZrymsQpgOEFeXMyJv0LrkoHQjuiPWBL2szVlfGUp66ODGz11pUBqVs/GRyiN6UhO8bjcEdGcVjC2/rNgEXzwXnZRg0fDK+hzJM5KJNxTV3/qqw20HA5f/pS05H1AQzcOlKkjf3weNnfXfreVMoEEs84NxKt02EI5R0QTBQioWQyMXgWFIDHW3LDD6FPAFC+8QIIQR4e7w8MeSxSBGMSTCYDYOratvQZbMf9lYsEEMJ+r4Wbgi9Pe27QMQbvEzMOY0sinDdZ6EWgwBWBRKnaMmCxVxm6IyQpZF/noQ0AAAQVSURBVJ5dTeTa99vYtYqYjdUrhMnoEYIzn+aBMFrSfsbYemImtMXoYrDBDUVg0U1ohAJGg5Rfknd09W/Jq5+iHptcmxY43jtSUN7rCr3plzUCBBbcT/VPXSFTeEPfxud9OEPKKx34m0rmBp7NIYEFHlzpQwxfOISPzPsUvFYeRcNLEa3QP20wTC2sSjh0iQxAE8pZdVsL5la7S3/DJxQyJaNvaHHsetYpmGQR7x+flTd6zckYPJAUeRgP3e/qU8jwpW/mJldXBg94Zu562E1lgD6gAW2ZP22hd/KGcpWmeFw+I4OMQuNgkNN4MK8Ux+BnfIwknjW8ogMwShnkN7JXlBQ/4CMOEENhLpVt4x28Z0ujNtL+fnl8rg9Hld9UypDZuu5waSddWSX0lbCEq694VyWhQzqiGLu6MUINDFcXEoYWAlHMCZOY5KzMra9tS18xJU+vvGSaMsNM5R7W+upE9TeFr67xeYeVlZfG4YrD8h0pAbvpQq+MzxPRUrAlfhlDxs1DaCUMO3atImLPucW8NFvjKJS6/4Hbaj95Y099pRCsmocbYcjSgCCI0WA5b63fxuBvem1a4Omf9/ZWTqMT9MHT/dRP/dRhexmjixBs9Q/OQ9/qC6sS4MJ5Vv7OecrpQ57mA70y7pIodsLQqvp14YFBOPLGheXhXx+FihkD0lIZgM4Y3HARYZk+7uepH4zKJdezTrUDN8aFnhO1SPkpGQPnSWO44MHhb3NsfuqrK9EAozJ7eQ9aBqABNBk5ht8TCdD3KR6Xj8Z5y+Ze3+51r3sNxrJ60hR8Zch00Rx1Re3q1wfGD3eR0crVrzmHhhp/0jaY2vDO25O8QPMcM+Fv+E9ECXwGIsftVE5NpQyZiLkVwlgyWBPGm8CkPpRgJrqs77d4A8rEC0gZjJn6CIh1Woc3UkY53/W9TP4Ht2zfb2U9dccEW1luKfyyfVYh+OU4ynzEZ1xl/8r8Jd/hBpwygWdMU3DV0796ntWBZ302PwkxmSN16tRqvy7T+l+7YAY35dy1+t/6rQXXb2AbW+hLG8ZRj3WsfmCkf+ZIXTBCTzV+6/4ZT9pX1v8t/E31QZ56abMsa87reS/zp76DaUwZn2cJS74+z+ELDMqIcvD9oBJeW3o961ybxjXWt/C38Zsb5WoZM4WL8Hdkg7aUL1N4KXRe00D6h37WTfoLrk8tP8HSVovH0442Sxqo+z4FX78TsUsfgofA9wyOM/5WP8vy+Z629Q/fpZ9+l/R9vzyetnbp2VTKu9TB3peOgY6B3cWAaJKQfPke8yB6S5ALX3tfWSuxg4DfYXQM7CoGulLe1Znp/eoYOAUwwFvhudQe1n67Dubpdj3rfnHW6x8PDHSlfDzmsY+iY+DYYUB41KenjoHTCQP/Hw0ikioh+0iUAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. cars 데이터\n",
    "* dist가 speed의 변화에 따라 어떻게 변화하는지 회귀분석\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   speed  dist\n",
       "0      4     2\n",
       "1      4    10\n",
       "2      7     4\n",
       "3      7    22\n",
       "4      8    16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = pd.read_csv('cars.csv')\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>speed</th>\n",
       "      <th>dist</th>\n",
       "      <th>speed2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   speed  dist  speed2\n",
       "0      4     2      16\n",
       "1      4    10      16\n",
       "2      7     4      49\n",
       "3      7    22      49\n",
       "4      8    16      64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['speed2'] = cars['speed'] **2\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>dist</td>       <th>  R-squared:         </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   47.14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>5.85e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:10</td>     <th>  Log-Likelihood:    </th> <td> -205.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   416.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    47</td>      <th>  BIC:               </th> <td>   422.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.4701</td> <td>   14.817</td> <td>    0.167</td> <td> 0.868</td> <td>  -27.338</td> <td>   32.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>speed</th>     <td>    0.9133</td> <td>    2.034</td> <td>    0.449</td> <td> 0.656</td> <td>   -3.179</td> <td>    5.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>speed2</th>    <td>    0.1000</td> <td>    0.066</td> <td>    1.515</td> <td> 0.136</td> <td>   -0.033</td> <td>    0.233</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.173</td> <th>  Durbin-Watson:     </th> <td>   1.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  11.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.991</td> <th>  Prob(JB):          </th> <td> 0.00396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.173</td> <th>  Cond. No.          </th> <td>2.16e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.16e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   dist   R-squared:                       0.667\n",
       "Model:                            OLS   Adj. R-squared:                  0.653\n",
       "Method:                 Least Squares   F-statistic:                     47.14\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           5.85e-12\n",
       "Time:                        21:05:10   Log-Likelihood:                -205.39\n",
       "No. Observations:                  50   AIC:                             416.8\n",
       "Df Residuals:                      47   BIC:                             422.5\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.4701     14.817      0.167      0.868     -27.338      32.278\n",
       "speed          0.9133      2.034      0.449      0.656      -3.179       5.006\n",
       "speed2         0.1000      0.066      1.515      0.136      -0.033       0.233\n",
       "==============================================================================\n",
