4_granger_casuality_ccn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from utility import grangers_causation_matrix,granger_test,adf_test,kpss_test\n",
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from scipy.stats import spearmanr,pearsonr\n",
    "from statsmodels.tsa.stattools import ccf\n",
    "from statsmodels.tsa.stattools import adfuller,kpss\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mins_played=pd.read_excel(\"data/Under21_minutes_played.xlsx\",index_col=0)\n",
    "all_teams=pd.read_excel(\"data/Under21_minutes_played_teams.xlsx\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mins_played[\"Minutes_played_italians\"]=mins_played[\"Minutes_played_italians\"]+mins_played[\"Minutes_played_oriundi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Minutes_played_under21</th>\n",
       "      <th>Minutes_played_italians</th>\n",
       "      <th>Minutes_played_foreigners</th>\n",
       "      <th>Fifa_points</th>\n",
       "      <th>Points_naz_u21</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>8.221823</td>\n",
       "      <td>2.669724</td>\n",
       "      <td>5.552100</td>\n",
       "      <td>1.064686</td>\n",
       "      <td>1.350</td>\n",
       "      <td>5.489717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6.539474</td>\n",
       "      <td>2.618820</td>\n",
       "      <td>3.920654</td>\n",
       "      <td>1.133279</td>\n",
       "      <td>2.555</td>\n",
       "      <td>6.272201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>6.049575</td>\n",
       "      <td>2.003721</td>\n",
       "      <td>4.045853</td>\n",
       "      <td>1.159438</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.480558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5.818846</td>\n",
       "      <td>2.303961</td>\n",
       "      <td>3.514886</td>\n",
       "      <td>1.386975</td>\n",
       "      <td>2.475</td>\n",
       "      <td>5.337017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>6.184476</td>\n",
       "      <td>3.170920</td>\n",
       "      <td>3.013557</td>\n",
       "      <td>1.312050</td>\n",
       "      <td>1.750</td>\n",
       "      <td>3.891005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Minutes_played_under21  Minutes_played_italians   \n",
       "2018                8.221823                 2.669724  \\\n",
       "2019                6.539474                 2.618820   \n",
       "2020                6.049575                 2.003721   \n",
       "2021                5.818846                 2.303961   \n",
       "2022                6.184476                 3.170920   \n",
       "\n",
       "      Minutes_played_foreigners  Fifa_points  Points_naz_u21   revenue  \n",
       "2018                   5.552100     1.064686           1.350  5.489717  \n",
       "2019                   3.920654     1.133279           2.555  6.272201  \n",
       "2020                   4.045853     1.159438           3.000  5.480558  \n",
       "2021                   3.514886     1.386975           2.475  5.337017  \n",
       "2022                   3.013557     1.312050           1.750  3.891005  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mins_played.drop(columns=[\"Minutes_played_oriundi\"],inplace=True)\n",
    "mins_played.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Granger Casuality Test and CCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Minutes_played_under21_x</th>\n",
       "      <th>Minutes_played_italians_x</th>\n",
       "      <th>Minutes_played_foreigners_x</th>\n",
       "      <th>Fifa_points_x</th>\n",
       "      <th>Points_naz_u21_x</th>\n",
       "      <th>revenue_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Minutes_played_under21_y</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5880</td>\n",
       "      <td>0.5880</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes_played_italians_y</th>\n",
       "      <td>0.4130</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.4130</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>0.0836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes_played_foreigners_y</th>\n",
       "      <td>0.2737</td>\n",
       "      <td>0.2737</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5093</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fifa_points_y</th>\n",
       "      <td>0.0738</td>\n",
       "      <td>0.4442</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.1093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Points_naz_u21_y</th>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.1597</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2739</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.5133</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Minutes_played_under21_x   \n",
       "Minutes_played_under21_y                       1.0000  \\\n",
       "Minutes_played_italians_y                      0.4130   \n",
       "Minutes_played_foreigners_y                    0.2737   \n",
       "Fifa_points_y                                  0.0738   \n",
       "Points_naz_u21_y                               0.2435   \n",
       "revenue_y                                      0.0000   \n",
       "\n",
       "                             Minutes_played_italians_x   \n",
       "Minutes_played_under21_y                        0.5880  \\\n",
       "Minutes_played_italians_y                       1.0000   \n",
       "Minutes_played_foreigners_y                     0.2737   \n",
       "Fifa_points_y                                   0.4442   \n",
       "Points_naz_u21_y                                0.0255   \n",
       "revenue_y                                       0.2739   \n",
       "\n",
       "                             Minutes_played_foreigners_x  Fifa_points_x   \n",
       "Minutes_played_under21_y                          0.5880         0.0285  \\\n",
       "Minutes_played_italians_y                         0.4130         0.0041   \n",
       "Minutes_played_foreigners_y                       1.0000         0.5093   \n",
       "Fifa_points_y                                     0.2491         1.0000   \n",
       "Points_naz_u21_y                                  0.0691         0.1597   \n",
       "revenue_y                                         0.0003         0.5133   \n",
       "\n",
       "                             Points_naz_u21_x  revenue_x  \n",
       "Minutes_played_under21_y               0.2245     0.0022  \n",
       "Minutes_played_italians_y              0.0389     0.0836  \n",
       "Minutes_played_foreigners_y            0.0010     0.0008  \n",
       "Fifa_points_y                          0.0504     0.1093  \n",
       "Points_naz_u21_y                       1.0000     0.0676  \n",
       "revenue_y                              0.0000     1.0000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grangers_causation_matrix(mins_played[['Minutes_played_under21','Minutes_played_italians','Minutes_played_foreigners','Fifa_points','Points_naz_u21','revenue']][27:-2], variables = mins_played[['Minutes_played_under21','Minutes_played_italians','Minutes_played_foreigners','Fifa_points','Points_naz_u21','revenue']][27:-2].columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FifaPoints and Minutes_played_italians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Minutes_played_italians</th>\n",
       "      <th>Fifa_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>-2.8789</td>\n",
       "      <td>-3.2954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0479</td>\n",
       "      <td>0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>-3.6791</td>\n",
       "      <td>-3.6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>-2.9679</td>\n",
       "      <td>-2.9720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>-2.6232</td>\n",
       "      <td>-2.6253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_italians  Fifa_points\n",
       "Test statistic                        -2.8789      -3.2954\n",
       "p-value                                0.0479       0.0151\n",
       "Critical value - 1%                   -3.6791      -3.6889\n",
       "Critical value - 5%                   -2.9679      -2.9720\n",
       "Critical value - 10%                  -2.6232      -2.6253"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf_test(mins_played[[\"Minutes_played_italians\",\"Fifa_points\"]][26:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Minutes_played_italians</th>\n",
       "      <th>Fifa_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>0.4013</td>\n",
       "      <td>0.1454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0766</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>0.7390</td>\n",
       "      <td>0.7390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>0.4630</td>\n",
       "      <td>0.4630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.3470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_italians  Fifa_points\n",
       "Test statistic                         0.4013       0.1454\n",
       "p-value                                0.0766       0.1000\n",
       "Critical value - 1%                    0.7390       0.7390\n",
       "Critical value - 5%                    0.4630       0.4630\n",
