Week1/original/SimpsonsParadox/Simpsons_Paradox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simpson's Paradox\n",
    "\n",
    "### Authors: \n",
    "- Christian Michelsen (Niels Bohr Institute)\n",
    "- Troels C. Petersen (Niels Bohr Institute)\n",
    "\n",
    "### Date:    \n",
    "- 05-11-2020 (latest update)\n",
    "\n",
    "***\n",
    "\n",
    "The aim of this notebook is to illustrate Simpson's paradox through a simple, quick example.\n",
    "\n",
    "For more information on the Simpson's Paradox, see: __[Wikipedia](https://en.wikipedia.org/wiki/Simpson%27s_paradox)__\n",
    "\n",
    "This small example is meant to show how aggregating different samples can change the overall statistics completely. In this specific case we will look at how the amount of exercise can change the risk of catching a highly infectioness disease.  \n",
    "\n",
    "***\n",
    "\n",
    "First, we import the modules we want to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the data located in the csv file `Simpsons_paradox.csv` into a Pandas DataFrame and see what it contains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exercise</th>\n",
       "      <th>Age</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.720159</td>\n",
       "      <td>20-40</td>\n",
       "      <td>20.988422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.498937</td>\n",
       "      <td>10-20</td>\n",
       "      <td>22.323348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.520742</td>\n",
       "      <td>10-20</td>\n",
       "      <td>5.875953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.441350</td>\n",
       "      <td>60-80</td>\n",
       "      <td>74.790105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.813937</td>\n",
       "      <td>40-60</td>\n",
       "      <td>50.120903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.362772</td>\n",
       "      <td>40-60</td>\n",
       "      <td>50.933656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.232431</td>\n",
       "      <td>20-40</td>\n",
       "      <td>36.104305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.192118</td>\n",
       "      <td>60-80</td>\n",
       "      <td>64.977362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.174650</td>\n",
       "      <td>60-80</td>\n",
       "      <td>76.653905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.463829</td>\n",
       "      <td>10-20</td>\n",
       "      <td>4.628246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Exercise    Age  Probability\n",