       "Omnibus:                       11.173   Durbin-Watson:                   1.762\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               11.061\n",
       "Skew:                           0.991   Prob(JB):                      0.00396\n",
       "Kurtosis:                       4.173   Cond. No.                     2.16e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.16e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "result = smf.ols(formula='dist ~ speed+speed2', data=cars).fit()\n",
    "result.summary()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 적절한 회귀 모형 찾기(산점도 & 잔차도 활용)\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x   y\n",
       "0  1   5\n",
       "1  2   3\n",
       "2  3   2\n",
       "3  4   3\n",
       "4  5   4\n",
       "5  6   6\n",
       "6  7  10\n",
       "7  8  12\n",
       "8  9  18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,2,3,4,5,6,7,8,9]\n",
    "y = [5,3,2,3,4,6,10,12,18]\n",
    "data = {'x' : x, 'y' : y}\n",
    "df1 = pd.DataFrame(data)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas 산점도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b04765b08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot.scatter(x='x', y='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seaborn 산점도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b04cd2a88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='x', y='y', data=df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x   y  x2\n",
       "0  1   5   1\n",
       "1  2   3   4\n",
       "2  3   2   9\n",
       "3  4   3  16\n",
       "4  5   4  25\n",
       "5  6   6  36\n",
       "6  7  10  49\n",
       "7  8  12  64\n",
       "8  9  18  81"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.copy()\n",
    "df2['x2'] = df2['x']**2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   16.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th>  <td>0.00445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:13</td>     <th>  Log-Likelihood:    </th> <td> -21.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>     9</td>      <th>  AIC:               </th> <td>   47.46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     7</td>      <th>  BIC:               </th> <td>   47.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   -1.1667</td> <td>    2.230</td> <td>   -0.523</td> <td> 0.617</td> <td>   -6.439</td> <td>    4.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>         <td>    1.6333</td> <td>    0.396</td> <td>    4.122</td> <td> 0.004</td> <td>    0.696</td> <td>    2.570</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.451</td> <th>  Durbin-Watson:     </th> <td>   0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.484</td> <th>  Jarque-Bera (JB):  </th> <td>   1.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.668</td> <th>  Prob(JB):          </th> <td>   0.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.051</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.708\n",
       "Model:                            OLS   Adj. R-squared:                  0.667\n",
       "Method:                 Least Squares   F-statistic:                     16.99\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):            0.00445\n",
       "Time:                        21:05:13   Log-Likelihood:                -21.732\n",
       "No. Observations:                   9   AIC:                             47.46\n",
       "Df Residuals:                       7   BIC:                             47.86\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -1.1667      2.230     -0.523      0.617      -6.439       4.106\n",
       "x              1.6333      0.396      4.122      0.004       0.696       2.570\n",
       "==============================================================================\n",
       "Omnibus:                        1.451   Durbin-Watson:                   0.696\n",
       "Prob(Omnibus):                  0.484   Jarque-Bera (JB):                1.008\n",
       "Skew:                           0.668   Prob(JB):                        0.604\n",
       "Kurtosis:                       2.051   Cond. No.                         12.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "result1 = smf.ols(formula='y ~ x', data=df1).fit()\n",
    "result1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24b04f7a608>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted = result1.predict(df1['x'])\n",
    "residual = df1['y'] - fitted\n",
    "\n",
    "sns.regplot(fitted, residual, lowess=True, line_kws={'color': 'red'})\n",
