statistics/multiple_linear_regression_moneda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import itertools\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grupo</th>\n",
       "      <th>y</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>astronautas</td>\n",
       "      <td>143.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>astronautas</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>astronautas</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>astronautas</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>astronautas</td>\n",
       "      <td>133.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Grupo      y    x1   x2\n",
       "0  astronautas  143.5  20.0  6.0\n",
       "1  astronautas   66.0  20.0  4.0\n",
       "2  astronautas   14.3  20.0  2.0\n",
       "3  astronautas  128.0  20.0  8.0\n",
       "4  astronautas  133.5  20.0  6.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('datos-moneda.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>   0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2017</td> <th>  Prob (F-statistic):</th> <td>3.61e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:26:59</td>     <th>  Log-Likelihood:    </th> <td> -434.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    83</td>      <th>  AIC:               </th> <td>   893.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    71</td>      <th>  BIC:               </th> <td>   922.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</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>  -19.5255</td> <td>   33.168</td> <td>   -0.589</td> <td> 0.558</td> <td>  -85.661</td> <td>   46.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.Los podridos]</th>                <td>    5.6532</td> <td>   28.929</td> <td>    0.195</td> <td> 0.846</td> <td>  -52.030</td> <td>   63.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.Redline]</th>                     <td>  -65.1590</td> <td>   26.910</td> <td>   -2.421</td> <td> 0.018</td> <td> -118.816</td> <td>  -11.502</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.astronautas]</th>                 <td>   -5.5133</td> <td>   27.275</td> <td>   -0.202</td> <td> 0.840</td> <td>  -59.899</td> <td>   48.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.el duo]</th>                      <td>    3.3463</td> <td>   24.819</td> <td>    0.135</td> <td> 0.893</td> <td>  -46.142</td> <td>   52.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.el duo dinamico]</th>             <td>  -92.4100</td> <td>   53.562</td> <td>   -1.725</td> <td> 0.089</td> <td> -199.209</td> <td>   14.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.los calzoncillos galacticos]</th> <td>   17.1445</td> <td>   27.722</td> <td>    0.618</td> <td> 0.538</td> <td>  -38.131</td> <td>   72.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.los liroy]</th>                   <td>   33.6840</td> <td>   25.726</td> <td>    1.309</td> <td> 0.195</td> <td>  -17.613</td> <td>   84.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.pitbull crew]</th>                <td>  -42.2132</td> <td>   24.300</td> <td>   -1.737</td> <td> 0.087</td> <td>  -90.666</td> <td>    6.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Grupo)[T.zelda]</th>                       <td>  -25.7128</td> <td>   25.740</td> <td>   -0.999</td> <td> 0.321</td> <td>  -77.036</td> <td>   25.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>                                      <td>    3.2508</td> <td>    1.469</td> <td>    2.213</td> <td> 0.030</td> <td>    0.321</td> <td>    6.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>                                      <td>   10.8202</td> <td>    2.705</td> <td>    4.001</td> <td> 0.000</td> <td>    5.427</td> <td>   16.213</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.943</td> <th>  Durbin-Watson:     </th> <td>   1.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  12.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.681</td> <th>  Prob(JB):          </th> <td> 0.00205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.313</td> <th>  Cond. No.          </th> <td>    184.</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.495\n",
       "Model:                            OLS   Adj. R-squared:                  0.416\n",
       "Method:                 Least Squares   F-statistic:                     6.321\n",
       "Date:                Tue, 14 Nov 2017   Prob (F-statistic):           3.61e-07\n",
       "Time:                        10:26:59   Log-Likelihood:                -434.60\n",
       "No. Observations:                  83   AIC:                             893.2\n",
       "Df Residuals:                      71   BIC:                             922.2\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===========================================================================================================\n",
       "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------------\n",
       "Intercept                                 -19.5255     33.168     -0.589      0.558     -85.661      46.610\n",
       "C(Grupo)[T.Los podridos]                    5.6532     28.929      0.195      0.846     -52.030      63.337\n",
       "C(Grupo)[T.Redline]                       -65.1590     26.910     -2.421      0.018    -118.816     -11.502\n",