       "Critical value - 10%                   0.3470       0.3470"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpss_test(mins_played[[\"Minutes_played_italians\",\"Fifa_points\"]][26:],3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peason Correlation: 0.0261, P-value 0.4456\n",
      "Correlation Minutes_played_italians e Fifa_points Lag 0: 0.0261\n",
      "Correlation Minutes_played_italians e Fifa_points Lag 1: 0.0176\n",
      "Correlation Minutes_played_italians e Fifa_points Lag 2: 0.0238\n",
      "Spearman Correlation: -0.0195, P-value 0.9231\n",
      "Lag1 Correlation: 0.007, P-value 0.9722\n",
      "Lag2 Correlation: 0.0814, P-value 0.6864\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import ccf\n",
    "#ccf(mins_played[\"Points_naz_u21\"][22:],mins_played[\"Minutes_played_italians\"][22:],adjusted=False)\n",
    "cross_corr_ita_fifa=ccf(mins_played[\"Fifa_points\"][26:],mins_played[\"Minutes_played_italians\"][26:],adjusted=True)\n",
    "corr_pearson_ita_fifa=pearsonr(mins_played[\"Fifa_points\"][26:],mins_played[\"Minutes_played_italians\"][26:],alternative='greater')\n",
    "print(f\"Peason Correlation: {round(corr_pearson_ita_fifa.statistic,4)}, P-value {round(corr_pearson_ita_fifa.pvalue,4)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Fifa_points Lag 0: {round(cross_corr_ita_fifa[0],5)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Fifa_points Lag 1: {round(cross_corr_ita_fifa[1],4)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Fifa_points Lag 2: {round(cross_corr_ita_fifa[2],4)}\")\n",
    "#crea una tabella con i valori di cross correlation\n",
    "\n",
    "\n",
    "corr_spearman_ita_nazu21=pearsonr(mins_played[\"Fifa_points\"][27:-2],mins_played[\"Minutes_played_italians\"][27:-2])\n",
    "corr_spearman_ita_nazu21_lag1=pearsonr(mins_played[\"Fifa_points\"][27:-2],mins_played[\"Minutes_played_italians\"][26:-2].shift(1).dropna())\n",
    "corr_spearman_ita_nazu21_lag2=pearsonr(mins_played[\"Fifa_points\"][27:-2],mins_played[\"Minutes_played_italians\"][25:-2].shift(2).dropna())\n",
    "\n",
    "\n",
    "print(f\"Spearman Correlation: {round(corr_spearman_ita_nazu21.statistic,4)}, P-value {round(corr_spearman_ita_nazu21.pvalue,4)}\")\n",
    "print(f\"Lag1 Correlation: {round(corr_spearman_ita_nazu21_lag1.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag1.pvalue,4)}\")\n",
    "print(f\"Lag2 Correlation: {round(corr_spearman_ita_nazu21_lag2.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag2.pvalue,4)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------\n",
      "Grenger Causality Test Results for: Fifa_points and Minutes played by Italian U21 players from 1993 \n",
      "---------------------------------------------------------------------------------------------------------\n",
      "   Lag  Test Statistic   P-value\n",
      "1  1.0        1.093067  0.578953\n",
      "2  2.0        1.220757  0.748030\n",
      "3  3.0        2.092111  0.718822\n"
     ]
    }
   ],
   "source": [
    "granger_test(mins_played[['Fifa_points','Minutes_played_italians']][26:],4,2,\"Fifa_points and Minutes played by Italian U21 players from 1993\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteration over ccf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peason Correlation: 0.3333, P-value 0.083\n",
      "Lag1 Correlation: -0.237, P-value 0.2246\n",
      "Lag2 Correlation: -0.4313, P-value 0.0219\n"
     ]
    }
   ],
   "source": [
    "corr_spearman_ita_nazu21=spearmanr(mins_played[\"revenue\"][28:],mins_played[\"Minutes_played_under21\"][28:])\n",
    "corr_spearman_ita_nazu21_lag1=spearmanr(mins_played[\"revenue\"][28:],mins_played[\"Minutes_played_italians\"][27:].shift(1).dropna())\n",
    "corr_spearman_ita_nazu21_lag2=spearmanr(mins_played[\"revenue\"][28:],mins_played[\"Minutes_played_italians\"][26:].shift(2).dropna())\n",
    "\n",
    "\n",
    "print(f\"Peason Correlation: {round(corr_spearman_ita_nazu21.statistic,4)}, P-value {round(corr_spearman_ita_nazu21.pvalue,4)}\")\n",
    "print(f\"Lag1 Correlation: {round(corr_spearman_ita_nazu21_lag1.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag1.pvalue,4)}\")\n",
    "print(f\"Lag2 Correlation: {round(corr_spearman_ita_nazu21_lag2.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag2.pvalue,4)}\")\n",
    "\n",
    "\n",
    "for i in range(len(mins_played[26:-15])):\n",
    "    corr_shift_1=pearsonr(mins_played[\"revenue\"][26+i:],mins_played[\"Minutes_played_under21\"][25+i:].shift(1).dropna(),alternative='two-sided')\n",
    "    corr_shift_2=pearsonr(mins_played[\"revenue\"][26+i:],mins_played[\"Minutes_played_under21\"][24+i:].shift(2).dropna(),alternative='two-sided')\n",
    "    corr_shift_3=pearsonr(mins_played[\"revenue\"][26+i:],mins_played[\"Minutes_played_under21\"][23+i:].shift(3).dropna(),alternative='two-sided')\n",
    "\n",
    "    #corr=pearsonr(mins_played[\"revenue\"][26+i:],mins_played[\"Minutes_played_under21\"][26+i:],alternative='two-sided')\n",
    "    #if corr_shift>corr:\n",
    "    #print(f\"{26 + i}  corr {corr.statistic},corr_shift_1 {corr_shift_1.statistic},corr_shift_2 {corr_shift_2.statistic}, corr_shift_3 {corr_shift_3.statistic}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Points national team  u21 and Minutes_played_italians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Minutes_played_italians</th>\n",
       "      <th>Points_naz_u21</th>\n",
       "      <th>Fifa_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>-2.8789</td>\n",
       "      <td>-4.5535</td>\n",
       "      <td>-3.2954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0479</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>-3.6791</td>\n",
       "      <td>-3.6791</td>\n",
       "      <td>-3.6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>-2.9679</td>\n",
       "      <td>-2.9679</td>\n",
       "      <td>-2.9720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>-2.6232</td>\n",
       "      <td>-2.6232</td>\n",
       "      <td>-2.6253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_italians  Points_naz_u21  Fifa_points\n",
       "Test statistic                        -2.8789         -4.5535      -3.2954\n",
       "p-value                                0.0479          0.0002       0.0151\n",
       "Critical value - 1%                   -3.6791         -3.6791      -3.6889\n",
       "Critical value - 5%                   -2.9679         -2.9679      -2.9720\n",
       "Critical value - 10%                  -2.6232         -2.6232      -2.6253"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf_test(mins_played[[\"Minutes_played_italians\",\"Points_naz_u21\",\"Fifa_points\"]][26:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.37357565899492545,\n",
       " 0.08854497457115282,\n",
       " 0,\n",
       " {'10%': 0.347, '5%': 0.463, '2.5%': 0.574, '1%': 0.739})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpss(mins_played[\"Points_naz_u21\"][30:])"
   ]
  },
  {
   "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>Minutes_played_italians</th>\n",
       "      <th>Points_naz_u21</th>\n",
       "      <th>Fifa_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>0.4337</td>\n",
       "      <td>0.3955</td>\n",
       "      <td>0.1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0626</td>\n",
       "      <td>0.0791</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>0.7390</td>\n",
       "      <td>0.7390</td>\n",
       "      <td>0.7390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>0.4630</td>\n",
       "      <td>0.4630</td>\n",
       "      <td>0.4630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.3470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_italians  Points_naz_u21  Fifa_points\n",
       "Test statistic                         0.4337          0.3955       0.1436\n",
       "p-value                                0.0626          0.0791       0.1000\n",
       "Critical value - 1%                    0.7390          0.7390       0.7390\n",
       "Critical value - 5%                    0.4630          0.4630       0.4630\n",
       "Critical value - 10%                   0.3470          0.3470       0.3470"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpss_test(mins_played[[\"Minutes_played_italians\",\"Points_naz_u21\",\"Fifa_points\"]][26:-1],3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross correlation between Italian U21 players and National Team U21 Points\n",
      "--------------------------------------------------------------------------------------------\n",
      "Peason Correlation: 0.4264, P-value 0.0094\n",
      "Correlation Minutes_played_italians e Points_naz_u21 Lag 0: 0.4264\n",
      "Correlation Minutes_played_italians e Points_naz_u21 Lag 1: 0.2931\n",
      "Correlation Minutes_played_italians e Points_naz_u21 Lag 2: 0.2027\n",
      "--------------------------------------------------------------------------------------------\n",
      "Spearman Correlation: 0.566, P-value 0.0011\n",
      "Lag1 Correlation: 0.4133, P-value 0.0232\n",
      "Lag2 Correlation: 0.3897, P-value 0.0333\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cross_corr_ita_nazu21=ccf(mins_played[\"Points_naz_u21\"][26:],mins_played[\"Minutes_played_italians\"][26:],adjusted=True)\n",
    "corr_pearson_ita_nazu21=pearsonr(mins_played[\"Points_naz_u21\"][26:],mins_played[\"Minutes_played_italians\"][26:],alternative='greater')\n",
    "print(\"Cross correlation between Italian U21 players and National Team U21 Points\")\n",