    "plt.plot([fitted.min(), fitted.max()], [0, 0], '--', color='grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   292.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>1.05e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:13</td>     <th>  Log-Likelihood:    </th> <td> -6.6251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>     9</td>      <th>  AIC:               </th> <td>   19.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     6</td>      <th>  BIC:               </th> <td>   19.84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    7.1667</td> <td>    0.787</td> <td>    9.103</td> <td> 0.000</td> <td>    5.240</td> <td>    9.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x</th>         <td>   -2.9121</td> <td>    0.361</td> <td>   -8.056</td> <td> 0.000</td> <td>   -3.797</td> <td>   -2.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>        <td>    0.4545</td> <td>    0.035</td> <td>   12.893</td> <td> 0.000</td> <td>    0.368</td> <td>    0.541</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.843</td> <th>  Durbin-Watson:     </th> <td>   3.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.656</td> <th>  Jarque-Bera (JB):  </th> <td>   0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.122</td> <th>  Prob(JB):          </th> <td>   0.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.962</td> <th>  Cond. No.          </th> <td>    173.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.990\n",
       "Model:                            OLS   Adj. R-squared:                  0.986\n",
       "Method:                 Least Squares   F-statistic:                     292.2\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           1.05e-06\n",
       "Time:                        21:05:13   Log-Likelihood:                -6.6251\n",
       "No. Observations:                   9   AIC:                             19.25\n",
       "Df Residuals:                       6   BIC:                             19.84\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      7.1667      0.787      9.103      0.000       5.240       9.093\n",
       "x             -2.9121      0.361     -8.056      0.000      -3.797      -2.028\n",
       "x2             0.4545      0.035     12.893      0.000       0.368       0.541\n",
       "==============================================================================\n",
       "Omnibus:                        0.843   Durbin-Watson:                   3.026\n",
       "Prob(Omnibus):                  0.656   Jarque-Bera (JB):                0.023\n",
       "Skew:                          -0.122   Prob(JB):                        0.989\n",
       "Kurtosis:                       2.962   Cond. No.                         173.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = smf.ols(formula='y ~ x+x2', data=df2).fit()\n",
    "result2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24b04f61e08>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted = result2.predict(df2[['x', 'x2']])\n",
    "residual = df2['y'] - fitted\n",
    "\n",
    "sns.regplot(fitted, residual, lowess=True, line_kws={'color': 'red'})\n",
    "plt.plot([fitted.min(), fitted.max()], [0, 0], '--', color='grey')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 최적회귀방적식 선택 : 설명변수 선택\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>104.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>95.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1  x2  x3  x4      y\n",
       "0   7  26   6  60   78.5\n",
       "1   1  29  15  52   74.3\n",
       "2  11  56   8  20  104.3\n",
       "3  11  31   8  47   87.6\n",
       "4   7  52   6  33   95.9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'x1':[7,1,11,11,7,11,3,1,2,21,1,11,10],\n",
    "        'x2':[26,29,56,31,52,55,71,31,54,47,40,66,68],\n",
    "        'x3':[6,15,8,8,6,9,17,22,18,4,23,9,8],\n",
    "        'x4':[60,52,20,47,33,22,6,44,22,26,34,12,12],\n",
    "        'y':[78.5,74.3,104.3,87.6,95.9,109.2,102.7,72.5,93.1,115.9,83.8,113.3,109.4]}\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>const</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  x1  x2  x3  x4\n",
       "0    1.0   7  26   6  60\n",
       "1    1.0   1  29  15  52\n",
       "2    1.0  11  56   8  20\n",
       "3    1.0  11  31   8  47\n",
       "4    1.0   7  52   6  33"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_columns = df.columns.difference([\"y\"]) # Target column 빼기\n",
    "X = df[feature_columns].copy()\n",
    "y = df.y\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. 전진선택법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processSubset(feature_set):\n",
    "    # Fit model on feature_set and calculate RSS\n",
    "    model = sm.OLS(y, X[list(feature_set)])\n",
    "    result = model.fit()\n",
    "    aic = result.aic\n",
    "    return {\"model\":result, \"AIC\":aic}\n",
    "\n",
    "def forward(predictors):\n",
    "    # predictor - 현재 선택되어있는 변수\n",
    "    # 데이터 변수들이 미리정의된 predictors에 있는지 없는지 확인 및 분류\n",
    "    \n",
    "    # Pull out predictors we still need to process\n",
    "    remaining_predictors = [p for p in X.columns.difference(['const']) if p not in predictors]\n",
    "    \n",
    "    results = []\n",
    "    for p in remaining_predictors:\n",