       "C(Grupo)[T.astronautas]                    -5.5133     27.275     -0.202      0.840     -59.899      48.872\n",
       "C(Grupo)[T.el duo]                          3.3463     24.819      0.135      0.893     -46.142      52.835\n",
       "C(Grupo)[T.el duo dinamico]               -92.4100     53.562     -1.725      0.089    -199.209      14.389\n",
       "C(Grupo)[T.los calzoncillos galacticos]    17.1445     27.722      0.618      0.538     -38.131      72.420\n",
       "C(Grupo)[T.los liroy]                      33.6840     25.726      1.309      0.195     -17.613      84.981\n",
       "C(Grupo)[T.pitbull crew]                  -42.2132     24.300     -1.737      0.087     -90.666       6.240\n",
       "C(Grupo)[T.zelda]                         -25.7128     25.740     -0.999      0.321     -77.036      25.611\n",
       "x1                                          3.2508      1.469      2.213      0.030       0.321       6.180\n",
       "x2                                         10.8202      2.705      4.001      0.000       5.427      16.213\n",
       "==============================================================================\n",
       "Omnibus:                       10.943   Durbin-Watson:                   1.895\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               12.381\n",
       "Skew:                           0.681   Prob(JB):                      0.00205\n",
       "Kurtosis:                       4.313   Cond. No.                         184.\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": [
    "modelo = smf.ols('y ~ x1 + x2 + C(Grupo)', data=df)\n",
    "resultado = modelo.fit()\n",
    "resultado.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EJwdU9RmN+jeJSFo6If7k7bdICU4OqOoz2lmD+jeJSFo6If7k7bcolQTHzH5k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\nnOCYmZlZzXGCY2ZmZjXnP8bbvqVdOstEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f12bd1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "ax.scatter(df.index, resultado.resid_pearson, color='blue')\n",
    "ax.set_xlabel('Observations')\n",
    "ax.set_ylabel('Standard error')\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "ax.scatter(resultado.fittedvalues, resultado.resid_pearson, color='blue')\n",
    "ax.set_xlabel('Mean response')\n",
    "ax.set_ylabel('Standard error')\n",
    "\n",
    "ax = fig.add_subplot(223)\n",
    "st.probplot(resultado.resid_pearson, plot=ax)\n",
    "\n",
    "ax = fig.add_subplot(224)\n",
    "ax.hist(resultado.resid_pearson, bins=6)\n",
    "ax.set_xlabel('Standard error')\n",
    "ax.set_ylabel('Frecuency')\n",
    "ax.set_title('Histogram')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hNeGyf3Dfd8gXYSwQZV+rN6dkB5uJBD9RUUb8Eb53dgiAYDTJiV3rq8lmNil+\n51gnsWTmTCgtXgffnxrGaTPT4s18IWxw2zd0kVyP7noX3fUudja5eefWNNsb3dyanCWWMIglYgz5\nwmsO3RqG3pJlnlYSjCZpr6mipdqxZo/ocGcNE8HYXABa+ndc6e/27qCfwZkwx7bVLXkf7Gh0cXkk\nmHWGHLvFzIc2MDJgNZuWLHsIRhN89/QgKUMz4ovmtORiM5HgV2EqvWDu4nSWRo65LZVSK34qHg/G\n6KhJzzybCMWodq6/0nghLE6/NjgT5tbkLG6HhY7aD2Z6/ubGFOPBKCd2NiwspTjVN80b1yfprnfy\nzN3tFTGTsM5lY3ujmyFfhEMrDDnO8zqsfPro0t5hPGnw4qkBpmfjfGR/y5Le/9u3pvh/37hFi9dB\nMJbk2d7OhZ99ZH8LD+9upKpEPS7NB+dHrufGZiLBT1SUtpoqPnGolUA0kdfUUfNqnTYsZhNmk8Jb\nVZ5lczpqnfzxIzuWPDYWiC5MfgAW1uldHgmgNfRNhpmNp4pSqb3UzCbFM0dyX6c4EYotZBp6fzSw\nEPySKYM3rk4yG0txc3KWE3ekVlNKlTTvptdh5VNH2hkNRDlYAfeEt/47WYg77Gou3L22w501NHrs\nOKxm6lzl0evLhsdhocpmJhJP0eT5YFbhka5afn19km0NLlxybzArzR472xpcTARjHF70ActsUjRV\nOzhoVNPotfPYntyHLAulq95JV4nWyhabBD8h8qzcp4wHowlevzaJ227hoV0NCz2O37u/m2A0uWRK\n/YH26i03M/TySICrY0GOdNYW5EJvMZsy9hyVUnyut5Op2RhNHkdF3UctRxL8hMgTrXVW98RG/dGF\n4bB8rrPL1ju3prkyV9C0o7Zq4V6g02ZZNux2YdDHG9cn2d7g4iP7Wzb9Pb9kyuCnF8cwtGYyFOcP\nV1lneadsX995U6EYf/mL62gN/+jDO6l327FZTCV5zcVykuFFiDy4OOzn//z5dV48ObBmodSXzw9x\ntt/H988NF6l1S83PMLSaFTVrTMg5P+AjljC4PBJkNl6+lcqzZTYp6lzpe7EN7uyHpdfz+s575dIY\nl4YDXB4J8NOLYzm1VxSO9PyEyIPLI0FShmbIF2E6HF9y3+xODquZ2Vgq77P6xgNR4iljyQzOTA53\n1tBa7cBhM+NdI/HyvrZq3rg2SU+Dc0vc81NK8eyxTiZDcZpXqbzhjySYno3TXefEZFJcGg5k/frO\n297owmaQCbqfAAAgAElEQVQ2oee+FuVFgp8QeXC4o5rJULqcT71r9TV8hztq+NXViTWn0a/H4EyY\n75weRGv4rf3N7G9L7zuZMvBFEtQ5bUvuMTVlWV/unu5ajnbVbPrhzsXsFvOqqcAi8RQvvH2bWMLg\nUEc1j+1t5u7OGqZm41m9vvPu3VbPn340fYndv0num751c4phX4QHdzZkXYNws5LgJ7aEYV+E/ukw\n+9q8a/ZmCmFXsyfrWaRvXJ8kaWjevDHFka7saudNhmK8dGYIpeAzRzuovWMmaTCaXFijFYh8kOrq\nb84OMTQTYWeTm6cOt2X3y9xhKwW+bMzGkpzt9xGKJrGaFY/tbV7X6ztvcCbMz94fB8BbZc1L9YxC\nmgrF+PF7o4SiSaIJgy8c7yp1kwpKgp/Y9GLJFH9zZpBEStM/FV7IlVgooViSyWCMzjrnumuIQToL\nTP90eF2frG+MhxbydN6cDHGPa2n6q7uaPczMxomlDI52p6fXa625MR5iajZOhcWvDdGA12EhlTJw\nbWDd3VggupAhZiwQzTr4zVdqONBeXdQUY4bW3JwMEUsYtFRv7V4fSPATW4BCzfVOCp+GK5ZM8cJb\ntwnHU+xr8/KR/S3r3sfTd7cxPRtf1zrAXc0e3hsOYFKwo3F5HkeTSfHAzuWp2mbCcW5MhPJaYHez\nG/ZFuDkxy742b8bXoMpqJpY0iCYNzBuIPfvbqhnxR9GarJeLBKIJXjo7RMrQjAaifOJQbr31XDis\nZu7uqCGcSL+3tzo5I8SqNkM6NJvFxGd7OxicibBnlWrX+RBPpiurA2vW41tJJJFiLBBLTzjJMrF2\nncu2rmn5kO75jQVj2MwmRv3La8VVopSheensEPGkwc3JEL93f8+ybRIpgx2NbnY0gtue+xC6w2pe\nKPGUbQ9OLfm6uN11j8PK79zbxVggyt5WCX5CbApNHkdWM/BWE4mncFhNq97j8jisPL63mcGZ8JKk\nwOvxN2eGmJ6NU+u08qUH1xfQ1sNkMnG0s4bBmQi7F92vmgzF+OnFMTwOC08eaMlbAeC/eus2p/qm\n+dihVn5r3/p7xPlwaTjAqdvT3NXs4XiGGogKmAjGGJwJr5jertZl40N3NfK3744wGYozHozm9N56\nfzTA3707CsBHD7ZkVevQ47Dy6aPtjAVi7C9B76vF66DOZdvyFR1Agp8QAPz127f51dUJDnfW8A8+\ntHPVbTea9SSeTK8Ti2e5XmwjvvTgNvqnwmxbNNX+bH+67NJYAG5PhTOWZpo37Itwcq6m3eJgEYmn\n0OiFRfGhaJIfnE+vW/z2qcGSBb