       "0   9.720159  20-40    20.988422\n",
       "1   2.498937  10-20    22.323348\n",
       "2   5.520742  10-20     5.875953\n",
       "3   8.441350  60-80    74.790105\n",
       "4  10.813937  40-60    50.120903\n",
       "5   8.362772  40-60    50.933656\n",
       "6   5.232431  20-40    36.104305\n",
       "7  11.192118  60-80    64.977362\n",
       "8  13.174650  60-80    76.653905\n",
       "9   8.463829  10-20     4.628246"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Simpsons_paradox.csv\", index_col=0) #index_col=0 to make sure that the first columns is used as the index\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a very quick, but illustrating, example of how to work with Pandas DataFrames, see the following __[link](https://jalammar.github.io/gentle-visual-intro-to-data-analysis-python-pandas)__.\n",
    "\n",
    "***\n",
    "\n",
    "To further see any relationships in the data, we plot the hours pr. week of exercise vs. the probability of catching the highly infectious disease. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AgAsoe4yecIwAwSP4AQDABZQ9Rk84RoDgEfwAAOCCKOxzZNsJOXZCKcuSYydU0cfu9LVtdx82dP2dXD+D3aJwjABhx/kGAIALwl72ONdi/NqZI3X3oy/pqb9sybk4nwX8pQn7MQJEAY9nAACuivNMQDqdkZXOKOk4stKZUA1qcy3Gr7vtaY37+gHZr7suzmcBf+nCfIwAUcDMDwDXZDKOHDvBE80YYyYgvPItxh/coVR3++L8ZA+/0/FnAMAk8XkcB8BTtp3Q5vc+Vu2yes2uW6/aZfVqammL1VN/MBMQZvkW4ze3tHX6uuPifL8X8Md5VhGAO7hqAHBFStKiXz7FoDfmKOUbXrkW49fOHKn1z2zOft11cb6fC/jbZxV5wAKgHMxKA+iRbSeUkgqms5H+AunTmYCOx0J2JiBNAGSyXIvx+9iWZp/0ZX33xH/Jee77uYA/36xi3ZzRolA0gGLxuARAQcU+bWX/CkiU8g27rovx23ale1yc79cCfmYVAbiB4AdAQcWu4UhKumLWkQx6Y67jTMCtteNUN2c0xQ7gCh6wAHAD4xIABRWbzpZOZ3TA8M+wfwV2zwTokxtM2lE64PYgGtpnFbtWEkxKHGMAikbwA6CgUtZwJBLW7vQXiUGvgYpZuwW4yc1jjg1CAbiB4AdAQTxtjQb234HfvDjmmFUEUC6CHwAF8bQ1GqiUBb9xzKErZp9hAoIfAD3iaWuw3BgwUIocfuOYQ0eFZgIBP1HtDQAM5tbGjlTKgt845tBRsZVDAa8R/ACAwdwaMLD/DvzGMYeO2KcJpuAaBAAGcyt1yM21W+TtoxisF0RHBSuHAj5i5gcADOZm6lA6ndlditxxZKUzvQ583EjDQzy4ccwhGpgJhCk45gDAYKaVGqeCF4DeYCYQpiD4AQCDmTZgoIIXgN6icihMQJ4CABjOpNQhKnghH9tOyLETSlmWHDtBKiQAI3FlAgAUjbx95MJaMABhwf0KAFA009LwYAbWggEIC08fyWzYsEE1NTU64YQTtGjRIknSpk2bVF1drfHjx2vp0qVevj0AwAMmpeHFjampZezhAiAsPJv5eeutt7RgwQLde++9GjZsmM4880w9/vjjWrBgge644w7ts88+Ovfcc/X4449rzJgxXjUDAIBIaE8t61r5b8iAisAD0IJ7uKQJgACYw7NHRo899pgmTZqk4cOHq6KiQkuXLlX//v11wAEHaL/99lMymVR1dbXWrVvnVRMAAIiMfKllqWCbJYm1YADCw7Pr0ubNm1VRUaHvfe97evfdd/XNb35TBx98sCorK7M/U1VVpS1btnjVBAAAIsPkMuOsBQMQFp5dL9PptJ599lndcccdGjBggM477zz169dPlvXp0kfHcTp9XYxhwwa53dSSVVYODroJ6ID+MAv9EbxMxlFT8045SVsVyYT2GNhXCUpRB67cc6OpeWfO1LJ+fZMaMrhfuc2LHa5VZqE/8stkHH20vVVtqYxv1/Qo94dnwc9ee+2lUaNGaejQoZKkb33rW1q3bp1s287+TENDg6qqqkr6dxsbtykT4ALKysrBamhoDuz90Rn9YRb6I3gmrwuJMzfODdtOaP7Mkd361mlLc96VyIRrlW0nlJKYKZMZ/WGqIK7pYe+PRMIqOFni2ZqfsWPHauPGjfr444+VTqf1hz/8QRMnTtTrr7+uzZs3K51Oa+3atTr22GO9agIAxI7J60JQno6pZbfWjlPdnNGeDIBMrSgXJeyLhGJxTXefZzM/X/nKV3T22Wfr9NNPV1tbm0aPHq3TTjtNn//853X++eertbVVY8aM0cSJE71qAjzGUyvAPCavC0H50umMLH1y8047Srv87zNz6A/2RUKxuKa7z9PP7ZRTTtEpp5zS6XujRo3Sb37zGy/fFj7gBgn0jtcPDSg5jHIwKPcHA1oUi2u6+5hfRa8wDQuUzo9UF0oOoxxsVuqP9gFtR9kBLdAB13T38dmhV3hqBZTOj6fq7etCllx4rHa2pkhJRUl4yuyP9gFt1+yJpOR6KiPCrTdl5L3MMIjCkgfGqegVbpBA6fx6aJBOZ1Q5dKAadjZ7si4k7KJw8/YKg3J/sC8SSlHKWj8vlyVEZckDaW/oFaZhgdKR6hI8qmwV5ldFOXwyoE1nlHQcWekMnzFc4eWyhKgseWCsil7hqRVQOp6qB48F/T3zuqIcAO94mWEQlSUPYWorDMMNEigNDw2CF5WbNwDk4uWyhKgseWCeHwB8RKpLsEg9BBBlXi5LiMqSh7C1F0CMsVAd5SL1EECUeZlhEJXsBYIfAJ5wO1CJSpWZqApLYBqVmzcA5OPlsoQoLHkg+AHgOi8CFRaqmytsgWkUbt4AgN5hzQ8A13lRDjPonedtOyHHTihlWXLsBKWRO4hK+VMA/uh6Pc34dB0HJGZ+AHjAi4paQVaZCdvMht+ooAagWLmup1fMOlJ79EtyPYUveHQJwHVeVNQKssoMMxuFUUENQLFyXU8X/fIprqfwDQ/lALjOi4paQS5UZ2ajsHz93ce2tEsJCgsAyOJ6iqBxnAFwnVeBSlAL1aOysZtXcvV3H9tSY/MuUgUBdML1FEEj7Q3wWFwXykdpM8+obOzmpa79vSvtkCoIoJtc19MrZh0ZyPU0rvfnuOPeDXiIhfLRwN4wpSO1BUAuua6ne+05QI2N23xtB/fn+CLEBTzEQvnoiNJMlh8oggAgn67X00QA1wXuz/FF8AN4KOi9adxAWgB6g1RBACaLwv0ZvcN9CPBQ2Bd2khaA3iJVEIDJwn5/Ru/xCBfwUNiffpMWgHKQKgjAVGG/P6P36GPAQ2F/+s2idQDwh20nlJJCea8Io7Dfn9F7jF8AjwW1N40bSAsAAO+RYhyMMN+f0XukvQHIi7QAoHe6FgrJsIgaBZBiHCwK+8QLYxgAeZEWAJQu11P8K2YdqT36JZVOZ0hvQjekGAeHWbf4IbQFUBCL1oHS5HqKv+iXTymlTwdatcvqNbtuvWqX1auppY0nzTHHvljBYdYtfrjaAoBHSKWIp0JP8RloIRdSjIPDfj/xw3kFAB4glSK+ChUKMTG9iTS84JFiHJwoF/bh3M6Nx5AA4AGe8MdXrqf4V8w6UkmZl95EGp45SDEORlRn3Ti38wt73wKAkUx8wg9/5HqKv9eeA9TYuE1JO6H5M0d2mxFMSoGU2c0XpNfNGS1WmyAOojrrxrmdH/dgAPBAlFMp0LOu+4ckPpnZMW2gRZAORHO/H87t/Jj7AgAPRDWVAuUzKb3JtDQ8AO7g3M6P+zAAeMC0J/xALu1BuilpeDATC+fDh3M7P4IfAPBIFFMpEC0E6dHjdqBC5cpw4tzOj7Q3AAAM5vV+USal4aE8XlT4onJleHFu50bwAwDwBJu8lo9ytSiFF4EKm4Aiarh6wngMoIDOwnBOMGh3B0/dUQovAhUWziNquAvBaAyggM7Cck4waHcHT91RCi8CFSpXImrMulsCXTCAAjoLyznBoN0dbgxmwzBTCHd4Eah0XDh/a+041c0ZTbEDhBqBO4zGJl0IAz/LwIblnIjTJq9e9n+55Wqp1BUvXlX4onIlosSkeyXQTZwGUAgnvweXYTkn4rLHhNf9X+5gNt9MYd2c0WLFRjRFKVBhfyF4gblvGI1cY5jO7zS0sJwTcUmV8aP/yylXS/ohwios6xsRPqbdL4FO2KQLpvM7DS1M50SUnkDnY3oaYlhmCoGumLWEVwifYTw26YLJgigDyzlhDtPLAIdlphDR4GZxDWYt4RWuf3ANubmIo7isbUFupvd/mGYKEW5ur39j1hJesRzHCdUR1Ni4TZkAo/7KysFqaGgO7P1NFVRFIfrDLHHtDxMD/7j2RRCK6X/6wyz0h/scO6HaZfXdgpW6OaNl9XA9zNUfVCoMTtjPj0TC0rBhg/K+zswPXEFuLuIsDmtbkB/9D3ROUztk/yGadtzBGjygQo6kpJ0oOWBh1hJeIfiBK0xf9AsAALzTnqY2ZHA/nTHpi7r+nufLnrHhwQK8QMEDuML0Rb8IDrvLIy441