    "        results.append(processSubset(predictors+[p]+['const']))\n",
    "    \n",
    "    # Wrap everything up in a nice dataframe\n",
    "    models = pd.DataFrame(results)\n",
    "    \n",
    "    # Choose the model with the highest RSS\n",
    "    best_model = models.loc[models['AIC'].argmin()]\n",
    "        \n",
    "    # Return the best model, along with some other useful information about the model\n",
    "    return best_model\n",
    "\n",
    "\n",
    "models_fwd = pd.DataFrame(columns=[\"AIC\", \"model\"])\n",
    "\n",
    "predictors = []\n",
    "for i in range(1,len(X.columns.difference(['const']))+1):    \n",
    "    result = forward(predictors)\n",
    "    if i>1:\n",
    "        if result['AIC'] > aic_before:\n",
    "            break\n",
    "    models_fwd.loc[i] = result\n",
    "    predictors = models_fwd.loc[i][\"model\"].model.exog_names\n",
    "    aic_before = models_fwd.loc[i][\"AIC\"]\n",
    "    predictors = [k for k in predictors if k != 'const']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>AIC</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.744045</td>\n",
       "      <td>&lt;statsmodels.regression.linear_model.Regressio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.634106</td>\n",
       "      <td>&lt;statsmodels.regression.linear_model.Regressio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.866285</td>\n",
       "      <td>&lt;statsmodels.regression.linear_model.Regressio...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AIC                                              model\n",
       "1  95.744045  <statsmodels.regression.linear_model.Regressio...\n",
       "2  65.634106  <statsmodels.regression.linear_model.Regressio...\n",
       "3  61.866285  <statsmodels.regression.linear_model.Regressio..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_fwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   166.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>3.32e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:05:14</td>     <th>  Log-Likelihood:    </th> <td> -26.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    13</td>      <th>  AIC:               </th> <td>   61.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   64.13</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>x4</th>    <td>   -0.2365</td> <td>    0.173</td> <td>   -1.365</td> <td> 0.205</td> <td>   -0.629</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.4519</td> <td>    0.117</td> <td>   12.410</td> <td> 0.000</td> <td>    1.187</td> <td>    1.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.4161</td> <td>    0.186</td> <td>    2.242</td> <td> 0.052</td> <td>   -0.004</td> <td>    0.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   71.6483</td> <td>   14.142</td> <td>    5.066</td> <td> 0.001</td> <td>   39.656</td> <td>  103.641</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.211</td> <th>  Durbin-Watson:     </th> <td>   2.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.900</td> <th>  Jarque-Bera (JB):  </th> <td>   0.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.202</td> <th>  Prob(JB):          </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.270</td> <th>  Cond. No.          </th> <td>1.27e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.27e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.982\n",
       "Model:                            OLS   Adj. R-squared:                  0.976\n",
       "Method:                 Least Squares   F-statistic:                     166.8\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           3.32e-08\n",
       "Time:                        21:05:14   Log-Likelihood:                -26.933\n",
       "No. Observations:                  13   AIC:                             61.87\n",
       "Df Residuals:                       9   BIC:                             64.13\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x4            -0.2365      0.173     -1.365      0.205      -0.629       0.155\n",
       "x1             1.4519      0.117     12.410      0.000       1.187       1.717\n",
       "x2             0.4161      0.186      2.242      0.052      -0.004       0.836\n",
       "const         71.6483     14.142      5.066      0.001      39.656     103.641\n",
       "==============================================================================\n",
       "Omnibus:                        0.211   Durbin-Watson:                   2.011\n",
       "Prob(Omnibus):                  0.900   Jarque-Bera (JB):                0.378\n",
       "Skew:                           0.202   Prob(JB):                        0.828\n",
       "Kurtosis:                       2.270   Cond. No.                     1.27e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.27e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_fwd.iloc[-1][\"model\"].summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. 후진제거법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "def backward(predictors):\n",