83b0wSjCZ5MzTF0e7aZYHd0BqH1USD245llSHyGqcNp83CbCzJ\nmdszPHkgc4WDO/8Oi4VjKW5MhID0JJpMgtEEv7o6idth4aGdDZhMio5a55rLVQohkTL41skBJoMx\nHt7dyD3d2U3G2qxkkbsQwKuXx5kJpy9EyWRhg9LTd7dxrKeOZ+7OPXlytqqrrBzsqF6SkLqn3olJ\nKVx2M80rlF2a9/P3x7k5McvPLo8Tjqcv4KP+KP/5jZv8p9dvMTAdBsBpMy1csHfnUFsuX+YLz3bW\nOTP2aC1mE931Lpq9DnpWKVLb6LHjtltQihW3Gwss/zssZmiN02bGaTNjrPCWOtk3zdWxIGduz3Br\najaL37BwfOEEk3MJuW/OBe2tTHp+QgB3d9Zw+vYMu5rdWO6ovv7qpTF+cmmU+7fXLytfk4smryPr\ndXaFsKvZw/O1TixmteaQZ6PHzkQwRnWVFdvctsP+CIlUehbjsC9CZ50Tk8nE//apA4wEonSWoNcy\n77G9zRzfXo/TasYwNL+6NkE4nuLh3Y0LHwA+e0/HwtrHlbjtFr70YA/JlF6x2O+wL0IwmuTGRIhE\nyuC///BO7JYPtm302BfyvDat8CGj0e0A/FjNitoSl79qcNs40F7NsC/CsRyH9DeTsgp+Sqk/B3qB\nM1rrf1zq9pS79U5GKVf5mFRzadjPn79yFYfNzL/+xL5136P5kw/tYCwYozFDkdlvvtNPKJZkYDrC\nJw61YbNs/gGTbKu3P7G3mYPt1dS5bAtV7/e1ehmYDmNovWT412Y1011f+kwm80Hu2liQs/0+AJy2\nDwq/WsymrIoJW80m5m99XRkN8rP3x2irruKpw22YTYq9rV5+enEUs0mRNDTXxkJL/h7d9a6FCTUr\nzew92FFNs9e+kG0nnjSWvb+uj4f4xfvjtNY4+NiB1oLNaFZK8cS+5oLsuxyVzVmslDoKuLXWDwE2\npdSxUrdJbB4vnR3i8kiQc/0+fnZpfN3Pt5hNtNdUZQxsPQ3pnkx7jaNkgS+aSPGjCyP83bsjxJLF\ny7FpMinaaqqWTIBwWM08fXc7nzrSgavM6vvdnAjxndODXBj0UeuyLdzXyybYrebCYDrH6a3JWaZm\n00ODDquZZ491sb+tGo/DQmuGtXF1LtuaS1qavA68DiuvXhrjP/ziOj9+b2TJz8/2zxCKJbk2FmJy\n7thi48rpnXsf8Mrc168C9wMnS9ccsZm0VjswmdJBrKV6Yxe6O/3pk3u4NRmmq4QZOt4d8nN1LF2J\nobnawdEsM8NUmp+/P04wmmRwJsw/fHQnv/dAD7FkasMzgfe3VTPqj9JaU7VkuHRbg4s/emg7JhNL\nhjxzcWXu9b06FuLJAx88vqfFy5Avkp6JWeKh0a2knIJfDXBz7ms/sP/ODZRSzwPPA3R1be3UO2J9\nPnl3OyaTospi5r7tyxd7b4TVYmZ3gdcPrqXZ48CkFEpB0yoJmStda3UVwWiQRk96Nmd1lRXYeLq7\nfW3eFRd+ZzuEvJbj2+o4N+BbNpP4YEc1+9q8OWUTEisrp+DnB+bfXV7Ad+cGWuuvAV8D6O3t1cVr\nmih3DW47X3qgcGvmSq2r3smXHuxBKUqSu3Sz+OiBFo5tq6XWadt0OUl7e+pWXDsqgS//yuaeH/Ab\n4LG5rx8H3iphW4QoO9VVVgl8azCZFE0eR94W7outS2ld/A6UUuo48OeAAZzUWv+PSik/EATcwIta\n6+dX20dDQ4Pu6ekpeFtFeevr60PeB0LeB2Le6dOntdZ6zU8/pRr2vA18WGsdVUq9oJQ6CLyrtT6R\n7Q56eno4depU4Vq4AYvXFz2yu7HsZsRtJb29vWX7PhDFU+nvg/6pMGcHZtjV5KmIpNSrUUqdyWa7\nklyVtdaji75NAClgr1LqdeDXwL/UpeiS5sn1iVDG9UVCCFEIr1weIxBJ0DcZZneze2FNplhZSf9C\nSqlDQKPW+hKwC3gYqAWeWmH755VSp5RSpyYmJorY0vWpdX6wvqhRZuYJIQps/jpT57LK5JgslWw8\nTilVB/wl8CyA1np67vHvAUeAl+98zmaZ7dnosedtfZEQQqzl4wdbGQ1EaXBvvlmupVKS4KeUsgB/\nBfwzrfWoUsoFRLXWKeBB4N1StCuf8rW+SAgh1mI2Kdrn8oiK7JSq5/dZ4Bjw1blPKf8S+A9KqRBw\nC/g3JWqXEJtOMXK8lqJIsRCFVKoJL98EvnnHw0dL0RYhhBCVR6YECSGEqDgS/IQQQlQcCX5CCCEq\njgQ/IYQQFUeCnxBCiIojwU8IIUTFkYzLQpSZYqzbE6LSSc9PCCFExZHgJ4QQouJI8BNCCFFxJPgJ\nIYSoOBL8hBBCVBwJfkIIISqOBD8hhBAVR4KfEEKIiiPBTwghRMWR4CeEEKLiSPATQghRcUoS/JRS\nx5VSbyql3lBK/fncY1+e+/4FpZS1FO0SQghRGUrV87sNfFhrfQJoUko9Ajw69/0F4JmN7PzisJ9v\nvtPPVCiWh6bCWCDKmf4ZIvHUksevjQW5OOxHa53xeUO+CK9dGeftW1MEo4k19z/si3CmfwZ/JLHq\ncYUQuZsKxTjTP7PqOQnQNznLhUEfyZSR8ee3Jmc5P+DjwqCP6+PBnNoSTxq8dGaIX14Zz+n580Z8\nEb75Tj9Xx3Jrx7xhX4Sz/TNEE1v/mlOSqg5a69FF3yaA/cBrc9+/CjwHfDuXfQcicb7yd+8TTxqc\nH/Dxlc8c2lBbo4kU3z41QCKl6Z8K88yRdgBuTIT44YURAGJJg6NdtUvbEU3wnVMDvNM3TU2VlQd2\nNvDc8e5V9983OUtPg4v3hvw829uZ8bhCiNwZhubbpweJxFO8PxLkC8e7Mm43FojyvXNDaA2+cIKH\ndzcu+fmwL8L3zg4x7IuQMjSddU6eOWJiW4NrXe35r7+5xauX0oHP7bByT3ftGs/I7M9+fIURf4Sf\nXBzlP/1uLxbL+vs1wWiC75weJGVoRvxRPnawNae2bBYlveenlDoENAI+IDD3sB+oWWH755VSp5RS\npyYmJjLu09BgzPXEVuiQrYvWH+zHWLTDxb09w1h+oPnnzf/LtM3S7TSpuX2mDL3kuKkVniuEWL/5\n8zi1ygXC0DrjeX/nPvTcv5W2W0tqUQcrZWTuYWZj/tiG1uS6F01lXXNKVs9PKVUH/CXwLHAP0DH3\nIy/pYLiM1vprwNcAent7M746NU4b//SJuzg/4OPJAy0bbmeVzcynjrYzNBPhQHv1wuM7mzz81n6D\neNLgUMfyWF1dZeVTR9vZ2+qlymbmUEf1sm3u3P+n7+lg1B9ld7NnyeP72zM/VwixPiaT4jNHO7g1\nOcueFs+K27VWV/HU4VZ84UTG87uj1snHD7XiC8cBcNut7Gh0r7s9X3qgG0+VhQaXnXu31a/7+fO+\n/Ft38crlMe7dVocth14fgNdh5VNH2hnxRzL+zluNWul+VUEPqpQFeBn4X7TW7yilmoD/orX+uFLq\nnwN9WusXV9tHb2+vPnXqVDGaK8pYb28vW+19UI7FbPu+8vFSN2FVW/F9IHKjlDqtte5da7tSDXt+\nFjgGfFUp9RqwA/iVUuoN4G7ge7nsVGvNeDBKLLn1b9YKIUovEE0sTFATm0upJrx8E/jmHQ//Bviz\njez3tasTnOv3UeO08sX7urGaZRmjEKIwhn0RvnN6EK3h6bvb6FnnZBdRWlsqOoz4okB6dlakAqbq\nCiFKZzwYI2VoDK0ZC0RL3RyxTiWb8FIID+9u4K2b03TXO/E6ZJ28EKJw9rZ6GPVHSBq6IiaIbDVb\nKvh11Dr57XucpW6GEKIC2C1mnjywtdfCbWVbathTCCGEyMaW6vklUwYvnx9mIhjjiX3NbF9h3c21\nsSA/e3+c1moHnzjUxlQoxsvnh3FYzXz6aDtO25b6swghiiiZMvj+uWEGZsIkUwYNbjvPHGmnxmlb\ntu2wL8KPLozgdlj41JF2HFZzCVpcmbZUz288GOP2VJhwPMW7Q/4Vtzs/6CcST3FzYpap2RiXRgIE\no0kmgjFuTc4WscVCiK1mLBijfzrM0EyE6+OzzIQTXBsPZdz24nCAUCzJqD/KwHS4yC2tbFuqi9Pg\nttNS7WAyGGNPi3fF7fa1ehn2RWjxOqhz2tjd7OHSSACHxUxXndwzFOJOuSy8L/eF8YXS6LbT7HUQ\nT6aocVpx2c1sX2EZxJ4WD1fHgrjtFtprq4rc0sq2pYKfzWLi8/d2obVGKbXidvvavOxt9Sxs01ZT\nxT94ZMeqzxFCiGzYLCa+cHzt6xBAZ52TP/mQXHtKYUsNe87L5o105zby5hNC5FO21xS59pTGlgx+\n6zU4E+YH54e5NBxYc9t3bk3zowsjzMzGi9AyIcRWcmU0yMvnh0tyf6+Uxy5HW2rYM1c/uzzO9Gyc\nmxOz7GhyYbdknnE1Fojy6+uTQLocyicPtxWzmUKITSyZMvjxe6MYWjMRjPGHJ7ZVxLHLlfT8gDpX\negpydZUFq2nlP4nbblmYitzgWj5tWQghVmI2KWpd6cxTDe7iXj9KeexyJT0/4GMHWxn2RWj02DGZ\nVh5/d9kt/O793QQiCdpqZGaWECJ7Sime7e1kIhijtdpRMccuVxL8SH8q6sxyiYPbbsFtlz+bEGL9\nHFZz1tearXTscrQlhz0nQzHO9s8wG0uuuI0/nOBs/wz+8Ae1uAxDc3HYz/VFC1KD0fR204smuAxM\nh7kw6CORMtbVrjv3n00716Nvcpb3hvykjLULFCdSBucHfAzOyM1vsXWEYknO9s8wFYqtuM31sSD/\n7WQ/I77IituM+COcG/CtWhs0m/PtymiQK6PB7BqfQcrQvDvo5/bU6sk3svm9s5HN771VbLkuTDJl\n8OKpAWIJg2tjIZ491plxu++eGcQfSXC238cfzN38PTvg41dXJwD41JF2ehpcvHx+mPFADKdtmj96\naDsz4TjfPZOu4TUVivPonqas27Z4/08dbuWVS+NEEymujgX53LGuDf3eI/4IL50dAiAQSfDAzoZV\nt//llQneHfJjUorfu7+bWrmHmRVZ7F3efnB+mFF/lCqbmecf2r7sNkYyafBv//YykXiK39yY5i9+\n5+5l+whGE3zn1CBJQzPsi/Cxg8uTVw/7Fp1v0QQP7Fh+vl0aDvCTi6NAOojta1s58cZK3rwxyam+\nGZSCz9/bRbM385DlWr93NrL5vbeSLdfz04Ce+yCWXOUTmTG30fz/wJJPcPPPNeb+T+kP/p9/SjY9\nrEzHBEim9ML36+xAZrS4LSm9drvmt9HoJe0SYjObP29Thmald/X8uZI0Mp94GjDWOMeXnG8rbLPS\ntWU95p+n9er7yOb3Xks2v/dWsuV6flaziU8fbadvMrzqJ61njrRzbSzEruYPkl/f012L2aSIJlJU\n2dKzOp863MblkSDbG12YTIomj4OnDrcxPRvncGd1xn3Hk0Y6fVq1Y0mi2qNdtZiUwm4xsafVS7XT\nmm5n6/o/Ed6po9bJxw62EowmONy5dm2xR3Y3UlNlpcFjp95t3/DxhSgHTx1q5fJIkG0NLswZej8W\ni4l/8eQe3r45xeP7mjPuw+uw8syRNkb8UQ51ZD7HO+ucfPRgC6FokiaPnclQjIY7zqP9bd6FIHKg\nfe1zPJZMMeKLLrluPLCjAZfdQnWVddVJdmv93tnI5vfeSrZc8ANora6itXr12ZgNbvuyN6vZpOis\nreJbJwd459Y0j+1t4lBHDffvqF+y3c6mzNUi5n3v3BBDMxEaPHZ+977uJfu/p7t2Xe1cj7taPFlv\n67CaOb69fu0NhdhEapy2ZefrnQ60V3OgffWLe3e9i+76zPk45+1p8XJx2M93zwxhUorP9nYsCVBK\nqaw+iM773tkhhn1Rmrx2njuevm7YLCaO9dSt+dxsfu9sZPN7bxUlG/ZUSrUppc4opaJKKcvcY36l\n1Gtz/9Z+xdcQjicZ9oXxheP4wnHC8bUnlgSiCVKGJmUY3J5aOhlEa81kKEYgklgyAeZOvnD6Z/5w\nHJ3FkKI/kiAUSxJNpJhc4YZ1NJHa8M1sIUR6XsBEMLZwSyNX4XiSwblsKYbWBKKJZdsEoomMjy82\nf22amZt8N+aPcnUsu0ky/nD62mEY6cXryXzcQ8mDbH7vUitlz28aeAx4adFj72qtP5SPnfvCcb7+\n+k3O9PuoqbJS77LRWlPFc8e7MtbVmrej0c3Rrlr+9r0RjJEA1VVWHt7dCMAvr07w9s1pro0H2dvq\n5dE9TRztql22j48eaOW9IT93tXjWzNt3a3KWl88NY2iNRqNQ3Le9fsmnuEg8xTfe6mM2luLEroas\nPgkKITL79ulBRv1R7mrx5DypwxeO88Lb/YRjSRo8dnY1edjdtHTkZWA6vDAp5lNH2jMuM5ifFGOz\nmHhkdwNXx0J85/QAb9+a5vG9TfzRwztWbMP18SA/vDCCxaRocNsZ8Udp9jr4/L2dJc0Xms3vXQ5K\n1vPTWke11jN3PLxXKfW6UuorKsOrp5R6Xil1Sil1amJiYtX9T4bi+MIJDCPdWwvGksSTBlNr5ORU\nSnF3Zw0tXgdWs4kR/wfToYd9UaKJFMFokkTKYMQXzbiP9P2A1hWL6S426o9iaE04nmQ8EJs7ztIp\n2P5IgtlYKuPPhBDZS6YMxgLp83Yj59JkKE48aWAxm+isdXJiV8OyGZbjwejcKJJeOOad5q8v8aSB\nw2rhUHv1wgS46xOrL28Y8UfRGhIpvbB8ajwYXXWiXzFk83uXg3K757cLmAH+I/AU8PLiH2qtvwZ8\nDaC3t3fVV3hbg4uHdzXisJppr66iym6m0W2nJ4vx7Gqnlft31DM4E1nSA3t4dwMWE7RWO2jw2Llv\n+8Z7YIc7q5kMxbCYFFU2M5OhOCd2LZ023ey109tTy3gglpdxfSEqlcVs4sN7mrgyGuRo9/JRm2xt\na3BxsL2aUCy54kjM/rZqRv3pD7Qr3WPs7a4jGE3itlsWJqs8vq+J6+OzPHd89eVPR7pqmZ6NY7eY\n2dbg5MKgn93NHqzm0k7iz+b3Lgcqm3tSBW2AUq8Bj2utk4se+yhwRGv971Z6Xm9vrz516lQRWijK\nWW9vL8V8HxRjnV8uxyhHxVzfWOz3gShfSqnTWuvetbYrm56fUsoFRLXWKeBB4N0SN0mIvNgqwUyI\nraRkwU8pZQX+DjgM/AT4V8D/rZQKAbeAf5PP4/kjCb5/bgit4ZOH24qS0SSZMvjBhXSGmEd2N3J+\n0EcwmuRjB1slMbYQZcowND98d4QRX4RH9zSxuzn7JUSlcvr2DO/cmiIYTeKtsnJiZ0NZDzmWg4IM\nDiulnlhrG611Qmv9uNa6Vmv9mNb6ba31Ua31w1rrvzfXA8yb6+NBpkJxpmfjWU8j3qjxYIy+yTDh\neIrXrk4w7IsSjCa5mEXRXCFEaUzNxrkxHiIcT3FuwFfq5mTlbP8M/kiC07dnCEWTm6bdpVSoO6P/\nuUD7zVlPvQuX3YzTZs5qFmY+NLjtNHsdmE2K+7bXUeu0YrOY2N1cnOMLIdav1mmlvaYKk1J5yb5U\nDPtavdgtZva0erCYVU55RCtNzsOeSqmXV/oRUHZTEuvddv7ooe0ARVsDY7OY+MLxLrTWc0soahe+\nFkKUJ4vZxLPHOjfVufrAzgbu31GPUmpTtbuUNnLP7yHgi0DojscVcO8G9lswpXpDLD6uvCmF2Bw2\n27k6397N1u5S2UjwewsIa61/eecPlFJXNrDfshZNpHj92iRWs+LEzgYsWa6p8YXjvHljikaPfWFd\nkNaat25O44/EeWBnA16HtZBNz4vJUIy3b07TVuPgSIbsNkKItHjS4I3r6WQcJ3Y2YrPkdpdpKhTj\nrZvTtNY4MmaUytaV0SDXxoMc7qgpaNaVfP3ehZZz8NNafxRAKbVPa33pjh//zxtqVRk72+/jvSE/\nkL6nl+2MqtevTXJ9PMSV0SBddU6avQ4GpiO8dXMKSH9a+8j+loK1O19euzLBwHSYq2PpDPKrpYoT\nopK9N+zn/ED6WlFdZVuS1H49fnl1gttT6XOuu86ZUxWWZMrgx++NYmjNWCDGH87VMC2EfP3ehZaP\npQ4vKqW+AXwVcMz93wvcn4d9r8kwNCmt18xqEE8aWM1qw0MCdXNLJExKLXydzTHrXTauA3arCavZ\nhNYab5UFq1mRSGnqN0kx2XqXjYHpME6beUm5pnIg6+kEpM+7jfY2tNYkUnrV/ax17alz2pivrrfW\ntWI1dS4bt6fCVNnMOG25XbLNJkWty8pUKF7wa02d08b8ZXYjv3eh5SP4HQf+DHgT8AAvkF6kXnDh\neJJvvTNAKJbkYwdb2NmUeT3Oyb5p3rg2SXtNFZ+5pyPneleQLhtU67RiMZtWfWHP9M/wyysTtFQ7\n+Ow9Hdy/o56ueid9U7P81zf7Fh7/3ft7mI0lN826vw/d1cjOJjd1LlvZBT8hfnl1gjO3Z9je6OLp\nu9tz2kciZfDiqQEmgjE+dFcTd2coSxSKJfnWO/2E4yk+cShzHl+n3byQp9Nly/1ceWR3+pyrcdoW\n6oyul1KKZ3s7mQjGaK3OXA0+X3oaXAslmRo95VsrNB+DsQkgAlSR7vnd0loXpa7GWCCGP5IuQTSf\n2DWT+XV9Q74IodjaZY3W0uR1rPmJ5trcMUf9UfyRBEopOmqdDM1Eljy+VpHKcqOUorPOicteNsmB\nhFgwf97dnJglkWN5H184wXgghtasuCZ41B8hGE2SMjQ3VkhA3T8VxmJSWEyK29PhjNtkY/7a4d7g\nOeewmumsc2Y9T2EjGj32sg58kJ/gd5J08DtGegbo55VS387DftfUUVtFT4OTWqd11aKRvd11eBwW\nDrZX43Ws7w00MB3mvSH/QkXmbN3TXYe3ysreVu9CoLw9NYu3yorbYWFvqxen1cRLZ4Z459bUuvad\nT7FkivMDPkb95Zt9XYhs3bstfa739tTmnOC53mVjb6sHb5V1xftVXXUunDYz0USK/Susqdu9qLj0\nSoWmDUPz3pCfvsnVKzgUWiCa4NyAj6ujQc4P+Igl85pjpCzl4+P7H2qt5zPKjgBPK6V+Nw/7XZPV\nbOJTRzrW3O6uFs+6qpzPGw9E+e6ZQbSGmXCch3Y1Zv3cnU3uJRXfR/wR/uZMusbV8e11PLCjgf/j\nZ1d58/oUSsG/ffoAO0uQRunVS+NcHQtiMSn+4MQ26dGJTe1QRw2HOrKvnp6JyaR48sDqdf6mZmOE\n4ykcVjN9U7MZR2/mR3kABqcj7GtbPpv77VvTC5PePness2SjQC+dGWLUH+XyaIDDHTUM+yJ8NMda\nh5vFhnt+iwLf4se+sdH9loOU1swXvdhojaxk6oPnz/ciE8n0/1pDvEQVmJNG+riGTlejFkKsLdP5\nfKfFj6+0zfz5t9o2xZA0NFprUnO/V6LENQGLQT7mr6K1uoqPHWzFH0lkvOm9Hp11Tp480EIollzY\n1x89tI16t43O2ir2tZUmCe3je5u54PHTWu3AswnWGQpRDjKdz3fa1+pd+NC80tDofdvrcVjNuO2W\nklY8f/ruNq6OBXlifzPxpOZw59ZPii3Bbw25DJeuZO8deQKrnTZ+/8HCrbfJhstukQK5QuTgzvP5\nTiaTWvNDs9VsWrEYbjE1uO005LB+cDMrz6X3W1AiZTAViqG1JpZMMT0bL3WThBAbEImn8IcTRTnW\n9K2v4k4AACAASURBVGy8IiahFJP0/IogZWi+9U4/k6E4e1o9DM2kp0nft71eel1CbEL+cIIX3rlN\nLGHwkf0tBa2i8OaNSd6+OY3HYeGL93XL+to8kZ5fEcSSKSZD6Z7erYlZgtH0WsNhX2S1pwkhytRE\nKEYskZ6sUujzeNiXXoYUjCbzsk5ZpEnPrwicNgsP7Wrg5uQsx7fVMTAdYcQf4cGdDaVumhAiB9sa\nXBxoryYUS9DbU9jclQ/urOeNa5q2mqqKuy9XSBL8iqS3p47euRvb3fWuErdGCLERZpPiiX3NRTlW\na3UVn+3tLMqxKknJhj2VUm1KqTNKqahSyjL32JeVUm8opV5QSsm8eyGEEAVRyp7fNPAY8BKAUqoJ\neFRrfUIp9S+AZ4Cc0qT5wnG+f24Yk4JP3t1OdZWV166M8/5okGPdtUyEYtyeCvPQrsYVb1QvbN9T\nV5SSHL+5McX5QR+HOqp5YEdxhkNP9k1z+vYM+9u868peI4RY26UhP1/+7nkA/v1nDrMvy/JnuTjT\nP8M7t6a5q8XDo3c1ZfWcEX+EH10YweOw8PTd7djMJn707ggj/giP3tXErhJknCqmkvX8tNZRrfXM\nood6gdfmvn6VDZREujIaZHo2zmQozvXxEMmUwdl+H5F4il/fmOLySJBwPMXZgZmMz1+8/ZnbmbfJ\nt9O3p4nEU5zuK87xAE71zaSPeXsGLdldhMirl84O4Qsn8IUTfPfsYEGPdeZ2+lw+1+/LOqH3e0MB\ngtEkw74oA9NhpmbT18vZWIqzA76CtrcclNNszxogMPe1f+77JZRSzyulTimlTk1MTKy4o22Nrrna\nV2Z66tNZzOcXq9/dWUNXnROlVl6kunj7tRay5sv8cQo5ZfpO88fa0+LdcJ1DIcRSj+9vxmE1Y7eY\neXxPYe8P7m31ohTsanZnndB7d7Mbq1lR40xXlql1WmmrcWBSir0txbsOlYoq9Sd+pdRrwOPAR4D9\nWuuvKqWOAl/UWv+TlZ7X29urT51allZ0wfzvtfiibhga01wtv8VfrySbbfKp2Mcr1THzqbe3l/n3\ngRSzLS99X/l40Y61+H1QTlKp9MJ0s7nwa/NyOZczPWezXxOUUqe11r1rbVdOPb+TwCNzXz8OvLWR\nnSm1vGr74hc0mxf3/2/vzqMku+oDz39v7BmZkftWuVXWXqq9pKzSUtoKCRuBQAgwFjRgbIxsdx+d\nbo/dM+A5Htsz091gZmy3sceNxuODDcgYGySQEGgXkpBUqn2vUq1ZuWfkEhEZ+3bnj4jMyj0jI2PP\n34fDUWXEi/d+EfHi3Xffu/f3y/UOkI8drph3ciEKndFozEnDB+n9lud7zWo5JuRztKdZKfUysBt4\nAVgHvKGUegvYAzyTr9iEEEKUtryN9tRaR0j08KY7BHw9D+EIIYRYRQrpsqcQQgiRE5LhJWnMF+bC\ngIe2GjsD7gA15RY2l/g8FyFE7nWP+uhzBdjVVk2F9eYheMQb4v3BCTY2VdDosOUxwtVBGr+k5071\nM+oN82/HemmrLkMpReV+M81VshMKITLDG4ryzPF+4loz7Anx8b2tU889c7yPiWCU031ufue+DXmM\ncnVYdZc9I7E4p3vdczKxmwyJj8JsUCgFSiXy902KxzXn+j1cH/HNeN2wJ8ipXteqqLUVjiY+u0F3\nMG8x9Iz5OdPnJhaXSfkid8LROKd6XSve9w0KJqfhGWeNqpycn2dKcZ7edL5QlJM9LsYXqRM67gtz\nsseFb5HKEEPJ41k4mtpE+XxL5X0vZNX1/H5x0cnpPjdGg+I37uykyp5IIfqxPS1cGfbSWl3GoCdI\nVZmZBsfNDOpHb4zz1qURAD51WxvttXb84Sg/ONJDJKbpGQvwkV1r8vKecuW1i8Oc6/dgNCi+eKCT\nSltu068Oe4L88FgvWoPLH+HuTVIVQ+TGqxeGOD8wgSm57zvS3PftFhOf7mpn0BOcSqQx6dFbW7nm\n9NGZRuL7Z0/2M+AOYrcY+fI96+edu/eDIz34wzHODXj4zP6OOevwhqL84HAP0bimbzzAQzsL/3i2\n1PtezKpr/KLxxBlNXOupfwNUWE3sbk8klal3zC0bEpl2JhRN9jpicc1kJqHp6ypV0Vjifce1Jp6H\nnlc0rpnMyZBqCichMiEyte/DSn/qjZU2Givn3k6ptJmnjkHLNfl7iMY18/0yNTePW9EFfjuxuGby\nZx0pkisrS73vxay6xu++zY1UlVlocFipW0ZtrH3rajGbDJSZjayrT5yZOWxmHtnTQr87wO629Hba\nYnJwawO15RYaK61U2y05335LdRkP7WzG7Y+wp6P0P29ROB64pZG6CgvNlbapq0WF5OFdLZwf8LCu\noXzO5VRIXGL9RLJnuXWBlI1VZWY+tqeFgSI6ni31vhez6hq/MouROzfULft1ZqOBfcl6fNN11pfT\nWb866vPZLaa0PrtM2roKcg6KwmO3mHJWbSUdNeUW7lqiOPaaqjLWVJUtusy6+vKpk/tikMr7XkjJ\nD3jxBCP4wwvf4F2IOxAhEC79QSxCiLkmghGuOb35DkNkUUn3/C4Pe3nuVD9mo4FPd7XPGMCymHP9\nHl48N4jFZODf7V9bkJc5hBDZMRGM8Ac/OIk7EOHBbY18+R6ZdlCKSrrnN+AOoHVimPLwROpDlPtd\nideFInGc3lAWIxRCFJoBdxB3IALApSHp/ZWqku757WmvZtQbxmoyLCtbS1dnDe5ABIfNVFTXv4UQ\nK7e5KVEN/cqId94pAaI0lEzj9+YlJ9dGfHxsd8vUSESHzTwjg0Kqqu0WPnlbW6ZDFELk2IVBD/5w\njF2tVcuaPP6796d2qTMW15zqdWE1GXNaiFqsXEk0fledXv72tctoDX3jAb764VvyHZIQIs+6R338\n7PQgAMFILCujNY9NS35hNRvY0FCR8W2I7CiJxk8BCoVGU6p1GC8MenjjfScdtXZ+dXvznEK9qQiE\nY/z4RB+BSIyHd7WkPABIiGJkmPYbMaTxe0ltG/NvrxjF45rnzwww4ApycGsDGxsLP7H/8RvjvHdt\njC3NDu7f0ris15ZE47euoYL/6YObuDri5yM7m/MdTlYc63bhC8U4PzDBHevr0ppkfnXEy0AyN+G5\nAQ/3ORoyHaYQBaO91s5Hd7cQCMeydklyb3sNVpMRq8lQ9OMDRn3hqQE+x264iqLxO9o9jj8c4/gN\nFwc21k/lR01FSTR+APvW1bFvXX4nYGfT1jUOhieCtFaXpZ1XsL3WjsNmIhSNs6GhuH+oQqRiY2N2\nL0MaDIodrVVZ3Uau1NjNrKmyMegJckuRJJPY2lzJ4etjbGysWFbDByXU+JW6Wztq2N1WvewUPtNV\n2sx86e51aM2yEsAKIUqfyWjgsf0dxOJ6RceZXLp7Uz13bqhLK96CmuenlOpUSg0ppV5XSr2Y73gK\nTSZ2SKWUNHxCiAUVS8M3Kd14C7Hn95LW+nNpv/jcIO9dG+ODtzSzf/3cXJzZpLXm0LUx3IEId22o\nS/vypBAiey4NTXBp2MuutiraauxZ204kFuety4mRoHcv835UvpzocTHgCrB/Xe2yEv8Xo0Js/A4q\npd4EfqS1/svlvHDIE+SpQzfwh2P0jgfY2VZFmcWYpTDn6h0P8M6VUSAxAvVXtpfm4BshilUsrvnZ\nmUFicc2gO8hv3b0ua9s63efmxA0XkLjlcNvamqxtKxPGfWFeuzAMQCAS4xO3lvZc50I7FRkANgMH\ngQeVUrumP6mUelwpdUQpdcTpdM55cbnVNNXbqiu3YDbmtvteYTVNbbOmPPclf4QQizMoqLQlzvlr\nyrN7ZabGbkEpUCoxmKTQ2czGqc5CTR5KluVaQfX8tNYhIASglHoO2AGcmvb8k8CTAF1dXXNqF1ZY\nTfzpx7bz/uAEO9sql5XRIRNqyi187o61eEPRrF5OEUKkRynFY/s7GPIEaalevLzPSq2rL+ezyfRo\n8xWvLTRlFiP/7vYOxn0R2muz+9kUgoJq/JRSDq31RPLPA8A3l7uO2nILd+Sx5ly13ZKXQq9CiNTY\nzEbW1uVmqk8xNHrTOWzmVTNWodAue96jlDqqlHob6NNaH8rlxke9IU71ughG8l/Hr98V4Eyfm2gs\nnu9QhBCrxLAnyJk+N+FocRx3/OEop3pdjPvCy35tQfX8tNbPA8/nY9uRWJx/OdJDKBLn0pA3r4mt\nx3xh/vVIL3GtcXpDHFxm2h4hhFgubyjKvxzuIRrX9I77+dCONfkOaUk/OdHPgDuI3WLky/esX9Y0\nrkLr+eVNXGtiscRtxEiee1vReJy4TsZSJGdgQojiFotr4smRFOHYnCEVBWnyWB2Na5YbcUH1/PLJ\najLy8b2t3Bjzs6Mlv+mKGh02PrJrDSPeELd2FPbwaCFEaagqM/PR3WsYdAfZ3V6d73BS8pFdLZwf\n8LCuvnzZk92l8ZumvdZOe21hjNLc3ORYVgFeIYRYqfUNFawvorJMteUWDmxMr1SVXPYUQgix6kjj\nJ4QQYtWRy55CiILQ+ZWfLmv561/7SJYiEauB9PyEEEKsOtL4CSGEWHWKvvFz+yP8+EQf3/7lNZwT\nwXyHI4QoQaFojJM9Lgbcgaxtwx2IcPzGOG5/JOfbXo2K/p7f9w/f4KlDNwA4N+Dhzz+1e8bzoWiM\nZ08O4AlEeGhnM2uq0kvYetXp5dULwzRX2fjwjjVSEFaIVeTV88NcGJzAZFB88UBnVvJfPnO8jzFf\nmGM3XHxpWqmlXGwbIJ4s9zTgDnD/lkY2Nhb+lIcTPS4OXxtjc7OD+zY3LOu1Rd/zi8Ri6OT/5svM\n0jMWoGfMjzsQ4VSvO+3tHL/hYiIY5dKQlxFvaCUhCyGKTDSZ+iSumcqCkq1txOLxeR/P5rYBRn1h\n3h+aYCIY5diN8extKIOOXB/DG4pyrHt82Zm5ir7n9+l9HVSWWRj3hfnEra1znm+ptlFVZsYXirJp\nBWcyW5od9Iz7aXBYpVafEKvMA7c00uCw0lyZOJ5kwyN7Wnh/aGJOjysX24ZEzcHmKhtDniBbm4sj\nwcbW5koOXx9jQ2MF5mWWsCv6xq/RYeMLd3Yu+LzdYuI3D3QS1yw7/c10O1qr2NrsyHmNQCFE/tkt\nJu5Yn91SafUVVuorrHnZNoDJaOAz+zuIxuJFc5y7e1M9d6yvTSveom/8UqGUIhNF3YtlhxBCiHQV\n23Eu3XiV1tm5iKyUagGeA7YBFVrrqFLKDRxPLvIJrfXYtOUdwFNALfAtrfU/Lbb++vp63dnZmZXY\nRfG4fv06sh8I2Q/EpKNHj2qt9ZItYjZ7fmPAA8DT0x47rbW+f4Hlvwx8P/n/15RS39daL1ihsLOz\nkyNHjmQq1iV1j/p4/aKTpkobv7KtKaujPWNxzc/PDDLqC/HALU20Vqc3QnW2YCTGT08NEIzGeGjH\nGmqL7N5lJBbn+dOJkbsf3NZMc5WNrq4ujhw5wi/ed3LV6eXODXVsba5MaR2/ur150UrbLn+Y508P\nYjYqHt7VQpnFuOCy3aM+XrswTHNVGb+6vQmlZDRwLk3uB0IopY6lslzW+rda66DWevaQoVuUUm8q\npb6m5h4d7gBe0lrHgJPA1mzFlo4j18cZ84U5P+DJ+mjPfleA94cmGPWGOdaduVFXV5xeboz5GfaE\nON2X/sjXfOkdD3DV6WPEG+ZEz83PxZcc7eXyR3jv2tgia4AbY/5p63AtuuzZfg9DniC94wEuDU8s\nuuzh6+OM+yOcH/DglNHAQhS8XF/c3QTcC9QAH531XDXgSf7bnfx7BqXU40qpI0qpI06nM6uBzrax\nsQKloL7CQrU9uz2mBoeVarsZpWB9Q3nG1ttWbcduMWIyKNbVZW69udLosOKwmTAoNaPsSpnZSGtN\none8YYlyLE2VtmnrWPwzWFtnx2xU2MxG2moWL3W1IbmueoeV6rLi6lELsRpl7Z7f1AaUeh14UGsd\nnfbYQ8BerfV/nfbYvwL/QWs9rJT6a+DvtdanFlpvV1eXzvVljmAkhsVoyMkE93hcE47FsZkXvtSW\njmgsTlyDxVRcN7UnxeKaaDyO1ZT4XCYvd2mtCUVT+7xmr2Mx4Wgcg0rtpnou9w8xk1z2FJOUUke1