    "print(\"--------------------------------------------------------------------------------------------\")\n",
    "print(f\"Peason Correlation: {round(corr_pearson_ita_nazu21.statistic,4)}, P-value {round(corr_pearson_ita_nazu21.pvalue,4)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Points_naz_u21 Lag 0: {round(cross_corr_ita_nazu21[0],4)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Points_naz_u21 Lag 1: {round(cross_corr_ita_nazu21[3],4)}\")\n",
    "print(f\"Correlation Minutes_played_italians e Points_naz_u21 Lag 2: {round(cross_corr_ita_nazu21[1],4)}\")\n",
    "\n",
    "\n",
    "print(\"--------------------------------------------------------------------------------------------\")\n",
    "\n",
    "corr_spearman_ita_nazu21=spearmanr(mins_played[\"Points_naz_u21\"][24:-2],mins_played[\"Minutes_played_italians\"][24:-2])\n",
    "corr_spearman_ita_nazu21_lag1=spearmanr(mins_played[\"Points_naz_u21\"][24:-2],mins_played[\"Minutes_played_italians\"][23:-2].shift(1).dropna())\n",
    "corr_spearman_ita_nazu21_lag2=spearmanr(mins_played[\"Points_naz_u21\"][24:-2],mins_played[\"Minutes_played_italians\"][22:-2].shift(2).dropna())\n",
    "\n",
    "\n",
    "print(f\"Spearman Correlation: {round(corr_spearman_ita_nazu21.statistic,4)}, P-value {round(corr_spearman_ita_nazu21.pvalue,4)}\")\n",
    "print(f\"Lag1 Correlation: {round(corr_spearman_ita_nazu21_lag1.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag1.pvalue,4)}\")\n",
    "print(f\"Lag2 Correlation: {round(corr_spearman_ita_nazu21_lag2.statistic,4)}, P-value {round(corr_spearman_ita_nazu21_lag2.pvalue,4)}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------\n",
      "Grenger Causality Test Results for: Points_naz_u21 and Minutes played by Italian U21 players from 1993 \n",
      "---------------------------------------------------------------------------------------------------------\n",
      "   Lag  Test Statistic   P-value\n",
      "1  1.0       12.867615  0.004932\n",
      "2  2.0       13.344389  0.009710\n",
      "3  3.0       11.957117  0.035380\n"
     ]
    }
   ],
   "source": [
    "granger_test(mins_played[['Points_naz_u21','Minutes_played_italians']][26:],5,3,\"Points_naz_u21 and Minutes played by Italian U21 players from 1993\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grenger Causality Test Results for: Points_naz_u21 and Minutes played by Italian U21 players\n",
      "   Lag  Test Statistic   P-value\n",
      "2  2.0       12.867615  0.004932\n",
      "3  3.0       13.344389  0.009710\n",
      "4  4.0       11.957117  0.035380\n"
     ]
    }
   ],
   "source": [
    "test_result=grangercausalitytests(mins_played[['Points_naz_u21','Minutes_played_italians']][28:-1],maxlag=5,verbose=False)\n",
    "#create a dataframe with the results of the granger causality test\n",
    "test_result_df=pd.DataFrame(columns=['Lag','Test Statistic','P-value'])\n",
    "for i in range(3,6):\n",
    "    test_result_df.loc[i-1]=[i-1,test_result[i][0]['ssr_chi2test'][0],test_result[i][0]['ssr_chi2test'][1]]\n",
    "print(\"Grenger Causality Test Results for: Points_naz_u21 and Minutes played by Italian U21 players\")\n",
    "print(test_result_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue and Minutes_played_under21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Minutes_played_under21</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>-3.3542</td>\n",
       "      <td>-3.8396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>-3.7112</td>\n",
       "      <td>-3.8893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>-2.9812</td>\n",
       "      <td>-3.0544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>-2.6301</td>\n",
       "      <td>-2.6670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_under21  revenue\n",
       "Test statistic                       -3.3542  -3.8396\n",
       "p-value                               0.0126   0.0025\n",
       "Critical value - 1%                  -3.7112  -3.8893\n",
       "Critical value - 5%                  -2.9812  -3.0544\n",
       "Critical value - 10%                 -2.6301  -2.6670"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf_test(mins_played[[\"Minutes_played_under21\",\"revenue\"]][29:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Minutes_played_under21</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Test statistic</th>\n",
       "      <td>0.3673</td>\n",
       "      <td>0.3944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0912</td>\n",
       "      <td>0.0796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 1%</th>\n",
       "      <td>0.7390</td>\n",
       "      <td>0.7390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 5%</th>\n",
       "      <td>0.4630</td>\n",
       "      <td>0.4630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Critical value - 10%</th>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.3470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Minutes_played_under21  revenue\n",
       "Test statistic                        0.3673   0.3944\n",
       "p-value                               0.0912   0.0796\n",
       "Critical value - 1%                   0.7390   0.7390\n",
       "Critical value - 5%                   0.4630   0.4630\n",
       "Critical value - 10%                  0.3470   0.3470"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpss_test(mins_played[[\"Minutes_played_under21\",\"revenue\"]][29:],0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross correlation between U21 players and Profit\n",
      "--------------------------------------------------------------------------------------------\n",
      "Peason Correlation: 0.324, P-value 0.0992\n",
      "Correlation Minutes_played_under21 e Entrate Lag 0: 0.324\n",
      "Correlation Minutes_played_under21 e Entrate Lag 1: 0.4178\n",
      "Correlation Minutes_played_under21 e Entrate Lag 2: 0.1756\n",
      "--------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import ccf\n",
    "\n",
    "cross_corr=ccf(mins_played[\"revenue\"][29:],mins_played[\"Minutes_played_under21\"][29:],adjusted=True)\n",
    "corr_pearson_ita_fifa=pearsonr(mins_played[\"revenue\"][29:],mins_played[\"Minutes_played_under21\"][29:],alternative='two-sided')\n",
    "print(\"Cross correlation between U21 players and Profit\")\n",
    "print(\"--------------------------------------------------------------------------------------------\")\n",
    "print(f\"Peason Correlation: {round(corr_pearson_ita_fifa.statistic,4)}, P-value {round(corr_pearson_ita_fifa.pvalue,4)}\")\n",
    "print(f\"Correlation Minutes_played_under21 e Entrate Lag 0: {round(cross_corr[0],4)}\")\n",
    "print(f\"Correlation Minutes_played_under21 e Entrate Lag 1: {round(cross_corr[1],4)}\")\n",
    "print(f\"Correlation Minutes_played_under21 e Entrate Lag 2: {round(cross_corr[3],4)}\")\n",
    "print(\"--------------------------------------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------\n",
      "Grenger Causality Test Results for: Revenue and Minutes played by U21 players from 1996 \n",
      "---------------------------------------------------------------------------------------------------------\n",
      "   Lag  Test Statistic   P-value\n",
      "1  1.0       10.303745  0.005789\n",
      "2  2.0       13.644901  0.003431\n",
      "3  3.0       21.863761  0.000213\n"
     ]
    }
   ],
   "source": [
    "granger_test(mins_played[['revenue','Minutes_played_under21']][29:],4,2,\"Revenue and Minutes played by U21 players from 1996\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BXgAAs9jHvE+FY3mV4EwWSO5hDJ89CZPO5Zda0eLeDjz3S/ga9sK24hI4N98I3hb7sYO7oxEAwOWVTXmvtcQ5i+Tn7ToLwwJtny/Zr9tEYIxFNMAu5iDjiSeegNPpxM033xzrJQAAgiDg+PHjEz7e1NQU13XJ1OjeApACyB3sBAdAyKuCaeAs2o/shtdjivmSdF/1k6x7a21phA3AkFdE5yTvUwpHbgVMPafRsud1+OZtTNwCNRDPvc1pOgKeMXjeew2uY9vhrTkPvqq1QAzHHdltp2EA0O5mCExzr7Vi8XOwAxg4W4/WPH2eL9PfE8xm84yPiTnIePrpp3H99dfDarXGegkAgMlkQk1Njfpnj8eDpqYmVFdXw2bTr+PbbET3NkTobUEfY+DMNuSvPA8j255ArjSKvCVLor4W3Vf9JPveDrXthAdAQXkVnNO8NlyejRjZdhq53h7kx/AaSoZ4763k86D7Rfk3dWNhJQK9LbCfeBXOrqPIvvA/YZm3OuJrsYAfXS8NAADmrzkXhqzcqNcTLa/JhcH6N5AleVGl8d9Zsl+3idDQEFmyfExBxokTJ9DS0oJrr702li8fg+M42O32CR+32WyTfpzEj+4tMDoqb4ObiyrhnLsUIwAC3U1x3Re6r/pJ1r0d9ss9Mqw5BdM+v3HROoxs+wuEtpOwmo3gjTP/hpcqYr23vmG5xwRvz0blp36GkUNvYGDbExAHOjDw9M9gm1+Hgss/BnNhxczX6ugAGANvz4ajsCyibfh4GUvnYhCAONQFm82my3Nm8ntCpPcrpsTPvXv3oqCgAIsXL47lywlJOiXp01xYAXNJNcDxEEf6EBgdSO7CSEoJtRSfOvETAEyFlTA48sACfvhaTyZiaUknDHQBkFt0c7wB2XWXofKzDyBn4/sB3ghP4wG0/vYr6H35kRmnnapJn0VVCQkwgFCvDMnrgkTTWHUTU5Bx7NgxLFq0SOu1EJIwQrB81VRYCd5sg6lwDgAqZSVjTTbmfTIcx8267p+BwVCQoeCtWSi49DZUfuZ+2GvXA0zC8J7n0fLQ5zG09wUwSZz0WomuLAGUMla5eRpVmOgnpiCjp6cnrooSQpItfCcDACxlCwBQkEHGiqSEVaEEGe7G2THHRGnJbcwtnvA5U34ZSm+6B2X/+R2YiqogeUbR99Lv0fr7r8LdODEI8/cEB6Pp3E58snUC1CtDTzEFGb/73e/wv//7v1qvhZCEYGIAQp/8pmIqUoIMOfnYT0EGCWKSCCnYA4KfYScDAGzVclMuf2djqB15BgsEg4zwnYzxbPNWouKTP0XhVZ8Cb3NC6GlB51+/j86//xBCf7v6uNBxSWLLf2kaq/5oQBqZdYSBTkAKgDNZYcwuBBC+k9EAxlgyl0dShBxgyK+FSNpOG515MBVVAWDwNGX+bkZ4TsZ0ON6A7LVXofKOB5G94RqAN8B9ai9afvMV9L36R/j72iG6hgBwMBdVJmDlIdSQS38UZJBZR1CPSuao8yjMxXMB3gDRNQRxpC+ZyyMpYszckgj7Ptjnhbp/ZjImCggMyxVaxtzpgwyFweZA4eW3o+JTP4dtwRpACmBo1zNo+/1X5evklYA3x9cSIVrqyHeaxqobCjLIrKPkY5gKQ7818SYLzEXyebCvnY5MSORJn+Fs88JbjGfujlhgqBdgEjijGQZHblRfay6sQNmH7kXph74JU8EcsIBf/ngCkz4VprzQTkYm/30lEwUZZNZRBqONr98PPzIhJJqkT4W1ahnAGxEY6s7o347VpM+8kphLTu0L6lDxqZ+j4IpPwFw6H9l1l2u5xIgY80oAAJLPrZYrE21RkEFmHaFH2cmYIsjopJ0MAogu+YdOJEmfCt5shbVCLu/P5FJWNR8jwqOSqXAGI3LWb0HFJ34C+4I6LZYWFd5kgSGYl0UVJvqgIIPMKkwSIfS1AcCEJDOlwsTXcZq2TomakxHNcQkQVsqawXkZgUGlsqQkySuJXyj5M3N3npKJggwyqwQGu8FEAZzRDGNO0ZjPmYsrAYMRkmdUbTREZi8phpwMALDNk0tZvU2Hp2w+le5CxyXx7WSkAipj1RcFGWRWUZM+C+ZMqBjgDCZYiqsBUFMuEvtOhqVsPnhrFiSfO2NfR5N1+0xX1JBLXxRkkFlFmCLpU0GdP4lCjHBuyXgcb4B17nIAmVnKyhiDMNANADDmZsBxSR4dl+iJggwyq4TKV6cIMsqVvAyqMJntlCAjmsRPhT14ZJKJyZ+iaxBM8AIcD1Nu0cxfkOLCdzIoF0t7FGSQWcXfozTimryzYGgnoxGMSQlbF0k9UgwlrArb/GBeRms9JL9H03UlWyBYWWLMLgBnMCV5NfGTy1g5MCpj1QUFGWTWYEyC0BfcySiafCfDVFgBzmgG83soEWwWY0xS549Em5MByLkKxtxiQArA23xM6+UlVSYlfQIAbzTDmE3TWPVCQQaZNQJDvWCCD+CNUyascbwB5tJ5ACgvYzaTPC4guJMVydySydiqg6WsGTbHRFCSPjMgH0NByZ/6oSCDzBpK0qepoHzaWRSU/EnUuSUWe8xHAsqRSablZYSmr2ZOkGHMo0FpeqEgg8wafnUw2uRHJQq1KVc7JX/OVvEkfSpsc1cA4CD0tCAw0q/RypJP6faZKcclAE1j1RMFGWTWmCnpU6HsZPi7zmRsMyUyvXiSPhUGuxOWsvkAMquUVe32GWdL8VRCQYZ+KMggs4Z6XDJF0qfCVFAOzmwFE3wQetsSsTSSYmKZwDoZW4aVsko+D0SXfJSUSccloZyMTipj1RgFGWRWYIxFfFzCcTwspTSRdTZTfpDGH2Rk1uh3JemTtznBW7OSvBrtGHOLAXBg/lAQRbRBQQaZFcSRfjC/R24gFPytZTqWckr+nM1C3T7jCzKsFYvBGc0QXYMQelq0WFpSBQYyr7IECJax5sjTWANUYaIpCjLIrOBXjkryyyKqFgifyEpmH7W6JI6cDADgjCZYq5YCANwZcGQiDCo9MjIryAAoL0MvFGSQWUGYoZ34eKHkzyYwUdBtXSQ1xTqBdTKZlJchqOWrmZP0qTDSNFZdUJBBZgV/jzIYbfrKEoUxtwS8NQtMFNSvJbOHVomfQCgvw3v2GFggvQNW9bgkA4MMasilDwoyyKwg9MlVIuYZKksUHMdRU65ZTNSghFVhLq6CISsHTPDB21Yf9/WSSUn8zMjjEprGqgsKMnTW/8Zf0Pvi7zIiszxdMcbU3QhThDsZADXlmq0YY6G5JXEmfgJytZLSYjydj0yYJCIw1AMgs3pkKGgaqz4oyNCR0N+BwXf/heF9L9I5XxKJriFI3tGIK0sUtJMxO0k+NyAFAMTX8TNceClrugoM9QCSCM5ggsGZl+zlaM6UWwJwPJjfC9E1mOzlZAwKMnTkOrlL/e8ABRlJozThMuYWgzdZIv46Nfmz5yykgF+XtZHUIwUrSzizFbzRrMk1leRPX8dpiJ5RTa6ZaKF24iXguMz70cEZTTBmy2Ws/p6zSV5N5si8V0oKCQ8y/P3tSVzJ7BZqwhX5UQkAGLIL5d9kJRH+rmY9lkZSkJZJnwpjdoFc2cQkeJoPa3bdRFIHo2VYj4xw1rnLAADDe19I8koyBwUZOgkM98EXluQlUJCRNKHy1TlRfd3Y5E/Ky5gtRJd2SZ/hQnkZ6XlkkslJn4rczTcA4OCu3wNf55lkLycjUJChE1f97uB/cQCo9jqZlEZc0e5kANSUazbSYycDSP+8jEzukaEwF8xB1rJzAQCD2/+R5NVkBgoydOIOHpVkLT4HACD00U5GsggRziyZDCV/zj6hbp8aBxlzlwO8AYGBTnVXIJ2EWopnbpABAHnn3giAg+vETvi76Zg0XhRk6EB0j8DTfBQAkLPpBvljI32Q/N5kLmtWEt3DoamRUR6XAKEgQ+htpb+/WSI0t0Tb4xLeYoN1Ti2A9NvNYIxldEvxcOaiKmQt2QgAGHiHdjPiRUGGDtyn9gBMgrm4GtbyGvA2B4DQdiNJHCXp05hTBN5si/rrjc58GBz5AJPg72rSeHUkFWnZUny8dM3LkNzDYH4vAC44sTSz5Z77QQCA6/gO6vgbJwoydOAad1QS6iRHeRmJFu3MkslQ8ufsohyX6BJkzA8GGU2HwZik+fX1ohzvGJz5mpX1pjJLSTXsi84BwDC4/Z/JXk5aoyBDY5LfA0+j3NUva1EwyCgoB0AVJskQT9KnwlJOyZ+zSai6RPsgw1JWA85sg+QZgb+zSfPr62U2JH2Ol3eevJsxemw7/MGxBCR6FGRozH36AJgowJhXClNRFQDAlE9BRrLQTgaJlpKTEe+Y98lwBiNswV4Mnqb0OTJRe2RkeD5GOEvpfNgXrgOYhMHt/0r2ctIWBRkac53YCQDIWrwRHCeXr6o98fvouCTR/D3BypKiOHYySucDkCuEJJ9bk3WR1MQY0zUnA0jP0e+hbp+zZycDAHLPuwkAMHrkLTrujhEFGRpiAQHuhv0AQkclAO1kJIvodUEc7Qcg17/HypCVA2NOEQA6Msl0zO8FE+Vx7PoFGcro9+OQBJ8uz6G12XhcAgDW8hrYFtTJuxnv0m5GLCjI0JCn6T0wvwcGR756jg8Apnz5H6bkGVGnOxL9KUclBmc+eGtWXNeifhmzg5L0yRnN4M1WXZ7DVDAHBkc+mCjA13pSl+fQWkDp9pnBLcWnknf+fwAARg5vS8v+JslGQYaGXCeCVSWLNowZIMSbbTA48wHIY4RJYmiR9Kmgzp+zg17dPsNxHBfq/pkGeRmS4IM4OgBgduVkKKxzamGbvwqQRAy++1Syl5N2KMjQCJNEuE7tASDnY4xHRyaJp0XSp4J2MmYHPZM+wylBhrsx9YMMpdMnb82CweZM8mqSI++84G7GoTfkkfckYhRkaMTbcgKSexi8zQFr1dIJn6fkz8RTkz41CDLMweTPwGAXRA8deWUqvZM+FUpTLn9nY8ofoSr5GMYMbyc+HWvlYlirVwBSAIM7nk72ctIKBRkacZ2Uq0rsC9eD4w0TPk87GYknKMclcVSWKAw2h5pZT7sZmUtpQW/I0jfIMDrzYCqqBMDgaT6i63PFS2knPhuPSsLlBStNhg++isBwX5JXkz4oyNAAYywsH+OcSR+j7mRQGVRCSD4PAsO9AABTQfw7GQA15ZoNQjkZ+h6XAOlTyqoORpvlQYZt7jJ5l1qk3YxoUJChAV/HaYgjfeBMVjlBaBLhXT8ZY4lc3qykdOgzZOXCYNfmHJnyMjJfIhI/FfY0mWOi9siYxcclCmU3Y+TAKwiMDCR5NemBggwNKGPd7TV1U/b1N+UWAxwPJvggjvQncnmzknJUokXSp4KCjMyn15j3yVirlsqj3we7Unp4YmAWHZc889Zp3POrdzA0Onn/Emv1ClgqFoGJAoZ2Pp3YxaUpCjLiJB+VBLt8LppYVaLgDCZ1eiHlZehPmb6qRdKnwlIyHwAHcbgXgdFBza5LUkeiEj+B9Bj9ziQRwqBcTZHpjbiEgIS/vHQCRxv78Ma+ySevchwXys3Y/zK9D0SAgow4Cb2tctBgMMJes2bax4aSPykvQ29Cj7KTEX/Sp4K32GAqlDuH+mk3IyOpxyVZ+udkAEj5fhmB4T5ACgAGo9rrJ1MdOd0LtzcAANh1dOqdJdv81bCULwQL+DG065lELS9tUZARJ2Wsu33eKvAW+7SPpeTPxFF3Moq028kAqClXpktkTgYQHmSk5uh3pdOnKad40qq5TBIeWBw7049hl3/Sx43Zzdj3olqRRCZHQUac1CBj0YYZH0tlrIkhCT4EBrsBaNPtMxxNZM1ckuADC84SSVSQERr9PpqSo9/VHhkZno/BGFODDKOBhyQx7DsxdQtxW80amEvngwk+DO1+LlHLTEsUZMRBGOyGv7MR4HhkLVw/4+NNBcpOBgUZehL62gAw8Dan5gl84cmfVCWUWZSkTxiM4My2hDynPPp9OYDULGWdLYPRTrcNoXfQA4vZgGvOmwdg+iOT8N2Mob3PU4O+aVCQEQdlF8NatSSiM1yzspMx0AUmibqubTYLT/rkOE7Ta5tLqgGOh+gapCqhDCO5QkclWr9uppPKeRmhHhn6BRmiKGH30U54fAHdnmMmu47IAcWaRcU4f7Wcd7X/RDeEwNTv0/ba9TAXV4P5vSm7m+Ftb8DAO/9I6s8bCjLioJSuTldVEs6QXQDOaAYkUd3OJ9rTI+lTwZssagdROjLJLIlsxBUulUe/h1qK63dc8sizR3HfH3bh0WeP6vYcM9l1VM6TO2dZKWoqcpHntMDjC+Dw6ak7e3Ich9zzPwgAGNrzPESvKyFrjVRgdACd/3cfBrb9Nak5ZBRkxCgwOghvywkA8tTVSHAcr459pyMT/eiV9Kmg5M/MpByXJCofQ2EqmAODUx797m09kdDnng5jTB1trlePjIbWQWx9pxEA8NaB1ml3DvTS1e/GmfZh8BywbkkJeJ7DhmXy+/TuaY5MALnDs6moCsznxvCerYlYbkQYY+h98XeQPKMwl8yDJTh7KRkoyIiRu343AAZLWQ2M2YURfx2VsepPy+mrk6GmXJkp0eWrijGj31OoX4bkGQXzuQHos5MhSQwP/fMQpGBqk8sbwL4Tid/hVQKJJfMKkOOwAIAaZOw62jlt7hXH8cg7L7ibsfs5SCmym+E6/q68084bUHTt58EZjElbCwUZMVLyMbIWTz6rZCpUxqovFhDULV6tK0sUlPyZmUJj3hO7kwGEprJ6zhxO+HNPRfl3ZHDkgzdZNL/+y7uaUX92EDaLERfWyb8QvHWgTfPnmYlyVLJxeSjvZNXCIphNBvQOenCmfXjar89avBGmwgpIXheG9r6g61ojIbqG0PvS7wEAuZs/AEtJdVLXQ0FGDESvC54meXKifYqBaFOhMlZ9Cf3tAJPAW+wwOPJ0eQ5z8VyAN0LyjCAwRLk1mUJ0JbZHRjhlJyOVRr/r2U58aNSHP249BgC49erFuP5COXDfleAE0FGPgCPBvAtl9wIALCYD6mqL1DVNh+MNyDtX2c14FpLPo9NqI9P78iOQ3MMwF1ch77wbk7oWgIKMmLgb9gFSAKbCCpgL5kT1tWqQ0UdBhh786lFJpW4VApzRJAcaoCOTTCIlKScDAIyOPJiKqiCPfk+N3Qx1MJoOlSWPPXcMox4B88tz8L7N87CgIgflhVnwCyJ2HUncLu/e410QJYbKEifKCx1jPneOmpcx83qylm6GKb8ckmcUw/te1GWtkXCd2AXXse0Ax6Poms+DM5iSthYFBRkxiGRWyVSU45LAcG/KZZJnAn+wskTLmSWTsZRTXkamSXS3z/FSLS9DCTJMGudjHG3sw6t7zgIA7vjgShgMPDiOwwXBI5NtCTwyUQKa8KMSxbqlJeA4oKFV7qExHY43IPdceddgcNczkASv9oudgegeQe+LvwUA5G66Xj3WTTYKMqIkCT54Th8AEH0+BiCf9/LWLABAIIUnL6YrNelTp8oSBSV/Zp5klbAq7CkWZISOS7TbyQiIEh76p9x07MqNc7F4bmgeygV18q7wgZPdU7b01pIQENVE03OWTfwe85xWLKqSj1z3HJv5vdqx/HwYc0sguYfhee91bRcbgb5X/gDRNQhTYQVyz78p4c8/FQoyouRpPAgW8MOYUwRzybyov57jOPXIxE95GZrz9yo7GfokfSrCy1hTceYEiV4yEz+B1Bv9rkdL8efeaURz5wicdjNu27J0zOcqS5yYPycHosSw/T393xsPN/TB4wsgP9uChZWT52+FV5nMJHw3w7X3eUAUtFvsDFyn9mL0yFvBY5LPgTeaE/bcM4kpyHj66aexZcsWrFixAu973/vwwgvJz6hNFLWqZNE5MZ/5q0cmVGGiKSYG1KodvY9LzIUV4IxmMJ8b4uDUMw5IemABAcwvb4kn67iEN6fO6HdJ8KkdbbXayegd9OCJl+Q+ILdfsxTZWRN/EF4Y3M1460CrJs85nZ3BXIv1S0vB85O/lys7HIdO9UaUkOpccSGMOUWQ3EOwNu7QbrHTEL0u9D7/GwBAzoZr1NdQqog6yPj3v/+Ne++9F7fccgu2bt2Ka665BnfddRcOHDigx/pSChMFuE/tBSCXLcVK3cnooyBDS8JAJyCJ4MxWGKLoXRILzmBUd7KErjO6PhfRn7KLAd6gHmcmQ6q0GFc6EnMWO3ibU5Nr/v6ZI/D4RCypzsel66smfcx5wZbeRxv7ZsyDiAdjTO2PsXF52ZSPqyxxoqwgCwFRwoGTM1eScQYj8s7/DwCA7fQ7GN2t/yj4/lcfgzjaD1N+GfIu/JDuzxetqIIMxhh+8Ytf4LbbbsMtt9yCqqoq3HHHHdi8eTN2796t1xpThqf5KCSvC4asXFjiiBZDvTLouERL6lFJgfYzSyaj5GVQkJH+1G6fNmdC55aMZ5u3CkBw9HsS502onT5zSzS5H/tPdGP7oXbwPIc7blw55c5BcZ4dS+flgzHg7YP6JYA2tA6ib8gLq9mAlTVT/0LCcVxURyYA4Fh5MRwbbwAAjG5/Ev1v/EW3fjru0wcwcuh1AJx8TKJDP5N4RRVknDlzBm1tbbj22mvHfPyRRx7BZz7zGU0XlorUse6168HxhpivQ70y9CH0JCbpU0FBRuYIdftMzlGJwlIeNvq9qylp6wgMaNcjwy+IePgpeWfm2vPmY1759Im1F65RGnPpd2SiDkRbXAyzafr3cuXIZM8xudx1JhzHwbHpA3AvugQAMPjuv9D3yh80DzQknxs9zz8MAMhefzWslUs0vb5Wouo1euaM/GbqdrvxiU98AseOHUNFRQXuuOMOXHLJJTEtgDEGt9ut/tnj8Yz5/1TBJAmuE3KQYaxePWbN0ZJs8j8yyT2M0f6ehG3Ppuq91YpHeVPOLonr7ydSLE/e2hW6moDlUsbe12RK1GvWM9Aj/4fFkZDXznTMFUvga9yPofp9cORMvZUfr+nurScYsMNREPf9ePL10+jodSHPacH151fNeL01C/PA8xwaWodwuqUHZQXavz/uOCz/gle3MH/G9cwtsSLLZsSI249DJ9uxeO7MTf48Hg988zYiv6gU3neewPCe5yF43Mi+9HZwvDb1FkOvPgpxuBeGnGJYz7kh4a9bxlhEu1xRBRmjo6MAgLvvvhuf//zn8bWvfQ0vvfQS7rzzTjz66KPYtGlT1AsVBAHHjx+f8PGmpqaor6Unw0ALst1DkIwWNLp4YJI1RyPH4gDvG0XDgZ0Qc8s1WmVkUu3easXZ3ggjgE63BCHOv5+IMAm5BhO4gA/8aB+amqhYSy96v2YtTQ2wAxgVgK5EvHamW4ulEHYAA8d3ocWhf6+Dye6to60RJgDdHgmtcdyPvpEAntom7xpcujILzWdORfR180vMaOjw4enXjuCiFdruLg2MBnC2axQcB2RhAMePD0W0nsNNAby0/QSYOzfi5+pwVMO84hrYD2+F58ibGOzrgXvFtUCcgYaxrwnOw3KZ7GDt5ehtSM5uqtk8cxVLVEGGySR3D/vEJz6BG26Qz5yWLFmCY8eOxRxkmEwm1NTUqH/2eDxoampCdXU1bDZb1NfTy/BbB+AGYK9Zh/Jly+O+Xt+RCgitJ1CZa4VtSWK2uVL13mqBSSK6XhkAAFSvOkfX0dTh+o7Mh9B2EsbhDpSvWJ9x9zXZEvWaHek7DBeA3NI5qErQv8epBEpy0Hv8ZZgG27B44QJwOpUjTndve3a5IQKoWLwKlqrY7gdjDD/80wGIErByQT4+eOWaiPM7rvDmoOFfR1HfEcBnb1qsaZ7M1nebAXRiSXUe1q5eFtHXXBroxOGmw2jqkbAkgtfHmHu7ZAk8ldUYevEhWDqOIjvLhtyr7wRnjK0bp+T3ou/d30IEYFt5KUrPuyqm68SroaEhosdFFWSUlMhv3LW1Y5Mea2pq8Oabb0ZzKRXHcbDb7RM+brPZJv14MjDG0Ht6HwAgZ9lmTdblKpSDDG60L+HfZyrdW60I/e2AKIAzmuEsrYorZyYanopaDLWdhGGoIyPva6rQ+966BPnIwJJdkPS/Q2ZbgAFnPsSRfnB9zbAHk0H1Mv7eMiZBHJaPjxylVTDFeD+2v9eOQw19MBp43HlTHbKyIj/2uHDtXPz+meNo73Wjc0DAgorcmNYwmYOn5NLczSvnRPx3vXFlJYz/PIL2Xjf6R0VUFEdWcaPcW3vdxbBmOdD1r5/C17AXw1sfQMkH/yumRM3et5+AONwLY04RSq64HbwlOb/YRBr4RbVns2zZMmRlZeHQoUNjPl5fX4+qqslLkjKBv6sJgcFucEYzbPNXa3JNU0Fikz+lgB/Db/wJxt7GhDxfovl75Ux0U8GchAUYQCj50zhE5cjpTEzi3JLx5NHvwSqTJPTLEEf6ATEA8AYYYywFd3sF/O5peQbLjZfUYE6RY4avGMtuNWH9UjnhUsvJrCNuP440ygPRJuvyOZUsmwnLF8j3YvfR2PriZNWuR+nN/w+cyQJP4wF0/u2/ox6m5jl7FMPBSa+FW+5IWoARjaiCDKvVik9+8pP41a9+heeeew5nz57FQw89hO3bt+P222/Xa41J5zopzyqxLagDb7Zqck1TXmJHvruOvgP3wVdgP/J8Ro4nF3oTM7NkPCXIMIx0gYmJmx5JtKV2+0xydYkimXNM1E6fOUUxB+z/90o9+oa8KMm346ZLYyv3vyCsMZcUQVVHJPYe74IkMVSXZaM0yoRSdWBaBC3Gp2KftwplH/4WOLMN3uaj6Pjr9yF6RiP6Wknwoee5XwMAnKsvg32+vjtcWok6++TOO+/EF77wBfzv//4vtmzZghdffBEPPPAAzjkn+jke6cJ1Uu4BkrVog2bXDN/JSMQPfc9ZeayywTuMQPAHciYJTV9NbJBhzCsFZ7GDk0QE+hI32IloS0rycLTxbNUrAAD+zjOhRmEJIgzEN7OkuWMY/35Lnunz2Q+shGWGEtGprFtSArvViN4hL4439cd0jfGU0tVodjEUSr+M42f6MDQa+3BLa+USlN/yXfA2B3xt9ej4y3chumZOPh148wkEBjphcBag4NLbYn7+RIspxfX222/Ha6+9hiNHjuDf//43LrvsMq3XlTL8fe0Qes4CvAH2mnWaXdeUWwJwPJjfC3F0ULPrTsUbDDIAwNe4X/fnSzR/sORO75kl43EcD1Ox0vkzM4+iZoNkD0cbb+zo9yMJfe6AMn01hiCDMYZf//MQJIlh04oyrFsSewK22WTAphXyju82DXpmCAER+0/K39uGGIKM4jw75pfnQGLAvhPxjRKwlNeg/Nbvw5CVA3/XGbQ//m0ERqYOpLytJzC0eysAoGjLZ5PalTZaVHM3A3ewAZetejkMtujOFafDGU0w5hQB0D8vIzDch0DYfA1fY2a1gGdMgtCX2EZc4Uwl1QAAobsp4c9N4sfEACSvC0Dq7GQAYVNZGw/N8EhtKd0+Y6nQen1vC46d6YfFbMAn3x9/FZ4y/n37oXYExPgGEcrzR0TkZ1tRE2MiabTdP6djLp6Lso/cB4MzH0JvK9r//C0IQxNbl8vHJL8CwOBYeRHsNWvifu5EoiBjBqGBaLHPKplKqPOnvnkZ3ha5zp0PJnEJnY0IjAzo+pyJFBjqBRN8AG/UdCx1pIzF1fI6KMhIS6J7RP4Pjgev4S8S8UrWHJNYu32OuP149LmjAIAPX74IxXnxV+msqilErsOCYZcfB+t74rqWEhics2zqgWgzUY5Z9p/ohl+Iv+27uWAOym/7AYy5xQgMdKL9T9+a8EvnwNt/h9DXDoMjDwWXpV/uIwUZ0wgM98HXfgoAB3vtes2vbypIzAwTJciwLliLQLCDoLthn67PmUhK0qepoDyhlSUKUzDIEHpaKPkzDSmVJbzNAY5LnbdEefS7EYHB7oSOfhdiPC758/PHMTTqR2WJE9ddoE0TMYOBx7mr5F/G4mkzLkkMu4NTV89ZHvsvIgsqcpCfbYXXL+K9ht6YrxPOlFuC8o/8AKaCORCHe9H+p2/B330WAOBtO4WhnfKQtcKrP6PpbnqipM6/qBSk7GJYKhbB6Ji5lWy0EjXDREn6NM9ZBKF4IQCo02QzgZL0mejKEoUhtxjMaAFEAQIlf6adVEv6VPBmG6wViR39LnpGIXnlaodojkvqzw7gxZ1NAIA7blwJk1G7Hy0XBo9Mdh7pgC/G3YOG1kH0D/tgsxinHYg2E47jQlUmGhyZKIzZBSj/yH0wF8+F6BpE++Pfhrf1JHqeexBgEhzLzkeWDr/oJgIFGdNQj0oW61M5E5rGqt9xiegZkRNXAZjm1EIokoMMz5lDkITYM6RTSbKSPhUcxyPglN+QfZ2U/Jlu1B4ZWamR9BnOVp3YUlblqMSQlRtxub4oycmejAEXr63AigWx/xCfzOLqPBTn2eDxidgTY/noziPye+yaxcUwGePb7QzPy9CyMtCQlYOyW78HS1kNJM8I2v94L4TeVhiyclBwxcc1e55EoyBjCqJ7WK3IyFqkV5Ch7GR06jbW2dtyQn6ugjkw2HMgOovBOwvAAn54mg7r8pyJph6XJCHpUyFmy288FGSkHzFFdzKAsLyM5sSMfleTPqPIx3jx3TM43TqELKsRt18bWZvuaHAch/NXKz0zYtspVPIxNsZQVTLeyppCWM0G9A97cbp15tLTaBhsTpTd8p3gRFU5gCm48lMp+dqMFAUZU3Cf2gswCeaSeXK5qQ6M2QXgDCZACiAwFF9S01S8LXKgpI4B5jhY59cBANz1e3R5zkRijCX9uAQAxBz5zcvfSWPf043oSq3y1XCW8hpwFnvCRr9Hm48xMOzFn1+Qc74+smUp8pzaNCscTxn/vvd4F1weIaqv7eh14WznCHiei6ukVmE2GVC3qBiANlUm4/EWO0o//C1kb7gG+RffCseS6GeCpRIKMqYQqirRrgHXeBxvgDFf/ses15GJ92ww6bNqqfoxywK5BMrdsA+MxVcWlmziSD+Y3wNwvHr8lAyBbOW45Eza39PZRu32mYK/LXK8Aba58u6A54z+paxqZUnuzEGG1xfAL/9+EC5vADUVObhqU7Vu66ouy0ZliRNCQMKOw9G9V+4KJnwun18Ah12bYXN65GWE400WFF5+O3I336DL9ROJgoxJSH6PWpuu11GJQs8yVsnvUbfvrWGTFM1zFoMzWyGODsDXkd7b+37lqCS/TN4VShIpqwAwmsEEb8JaxRNtpNLckskkMi9DGAy2FJ/huKR7wI27H3wHe493wWjgcOcHV8EQY1loJDiOw4XBNuPRNubaqXT5jKOqZLx1S0rAc0Bj+xC6B9yaXTcTUZAxCffpg2CiAGNeabDrnn5CyZ/aV5h42+oBSYQxuxCmnGL145zRBHtw0Jv7VHofmQhJaic+AcerrxU/5WWklVStLlHYgjMqvC0ndE/WjuS45GhjH+66fxsa24eQ4zDjB589Fwsrta++G+/8YJDx3qkeDIx4I/qaoVEfjp+RB6JtXKbdTmeOw4LF1fkAgD067WZkCgoyJuEOOyqJdJxtrPQsY53sqERhXyiXQ7nr07uU1d+jDEZLTmVJOKVfBiV/ppdUTvwE5PcIg7MATBTgbT2h2/OwgABxWP6BPFX56gs7mnDvQ9sxNOrH/Dk5+PmXL8Sy+QW6rSlceaEDCytzITG5A2gk9p3ogsSAeeXZKM6PvzlYuHM07P6ZySjIGIeJgtqoSu+jEgAwK4PS+rTfYp+Q9BnGXrMG4Hj4u5t0SzpNBDXpM4mVJQqjGmRQ8mc6SbW5JePJo9/1PzKRW1ozcCbrhHLegCjh1/88hF//4xBEieH81XPw48+fp0lXz2goCaDb9kd2ZKIelWi4i6FQSlkPn+6F2xtdMupsQkHGOJ6mI5B8bhiycmGZE9uI4mgYgyPfA0M9kAJ+za7LRAG+tlMAJt/JMNizYa1YBABwpWljLsZY2HFJ6uxk+DsbEzJZl8SPSSKk4KjtVEz8VCQiyAhvJx6+gzs06sM3H34XL7zbBI4DbtuyBP9161pYzUbd1jKV81aVg+OAE80D6OqfPhfCL4g4cFKeBaJlPoaiotiJOUVZCIgM+09OnDlCZBRkjKOMdbfXbkhIi2FDVg44ix0AU/+Ra8HXcRos4Advz4apYM6kj7EvlKfKpmv3T9E1KHcnTHJlicJYMAcwGCF5XQhMMuiIpB45wJADQoPdmdzFTENJ/tRz9LuSj2EMy8c40z6Eu+7fhqONfbBZjPjm7efgpktrdT9GnkpBjk1t9jVTm/FDp3rg9YsozLFiwRx9dqk2BHdI6MhkahRkhGFMgrteDjL0LF0Nx3EczEryp4ZHJkojMWvlkinfEJQgw9N8BJLPo9lzJ4qyi2HMLQZvsiR5NQBnMMJcNBcA5WWkizFzSxIw96aj1xXTb71GRy7MxcHR7zo10RPGDUbbfqgd//XA2+ge8KCsMAs//eL5MY1I19oFdZE15lJ+8G9YVqpbUKTkZew91gUxzimxmYqCjDC+tnqIrkHwFjts1fGPKY6Umvw5oF2Q4QkmfdomOSpRn7dgjvxbixiA+8zBiK57tLFPt9rwaKVS0qfCUjoPAOBP89Lg2SKR+Rijbj++/uDb+M5vd+DI6eiHa+ldyhoIdvs05JTg8ReP40d/2gOfX0RdbRF+/qULUFWaGsdJm1eWw2jg0NQxjObOyXd15IFoSumqfruci+fmwWk3Y9Qj4FhTv27Pk84oyAijNOCy16xNaM8FNcjo06bChEmimoVurZw6yOA4Th26E0mViSQx3PeHXbjvD7vQ0jWiyVrjIaRQ0qfCUjofACV/pgvRlbgeGY8+dwyDI3IJ6ut7W6L+ets8uZRVr9Hvyk7GP/YM4m+v1AMArr9wAb7zyY2aNbHSgtNuxppF8m7LVLsZ9S0DGBjxwW41aj5LJZzBwGP9UnktqfLLV6qhICOIMRbKx0hAVUk4rUe++7vPgvnc4Mw2mEvmTvtYNS+jYd+MsxF6Bz1qS99YBxVpyZ8qPTLCmNUg4zQlf6aBRHX7PHy6Fy/valb/vP29dvijnChqrVqi2+h3xiQ1J+OdxgBMRh5f+XAdPnHdchgMqfdjInRk0jrpv7NdwaqStYtLNJ0IOxl1YNoRbQemZYrUe/UkidBzFoGBTnAGE+wLVif0ubXu+qmWrlYsnvGc2VqxGLzVAckzAl9b/bSPbe8dVf9797Gu+BcaJ6XbZyodl5iLqwCOh+QehjhC26epLhHHJX5BxK+elDsIX3HOXBTm2uD2BrAnyn9Deo5+P/xeAyAKEBkHzpGPH955Li5Zp28jwnics6wUFrMBnX1u1J8dmPB5pZX4OQnIIamrLYLRwKOjz4XW7tGZv2CWoSAjSDkqsc1bCd5sS+hzK5URcrWEK+7rhZpwTeyPMR5nMMK+QB6YNlMpa3tvaG3Hm/ox6tau5DZaomtI7dQ4VfVMMvAmi3p8Q8mfqS8R3T6ffO0U2npGkee04PZrl6ntsd/cH8eRiUZBBmMML+w4i0f/+iYAYJR34qdfvhiL5uZrcn29WC1GNYAYf2TS3jOKlq5RGHgOazUYiDYTu9WElQvlIxmqMpmIgoygZB2VAPLUPUNWLoD4dzMYY+pOxnRJn+HsSl7GTEFGTyjIkKTk1ob7+4KVJTlF4M36TH6MVejIhIKMVKfOLcnSJ8g42zmMf7wu7xB++oYVcNhMuHitvPO293gXRqIM1LUc/S4EJDyzawCPPX8S+ZycY1VcNRcFOYn9JStWF9bJwfzbB9sgSqFjCqUB14oFhXDYEpNbt1HngWnpjIIMAMJgF/xdZwCOR1YwRyHRQjNM4gsyhP4OiK4hcAYTLGU1EX2Nff5qgDdA6G2d9vmV45Ich5wEFu12r5YCg3KX0khHUieSkvxJM0xSn54txSWJ4Vf/OISAyLBuSQnOXSkfi84ty0Z1WTYCIou4PbbCUrYgNPo9juRiISDiB4/tw4FGNzgOuGyJHFiYU6DfTKTqFhXDYTNhYMSHIw2hah31qESHBlxTWb9Ufq4Tzf1qci+RUZCB0C6GtXLJhHa6iaJVXobSH8MyZyE4Y2RRPG/NUnc9pjsyUXYyrt4kl2nuO5G82vDAsPymYsguSsrzT8dCOxlpQ8/Ez5d3NePYmX5YzQbc8YGVY3o1XLxW/i38zQjbYyvCR7+74hhu+M6hdpxoHoTFxOGej9Rhfrb8g9E0w/TVVGIy8tgcDNyUyaxDoz6cCJaSJrKnR2GuDTUVOWAM2HucdjPCUZCBsQPRksWkzDCJs8LE2xLMx5hkXsl0Qt0/J3/jEkUJXf1ykHHp+ko4bCaMuAWcaJ6YdJUIyrwVY45+5WmxMhdXA+AgjvQjMDqY5NWQ6Ug6JX72D3vx2HNHAQC3Xr1kwnCuC+oqwHFy35nuGdpjj5e1ZDMAYGjnM2qFVbSe3y7vgpy7xInVCwsRiGD6aiq6cI2c3/Lu4Q4IARF7jnVCYsD8OTkJn6tC3T8nN+uDDNE1BG+L3FPCnswgQ6OR72qnzwjzMRRKkOE9exyiZ2KGdM+gBwGRwWTkUZxnx9rF8m88e48n58hE2ckwZqdekMFbbGpZsr+L+mWkKsYkiG45F0Hr45LfPn0YLm8ANZW5uOa8+RM+X5hrw/L58mt32wztscdzLDsftvmrwAJ+9Dz7YNS5GadbB3GieQAGA4e6BVkAQj0yppq+mqqWzS9EfrYFLo+A/Se61R/wG5PQmVRJRD1Q3xN1eXImm/VBhrzlyGAunQ9TTnHS1qEcl/j7O2KutQ4M98ozMzge1jmLonv+vFKYiioBJsFz+sCEzytHJaUFWeB5Tm1Ak6x+GWqQkYI7GQAlf6YDyeMCmHzcp+Xckt1HO7H9UDt4nsPnP7gKBn7yltYXhR2ZRPNvnuM4FL3vc+AtdvjaT2Fwx9NRre+FHU0AgHOWFsNpM0DyuSF55GAr3XYyDDyH81bLuxmv7D6LA/XyDqeeXT6nMq88G4W5Nvj8Io40Uvm6YtYHGe6TyqySxFeVhDPmlQDgwHxutQthtJTSVUvpPPCW6DPElaTXyc56laTP8kL5N581i4vBc0Bz58iM0xC1xhhDYEjZyUi9nAyAkj/TgTq3xGLXrMOv2yvgoX/J5aXXX7AACypyp3ys3B6bx9nOETR1RDf0zJhdgIIrPwEAGHjr7/B1NUX0daMeQc0DuWKDXOUiBof58fbsmN43kk2pMtl1tBM+v4jiPBvmlSe+BTrHcaFZJid6Ev78qWpWBxmSzwP3GblJTjLzMQCAN5phzJF/YMZ6ZOJpCQ1Fi4V9oVzK6jl9AEwMjPmc0iOjvMgBQG7tu2ReAQBgb4J3MyTvKJjgBSC/2aYiSv5MfaHyVe3yMf7y4gn0DnpQkm/Hh6+YfjfRYTNhwzJ5R/CNfdHnVjiWXyiX3EsB9DzzS7CAMOPXvL73LHx+EXNLnVg8NxcAIA7KQUa67WIoFlbmoqwgS/2zngPRZqIkm+4/2QOJun8CmOVBhvv0fkAMwJRfBlMKdI2Mt714rPkYCkt5DXh7NiSfW00gVbT3jN3JAID1wUY3exKcl6HsYvD27JSYvjoZc4lcgRMY7J40x0UvwkAnvK0nE/Z86UzrypL6swN49h05qLzzxlWwWowzfs1Fa+Tfwt860Dqm10MkOI5D0dWfAW/Phr+7GQNv/33axzPG8MK7TQCAqzfPU38QB4bSO8jgOE5tMw4AG5clrwx3xYIC2CxGDIz40dE/c9A3G8zqIEMdiLbonKRFvuHiKWMV3cPqwLBYdzI43gB7TfDIpH7skUloJyMsyAjmZbzX0Auvb+zOh55CSZ+peVQCAAabA8ZcOccnUcmfjEno+Mt30f7H/zfh749MJLm065ERECU8+ORBMCYHDmsWR5bftW5JCbJsJvQNeWOazGrIykHR1Z8BAAzueBreaUYDHD7di9buUdgsBrWEFgDE4PRV5fWaji5cUwGe55DrsGDZguTtbpqMBvXv/o33hjBAPTNmb5DBAgLcDfsBJD8fQxFPkKHsPJgKK+J608wKK2VVktECoqTmXZQXOtTHVpY4UZxvhxCQcOhU4s4gU7l8NVyij0x8HY3qvel9/qGYc3tmCy3nlvx722mcaR+G027CJ65bHvHXmYwGnLcq2Oshyp4ZiqzFG+FYfgHAJPQ88wAkYfIfbM9vbwIAXLS2EnZrKAdFHErdxnaRqixx4sefOw//353nwpjkgW5Xb6wGxwENHT585Rfb8fS2BgSS1E8oFczaIMPTdBjM74HBkQdLeWSdMfUWTxlrvEclCtv8leAMJnnSY3AAWXe/G5LEYDYZkJ8dauHNcRw2JOHIJJXLV8OZSxcASFyQ4W7Yp/636BpCzwu/oamQ09Cq22dHrwtPvCwfUX382uXIdUZ3hKccmcQymVVRcMUnYHDkQ+hvR/8bf5nw+b4hD3YckX952bJ53pjPiUPp2SNjvMXV+ags0a5KKFaraovwg09vwJwCEzw+EY88cxRf/NkbCf1FLJXM2iBDOSrJqt0AjkuN26A05Ar0d0Zd+67sZNgq4wsyeLMN1mr5NzFllol6VFIol6+GU9rp7jnWlbAfaKlevqqwlMpv5omqMPEEg4zsdVcDvAHuk7swemRbQp47HanVJXEEGYwx/Pqfh+AXRKysKcSl66PP7Vo6ryDmyawKg82BomvuBAAM79kKT9PhMZ9/eWczJIlh2fwCVJeFfb+SCHGkDwBgzE3vICOV1FTk4BNXFOOz1y9FdpYZLV2j+ObD7+LHf9qDngFPspeXUKnx0zXBmCSqnS2TMRBtKsbsQsBgBBMFBIb7Iv46yeeBLzjHIJLJqzPJClaZuOqVIENOXCwLS/pULF9QAKvZgP5hLxrbErM9n+rlqwrluETo64Dk0/eNJTA6AF/HaQBA7uYbkXf+fwAAel96RA3KyFhaTGDdtr8VB+t7YDLy+NwHV8WU28XznLqb8ca+6CezKuwL6uCsuwIA0PPcryD55CPOgCjhxZ3NAIAtm6vHPrdnCGAMnNEMgyM35ucmE/Ech4vXzsFv7rkU15w7Dzwnt3O/439ew5Ov1UMIzI6GXbMyyPC11UN0DYG32NU5AKmA4w3qlmU0RybetpMAk2DMKdbkCEHp/qncp46e0E7GeGaTAasWyj/sE3VkEhgO5mSk+HGJISsHBmcBAAZ/d5Ouz6U0UDOXzofRmYfczTfAMqcWzOdG97MPgrHZeyY8lXiPS4Zdfvzu30cAAB+6fJFa3h0LJcjYdyL6yazhCi69DcbcYgSGetD36h8ByP0j+oe9yHVYsGlF+ZjH8255LIAxryQlkt8zkcNuxmc+sBL333URls7Lh88v4k/PH8fnf/IG9p1I3pDJRJmVQYZaVbJwHTjDzGVmiaTmZfRFEWRolI+hMGYXBDtWMrgb9k3okTFe6MhE/34ZTAxADM4DSfXjEiBxyZ9KPoa9Zg0AOWAtvu4L4EwWeJsOY3jvC7o+fzqKN/HzD88ewbDLj7mlTtxwUXx5XfFMZg3HW2wouvbzADiMHHwV7lP71DklV2ycC5Nx7Fu+wTMIADDRUYnu5pXn4EefOw93/eca5DktaO914bu/24kf/GEXOvtcyV6ebmZdkMEYCytdTW4DrsmoFSYDkVeYxDoUbTp2tfvn3mmPSwBg3RK5ZOtUyyAGRryarWEygdF+uRW0wZi0ibnRSESQwUQB7ka5qZy9Zq36cVN+OQouvQ0A0P/64zEP08pEjLHQ3JKs6HcyDp3qwWt7WsBxwOdvWj3hh3csYp3MOp6tahlyzrkGAND57K/QcLoVPAdcuXHuhMcqOxnpNH01nXEch4vXVuLhey7F9RcugIHnsOtoJz73P6/jiZdOwJeBM09mXZDh725GYLAbnNEM+/y6ZC9nAjXI6IssyGABAb62UwC028kAQnkZnsaD6O+X34wnOy4BgIKc0Jjjfce7NVvDZEL5GIUpk7A7HXMCkj+9LSfA/B7w9uwJlVLONVfCNn+1PEzrmV9O6OQ6W0k+NyDJ9yLaxE+fIOJX/5CDui2b52Fxdb4ma4pnMut4eRd+GKbCCsAzhA/ad2P90tJJp5Ly7kEAgDHNK0vSjd0qlzr/8qsXYWVNIfwBCX99+STu/J/XsfNI7POrUlHqv0trTNnFsM1bBd5sneHRiRdt109fRwOYKMCQlaMetWjBXDoPBmc+mODDAmMnrOax5avjrVsSPDI5ru+RSbqUryrUGSY9rVP2L4iXelSyYM2EwEsepnUneKsDvo7TGNj+T13WkG6kYGUJZ7aBN5qj+tq/vXISHb0uFORYcdsW7XYPC3NtWLEgtsms4/EmC3KuvAMi47DW0oT3z518YJdB3cmgICMZqkqz8YPPbsbdt61DYY4V3f1u/Peju/G93+9Uuyynu1kXZLiV0tUUPCoBQjsZgaGeiGYReJR8jMqlmiZucRynHpksN7WgrDBr2usr3T8PnOyGENAvyVDdyUiDfAwAMDjz5WMdJsHffVaX51CDjIVrJ/28MbsAhVd9CgAw+M4/4G1v0GUd6USMsdtnU8cw/vWGfP8+c8PKMU2ttHChWmUS3WTWyezotOIV7woAgPPQ3xAYHRjzecYY+GBORrqNeM8kHMfhvFVz8NDdl+KmSxfCaOCw70Q3PveTN7D1nfSffTSrggxhoBP+7maA49VhYKnGkJULzmwDmARhcObMY2Xyqhalq+Mp3T+XmVpRXjD5UYmipiIXuU4LPD4RRxv1K5lMl8oSBcdxMJfoN5FVGOiUk4R5A+zzVk35OMey85C19NxgV8hf6Larki7U4WhRBBmixPDg3w9ClBg2Li/FphXaz8jYvLIcJiOPlq4RnGmPbjJrOMYYnt/ehJc9K+DJKofkGUHv82Obs0nuIXCiAHAcTLmpXQ4+G1gtRty2ZSke/K9LsGZxMQKihN8/cyTtS11nVZDhCo51t1YthcGe/M5wk+E4LuIKEyaJ8LaeACDvZGjNWr0CAc6EPIMbCx3Tv+HxPJeQgWnp0iMjnNKUS4/kT2UXw1q5GLx1+kCw8KpPweDIg9DXjv43Htd8LekklvLVF989g5NnB2CzGPGZG1bqsi6HzaTuCsaTAHry7AAa24dgMJpQ/oEvAQYj3Kf2YPTwm+pjlOmrvLNAs1H3JH5zihz47ic3IstmQkBkaOtJ78qTWRZkKEclqdOAazKRthf3dzWB+T3gLHaYi6s0XwdvNKPNKGekzxVm/gGpvDnuOapf98906fYZzlymVJhoPygtVLo6+VFJOIPNiaJrPgcAGN7zPDxn3tN8PekiNIF15gql7n43HnvuKB7dKh9NfnTLEhTm2nRb20Vr5K6h2/ZHP5lVoZStnl83B3lVNci/4EMAgN6X/6DOtxGD01eNOek7GC1TcRyHqmCL9LOdse9opYJZE2QERgfhC47ATtV8DEWkg9LU0tWKxeB4gy5rec8vj1DOHTwx42NXLSyC0cCho8+FNp2SltIt8RMIT/5sBhO1G/8s+T3wNB8FEFmQAchdIbPXXAkA6H72QYje9P4tKVbqTsYU5auMMRw42Y0f/GEXPvX/vYJ/vtEAn1/EqoWFuGrc7A+trVtSjCybCf3DsU1mHRr14e2D8i8oypySnI3XwTJnEZjPjZ6tvwZjEgLB41gDBRkpqapUCTJGkryS+MyaIMNdvxsAg6VsQcr/gFJmmMwUZChJnzYd8jEAwC+I2DVUDIkBfH/zjK3O7VYTlgez42OdwTAdyesCC7ZKTvW/w3DGnGL5KEMMwN+jXa8Kz5nDgBiAMbcYpoI5EX9d/qW3wZhXCnGkD30vP6LZetLJVC3F3V4Bz77diDt+/Dq+/dsd2HW0ExIDVi8swr23b8D3Pr0ZBl7fzpjhk1nf3Bf96+XV3WcRECXUVOaitioPgNKc7fPgjGZ4zryH4X0vqzsZFGSkJnUno4uCjLTgqpfzMVJpVslUQjsZUx+XMMZCOxka9scI19nnwohkQ4sk5z+ET/mcinJksleHvAxlF4O3OVKy/HgqHMcFO6hqm5cRflQSTWURb7ai+LovAhyP0cPbMHpih2ZrShfjEz+bO4fx638ewse+/xJ++/RhtPWMwmYx4prz5uHXX78E9312MzYuL9M9wFAobcbfPdweVYMmUWJ4YUcTAOB94+aUmPLLkX/JRwAA/a//CUK73F/HmEtBRiqqLKGdjLQh+dzqVMJUz8cAQjkZ4ujAlIO1hL42SO5hcEYzLGULdFmH0k68zSo3eHLV75nxa9YH+2UcbezDqEe7owEgPZM+FVpPZGWMwX16P4DIj0rCWSsWIXfT9QCA3ud/M6G8MdMpJawnuwK496Ht+PxP3sAL7zbB4xNRWeLAZz+wEo99+wp85oaVSRkfPnYya+S9Zw6c7EZXvxsOmwnnrZ64u5W97ipYq1eACT6IwUotQw6Vr6Yi5biko8+V1hUmsyLIcDfsB8QATAXlMBdWJHs5MzJYs9SW2VMdmSjzSixzFuqWGd4ezGoeLZCHyHmbDkPyT982vKwwCxXFDoiSfKatpXQrXw0Xai+uTfKnv6sJ4kg/OJMF1hiH/OVd8B8wl8yTyxu3PpRRXQanMzjiw+iA3Jzq0VfO4r2GXvAcsGlFGf77js341X9dgvedO0/zHhjRCJ/MGs2RydZgwudlG6pgNU+cy8RxPIqv+ZxcJh9koJ2MlJSfbUWWzQRJSu8Kk1kRZKRLVUk4Y970FSaheSX6HJUAoRHvjrK5MOYWg4kCPGcOzfh1eg1MS8fKEoVyXOLvOgMmxf9biXJUYqteEXXHSgVnMMnHJgYj3A37MHLwtbjXlaoYYzjR3I+f/WUfbr/vJXA+eQuaszlx06UL8bt7L8f/+9gGrKwpSplppNFOZu3sc6lTPa/eVD3l44w5RSi84uMAAMniAG+Z2G6cJF+mVJik1ghSHUgBf2hbuTZ9ggxTfjl8rSemDjLUyav6JH0CQIcyfbXYAbtxHYb3PA/3qb0zBmvrl5bgqTcbsO9EN0SJaXaOHT63JN2Y8svAma1gfi+EvnaYiyrjup67IfajknDm4irkX/Sf6H/tT+h79VHYqpenfYtpxhj6hrxo6x5Fa88oWrtHcOxMPxrb5DwMCwSYOLkr7c/ufh8s9un7iyTL3LJszCvPxpn2YbxzqH3awAEAXtzRBMaAutqiGcfOO1ZeDL/I0NKfGa2rM1VVqRPHm/rTOi8j44MM75nDYH4vDM58WMr1yV3Qg1mdYTLxuEQY6pZ/q+cNsM5ZpNsalN755YUOZBWsl4OMhn1gTJp2ONmS6nxk2UwYdvlx6uyAZgOkQjsZ6ZeTwXE8LCXz4G05Dl9nY1xBhugehq+tHkBotHs8cjZcA/epvfCePYaeZx9E2a3f060kWktefwDtPa4xwURbzyjae0bh8U3cLTIZeVxQNwfvW+kAnvorOJMlZQMMxUVrKnCm/Ri27W+dNsjwCyJe2S23rd9y7swlthzHwbZ4E8Tjx7VaKtFBJlSYZHyQoR6V1G5Ii6mdiul6ZSitxC2l83WrsvD6A+gdkvMvygqzYLUtAWexQ3QNwdfeAOuc2im/1mjgsWZRMd4+2Ibdxzq1CzKG0jcnA5CHznlbjsvJnysujPk67tMHADCYi+dqci843oCiaz+P1t/dBW/LcQztelZNCk02xhiG3AG8d7oPfUOdwWBiFG09o+gZmDwpGpBzGsoK7JhT5MScYgcqix3YsKwUOQ4LvG31aEf0c0uS4YK6Cjy29Zg6mbU4f/Kjje3vtWPY5Udhrk3tvEvSXyb0ysjoIINJIlyn5IoIe4o34BovvIyVMTbmnDgRRyWdfXI/iiybCdlZZnlg2oI6uI5th7t+z7RBBiAfmbx9sA17jnXhti3x540wSURgRE7WS9cgQ6vkz2i6fEbKlFuCgss/jt6tv0b/tr/CNn81LCXVml0/Fp19LvzPn/fgVMsQgMnze5x2EyqKnZhT5EBFsQNzih2YU+RAaUEWTMbJf6kQXdHPLUkWZTLrew292HagFTddOvm/O6XD51Wb5sJgSJ9fpsj0lMompcLEZEz9HcbxMjrI8LaegOQeBm91wFYVWwZ+shjzSgBwkLwuSO5htdoESFDSp3pUEpq+al+4Dq5j2+E6tRf5F98y7devWVQMnpOnVvYMeFCUF18bZnF0AGASwBtgcOTGda1kUYOMrjMzHjlNhUkiPI0HAWgbZACAc9UlcNfvhvvUXvQ880vMuf3H4IzJqbB4+2AbHnzyINzeADgOKCuwo7Ike0IwkeOwRH3tUEvx1A8yAPnI5L2GXryxrxUfvGThhMTUxrYhnGgegNHA4YoNc5O0SqIHpcLE5RHQ2j2KeeUzt8FPNRkd8ioD0ewL14IzpFc8xZssahVF+JGJ6BqC0NcGQB6KpRelR0Z5YSiBzL6gDuB4CD1n1V2FqeQ4LFg0Vz4m2Xs8/ioTNenTWZAW+QKTMRVWgDOawXxuBAZia1bmbT0JyesCb3PAMmehpuvjOA6FW+4Ab8+Gv7sZw/tf0vT6kfAJIh588iD+58974fYGsKgqF1+6rhT/+6Vz8c2Pn4Pbr12Gy8+Zi6XzCmIKMIDwbp/p8YY902TW59+VdzE2rShHXnb6NKkjMwuvMGlJ07yMjA0yGGNwq/kY6VNVEm6yQWnKLoapqAoGm35NgtSdjKJQYpzB5lQrD/y9LTNeQ+n+uVuDFuNqj4w0LF9VcLwB5mL5N81YO3+qRyXz63QJtoyOXDUfw9N0RPPrT+ds5zC+ev82vLSzGRwH3HTpQnzn42uRm6XtLwixTGBNpqywyaxv7Bv7787lEdRprVvGdfgkmSHd8zIyNsjwd51BYKgHnNEM24LVyV5OTCZrLx6aV6LfUQkQvpMxNvtenasywxh6INQv471TPfD6A3GtJ53LV8OZ4xz7rkc+xnjWCnmHzNden5AGXYwxvLKrGV+5/y00d44g12nB9z+9CbdtWapLfkG6BRlAaDLrWwfaxkxmfX1vC3x+EVWlTiybX5Cs5REdpXuFScYGGUpViW3+avCm2LZVky20kxE6LlGTPiv1S/oEgI5eZSdjbL19NEHG3FInivJs8AckvNcQ/TTJcOk4fXUy6kTWGIIMYagbQk8LwPG6Bs7m0nkAb4ToGlIrevTi9gr42V/245d/Pwi/IGJ1bRF++dWLsLpWvy6UytySdMnJAMZNZg3+W2KMqUclWzbPS5kmYkRboZ2M9GzIlcFBhpyPkU5dPscbv5MheV3wdzcD0DfI8PgC6B/2AZhkJyN/zpg1TYfjOLWcbm+cRyZq+Woa9sgIZwkblBbtLoH7lNyAyzKnVtejMt5oVitLlH4cemhoHcSX/3cbth1oBc9zuG3LEnzvU5uQ59Q3r2CqCaypbMxk1uDxyOHTvWjtHoXNYsDFa1N/XAKJTVWp/Drt6E3PGSYZGWQI/R0Qes4CHA/7Qv22lfUWGvneCcYkeFtPAkyCMbcExmz9tkaVTp9OuxkO+9iW1SalSVgEOxnA2Bbj8Wy9K2Pm030nw1xUBfBGSJ5RNc8kUok4KlEoSaXe4KROLTHG8OzbjfivX76Njl4XCnNt+OGd5+KmS2vBJ2DKqXpckpUeiZ8Kpc349vfkyazPb2+SP762MqlzVoi+8pwWeYYJA1q7069Da9RBRldXFxYtWjThf//617/0WF9M1KOSuct0/Y1Pb8acIoA3ggX8EIf7dB/trmjvnZj0qVB2MgJDPZAE34zXWlFTCLPJgN4hL5o6Yt/uS+e5JeE4o0nt9unviLxfhiT44G2WEzG16PI5E0uwD4rWOxkjbj/++9Hd+O3ThxEQJZyzrBS//OpFWDovcfkE6ZiTAciTWYvybPD4AnhpRxN2HJGPUbdsnrnDJ0lfY2eYpF9eRtRp2ydOnIDFYsGrr7465gzQ6UydH+bK1nrW0nOTvJL4cLwBprwSCH1t8Pe3Jy7ps2fypE9A/u2Pt9gh+dwIDHSq1RJTsZgMWL2wCLuPdWL3sc6Y6rwlnweSVw580n0nA5CPTPxdZ+DrbETW4siO87xNR8ACfhicBTPecy0ozdZ8nY1gAUGTfhnHz/Tjfx7fi95BD4wGHh+/dhmuOS+xuQSS4AMLBsfpUsKq4HkOF9ZV4B+vn8Kjzx2FJDEsm1+A6rL0CpZI9NQZJmmY/Bn1TkZ9fT2qq6tRXFyMoqIi9X9Wa+rUZ+dd+GGU3Ph1OFdfmuylxE3Jy/B3N8PX0QBA/6TP9imSPgE5qjYVyLsZ/oiPTOLLy1B2MXhrVkZMjDSH5WVEKvyoJBE/lI25JXJipBiAr6sprmtJEsOTr9Xjnl+/g95BD8oKs/CTL56Pa8+fn/BkRSXpkzOYwOnUkl9PFwVzLwKifPRIZauzQzr3yog6yDh58iQWLEjtQWMGmwNZi89Jq1klU1EqTEYPvwWIARgceTDqPCWzY4ryVXVNBRNLa6ejBBknzw5gaHTmI5bxMqWyRGEpi67ChDEWFmTof1QCyMGktVzOy/C1x35kMjDixXd+twN/ev44JInhojUVuP8rF6KmIlejlUZHdIW6faZjNcbcUnkyKwDkOizYtKI8ySsiiZDOFSZRH5fU19cjLy8Pt9xyC86cOYO5c+fijjvuwAUXXBDTAhhjcLvd6p89Hs+Y/5/tmEM+q/Z3yef3pvLamO9NpPe2LdiIK99hHPN3o3LKP+w9XWdhmezz49hMQHWpE02dI3j3UAsurIvujdHdI3c45bLyJl9PkkX7mmWOIoDjILoGMdLdPmObdKG3RQ60DCawkgUJuwd8UTXQsA+us8dhWnZx1F//3uk+PPiPIxga9cNi4nH7NYtxUV05IAlwu4WIrqH1+4FvoBsAwNkcKflaisQVGyrwm6ePYcvmKgh+LwR/bNeh91r9aH1vi3Lk48r2XhcGh0ZgNiW/6/H4mVpTiSrICAQCaGxsRE1NDe655x44HA5s3boVn/70p/Hoo49i06ZNUS9UEAQcn2TccFNTU9TXShUBkaF7SEBbnx9tfX54/BIsRh4WEweLaez/W038hI9ZTDyMBvkvzzjsR3i2S78hBx1xjmee7t56BQlDo/K71lBfC44PT9wNMrklOACMtjdGvJaqAqCpE3hzbyOKrUNRrdd65iRsAIZEHp0pPJo6mtdsdlYBDKO9aNz3NgLFNdM+1tK4A3YAQl4lTjbEN1wtGkbBDCcAV/PxqF9zR8+68Y/t/WAMKM4x4qbzClBkG8aJE7H9JqbV+4G57SSyAHhEftL3nXRQZge+cn0psm1uTb6HdH6vTXVa3VvGGKxmDl4/w/Y9R1CaZ575ixLAbJ55HVEFGUajEbt27YLBYFBzMJYvX45Tp07hkUceiSnIMJlMqKkJvcl6PB40NTWhuroaNlt8Q7USQZIYOvvcaGgbwum2YTS0DqO5cwRCQIrrukYDB5vFiGILhy+GBYuVay+EqagqpmtGcm8b24cBtCMny4y6lZMPlRMKs9B38CmYvINYvHhxZNGsYwhvHd2NM10CFtYugjGKTo6DzdvgBVBYtRCOJfrmo8QiltfsYPMieI/3oswizvg99R3+JwQA+SvOQ2UCv3/JNxfde/8PBs8gFs2dE3HzqsOn+/DUjgNgDDh/VRk+/f4lMf/mpfX7gcvViBEAzqKyhN7LVJRu77XpRI97O3e7CyfPDsLsKMaSJWWaXDMeDQ0NET0u6uOSrKyJ5/QLFy7EO++8E+2lAMhnv3b7xGQ+m8026ceTrW/Ig/qzgzjVMoD6swNoaBmEyzuxZbbDZsLCylzUVuWhIMcKjy8AtzcAty8At1eA2xuAxxuA2yf/t9sbgMcnwOOTm60ERIYRt4ARNwdfnhEWLoCAwYrsqtq4c02mu7cDIwMA5KTPqR4jlc9DHzgwnxtWBCLK0l+x0IYchxlDo340dXmwsibyplqDrkEAgL2wLCVfE4poXrNCRS28x7dD6muZ9mtEzyiEYK+K3KUbYUrk92+3Y6BwDoTeVnADrbAXrpvxS061DOBnfz2EgMhw7spyfPXWdTBo0PtCq/cDT8ALADBn56f0aymRUvW9NhNoeW+ry3Nw8uwgOgf8KfH3FWlOU1RBxqlTp3DzzTfjoYcewjnnhErvjhw5MmY3IlO4PAJOtQzgVMsg6s8OoP7sIPqHvRMeZzbyWFCRi4VVuVhYmYfaqlyUFWTFlFgmSgxeNSAR4PEG4P/Xq7B4OtDKlaFW52TW6XpkKJQJsYGhHvj72mCLoKkRz3NYu7gEr+9twZ5jXVEFGepwtOz07vYZTplhMlPyp6fxIMAkmAorYMotScDKxrKU10LobYWvrR5ZC6cPMtp6RvHd3+2ExydiZU0hvnrLGk0CDC0piZ/p1iODkHRN/owqyFiwYAHmz5+P73//+/je976HvLw8/P3vf8fBgwfxz3/+U681JsWZ9iF87Zdvwy+MbePKc3KbV2WXorYqD1Wlzqi2/6dj4Dlk2UzIspkAyNtszVU1EE924OBIITb7ArBa9BtbrwxGK5uiskRhKihHYKgHQn97xH07NiwtDQYZnfjEdcsj+homiQgMy2Pl9WjE5RdEBEQp4R0TLSVykBEY7oXoHp7yh16iq0rGs85ZiNH3Xodvhs6ffUMefPs372LY5UdNRQ7uvX0DTMbkJ6eNJwVLWCnIIOkmXctYo/ppxfM8Hn74YfzsZz/Dl7/8ZQwPD2Pp0qV49NFHUVtbq9cak+L1vS3wCyJyHRasqClEbXCXYsGcHF1/yE+m/Mrb8PAJDts8VbikuV/X4VHqiPfCiT0ywpnyy+FpPBRxe3EAqFtUBAPPoa3Hhfae0Un7cIwnuoYAKQBwPAyOvIifKxIjbj/ufvBtDI748OB/XYL87MT1TeAtdpjyyyD0d8DXeQb2+asmPIZJItynDwBITCvxySidP71tp8AkcdLx8iNuP7792x3oHvBgTlEWvvPJTSnb5jpdu30SEj7DxC+IKVFhEomof1oWFhbihz/8oR5rSRmMMewMtuy948aV2LwyubXoJmc+AgvOh7S3BYdP9+kbZMzQI0NdkzK8LYogw241YfmCAhw61Ys9x7vw/giCDKVHhsGZP+kPuFiJooT/+fNetHTJQdXW7WfwkasTmwhoLp0Pob8D/s7GSYMMX3sDJM8IeItdHb+eaOaiSnAmK5jfA6GvTZ69EsbrD+D7v9+Js50jyM+24nuf3oxcZ+pOPQ4FGenV7ZOQPKcFDpsJox4BbT2jMXVPTob071alg+bOEXT2uWE28lizSL8f6NFYsUDul3E4zpHp0xn1CBh2yeWrMx+XKNNY26J6jnVLQgPTIqFOX9W4EddjW4/hYH0PlLSZF949A69/YgKvniwzdP5Ujkps81eDMyR290zB8QZYyuTme962sUcmAVHCj/+0FyeaB5BlM+H7n96EkvzkJ6RNJzQcjXYySHrhOC4sLyN9jkwoyJiEsotRt6g44UcjU1m+QP4hW392AF6fPj8MlaOSPKdlxu1us9L1c6ALTIx8PRuC3T+PnO6D2ztzQyY9BqO9sa8FT287DQD42i1rUZJvx4hbwBv7WjV7jkgoyZ9TBxnyaPdkHZUolIms4cPSJInhF387gL3Hu2A2GfDtT5yDuSk+Q0MK+MH8cnMknnYySBqqVAalpVFeBgUZk1CCjI3L9W3fHY3SgiwU59kgSgzHm/p1eQ71qCSCYwxDdgE4oxmQRASGuiN+jvIiB+YUZUGUGN46MPMuSGBI25bip1oG8MDfDwIAbrp0IS6oq8B158s7Cv/edhqSFPs4+mhZSuTnDQx0QvK6xnwuMNwX7PLKwb6gLmFrmoy1PDgsLZj8yRjDH549ijf3tYLnOdxz27qETlGNlRTcxQBvzIgZOGT2SccKEwoyxunud+N06xB4Dli/NHWCDCC0m3H4tD5HJh1q0uf0RyUAwHG8Olcl0kFpigvXyKPOH/7Xe3j3vem/Vsvy1YFhL/6/R3dDCEhYv7QEt14l52BctqEKdqsRbT2j2H8y8oApXga7E8Yc+fsaP4TMfVrexbCU18AQQYmwnpSdDH9PCySfB/94/RT+/Za8E/Slm+tS7t/JVELlq860nFtCyNwSebeQjkvS2M6j8i7GknkFyHGkVgLbimCQceR0ny7Xj7R8VaEOSosyyPiPSxfiwroKiBLDj/+8F9sPTf316k5GnMclQkDCD/+4B71DXswpcuCr/7kWfLCHg91qwhXnyOPTn94WWRc7rUw1kTV86mqyGZ358k4Sk/DOa9vxp+flVtafuG45LllXmeTVRU6k8lWS5iqDOxmdfa4J7RVSFQUZ4+w6Iickblye/Lat462o0TcvY7oR75Mx5QeTP/uiS/40GHh85T/X4KK1FZAkhv