    "    results = []\n",
    "    \n",
    "    for combo in itertools.combinations(predictors, len(predictors)-1):\n",
    "        results.append(processSubset(list(combo)+['const']))\n",
    "    \n",
    "    # Wrap everything up in a nice dataframe\n",
    "    models = pd.DataFrame(results)\n",
    "    \n",
    "    # Choose the model with the highest RSS\n",
    "    best_model = models.loc[models['AIC'].argmin()]\n",
    "    \n",
    "    # Return the best model, along with some other useful information about the model\n",
    "    return best_model\n",
    "\n",
    "models_back = pd.DataFrame(columns=[\"AIC\",\"model\"], index = range(1,len(X.columns)))\n",
    "\n",
    "predictors = X.columns.difference(['const'])\n",
    "aic_before = processSubset(predictors)['AIC']\n",
    "                       \n",
    "while (len(predictors) > 1):\n",
    "    result = backward(predictors=predictors)\n",
    "    if result['AIC'] > aic_before :\n",
    "        break\n",
    "    models_back.loc[len(predictors) -1] = result\n",
    "    predictors = models_back.loc[len(predictors) - 1]['model'].model.exog_names\n",
    "    Bmodel_before = result[\"AIC\"]\n",
    "    predictors = [k for k in predictors if k != 'const']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>AIC</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.3124</td>\n",
       "      <td>&lt;statsmodels.regression.linear_model.Regressio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.8663</td>\n",
       "      <td>&lt;statsmodels.regression.linear_model.Regressio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AIC                                              model\n",
       "1      NaN                                                NaN\n",
       "2  62.3124  <statsmodels.regression.linear_model.Regressio...\n",
       "3  61.8663  <statsmodels.regression.linear_model.Regressio...\n",
       "4      NaN                                                NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   166.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>3.32e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:19:20</td>     <th>  Log-Likelihood:    </th> <td> -26.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    13</td>      <th>  AIC:               </th> <td>   61.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   64.13</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>x1</th>    <td>    1.4519</td> <td>    0.117</td> <td>   12.410</td> <td> 0.000</td> <td>    1.187</td> <td>    1.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.4161</td> <td>    0.186</td> <td>    2.242</td> <td> 0.052</td> <td>   -0.004</td> <td>    0.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -0.2365</td> <td>    0.173</td> <td>   -1.365</td> <td> 0.205</td> <td>   -0.629</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   71.6483</td> <td>   14.142</td> <td>    5.066</td> <td> 0.001</td> <td>   39.656</td> <td>  103.641</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.211</td> <th>  Durbin-Watson:     </th> <td>   2.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.900</td> <th>  Jarque-Bera (JB):  </th> <td>   0.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.202</td> <th>  Prob(JB):          </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.270</td> <th>  Cond. No.          </th> <td>1.27e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.27e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.982\n",
       "Model:                            OLS   Adj. R-squared:                  0.976\n",
       "Method:                 Least Squares   F-statistic:                     166.8\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           3.32e-08\n",
       "Time:                        21:19:20   Log-Likelihood:                -26.933\n",
       "No. Observations:                  13   AIC:                             61.87\n",
       "Df Residuals:                       9   BIC:                             64.13\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             1.4519      0.117     12.410      0.000       1.187       1.717\n",
       "x2             0.4161      0.186      2.242      0.052      -0.004       0.836\n",
       "x4            -0.2365      0.173     -1.365      0.205      -0.629       0.155\n",
       "const         71.6483     14.142      5.066      0.001      39.656     103.641\n",
       "==============================================================================\n",
       "Omnibus:                        0.211   Durbin-Watson:                   2.011\n",
       "Prob(Omnibus):                  0.900   Jarque-Bera (JB):                0.378\n",
       "Skew:                           0.202   Prob(JB):                        0.828\n",