\n1l1LLZezI6BSqlwpNXm0OQBcmbXIO8ADyWX2ABdyFVuqbGZjzg5sBoPKeMMHiYN4sTZ8kJiuMl+j\npVTqn9dC65iPxWRIeTRZLvcPIcTKZG3Ai1LKDPwM2A28APwR8HdKKS9wDfiT5HLf1Fo/Afw9idGe\nTwBPLjbYRQgh8mm55ZdASjAVmqw1flrrCPDgrIdvnWe5J5L/9QAPZyseIYQQYlLxXv8SQggh0iSN\nnxBCiFVHGj8hhBCrjjR+QgghVh1p/IQQQqw60vgJIYRYdaTxE0IIsepI4yeEEGLVkcZPCCHEqiON\nnxBCiFVHGj8hhBCrjjR+QgghVh1p/IQQQqw6WWv8lFItSqljSqmgUsqklFqnlHpTKfWGUuqpabX9\nJpf/olLqolLqdaXUn2crLiGEECKbPb8x4AHg3eTfLuBhrfW9JOr5fXie13xDa32/1vp/zmJcQggh\nVrmsNX5a66DWenza3+Naa3fyzwgQm+dl/ynZM3wgW3EJIYQQOb/np5RqAT4IvDjrqWeAXcAngf9r\n9mXR5GsfV0odUUodcTqd2Q9WCCFEScpp46eUsgL/CHxZax2d/pzW2qW1jmutncD7QNPs12utn9Ra\nd2mtuxoaGnITtBBCiJKT657fk8Dfaq3PzX5CKVWZ/G8ZsAmQrp0QQoisyOZoT7NS6mVgN/CCUuo+\n4BMk7uu9rpR6NLncN5Mv+X2l1DvA68DXtNaRbMVWKIKRGN871M3fvnaZ6yO+BZeLxOL84HAPf/Pq\nJS4NTeQwQlGITve6+eYrl3jmeB/xuF502VfOD/HXr1zil5dHchSdEMXBlK0VJxuvB2c97JhnuSeS\n//0z4M+yFU8hGnAHGfaEADg/4KGzvnze5Ua8IfpcAQDO9nvY1DTnYxSryJl+N9G45tqID3cgQk25\nZd7l4nHN6T43WsOpXjcHNtbnOFIhCpdMcs+jlmoba6ps2MxGtrdULbhcQ4WV9lo7VrOBHa2VOYxQ\nFKKdrVVYTAbWN5RTVWZecDmDQbG7rRqLycDu9oX3LyFWo6z1/MTSrCYjj+3vWHI5k9HAp25ry0FE\nohjsaK1iR2tqjdnBrY0c3NqY5YiEKD7S8xNCCLHqSOMnhBBi1ZHGTwghxKojjZ8QQohVRxo/IYQQ\nq440fkIIIVYdafyEEEKsOtL4CSGEWHWk8RNCCLHqSOMnhBBi1ZHGTwghxKojjZ8QQohVJ5v1/FqU\nUseUUkGllCn52H9WSr2llPqeUso8a3mHUupZpdQvlVJfyFZcQgghRDZ7fmPAA8C7AEqpRuCg1vpu\n4BTw8VnLfxn4PnAv8NtKqfmLlC2hd9zP08d7OdnjAhI1zV6/OMzfvX6Zpw51L1o09mSPi6eP99Iz\n5k9n03McujrKj0/0MTwRnPf5cV+YH5/o461LI2i9eFHSAXeAZ473cbR7fN7nJ9/nsyf78QSzUwfY\nORHixyf6ePfqaFbWvxKpfnfTPyd34ObndNXp5enjvZwf8Ew9FgjHeP70AC+dGyISiy+63uGJIM8c\n7+NQCp/NpaEJnj7ey/spFCZ+79oYzxzvY9gz/z6UDk8wwrMn+3n94vCSxXCX40yfm6eP99I9uvBv\nTIhCkbXGT2sd1FpPP1J3kajSDvAycOesl9wBvKS1jgEnga3pbPe1i06uj/h59cIwgXCM66M+jnaP\n84uLTt65MsrL54fmfV0gHOO1i8NcH/Hz+sXhdDY9w4g3xNtXRrnq9PHWpZtVtKc3cu9cTTx/+PrY\nVLHahfziopNrIz7eeN8546A96fqoj+M3XFwe9vLe1bEVxz+fty47uer08c6VUZwToaxsIx2BcIxX\nL6T23XWP+W9+Ttdufk6vnE+8/sWzQ1MNwvGecS4OTnCmz83FwcUbqrcujXBtxMfbV0YZ8S7+2bx4\nbojrI35eOjf/vjhp1Bvil5dHuDbi5Y1LmavEfvjaGJeHvRy/4eJahhqqSCzOy+cT7+uV8yv//QiR\nbbm851cNTJ5Wu5N/L+d5lFKPK6WOKKWOOJ3OeTfS6LACUGM3YzEZqC23YDUZKLMYKbeaaKy0zfs6\ni8lAdbIw6ELLLEeF1US51QhAU6WNQDjGP759nf/n9SvcGPXPiNVqNixalHRyHQCVZWbKzMY5z9fY\nLVhMhhnLZlqTI7HecqsRh61wSkFaTAZq7Kl9d5P7BUBTpXXq8cbkvxscVgwGlXjMYUUpMBoU9RXW\nuSubpnHaZ1NhXfyzmfx+GhyLr9NmMnDF6eXQtTGCkeiiyy7HZKwWk4Fae1oXWOYwGRR1yc+osXLx\n9yVEIcjlEcwNTFZkrQRc8zxfCQQXeB6t9ZPAkwBdXV3zXq/54C1N7G6rpqbcjNGgqLZb+OKBdXzi\n1jYMSi14wDEaFJ+5vYNxX2SqUVoJm9nI5+/oxBOM0FRp4/LwBGO+MAAXhyboqLPT1VlLR52dcouJ\n8iUOmPdvaWBbSyVVZTcP3tPVlFv4jbs6CUZiSx6o03XXxno2NlbgsJmxzdMA58tyvrtqe+JzCoRj\nM/aFh3e14JwIUVdxszHY2OjgC3daMRrUkicnd2+qZ1NTBZUpfDYf39PCiDdMfcXiDY8/Eqej1k5T\npQ2LKXOf9862KtZU27CZl26oU6WU4te72hnzhZds1IUoBLls/A4D/x74c+BBkvcCp3kHeEAp9QNg\nD3AhnY0YDIrmqpln/xVWU0o/cqvJSHNV4iDj8oe5MeZnQ0PFkg3TQsosRsosifW11dhprrLhDUbZ\n1lI5tczkWfhSlFJL9uhSfZ8rkYlecTZM/+6WMt/nZJxnvwGoLU+9Z5Rqj9sfiTE8EcRuNVJpXPji\nS225hS3NDvpdQXa3zbkQsiLZOEGymAzzfoZCFKKsHSmTozl/BuwGXgD+CHhDKfUWcAP4q+Ry39Ra\nPwH8PfAU8ATwpNY6nK3YlhKPa35wpAdfKMbZfg+f2d+x4nXazMaMrEcUvx8d7WXcH6HabuY3D6xb\ncDmjQfHIntYcRibE6pG1xk9rHSHRw5vuEPD1Wcs9kfyvB3g4W/EshwYiscRV1aVG+QmxXLJvCZF/\nhTNqoYAYDYpH97Zy1embcYlSiEx4ZG8L7w962dxUke9QhFi1pPFbQEt1GS3VZXMej8U14Wh86l5e\nPK4JRmPYLUt/lIFwDIvJgDE5mlCUllS/30aHLeV7vQvxh6PYTMapkamZMHvfFqKUSeO3DMFIjO+/\ndwNXIMIHtjZyy5pKvn+4h5GJEHdvqmdfZ+2Crz3V6+LVC8NUlZn5zP6OghotKVbu2I3EXNK6CguP\n7euYd0Ruphy6OsrbV0ZprLTy613tmBYZNJOq2fv2rgwPsBGi0Ehuz2UY84UZ90fQGq6N+JgIRhlJ\nTva+6vQu+tprIz60Bpc/MjXlQZSOq87EZPFRbxhXILvf79VklqJhTwhvKDPz/8b9N/ftyfciRCmT\nnt8yNFfauGVNJU5viNvW1lBjN7O7vYre8QC3r6tb9LW3ra3BE4zSUGGluUCnC4j07e+sJRCO0lxV\nRkOW5llOun1dLW9dHqG9xk51hiapNzlu7ttdnTUZWacQhUwav2UwGBQf2tE847EPbG1K6bVtNXY+\nf8fabIQlCkBHnZ3P39mZk22tb6hgfUNmB8vMt28LUcqk8RNCiBzo/MpPl7X89a99pCC3USrknp8Q\nQohVRxq/LAtGYktm+RciE7yhKG5/dspZCVFqFr3sqZSqBBq01ldmPb5La30qq5GVAH84ynff7cYX\ninFgYz371y08FUKIlRj2BPmXwz3EtObhXS1sbJQJ9EIsZsGen1Lq0ySSS/9QKXVWKbVv2tPfznZg\nhSYW15zscXEphQKkAJeHJ3j3yijDnhD9rgBXnV6uOL2c6HERnZXWasAd4PWLQzx9rJfXLw4RjMSy\n8RZmCEVjHO0enyqvJFYmGIlxtHsspULI474wR66PMZ7BKS/DEyEGPUEG3UEG3DdrQ456Qxy5Pjaj\nRxiNxTnR4+LKEtNzluvcgJvvvnOdPlf296khT5Aj18cyNtVDrD6L9fz+CLhNaz2glNoPfEcp9VWt\n9dPAqktRcvj6GO9cSVTp/uStRjrq7Asue2PUz7MnB9Bac8XpxWw00D3qZ8CdqMbtC0U5sLEeSBw0\nf3i0l7evjDLkCdJaXYY/HOfDO9dk9f288f4IZ/rcGJTiC3eupWYZ1QvEXK9eGObi4ARGg+KLBzqp\ntC1cAulHx/vwBCKc6HHx2/esz8j2zUaFOxAhFtcYVOLnqbXm34724g/HODfg4QvJ0aiHro1NFfL9\n9X3t82YyWq5wNM7XfnaBUCTOkW4Xf/XYnhWvcyGhaIx/O9pLOBrn2oiPX+tqz9q2ROlarPEzaq0H\nALTW7ymlDgLPKaXaSeR+FilorkqksjIZFdFkQuNpxdzROvl38rFcfbCTFeU1Wr7MDJr6PnPMaDCw\nITn9wWaee0Fn9j439e9sB5ZlxR5/vq3m0aGLNX4TSqkNk/f7kj3A+4FngO25CC5ftNa8fH6YnjE/\n925uYGNjBfs6a7GZjdgti/f6IDHn6yO71uAPx2iutNEz7mdzkwPnRJCJYJSdrVVTy5ZZjHzytja2\ntVTiDoSpLrNwV7JXmE33bWmgrsJCfYV1WTXrxPw+sLWRRoeVpkrbkoVvH93byhWnd6qxyoSNjRU8\ntLOZcDTOjpbE/qWUYm2dnXevjtG19ubE9TvW11JuNeKwmWjNQK8PErX8vvKhrRzpHufg1saMrHMh\nVpORT97aRs+4n1vWSOJ5kZ7FGr/fAwxKqW1a63MAWusJpdSHgMfS2VjytV9J/rkF+D2t9TPJ5/4U\neBQYB36itf6LdLaRCS5/hDN9bgCOXB9jY2MFRoNiT3vq+Q43Nzmm/j1Z4HOhg+JCSbSzyWoyyY6y\nUwAAHypJREFUcttaGYCTKTazka5FcrtOV1tuobY885/91uaZDUE8rrkwOEFVmZkz/R7u2dwAgMlo\nYG9H5rO4bGupYltL1dILZkBzlU0K54oVWXDAi9b6pNb6EvADpdT/ohLKgL8gUZF92bTWP9da36+1\nvp9EQduXZy3yB8nn89bwAThspqkf1iYpOyOKlMGgpkZ9SvkkIWZKJcPL7SQK0L4NOIDvAQdWslGl\n1HpgSGs9e7jZ15VS48Afaq1PzPO6x4HHATo6slcV3WQ08Ni+dsKxOFaTVF8QxevhXS0EIzGpIiLE\nLKlMco8AAaAMsAHXtNYrLUH9CeDpWY/9tdb6NhKXW78534u01k9qrbu01l0NDQ0rDGFxSilp+ERJ\nkIZPiLlSafwOk2j89gH3AJ9RSv3rCrf7UeAn0x/QWo8l/3tphesWQgghFpXKZc8vaa2PJP89ADyi\nlPp8uhtUSjUDYa316KzHK7XWHqVUfYpxTXnr0ghXR7zcub6OTdMGmhSSty6NcG3Eyx0FHGMqukd9\nvPG+k9aaMg5uaUSpwpjy6fKH+fmZQWxmIx/a0Vx0vZ1xX5gXzg5SZknEv9hVh2sjPt665KSt1s7B\nLdkdWSlEqVqy5zet4Zv+2HdWsM1HgB9P/qGUmrzE+Q2l1C+BZ7k5InRJ/nCUw9fHGPWGeefq6NIv\nWKZ3r47yzVcu8dK5obTX4QslYhzJUoy5dOhq4n2c7HEXVFHeU71uBtxBro34Mp65JBdO9roYcAe5\n6vQtWUz20NVRRrxhTtxw4fIv/zvoGfPzd69f4TvvdhMIZz+bkBCFKOeJrbXW39Ja/820v59I/vd3\ntNYHtNZ3aq1/ker6bCYja5IjMzvryjMe7+leN9G45kyfe05aslSVmW/GuK4+8zHm0rqGRPz1FRYq\nl5jPlksdtXaMBoXNbKSlKrfTRjJhbV05RoOizHJzX1lIZ3IfanBYqbAuvyrZ+QFPIuH6RCgnqciE\nKERFX8/PYFB8uqudQCRGeRoHgqXsaqvi8PUxtjRXYjKmd66Q7RhzaV9nLdvWVGIzGzEaCuOSJyQa\nhMfvXY9BKSym4itWsm4Z8d+xvo6drVVpfwe3rKnk6oiPCquJtprFEzYIUaqK+0icZDCoBRuVM31u\nApEYe9qrMafReN2+vo7b19fNeOz9oQnGfGH2tFenfG9psRiLTaG+j2K7zzfbcuJP9Ts40+fmnSsj\nHNzSyMbkveb2Wju/e9+GtGIUolQU5lEsQ66N+Kbu1UVice7asPK0YcOeID89NQDARDDKB7c1rXid\nQmRDPB7nGy9cTFacGOd/fL4r3yEJUTCK7/rQLKd6XTx16MZUOrLpTNMuCaXa63NOhPjB4R5ePDtI\nPD43ba7RoKay5puM819yOtfv4alDNzjZ40ppm2Ll3IEIPzzay3On+glHVzoNNX0uf5h/PdLD86cH\niKR5jziTzMl9NBSN88/v3eBQkQ+4EiJTir7n9/pFJ7G45hfvh9nROjOvYHutnUf3tuIPx9janNr0\ngiPXx+hzBehzBdjaXDkniXVdhZVPdbUx7gsvuM43LjkJhGM4J0LsaqsqmOkApexkj4sbyVp66+on\n2J6jHJOzHb/honc8UU9vY2PFjByvuWYwGPiTh7dz6Noo/e5Erb9Bd5BdbdWUWYr7ErEQK1X0Pb+O\nWvuM/wbCMYYnglPPd9aXs62lEkOKAwPak+sptxqprbDgC0UZ8YZmLNNaXcaO1qoFB8C0JwcRtNWU\npdTwjfvCuAMRIrE4Q54gsWSPMxiJMeQJTpUfEgtrrSmbGizSXJm/hMeJ7xysZgONDuuK1xeMxBj2\nBJdecAHN1Tbu2dzAtpZE0uumShvWZQwI0loz5AkSii49JcIbjCZGReex5y1Eqoq+5/ex3S14ghEq\nbWb84SjfeacbfzjGXRvmDlRJxY7WKjrq7FhNBoKRON871E0oEufg1saUqzp8eGczBzbWLVrQdNJV\np5efnOzHoBRWkwF/OMa6+nI+smsNTx26gTsQYXd7FR/YKvcWF7OhoYIv3bMOU3K6Q75sanLw29Vl\nGYkjGInx3Xe7mQhG2ddZy92bln/P+odHexlwB1nfUM5vHVhHudWY8okgwMvnhznT56bGbuZzd6xd\n8IQvHo/z1adPMewJsaO1ij9+eNuyYxUil4q+8TMYFNX2RD06TyCKPzlpd2gitNjLFjXZaA26g4Qi\nibPYoWWcfSt1M6alDHlCaA0xHad7NEiDw8qgJ0gwEsMdiCTjSP+9rCbpzHnLhkzF4QtFmQhGARhM\no/cXj2uGk7+DQXeQKvvy52VObnfcHyEUjS/Y+AWjcZzJbfWO5X/u4HKLtIrVpzCOFhnSXGVj/7pa\nnBMh7tqw/F7fbB21dvZ2VOMORLh9XXZq3+1pr2bcH8ZoUKypsnF52MuutiocNjP3bq6ne9SfVg9W\nFL+6Cit3bqhjwB3gQBojlQ0GxYO3NHFh0MOuttRrUU53/+YGDl8fY119+aLTK+wWE7/e1cHh66N8\neOeatLYlRC6VVOMHcCDNKuiXh70cvzHOlmbH1IFCKcX9Wc6dWGYxzjhYTD9I3ba2VgrOFhl3IMLr\nF4cpMxv5wNbGtBMjTLpjhSc+21oqp+73paO91j51H3wpj97ayqO3tqa9LSFyqeQav3Sc7nXz3195\nn3KriT5XgO0tVQWVvUQUj2Pd41O5OTvry/M62nM5YnHN86cHGJ4I8eAtjazNQqpAIQpJ0Y/2zITD\n18ewGA0MuoPUlluk4RNpW1NtQymwmAzUV6x8tGeuDE8EuTzsxROIcPyGzE8VpU96fsCWZgcuf5g7\nHVYe29ee73BEEdvaXElzpQ2LyYDdUjw/r9pyC/UVFkZ9YTY1VeQ7HCGyLqe/TqVUJ3AIOE+ipt+v\nTHuuBfguiWrx/5vW+uVcxXVgYz3719WmlftTiNlSHelbSKwmI5+7Yy3RuJbfgVgV8nFq+pLW+nPz\nPP4V4I+Bk8BzQFqN37l+z9S0BLvFyL7OWsKxOIevj+GwmdnTXs3JHhe/eN/Jnetr2bcuMaAg1R98\nJBbn8LUxzCYDXWtrlp29xR2IcOzGOK3VZcTicV46N8ytHdXcvalheW80A4Y8Qc71e9jUVLHqs/sP\ne4Kc7fewsbEi5QEe6Xrl/CA/OTnAwzvX8MHtzYsue3FwggF3gN3t1VwYmMCgYHOTgxO9Ltqqy5Ys\njNw77ufSkJdtLZU0JSf/ByOxGb+HSUqpqXRok8Z8YU72ulhba2d9g/QIRenIR+N3UCn1JvAjrfVf\nTnt8J/AftdZaKTUxWdl9OSt2ToR44ewgA+4A3mCUTU0O7BYTo77Q1H0Mm8nA//jFFcZ8YU73udnY\n6KCmPPUz9eM3XBy6NgaAw2Zia/PyRtK9cn6I7lE/x7rHuTzsZcwX5viNcTY2Omheoo5bpj17sp+J\nYJRzAx7+/f0bVnUatudODeAORDg34OH37tuwrIngy/Vfn79AMBLjdK970cbP7Y/wszMDaJ1I3zaZ\navadq6NTj32pyoZjgWQKWmt+fCKR6/TqiI8v3b0OSBRonvw91Notc1L4Tffi2UEG3EFO97p5/N71\nRV85Q4hJub6+MQBsBg4CDyqldk17zqhv5vFyA3MmJimlHldKHVFKHXE6nXNWbjEZMBkUZqNhqidX\nZjFO3XtRCuxWI7ZkXkOryYDZZGDMF+apQzf40bFegpHF0zjZp+VELEvjQDD5eovJQIX15r+Xk3Iq\nUyY/lzKzcVU3fHDze7GZjWT7o5jcb8osi3/nJqOa2o+nN3AOW+J7s5gMi16xUErd3Na0fXX678Fm\nXjwGVyDMyR4XveP+qYTuQpSCnPb8tNYhIASglHoO2AGcSj49PSFgJTBnyJnW+kngSYCurq45CS+r\nysw8tr8jMWlcJQ4cHXV2tNbUlpupsJpprrLxRw9t5Wj3OHs6qqmwmvhFt3PqUulVp2/ReVE7Wquw\nW4yYjYa0Lo89eEsTnfXlNDpsGIB3r42yvaVyWb3PTHl0byvXR31Zv8xXDB7Zk/gsUs3HuhJ/89m9\nvHh2iF/ZvnjKunKricf2tTPiDbOhoZze8QAGpVhTbeOK00ujw7ZkT+zT+9rpGfPTOW3qwr7Omqnf\nQ+MSeVCtJiOtNWU4rCbCsXhRFgoWYj65HvDi0FpPJP88AHxz2tOnlFJ3kmgMl33Jc1KDw0rDrITC\nSik2NjqmLWPjQztuTizvrLNzsseFxWSgpXrpS48rufdhMhpmXCqdHkeulVmM3LIm/QnQpSSXn0Vn\nfQWP35faPlRXYaUuOWWis/5mA5bq5fYKq2nO+5r9e1jMliYHLn+ElmobdrnkKUpIru/53aOU+j9I\n9P7e1FofUkp9U2v9BPDnwD8BZcCf5DKotXXl/M596zEoJSPdhJjmro317O2owWoyZPU+qBC5luvL\nns8Dz8967Inkf3uBD+QynumsJjmrFWI+UvtPlKKS6eac6HHx01MDc2rvLVc8rnnr0ggvnB3EH44u\nuFyigK6Tl84NLTlIRhS3njE/z53q59LQxNILF6gBd4DnTvVztt+dtW1cHJzguVP99I7nv6qDEEsp\nnhQUi3D5w7x2YRiXP8yrF4Z4dG9bWrXPAK6OeDl8PTGVwWoyLJjY+sKgh2Pd40Ci8O1daWTdF8Xh\nhbODTASjXHX6WN9QUZTp714+P8zIRIjLw142NFSsaMpCIBzjhbODxOKaX93RTIXVRCQW5+dnBolr\njXMixG8eWJfB6IXIvJJo/GxmI3aLkdN9fiqsZg5fH2NnWxVVZcuvX1ZZZsZoUMTimrpyK1prfnp6\ngO5RP63VZfS5Aqyts7O3vRqDUsS1pnaekZrnBzy8dnGY1uoyPrqrBYNBMewJ8syJPi4OTtBWa+cj\nO9fkPfHxfHGKmeoqLEwEo1TbzVlr+AbdQZ492Y/NYuSTt7Zit5iIxTXPnuyn3x2ga20tZ/rcKAWf\n2NuWcm2+1y4Oc67fQzhZXd1hMzPkCfLC2UEqrGY+cWvrshvC84Mero0kknef6XNzx/o6TAbFgDvA\njTE/+zulEokofCXT+H3ujrU0OmxcG/FS77BSnuZ9ikaHjc/fsZZQNE5zlQ13IMKlIS+QOJB01pVz\nacjLPZsa+Pyda4nG4vMOFz/V6yIUiXPV6WPcH6auwsqFwQmGJ0L0jgewGA2c6nXnvfE73eueE6eY\n6eFdLQy6g3NGEWfS+UEP3lAUbyhK96ifW9ZUMuYLTzUyr14Ymrovfdk5kVKpq3hcc7LHhdaJOYEf\n291CfYWVNy858YVi+EIxesf9KY/8nNRaXYbZqNAa2mrKAIjENHXllqkTUSEKXUk0fpCYE/Xora14\nghHKLaYVnaFPn3PnsJpYV1/OjTE/92ysp98dpKPWjsNqWrSXtL2liiFPiNbqsqlcj5ubHJzpc9NU\naaOu3ML2FdRZy5RtLZUMeoIz4hQzpTunczm2NDm4MDBBmfnmtmrLLbTX2hlwBdi/qZ6z/R4MSrG+\nPrVpEgaDYkdLFWf7Pexuq55a7y1rKrk07KXSZqK1evnvq6nSxm/fsx6tbw6GsZgM7Gyr4uKgl13t\n6RXOFTNJNfrsUjeTqhSXrq4ufeTIkXyHIfKsq6sL2Q/E7P1AGo7suP61j