95fC/ePjj5NbSYwMoYw2+eeg/Hm/qRZTXimx/fEOxDEnLtefPBc8ChU7040x7dbJV4KMmf4U25WECA58xhAKkRZACh3YyD23cCAD54yUJcf2FqT2Mej8pXSbpTKkwkFhpkmeooyAgz7PLjSKO8S5BK+RiKkny7rnkZ7T2Rla8qoh35Hs7Ac/jyh9bgknWVkCSGn/5lH946MDbxUvJ7IXnkbUHlWCEWL+xowks7m8FxwNduXYeKYueExxTn27EpWKqsHAUkgmWS5E/P2aNgghcGR56aHJps/RY5oKwy9uLyDVW4bUv6zf1QgoxIZ7AQkmrCK0ya0+TIhIKMMHuPd0KSGOaVZ6O0ILIftImmV17GsMuPUY9c7RH5cYmykxF9kAHIgcYXb67DZeurIEkMP/vLPry5PxRoKLsYnNkWc6LekdO9+O1T8q7AbVuWYt2SqVtzK7+Zb9vfhoFJ2sfrQTkuEfraIfnl51SPShasSYncgdOtg/jDDrkqY6G1H3feuDIl1hUtiXpkkAygNOVKl+RPCjLC7EzhoxKFXnkZylFJQY4VVnNkZbvmYOKn6BqE5HPH9LwGnsMX/mM1Lt9QBYkB//vEPryxrwXA2PLVWH6odQ+48aM/7YEoMVyweg5uvHj6+TqL5+Zj0dw8BEQJW99NzEh1oyNP7mTKJPi7m8EYS5nSVUB+XXz3dztx2pMDCTxskgvMpU9OkN7ouIRkgsoS+Tg7XdqLU5AR5PUHsO+EXFmQ0kGGTnkZoaOSyPIxAIC3ZsGQlQsg+gqTMdfhOXz+ptW4cuNcOdD46368tudsXOWrXn8A//3obgyN+jF/Tg6+cPPqiAIVZTfjhXeb4EtQYlV4Uy6hvx2BgU6AN8I2b2VCnn8q/cNefPs3OzA46kNleQHMJdXyOtumn2OSqijxk2SCdKswoSAj6GB9D/yCiOI8G+aVp+6bkF55GZFMX51MqMIkuuTP8Xiew503rsLVm6rBGPCLvx3AqRNybkS05auMMTzwt4NobBtCjsOMe2/fEPHuzKblZSjOs2HY5cebwR0VvYVPZFW7fM5dCt5iS8jzT2bUI+A7v92Brn43ygqy8N1Pb4S9ItgvI6wpVzpRczKo2ydJY1VpVmFCQUZQqAFXWcqfN+uRl9ERZdKnIpYZJlPheQ533LgSWzbLgUb98WCQEWX56r/eaMBbB9tg4Dncc9t6FOdFns9hMPC4VmnO9dZpMKZ/c67QTsYZuE8lv3TVJ4j4wR92oaljGHlOC77/mU3Ic1rHDEtLR5STQTJBbppVmFCQAXlY1u6jXQBS+6hEoUdehrKTURbFcQkQ+wyTqXAch89+YCWuOXce8ng58DkeRX+svce78MfnjwEAPn3DCjUgi8blG+bCZjGgpSsxzbnUMtaes/C2yD0okjXaHQAeeeYIjjb2IctqxPc+vUlNgrYqTbk6G8HEmVvCpxrKySCZIN0qTCjIAHCsqR8jbj+cdjOWzstP9nJmpHVeBmMsrKV4rMclHXGvQ8FxHD59wwpUZMnD2v6+sw8vRJCI2dYzip8+vheMAVdunIurN1XH9PxZNhMuDzbn+vc2/ctZDdmF4G1OQBIBSYQpv0zdIUq0Uy0DeHFHEwDg7tvWj5n0aMwrA29zgIkCfF3NSVlfrJgoqK3baSeDpLt0qjChIAOho5INy0pgMKT+LdE6L2No1A+3NwCOA8qiLN1Vj0v628GYFPdaQhiyRDlKH5Cy8Ot/voet26cONNxeAT/4wy64vAEsqc7HZ26Ir8xSac51oL4HzR36/kPmOE7dzQAAW5KOSiSJ4aF/vgfGgIvWVKBu3ARijuNgKU/PvAzRHfyNj+PB21KzPJ2QSFWVpM801tT/iaozxlhalK6Op2VehnJUUphrg9lkiOprTbnFAG8AC/ghDmt3fCO6hoNb8hwuOn8FAHnWyXPvTJxYqjTzau0eRUGOFd/46HqYjPG9tEsLsrBxhfx6SERzrvCmW8k6KnlldzNOtQzCbjXi49cum/QxypGJMiwtXYSOSpzguFn/tkfSnBJkpEMZ66z/13amfRjd/W6YTQasro1/CFeiaJmXEW2nz3CcwQhTntzgKp4y1vGUHhkGZx4+du0KtcfFb546jGfG/dD/y0snsOdYF8xGHvfevgF52VZN1vD+C+Ry1jf3t2JwxKfJNaei7GRwZitsVUt1fa7JDI368Metci7LLVcunvIehpI/020nQy5fpW6fJBOkU4XJrA8ylKOSNYuKIi5zTAVa5mWo5atRJn0qYp1hMp3w6ascx+Gj71uKmy6Vf4v+3b+P4OlgrsQ7h9rw91flH3if/4/VWFiZp9kallTno7YqF0JAiignJB72mrWwL1yP/ItuAWcwzfwFGvvzC8cx4hZQXZaN9507dStzS7n8dxAY6FR3B9KBREmfJIPkOi1w2uUKk9bu1K4woSAjrHQ1nWiZlxFr0qciNMNEu+TP8dNXOY7DR65egv+4TP5N+pFnjuC3Tx/G/f93AIDcROvitdpOBOU4Tt3NeP7dJl1/Y+DNVpT+xz3IWb9Ft+eYysnmfry8S07k/OwHVk6bl2SwZqkVRenUlIsqS0gm4TgOlUpeRoofmczqIKOzz4Uz7cPgOWD90tQbiDYTrfIyOmLo9hkuFGRouZMxsdsnx3G49arF+NDliwAAz77dCJ9fRF1tET72Pn2OGDavLEdhrg2Doz5s29868xekGVFieOhfcrLnJesqsWx+wYxfk45HJqKLemSQzJIuFSazOsjYdVRO+Fw2vxDZWeYkryZ6WuRlyOWrSo+MGHcyNGzIpQgMKcclY/tccByHW65ajP+8Qg40ygqz8F8fWadbVZDRwOPa8+Tjg0Q150qkl3Y24XTrELKsRnzsmsgCNWt5+iV/0gRWkmnSpcIkfZIQdKAelaxIv10MYGxehscXgM0S/V/n4IgPXr8InkPMk2fNwe3zwFAvJMEH3mSJ6TrhRHU42uTJuB++cjE2rihDSb4ddqu+OQxXbKzGX18+iebOERys75lQ2pmuhkZ9+NPzcvOvW69egjxnZAmz6k5G+ykwJqVFtQbNLSGZRkn+pOOSFDU06sOxRnkHYOOy9MrHUGiRl6HkYxTl2WMu++Tt2eCtWQCYZnkZkx2XjDevPEf3AAMAHDYTLttQBSAx5ayJ8setx+DyCJhfnhNV4zJzcRU4kwXM59Z090pPlPhJMo2yk9HV50rYMMdYzNogY8+xTkgMmD8nB8X5kc+2SDXL1SOT2PIy2nuUypLYGxRxHDemKVe8JMEH0SX/5hnt3BK9XHf+AnAcsO9Ed1rUps/k+Jl+vLL7LADgjhunT/Ycj+MNsJTJCbHp0pRLTfzMopwMkhnCK0zaUrjCZNYGGenYgGsySl7G4YYYgwy1siS2pE+FOsNEg99sxRF5h4kzWcFb41uXVsoKs3DOMvlYLd13M0RRwsP/eg8AcPmGKiyujr6Vfrolf1J1Cck08gyT1E/+nJVBhtcXwIHg4KuNy9MzH0Oh5GWcahmEJ4Z+GaEeGfG1Wjbly8GaFjsZavlqdkFKTcS9/kK5Idgbe1swNKpvcy49vbCjCY3tQ8iymfDRGKty1OTPNChjZZIIySO/zqm6hGSSdChjnZVBxoH6bvgDEkry7aguS+/fbEry7SjOt8ecl6F2+0yhnQw1HyNFjkoUS+flo6YiB/6ApA4RSzcDI148/oKc7HnbliXIccSWpKvsZPh7zkLyezRbnx7kuSUMAAfelho7Y4RoIR0qTGZlkBF+VJJKvynHasUCubdBtHkZjDF09MXeUjycWZ3G2hZ3mWdoJyO12rxzHIf3B3czntt+BkIgdZOtpvLYc8fg8gZQU5GDKzdWx3wdozMfBmcBwCT4OibOk0klStInb3OA46ObzUNIKkuHCpNZF2SIooTdwf4Ym1akdz6GIta8jP5hL3x+ETzPxZ38aswrBcBB8rnVpM1YhVqKp9ZOBgCct6ocBTlWDI748NYB7ZqPJcLRxj68vrcFHAfcceMqGPj4AmzrnPSYyErlqyRThc8wSdUKk1kXZBw904dRj4DsLHNMCW+pSKkwiTYvQzkqKcm3wxhnMyveZFF7WsSbl5GqxyWA3JxLme3x9Lb0ac4Vnux5xTlzUVsV/4yXdEn+pKRPkqlyHXKFCUvhCpNZF2TsOCz3cThnWWncv8mliljzMrRK+lSo7cXjzMsIHZekXpABAFdtqobFbEBTxzDei7GqJ9Ge234GTR3DcNpNuG2LNi3Y1bHvbfUpHWxRt0+SqdKhwmRWBRmMsYwpXR0vlrwMZScj1nbi44V6ZcR+jMAYC9vJSK2cDIXTbsal6+RhbMo02FTWP+zFX148AQD46PuWatZC31w6H+ANEF2DaofWVMIYg6fpMEYPbwNAlSUkM1WleIXJrAoyTrcNoXfQA4vZgFW1qfkDLFax5GXEO+J9PC12MiT3MFjADwAwOmce1pUs110gN+fae7xLDdZS1aPPHoXHF0BtVS4u3zBXs+vyJgvMxfL1vCk0x0TyezG8/2W0/u4r6PjLd9UZK7a5y5K8MkK0V5niFSazanaJMqtkzaJiWEyZlWU+Pi8jkjkm8Y54H0+LIEPZxTBk5YIz6t8yPFZzihxYv6QUu4914vkdZ3HuwmSvaHKHG3rx5v5WcJw8xp3X+IjQOqcW/s5G+Nrq4ViyWdNrR0sY7MbwvhcxcvA1SF45gOZMVjhXXoTsdVfDXFiR1PURoodUrzCZVUHGrgw9KgFCeRnd/W4cb+rHmhmGeEkSQ2dvfCPex1MGpQmDXWBiAJwh+pdXqh+VhLv+wgXYfawT2w62o66qJNnLmSAgSngomOx51aZqLKyMP9lzPMuchcC+F5OW/MkYg7f5CIb2PA/3qb0AkwAAxtwS5KzfAsfKi2GwahNEE5KKxleYpNov0LMmyOjodaGpYxg8z2HD0tT7gaCFFQsK8Fq/G0dO984YZPQOeeAPSDDwHIrzbJo8v8GZLw/OEnwQBrvV3hnRiGQwWqpYvqAA88tz0Ng+hL0NLqxdnewVjfXs241o6RpBdpYZH7l6iS7PYSkPNuXqPAMmCuAMidl9kgQfRo+8haE9z0PoOat+3DZvFbLXb4F9QR31xCCzglxhYsaI24/WrhEsqMhN9pLGmDU5GbuOykclKxYUwGHXJvEt1USTl9ERzCMoLbBHNRxrOhzHw5QXbC/eF1vyp1pZkoLlq+PJzbnkQWG760cRCEhJXlFI35AHf31ZTvb82PuWwqnTa96UXwbe6gAL+OHvatblOcIJQ93oe+1POPvLT6P3+Ych9JwFZ7Iie82VqPj0/Sj7z28ja+E6CjDIrCFXmMi7Gak4vHHW7GQopauZeFSiiCYvQ0n6LNPoqERhKiiHv7sp5l4ZqdyIazLnr56Dx547goERP750/3bMm5ODuaXZmFvqRFVpNiqKHTAnYfvykWeOwuMTsXhuHi5dX6Xb83AcB0v5QngaD8DbfgqW8hrtn4Qx+FqOY/jwa3DX7wk7EilG9rotcK66hI5EyKxWVeLE0ca+lMzLmBVBxuCIT+0fcc6yzA0yosnL0DrpUxFv8meqthSfisnI4wMXzccjz55A75AXvUNe7DnWpX6e5+RArqrUKQcfZU5UlThRXuSIuwHaVA7V9+Dtg23gdUr2HM86pxaexgNy5891V2t6bd+Zg3Bu/xMGRnvUj9nmrUT2ui2w16yhHQtCEJb8mYIVJrMiyNh9rBOMATUVOSjSKP8gVUWal6EORtN8J0MZlBbjcUkKd/ucyhUbKlFgHoItdw66Bvxo7hzG2c4RnO0cxohbQFvPKNp6RtXdNAAwGjhUFMsBR1WZE1UlcgCS67DAbDLAwHMxzdURAhIefkpO9tyyeV5CzmctwaZcWid/+jpOY+DZX8AoBsAZzXCsvAg567bAXFSp6fMQku5SuYx1VgQZSulqJh+VKFYsKMRre1pmzMvQutunwqw25OqY4ZETsYAAcXQAQPoclyjsFgOWVOdh3dLQDBjGGAZGfDjbOYzmzhGc7RwJBiDD8PhENHUMo6ljGDg48Xo8B5hMBpiNBphNPMxGA0wmHmaTAWbjJH82GWA2GdA76EFr9yhyHGbcolOy53iW4Nj3wEAnRPcIDHZn3NeUvC50/etngBiAv2ghKj74VTjy02N3i5BEUytM+lOvwiTjgwyPL4CD9fJW62wIMiLJy5Akhs4+N4D4R7yPpxyXiK5BSF4X+CjOygMjfQAAzmjOiBbQHMchP9uK/GwrVteGdpUYY+gZ9MhBR8cwznbJwUdL5wj8weRRiQE+vwifP7ahR7dfswwOW2IqPQw2B0wF5RD62uFrr4e9Zm1c12OMoef5hxAY7IIhuxDulddE9ToiZLZJ5QqTjA8y9p/shhCQUFaQpUZ7mSySvIzeIS8CogSjgUdhrrbHR7zFDkNWLkTXIPx97ep8i0iEl6/GclSQLjiOQ3GeHcV5dqxbEiqnZoxBCEjwByQIggifIMp/FkT4BQn+gPxnnyBCEMSwx0kQAvKf/YKIojwbLlmX2CMFS3kthL52eNtOxR1kDO97Ca7jOwDeiJz3fR69A36NVklIZlIqTJTkTwoyEkg9KllRltE/uMIpeRmHGybPy+gI7mKUFdp1GRJnKpgD0TUIob8tuiAjjcpX9cBxnHrsgQTtQmjFOmchRg+/CV97fHkZvs5G9L36KAAg/5JbYS5dAAwc12CFhGQ2pcIk1cpYM7pPRkCU1Ez/jctLk7yaxFH7ZUwxLE09KtE46VMRa4VJOjXiImMpTbl8bafAWGz9QiSfW83DsC9cj5wN12i5REIyWqpWmGR0kHHkdC9cHgG5DgsWzc1P9nISZnxexnihnQx9zrnVICPKXhmBIaVHBiX4pRtzcRU4oxmSzx1T+bKch/EwAgOdMGYXoujaz82anUdCtEBBRhIoY903LCvV5VggVSl5GZLEcPxM/4TP65X0qTDnK2WsMe5kzNLjknTGGYywlMndT30xlLKOHHgFrmPbAd6A4g98FQZb5udPEaIlpYy1s98Fr3/iL5fJkrFBBmMsrHR19hyVKFYskMekT3ZkEjou0WsnI9havL8jqq1zOi5Jb2q/jCjHvvs6z6Dv5T8AAPIvvhXWObWar42QTKdUmDAGtHWPJns5qowNMhpaB9E35IXVbMCqhbNv+31lzeR5GaLE0D3gAaBfToYxtwTgjWABvxo4zIQxNusTP9OdZU4oLyNSks+D7qd+BiYKsNesRc451+q1PEIyWvgMk1RqL56xQYZyVLJ2cUlSZkck2/L5k+dlDLpEiBKD2cijIMeqy3NzvAGmPLk0M9IjE8k7CiZ4AQAGZ4Eu6yL6sioTWbubIfm9Mz6eMYaeFx6G0N8BQ3Yhiq79AuVhEBKHVMzLyNggQx2ItiLzG3BNpjjfjpJJ8jL6RwQActKnnjMtoq0wUXYxDFk54E0W3dZF9GPMLoDBmQ8wCb7O0zM+fuTgq3AdfQfgeJTc8BVNOoUSMptVpWB78YwMMtp6RtHSNQIDz41pdjTbTFbK2jci72rolfSpUGeYRFhhohyrGJx0VJLOrBEemfi6msLyMG6BtWKx7msjJNOl4sj3jAwydgUTPlfUFCastXIqWlEzMfmzf0RuU61X0qfClB/tTkawfJXyMdKaMsdkumFpkj+YhxHww7ZgDXI2Xpeo5RGS0apK5HEMqVRhkpFBhpLYeN6qOUleSXJNlpcROi7RdyfDrB6XRDaNlSpLMkMo+bMejLEJn2eMofeF30Loa4fBmY/i674AjsvItyFCEi7HYVYrTFpTpMIkI/9133r1Evy/j23A5Ruqkr2UpJosLyN0XJKYnYzAcC8kwTfj46lHRmawlC0AOB7i6ADE4MC7cCOHXsPokbeCeRh3wZABg/AISRXhFSapcmSSkUGGw2bCphVluiY2povwvIyAKGHQlZjjEt6eDd4q75ZEMvZdLV+lbp9pjTdZYC6eCwDwjsvL8Hc3o++lRwAA+Rd9GNbKxIyiJ2Q2SbUKk4wMMkhIeF5G94AHjAEWswH52fqUryo4jouqvXhgWGkpTjsZ6c4admSikPwedP0rmIcxvw45m65P0uoIyWxzU6zChIKMDBeel9HUIb/oSvNtCelHEGkZKxMDEEcHAdBxSSZQOn/6gp0/GWPoffF3EPraYHBQHgYheqpUG3INJ3klMvqXnuHC8zK2HZB/2JcW2BPy3KEKk+mTPwOj/QCTAIMRhqycRCyN6EhN/uw4DSYGMPreGxg9vA3geBTf8GX6OyZER0qFSVe/OyUqTCjImAWUvIxDDXIiXlmigowIdzJC+RiF9BtuBjDll4G3ZoEF/Bg9+g56X/wdACDvgpthq1qW5NURktlynRZkZ6VOhUnM7+hnzpxBXV0d/vWvf2m5HqIDJS9DqShM1E6GMo3V398+aTmjgspXMwvH8Wq/jJ6tv5bzMOatQu65H0jyygiZHSpTKC8jpiBDEAR87Wtfg9vt1no9RAdKXoYiUTsZxvxSAByYzw3RNTjl42gwWuZRjkwgiTA48lD8/i/RLhUhCZJKZawx/at/4IEH4HDo28yJaEfJy1AkaieDN5phzJVLUqerMKHKksyjjmvneBRfT3kYhCRSKlWYGKP9gj179uBvf/sbnn76aVx00UVxL4AxNmZHxOPxjPl/oo0l1bno6nfDbORg5sWE7ULxOSXAYDdcHU1ghfMmfYx/oAsAIFlz0nJ3jF6zE7GShbCv3QJTSTVY0fyY/17p3uqH7q1+kn1vi/PMAIDmjiHd3lMZYxFVKUYVZAwPD+PrX/86vvnNb6KsTJvppoIg4Pjx4xM+3tTUpMn1iSzPIr/YC7ONaG5uTtjz2mCBFUB3w1F4jKWTPia7px0GAG2DLgQmeS2kC3rNjlO0GpAAaPB3SvdWP3Rv9ZOse+v2yk0XuwY8OHT4KMxGfY4qzWbzjI+JKsj47ne/i7q6Olx77bUxL2o8k8mEmpoa9c8ejwdNTU2orq6GzWbT7Hlmu9paCcx4CgVWT0LvrdvfhuHmvcjhfKheMnmHx67XR8EAzFtWB2Ow7DWd0GtWP3Rv9UP3Vj+pcG+dL/ZixC3AWVCJ+eXat+9vaGiI6HERBxlPP/009u7di2effTbmRU2G4zjY7RNzBGw226QfJ7H78BWLcPz48YTeW660GsMApKGuSZ9T8rrA/PIui6O4ArxZ306keqLXrH7o3uqH7q1+knlv55Zl48jpPnQPClheo/0aIm3oGPEeyj//+U/09fXhoosuQl1dHerq6gAA3/nOd/DJT34ytlWSjGcqkMtYhYEuMHFiYxilfJW3OdM6wCCEkFQSKmNNbufPiHcyfvrTn8Lr9Y752BVXXIEvfvGLuO666zRfGMkMBmc+OJMFTPBBGOyCORh0KMIbcRFCCNGGUmHS0pXchlwRBxklJSWTfrygoGDKzxHCcRxM+eXwd52B0Nc+MchQylepRwYhhGimqlTOw0j2DBPqjkN0F2ovPnGGSajbJ414J4QQrSgNuZI9wyTqPhnhTp48qdU6SAYLDUqb2JCLun0SQoj2chzyDJNhlx+tXaOoqcxNyjpoJ4PoTjkimazrJ80tIYQQfSydlw8AYJh6dpTe4trJICQS6nHJZEHGkJKTQcclhBCipS99aA1u7nOhpiI3aWugnQyiO1O+3B1WdA1B9LrUjzNJRGCkHwDtZBBCiNYcNlNSAwyAggySALzFDoMjD8DY5E9xdABgEsAbYXDkJml1hBBC9EJBBkmIUIVJ6Mgk1CMjn8aAE0JIBqJ3dpIQpvxg8mfYTgaNeCeEkMxGQQZJiFDyZ4f6Mer2SQghmY2CDJIQZjXICN/JoCCDEEIyGQUZJCHUhlz9nWBMAkDlq4QQkukoyCAJYcwtBngjWMCvHpMEhvvkz9FOBiGEZCQKMkhCcLwBpvxSAKHkT/W4hFqKE0JIRqIggyRM6MikA5LPA8krjyCmnQxCCMlMFGSQhAmfxqrsYvDWLPAWezKXRQghRCcUZJCECe1ktFNlCSGEzAIUZJCEUaax+vvaQ5UlFGQQQkjGoiCDJIxyXCIO98IfTP6k8lVCCMlcFGSQhDHYs8HbHAAAb9N7AGgngxBCMhkFGSShlBkm/u6zAKh8lRBCMhkFGSShlCMThTGbjksIISRTUZBBEkqpMFHQTgYhhGQuCjJIQpnDdzI4HgZHXvIWQwghRFcUZJCECj8uMTrzwfGGJK6GEEKInijIIAllyisDOPllR+WrhBCS2SjIIAnFGU1qcEHlq4QQktkoyCAJpyR/UtInIYRkNgoySMJlLdkIzmyDbf7qZC+FEEKIjozJXgCZfbJXXwbnqkvAcRTjEkJIJqN3eZIUFGAQQkjmo3d6QgghhOiCggxCCCGE6IKCDEIIIYTogoIMQgghhOiCggxCCCGE6IKCDEIIIYTogoIMQgghhOiCggxCCCGE6IKCDEIIIYTogoIMQgghhOiCggxCCCGE6IKCDEIIIYTogoIMQgghhOiCY4yxZD35/v37wRiD2WxWP8YYgyAIMJlM4DguWUvLSHRv9UH3VT90b/VD91Y/s+He+v1+cByHNWvWTPs4Y4LWM6nJbj7HcWOCDqIdurf6oPuqH7q3+qF7q5/ZcG85josogErqTgYhhBBCMhflZBBCCCFEFxRkEEIIIUQXFGQQQgghRBcUZBBCCCFEFxRkEEIIIUQXFGQQQgghRBcUZBBCCCFEFxRkEEIIIUQXFGQQQgghRBcUZBBCCCFEFxRkEEIIIUQXFGQQQgghRBcJCTJ+85vf4CMf+ciYj7399tu48cYbUVdXh2uvvRbPPffcmM/39PTgrrvuwsaNG7F582bcd999cLvdYx5zxRVXYNGiRWP+d8899+j+/STT4OAgvv3tb+OCCy7AmjVr8OEPfxh79+5VP79jxw584AMfwKpVq3DVVVdh69atY77e5/Phe9/7HjZt2oS6ujp89atfRX9//5jHzHSNTJWIe3v77bdPeM2O/7eRieK9t+G+/e1vT/rvfDa+bhNxX+k1G9u97ejowF133YVzzz0X69evxyc+8QmcOnVqzGNeeOEFbNmyBStXrsT111+PHTt2JOR7Syims8cff5wtXryY3XrrrerH9u7dyxYtWsS+//3vs4aGBvbcc8+xuro69tRTTzHGGPP7/eyaa65h11xzDduzZw87cuQIu/nmm9lHP/pR9Roul4stXryYvfHGG6y7u1v93/DwsN7fUlLdfvvt6n1pbGxk3/ve99jKlSvZ6dOnWUNDA1uxYgX7+c9/zhoaGtjvf/97tnTpUvbuu++qX3/PPfewyy67jO3Zs4cdOnSIXX/99eyWW25RPx/JNTKV3veWMcY2bdrEnnjiiTGv2YGBgQR/p4kX771ljDFRFNnPfvYzVltby+6+++4xn5utr1u97ytj9JqN5d76fD52zTXXsFtvvZW99957rL6+nn3hC19gmzZtYn19fYwxxnbs2MGWLVvG/vjHP7KGhgb2ox/9iC1fvpw1NDQk89vWnG5BRmdnJ/vMZz7DVq9eza666qoxQcYdd9zBbrrppjGP//Wvf80uvvhixhhjr7zyCqutrR1zszs6OtiiRYvYrl27GGOMHTp0iNXW1rLBwUG9voWU09TUxGpra9nevXvVj0mSxC677DJ2//33s29961vsgx/84Jivueuuu9jHP/5xxpj8d7J48WL25ptvqp9vbGxktbW1bP/+/YwxNuM1MlUi7m1vby+rra1lR48eTcB3lDrivbeMyUHEzTffzDZu3MguuuiiCT8MZ+PrNhH3lV6zsd3b7du3s9raWtbZ2al+3uv1slWrVrEnn3ySMcbYxz/+cfalL31pzDVuvvlm9q1vfUun7yo5dDsuOXr0KEwmE5555hmsWrVqzOeam5uxdu3aMR9bunQp2tra0N7ejqamJuTl5WHBggXq50tLS5GXl4fdu3cDAE6ePInCwkLk5OTo9S2knLy8PPz2t7/FihUr1I9xHAeO4zA8PIy9e/di06ZNY75m48aN2LdvHxhj2Ldvn/oxxbx581BSUoI9e/YAwIzXyFSJuLcnT54Ex3GYN29eAr6j1BHvvQWAnTt3YsGCBXjuuedQUVEx4Tlm4+s2EfeVXrOx3duFCxfit7/9LUpKStTP87z843Z4eBiSJGH//v0TrnHOOeeo7xeZQrcg45JLLsEDDzyAysrKCZ8rLi5GR0fHmI+1trYCAPr6+lBcXIyRkRGMjo6qnx8dHcXQ0JB6xn3y5EnY7XZ88YtfxHnnnYdrr70Wjz32GCRJ0utbSrrs7GxceOGFMJvN6sdeeuklNDc34/zzz0dnZydKS0vHfE1xcTE8Hg8GBgbQ1dWFvLw8WCyWCY/p7OwEgBmvkakScW/r6+vhdDrx/e9/HxdccAGuuuoq3H///fD7/fp/g0kU770FgFtuuQX//d//jYKCgkmfYza+bhNxX+k1G9u9LSoqwoUXXjjm83/+85/h9Xpx7rnnYnh4GG63e9JrKO8XmSIp1SXvf//78fLLL+OZZ55BIBDA8ePH8Yc//AEAIAgCLrjgAjidTtx7770YHBzE6OgovvOd74DjOAiCAAA4deoUhoeHceWVV+KRRx7Bhz/8YfziF7/AAw88kIxvKSn279+Pb3zjG7jiiitw0UUXwev1jvlHAUD9s9/vh8fjmfB5ALBYLPD5fAAw4zVmCz3ubX19PXw+H1auXInf//73uOOOO/Dkk0/im9/8pv7fUAqJ9t5Ggl63+txXes3K4r23r7zyCn72s5/hYx/7GBYtWgSv1zvmaxTh7xeZwpiMJ73++uvR1taGb33rW7j77rtRVlaGT33qU/jud78Lp9OJ3NxcPPTQQ7jnnnuwceNGWK1W3HrrrVi2bBkcDgcA4He/+x18Ph+cTicAYNGiRRgdHcVDDz2EL3zhC+rWVKZ69dVX8bWvfQ1r1qzBT3/6UwDyC3T8C1z5s81mg9VqnfQfgM/ng81mi+gas4Fe9/b73/8+7r77bvWIr7a2FiaTCV/5ylfw9a9/HYWFhXp+Wykhlnsbidn+utXrvtJrNv57+9e//hX33XcfrrvuOnz9619Xvz78axTh7xeZImk/iT/3uc9h//79ePPNN/Hqq6+ivLwcBoMB5eXlAIC6ujq89NJL2L59O3bu3Imvfe1raGlpQVVVFQA5AlQCDEVtbS3cbjeGhoYS/v0k0uOPP44vfOELuPjii/Hwww+rL9iysjJ0d3ePeWx3dzfsdjucTidKS0sxODg44YXd3d2tnh3OdI1Mp+e9NRqNE3KIFi5cCAAZt0U6mVjvbSRm8+tWz/tKr9n47u1PfvITfPe738Vtt92GH/7wh+ovv7m5ubDb7ZNeIzyPIxMkJch4/PHHcd9998FgMKCkpAQ8z+Oll15CXV0dsrKy0NzcjA996EPo7+9HQUEBrFYr9u7di8HBQWzevBmMMVx22WV48MEHx1z38OHDKCoqQl5eXjK+rYR44okncN999+GWW27Bz3/+8zHbbevWrVMTYxU7d+7EmjVrwPM81q5dC0mS1CRFADhz5gy6urqwfv36iK6RyfS+tx/5yEfwjW98Y8w1Dh8+DJPJhOrqav2+sRQQz72NxGx93ep9X+k1G/u9/clPfoLf//73uPvuu3HPPfeA4zj1sRzHYc2aNROusWvXLqxbt07H7yoJElHCcvfdd48pYX333XfZ0qVL2VNPPcVaWlrYb37zG7Zs2TK1PFUQBHbJJZewL3/5y+zMmTNs586d7IILLmDf/OY31Wv86Ec/YqtXr2Zbt25lzc3N7P/+7//YypUr2d/+9rdEfEtJ0djYyJYtW8Y+97nPjalZV/qD1NfXs2XLlrGf/OQnrKGhgT3yyCMT6uLvuusudskll7CdO3eqvRzC/24iuUYmSsS9/fOf/8yWLFnCnnjiCXb27Fm2detWds4557Cf//znyfiWE0aLexvu1ltvnVBqORtft4m4r/Saje3e7ty5k9XW1rL77rtvwtePjo4yxhh7++232ZIlS9gf/vAH1tDQwH784x+zlStXUp+MWIwPMhhj7Mknn2SXX345W7lyJfvABz7A3nrrrTGfb2hoYB/96EfZ6tWr2QUXXMB+/vOfM0EQ1M8LgsAefPBBdumll7Jly5axK6+8MqMDDMYYe+ihh1htbe2k/1PeHLZt28auueYatnz5cnbVVVexrVu3jrmGy+Vi9957L1u3bh1bt24du+uuu1h/f/+Yx8x0jUyUqHv7+OOPs6uvvpotX76cXXzxxeyhhx5ioigm7PtMBi3ubbjJfhhGe41MkKj7Sq/Z6O/tN7/5zSm//pe//KX6uKeeeopdfvnlbMWKFeyGG27IyKCYYyxDi8gJIYQQklSZe1hJCCGEkKSiIIMQQgghuqAggxBCCCG6oCCDEEIIIbqgIIMQQgghuqAggxBCCCG6oCCDEEIIIbqgIIMQQgghuqAggxBCCCG6oCCDEEIIIbqgIIMQQgghuvj/ASm4sFNB0g4mAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mins_played[\"revenue\"][29:].plot()\n",
    "mins_played[\"Minutes_played_under21\"][29:].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stampa il grafico della correlazione tra i minuti giocati dai giocatori under 21 e il ranking uefa come retta di regressione\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=mins_played[\"Minutes_played_under21\"], y=mins_played[\"revenue\"])\n",
    "plt.xlabel(\"Minutes_played_under21\")\n",
    "plt.ylabel(\"Revenue\")\n",
    "plt.title(\"Correlazione tra i minuti giocati dai giocatori under 21 e il ranking uefa\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                revenue   R-squared:                       0.146\n",
      "Model:                            OLS   Adj. R-squared:                  0.111\n",
      "Method:                 Least Squares   F-statistic:                     4.112\n",
      "Date:                Thu, 07 Mar 2024   Prob (F-statistic):             0.0538\n",
      "Time:                        20:09:46   Log-Likelihood:                -20.655\n",
      "No. Observations:                  26   AIC:                             45.31\n",
      "Df Residuals:                      24   BIC:                             47.83\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "const                      3.9111      0.590      6.628      0.000       2.693       5.129\n",
      "Minutes_played_under21     0.2054      0.101      2.028      0.054      -0.004       0.415\n",
      "==============================================================================\n",
      "Omnibus:                        0.483   Durbin-Watson:                   1.435\n",
      "Prob(Omnibus):                  0.786   Jarque-Bera (JB):                0.596\n",
      "Skew:                          -0.137   Prob(JB):                        0.742\n",
      "Kurtosis:                       2.311   Cond. No.                         32.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#fai la OLS regression tra i minuti giocati dai giocatori under 21 e il ranking uefa\n",
    "import statsmodels.api as sm\n",
    "\n",
    "X = mins_played[\"Minutes_played_under21\"][29:-1]\n",
    "y = mins_played[\"revenue\"][29:-1]\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stampa il grafico della correlazione tra i minuti giocati dai giocatori under 21 e il ranking uefa come retta di regressione\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x=mins_played[\"Minutes_played_italians\"], y=mins_played[\"Points_naz_u21\"])\n",
    "plt.xlabel(\"Minutes_played_italians\")\n",
    "plt.ylabel(\"Points_naz_u21\")\n",
    "plt.title(\"Correlazione tra i minuti giocati dai giocatori under 21 e il ranking uefa\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         Points_naz_u21   R-squared:                       0.244\n",
      "Model:                            OLS   Adj. R-squared:                  0.217\n",
      "Method:                 Least Squares   F-statistic:                     9.018\n",
      "Date:                Thu, 07 Mar 2024   Prob (F-statistic):            0.00558\n",
      "Time:                        20:08:08   Log-Likelihood:                -28.919\n",
      "No. Observations:                  30   AIC:                             61.84\n",
      "Df Residuals:                      28   BIC:                             64.64\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       1.7491      0.357      4.905      0.000       1.019       2.479\n",
      "Minutes_played_italians     0.3168      0.105      3.003      0.006       0.101       0.533\n",
      "==============================================================================\n",
      "Omnibus:                       26.418   Durbin-Watson:                   1.980\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.807\n",
      "Skew:                          -1.918   Prob(JB):                     3.41e-12\n",
      "Kurtosis:                       8.247   Cond. No.                         10.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#fai la OLS regression tra i minuti giocati dai giocatori under 21 e il ranking uefa\n",
    "import statsmodels.api as sm\n",
    "\n",
    "X = mins_played[\"Minutes_played_italians\"][25:-1]\n",
    "y = mins_played[\"Points_naz_u21\"][25:-1]\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  }
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