       "Kurtosis:                       2.270   Cond. No.                     1.27e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.27e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_back.loc[3][\"model\"].summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C. 단계적 선택법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forward\n",
      "forward\n",
      "forward\n",
      "forward\n",
      "backward\n"
     ]
    }
   ],
   "source": [
    "def Stepwise():\n",
    "    Stepmodels = pd.DataFrame(columns = [\"AIC\",\"model\"])\n",
    "    predictors = []\n",
    "    aic_before = processSubset(predictors + ['const'])['AIC']\n",
    "    \n",
    "    # 변수 1~10개 0-9 -> 1-10\n",
    "    for i in range(1,len(X.columns.difference(['const']))+1) :\n",
    "        Forward_result = forward(predictors = predictors) # constant added\n",
    "        print('forward')\n",
    "        Stepmodels.loc[i] = Forward_result\n",
    "        predictors = Stepmodels.loc[i]['model'].model.exog_names\n",
    "        predictors = [k for k in predictors if k != 'const']\n",
    "        Backward_result = backward(predictors = predictors)\n",
    "        if Backward_result[\"AIC\"] < Forward_result[\"AIC\"]:\n",
    "            Stepmodels.loc[i] = Backward_result\n",
    "            predictors = Stepmodels.loc[i][\"model\"].model.exog_names\n",
    "            aic_before = Stepmodels.loc[i][\"AIC\"]\n",
    "            predictors = [k for k in predictors if k != 'const']\n",
    "            print('backward')\n",
    "        if Stepmodels.loc[i][\"AIC\"] > aic_before:\n",
    "            break\n",
    "        else :\n",
    "            Smodel_before = Stepmodels.loc[i][\"AIC\"]\n",
    "\n",
    "    return (Stepmodels[\"model\"][len(Stepmodels[\"model\"])])\n",
    "\n",
    "Stepwise_best_model = Stepwise()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   166.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Dec 2020</td> <th>  Prob (F-statistic):</th> <td>3.32e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:28:23</td>     <th>  Log-Likelihood:    </th> <td> -26.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    13</td>      <th>  AIC:               </th> <td>   61.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   64.13</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>x4</th>    <td>   -0.2365</td> <td>    0.173</td> <td>   -1.365</td> <td> 0.205</td> <td>   -0.629</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.4519</td> <td>    0.117</td> <td>   12.410</td> <td> 0.000</td> <td>    1.187</td> <td>    1.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.4161</td> <td>    0.186</td> <td>    2.242</td> <td> 0.052</td> <td>   -0.004</td> <td>    0.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   71.6483</td> <td>   14.142</td> <td>    5.066</td> <td> 0.001</td> <td>   39.656</td> <td>  103.641</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.211</td> <th>  Durbin-Watson:     </th> <td>   2.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.900</td> <th>  Jarque-Bera (JB):  </th> <td>   0.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.202</td> <th>  Prob(JB):          </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.270</td> <th>  Cond. No.          </th> <td>1.27e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.27e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.982\n",
       "Model:                            OLS   Adj. R-squared:                  0.976\n",
       "Method:                 Least Squares   F-statistic:                     166.8\n",
       "Date:                Wed, 30 Dec 2020   Prob (F-statistic):           3.32e-08\n",
       "Time:                        21:28:23   Log-Likelihood:                -26.933\n",
       "No. Observations:                  13   AIC:                             61.87\n",
       "Df Residuals:                       9   BIC:                             64.13\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x4            -0.2365      0.173     -1.365      0.205      -0.629       0.155\n",
       "x1             1.4519      0.117     12.410      0.000       1.187       1.717\n",
       "x2             0.4161      0.186      2.242      0.052      -0.004       0.836\n",
       "const         71.6483     14.142      5.066      0.001      39.656     103.641\n",
       "==============================================================================\n",
       "Omnibus:                        0.211   Durbin-Watson:                   2.011\n",
       "Prob(Omnibus):                  0.900   Jarque-Bera (JB):                0.378\n",
       "Skew:                           0.202   Prob(JB):                        0.828\n",
       "Kurtosis:                       2.270   Cond. No.                     1.27e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.27e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stepwise_best_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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