+Q7hCUppY5qrbuWWq4k\nRnteHp7g52cGGXQHl1zWHYjw4tlBTvRIzbJSEozEeO3CMG9fGSEez/0Jncsf5oWzg5zuzd5oSiFE\n5hT9Zc9ILM7zpxMjz4Y8QX7jrs5Fl3/jfSeXh72c7ffQUm2j0ZHbZNIiO452j0+d0NSVW9nSnNt7\nqa9fdHJtxMe5fg+tNWXzDoISQhSOou/5GZWi3JpowyvLlm7LJ0eAWkyGtBJTi8I0+b0qdTPxcz62\nbzUblkwWLYTIv6Lv+RkMis/sb2fQHZy6oR+JxXH5I9RXWOYkKb5nUz0dtXaq7eYFS8GI4rOjtYpq\nuxmLyZCX3vx9mxvorC+nttxSVBXcM2nyd1dXbpEpMyJt6dyvTedeZEn8Su0W01Sy6Xhc8/3DPYxM\nhLhlTSXb1lRiNEIgHCcWj1NhM89IEDxbz5gfi8lAIBzDbjXKZdEikquCvD3JArF3ra/FlmzoDAbF\nukX2q0yYLCvUUl027/OD7iCRWDylkanXR3yUW00Zm76hteYHR3oY9oTY2uzgoZ35S9guRCpKovGb\nLhKPMzKRSHF2tHuc8wMeLg9PYDMbGfQE2dNezcf3tM5bAft0r5uXzw8x4A5QbjFRbbfwmdvbpQEU\nUyaCEf74mTMEwjEOXR3hqx/elpPtXhqa4LlTAwA8sqdlTmWRnjE/PzzWi9bwwW1N7GitWnBdR66P\n8ealEQxK8dnbOzLSAEZiicwuAP0pDDwTIt9K7uaE1WTk/i0NtNWUccuaRAMXisbxhWJEY5p4HDzB\n+XN2ToQiU8uHY3HiWuMPSd5OcZM/HJvK5Trmj+Rsu9P32Yl59l9vKMrkrKX5np9u8vm41vhCiy+b\nKovJwMEtjbTVlHFwS0NG1ilENpVczw9gb0cNeztqCEVjvHt1jO0tlcTjGlcgQluNnd1t858V37a2\nhkhMc2tHDdF4nKqyxS+RitWnqdLGb9zVyZk+D5+4tTVn293dVkUgHEMp5u3VTdbdC8fi3La2ZtF1\n3bG+DkgMDMrk/r27vZrdMsFdFIlcF7O9HfhLElXbD2utf3/ac38KPAqMAz/RWv/FSrdnNRm5b3Pq\nZ6HLXV6sTg/tWMNDOS5CbDIaFk3WbjAo7txQl9K6yixGDm6dP2G7EKtFrnt+3cAHtNZBpdT3lFI7\ntdanpz3/B1rrl3MckxBCiFUmp/f8tNaDWuvJu+ERYPYNta8rpV5WSu2Z7/VKqceVUkeUUkecTmdW\nYxVCCFG68jLgRSm1C2jQWp+b9vBfa61vA34P+OZ8r9NaP6m17tJadzU0yOVJIYQQ6cn5gBelVC3w\nN8Cnpz+utR5L/vfS7InpQgghRCblesCLCfgu8Ida68FZz1VqrT1KqfqVxnX8xjjdY36MCkwGA/ds\nbqDCOnOVl4e9nBvwsL2lkg0NqZWImfTOlVHGfGHu3lg/o6io1pq3r4ziDkS4e1M9lZJBpmh0j/o4\n2etma7Mj7zUWM+n9oQkuDE6wq7VqamTngCvAP/zyGjXlFn733vUYDJm5AHR+wMOlYS9726uzXgJK\niJXKdc/v14B9wJ8ne3dfBT6rtX4C+IZSageJS7FfSXcD7kCE1y86GfeF6XcH2N5ShdVs4ANbm2Ys\n98LZQcLROD1jfv7DwY0pr7/PFeDdq6NAIo/kh6dlsugZC/DetTEAjAbFr25vTvdtiBx78ewQ3lCU\n6yM+NjZUlER6Lq01Pz+TSPo+6A7w+L0bAPjn925wKll9YvuaSu7bsvKRn5FYnBfPDhHXmlFviN88\nsG7F6xQim3La+Gmt/xn451kPv5N87ncysQ2b2YDDZsIfjk4lOK6fpzp5fYWFfleQhmVWLnfYTFhM\nBsLR+Jz1VpWZF3xOFLZ6hwVvKEptCeWlVEpRV2Fh2BOasT921No5dG0Ms1HRlqEemsmgqCk3M+oN\nUyf7fsnKVd7NXCi5Se5Wk5HP3t7BuD9ChcVIMBqnqXJuerJH97YxPBFcduqySpuZL9y5Fm8oypqq\nmTkWq+xmPn/nWvyhGM1VkhKtmHx0VwuDnmDGcl0Wik/d1oZzIjTjN/Cprna2rqmkym6mPUP5UJVS\nfLqrnRFvaM7vQohCVHKNHyQSXU9m1l8ow6HFZEg7EbLDtnBFiEqbWe71FSGTMf39oZBZTcZ539di\nuT/TZTPPvy0hClHJ5PZ8f2iCQ1dHCUUlF6fIrDFfmLcvjzAoCZuFKBkl0fMbdAf5aTLjvS8cnTO4\nRYiVeO5UP6PeMCd6XfzuvRtK5p6gEKtZSfT8jAbF5NRAU4aGbQsxyZhs7EzT9jMhRHEr+p5fPK6p\nKjPzyVvbcAcibG12oLUmFI1jMxvzHZ4oAY/saeXysJe1tXbymYAhGosDifuTKxWOxjEa1FTDLsRq\nU9SNnz8c5Z/f68EbjPLhnc3saK1Ca83Tx/voHvXT1VnDPZskDZpYmQqriT15LtUz6A7yw2O9KJUY\nwbmSAstXnF5+emoAu8XIY/s75iSAEGI1KOprhEOeEJ5AhLjWXHF6gUQh2u5RPwCXhrz5DE+IjOke\n9RGOxglFEokZVuKq00csrpkIRhl0BzIUoRDFpahP+dpqyljfUI47EGFPe6KAp81s5La1NVwe9nL7\n+to8RyhEZmxdU8nVER8GBVuaK1e0rt1tVQy6A1TYTHTUSrFmsToVdeNnNhp4ZM/catr3bm7gXilK\nK0pIVZmZz+zvyMi6GittfP7OzoysS4hipbTW+Y4hLUopJ4niuAD1wEgew1lMocZWqHHB8mK7FTiW\nxViyqZC/g3Tl6z0V8n5QbN9zscULM2Neq7VesvdTtI3fdEqpI1rrrnzHMZ9Cja1Q44LCji2TSvF9\nluJ7Wqli+0yKLV5IL+aiHvAihBBCpEMaPyGEEKtOqTR+T+Y7gEUUamyFGhcUdmyZVIrvsxTf00oV\n22dSbPFCGjGXxD0/IYQQYjlKpecnhBBCpEwaPyGEEKuONH5CCCFWnaLM8KKUug24E6gGXMC7Wusj\n+Y1KCCFuUkrtAHYAV7TWh/Mdj5ip6Aa8KKX+ErACLwNuoBJ4EIhqrf9jPmMTYiFKKSPwcWadtAHP\naK2j+YxtJUr1faVLKfVzrfWHlFL/CXgA+ClwAOjVWn81v9HNr9i+Q6VUtdbalfz3wyRPMIB/08to\n0Iqx8XtDa31vqo/nklKqAvhdEjtRFTd3om9prSckruKKLZOUUt8BTgGvMPOkbbfW+nP5jG0lSvV9\npUsp9arW+gNKqV8AB7XW8eTjb2mt785zePMqtu9w2mf830g01j8mcYLRprX+zVTXU4yXPY8opb4F\nvAR4SHxRD1AYef2eAr4D/AMzd6KngI9KXPMq5NgyqVNr/flZjx1XSr2Zl2gyp1TfV7q2KaX+CdhA\n4grVZM2o9AswZl+xfod3aa3vS/7750qp15fz4qLr+QEopfYCd5Bo9d3AO1rr4/mNCpRSvwTumTzb\nSz5mAN7UWh+QuOYq5NgySSn1h8D9wOvcPGm7D3hDa/2N/EW2Mkqp/0zifbxOCb2vdCml1k77s19r\nHUle3bhHa/2zfMW1mGLbN5VSLuA0cAuwUWvtSh4zDmutb0t5PcXY+BUqpdRnSVzCO0ViJ6oCtgP/\nr9b6exJXccWWaUqpBqCLmydth0mcdRf1YAil1L3ANhKXrD0k3td6rfWhvAYmUlbs+6ZSyg7s0Fq/\nl/JrpPHLLKWUCdhE4iDuBi4Vwk3jQo0LCju2TEmemc7nBa31B3MaTAYppf5voBGIkigr81taa+fk\nfZn8RidSUWz75gLxKuDny4m3GO/5FazkqKlHmDVqSimV11FThRpXoceWYV4SA3mmU8CuPMSSSfsm\nB5oppXYB/5q8jCaKR7Htm5PxKmCy97bseKXxy6xvk7gW/RQzB298G8jnqKlvU5hxQWHHlknngUe1\n1u7pDyqlXspTPJliVEpZtNZhrfUppdSjwHdJXLoWxaHY9s2MxCuXPTNIKfWm1vqeVB/PlUKNa7EY\nCiG2TFJKrQFGtdbhWY+birmHq5TaD1zXWg9Pe8wI/JrW+vv5i0ykqtj2zUzFKz2/zPqxUuo55o6a\n+kk+gwJ+UqBxwcKxPZvPoDJNaz2wwOMFd3BZjvkGGGitY4A0fEWi2PbNTMUrPb8MK9RRU4U8Ii/Z\ne9hA4mQsBhi01t/Nb1RCiFImjV8GFeqoqUIekaeU+v+S/wyTiLGPROPcqLV+PG+BCSFKmlz2zKxC\nHTVVyCPyNk5maVBKndZafzL579fyG1ZxUUr9nETih7e01g/nOx6Re0qpPcDfkbh1EAP+i9b6X/Ib\nVeGSxi+zCnXUVCGPyJu+D/7RtH+rXAdS5L4B2IHfyXcgIm/8wBe01peUUi3AUaXUC5NJoMVMUs8v\nsx7mZi6/6R7KdSCz/D6Je5AAaK3HgY8BhVAF4/Hk6EC01s8CKKUswF/kNaoCpZTap5Q6pZSyKaXK\nlVJnlVI7tNavACWTCFwsbr79ALBorS8BaK37gWGgIa+BFjC55ydEkVFK/Z8kEiWXkSiV89+Sj98P\n/KFc9lwdFtoPks/tB/4R2D49b664SRo/IYpMsmd8GAiSyGwfSz5+P9L4rRqL7AdrSEwd+g2t9ewx\nCCJJLnsKUXzqgArAQWGXyhHZNWc/UEpVkiig+79Kw7c4afyEKD7fAv4Y+B7w9TzHIvJnxn6Q7Ak+\nDfyT1vrf8hpZEZDRnkIUEaXUF4CI1vqp5ECht5VSHwD+DNgKVCileoEvaa1fyGesInvm2w+Ax4B7\ngTql1BeTi35Ra30iT2EWNLnnV4SUUv87MKa1/qvk3/8FGNZa//f8RiaEEMVBGr8ipJTqBH6ktb41\nmVXmErBfaz2a18CEEKJIyGXPIqS1vq6UGlVK7QWagOPS8AkhROqk8Stefw98EWgG/iG/oQghRHGR\ny55FKjmy6zRgBjZNzvERQgixNOn5FSmtdTiZ/NklDZ8QQiyPNH5FKjnQ5Q7g1/IdixBCFBuZ5F6E\nlFLbgMvAK5OJbIUQQqRO7vkJIYRYdaTnJ4QQYtWRxk8IIcSqI42fEEKIVUcaPyGEEKuONH5CCCFW\nnf8fLKcEmilZkRIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f13d09be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(df, figsize=(7, 7));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}