hFn7evfpo//Qjbwkbwv/w+UiofycIXpi34RDHK2EWalrOXiWEfctaepDeiXJBMERuM4hCvIzUUurAVDWJUazHCsA7vv+badoEobjMacPFzD3iPoin0aEFb5gpl8qTsc68Bu7C0F03EswngmlhFGcdinAWFVahEXjnVgNzJBYDpmfmC09tST2mX1ml23XrXL6tXU0hbIQmIWM5eOJ4AIq1KLuHCsA58iEwQmK2qT0w0bNmjs2LGyrOAzl9nkNF562jTNr/5gMXNx8m1Ux8yd/7hWlSffOb/n4D6y0k7OY7jQsU5/mIX+MAv9YZaw90dPm5wW9ej6jjvu0Lhx47Rs2TI1NDS41jigJ6bk0Zea/49P8QQwmqI+E9qeunP1nKN1y2XjdN60w3TT/S/o0hs25p199vpYj/pnDgB+KGpGfvny5XrzzTe1cuVK/eu//qsOO+wwTZ8+XaNGjfK6fYg5U/Lo2cQV+FRcZkLT6YwcO6Ef3bKp0/kfRBW3uHzmAOC1oh8b7b///po3b54uu+wy/fnPf9bFF1+s6upqvfDCC162DzFnSh49m7gCn4rTTCizzwAQLUWNITdv3qyVK1dq9erVOuSQQzR//nyNHTtWf/zjH3XRRRdpw4YNXrcTMWVK1Rg2cQU+FaeZUGafASBairpmnnrqqTr55JP161//Wp/73Oey3z/88MM1cuRIr9oGSPokj16fHKxpJ5Bgw5QgDDCBKQGBH0x58BGnzxxAuIStsFFR1d7WrFmj6urqTt978MEHddJJJ3nWsHyo9oaO6A+z0B/m8LIv4rb+xI0be7n9EbfP3Gtcq8xCf5illP4w8drUU7W3gjM/GzZsUCqV0nXXXad+/fqpPU5KpVK64YYbAgl+AADBittMKLPPQDSEbYYiDPKtR/S7KEwpCgY/L774op588kk1Njbq9ttv//SXkknNnDnT67YBAAxlQkAQN3zmQO+ZOEMRBWFcj1iwXXPnztXcuXO1YsUKfec73/GrTQAAAIBrwjhDEQZhXI9YMPhZvXq1pk6dqtbWVi1fvrzb62eddZZnDQMAAADcEMYZijAwpShMKQr29+bNmyVJr776qi+NAQAAkFifAXeFcYYiDMK4HrFg8HPBBRdIkurq6nxpDACYioEY4B/WZ8BtYZyhCIuwrUcsGPx0LW/d1Zo1a1xtDACYiIEY4C/WZ8BtYZyhgDcKBj8/+tGP/GoHABiLgRjgL9ZnwAthm6EISibjyLETkQ0SC15Dhg0bpgMPPFB/+ctf/GoPABiHgRjgL9ZnIC5MS6m27YQ2v/exFv3yqchmOhS8b1977bW65ZZbdP7553d7zbIsrV+/3rOGAYApGIgB/mJ9BuLAxJTqlJQNfKRoZjoUDH5uueUWSdKGDRt8aQwAmIiBGOAv1mcgDkxMqY5DpkNRf0dLS4tuuukm1dfXq6KiQscee6zOOecc9enTx+v2AUDgGIgB/mN9BqLOxEAjDpkOiWJ+6Cc/+Ynee+89XXLJJbrwwgv16quvatGiRV63DQCMkU5nZKUzSjqOrHSGwAcAUJb2QKOjbKARkKSkK2YdmW1Xx0yHqCjqb/nrX//aqaz1kUceqalTp3rWKAAAACDKTEypTqczOmD4ZyKd6VBU8LPHHnvoww8/1J577ilpdxrc4MGDPW0YAAAAEFWmplQnEtbuTAcpkimnBYOf9tS2ZDKpmpoajR8/XolEQhs2bNBBBx3kSwMBAADixrQSyPAGa9v8VzD4aZ/pOeKII3TEEUdkvz9lyhRvWwXAKNyEd+NzAOAHE0sgA1FRMPj5/ve/n/e1lpYW1xsDwDzchHfjcwDgla4PVtKyjCuBDERFUWt+fve73+n6669XS0uLHMdRJpPRhx9+qOeff97r9gEImIn7EASBzwGAF3I9WPn3c79hXAlkICqKOoeuvfZaXXTRRbrrrrt0zjnn6He/+50GDhzoddsAGMDEfQiCwOcAwAu5Hqy88/62yO+1AgSlqH1++vfvr0mTJmnEiBHq27evrrzySv3+97/3uGkATGDiPgRB4HMA4IVcD1bufvQVzZ85MtJ7rQBBKSr46du3r3bt2qX9958hnhEAACAASURBVN9fL774ohKJhCyLGz7gNdtOyLETSlmWHDsh2y7qlHVV+z4Ecb8J8zkA8EKuBytNzTu15+A+qpszWrfWjlPdnNGsLwRcUtR9+7jjjtPs2bN1zTXX6Nvf/raee+45DRkyxOu2AbFmygJ7U/ch8BufAwAv5Nvo0ko7kd5rBQiK5ThOUcmj77zzjv7pn/5Jf/3rX/XMM89oypQpGjZsmNft66axcZsymeDyXSsrB6uhoTmw90dnUe4Px06odll9t5zvujmjZRk64I5yf4QNfWEW+sMspvVH3Mvom9YfcRf2/kgkLA0bNijv60VnbLz22mu64447lEwmdcwxxwQS+ABxwgJ7AIgHNroE/FPUAoKbb75ZdXV16tevnxKJhH70ox9pxYoVRb3BNddco8suu0yS9OKLL6qmpkYTJkzQ5ZdfrlQq1fuWAxHHAnsAAAB3FRX8rF27VitXrtSFF16oefPmaeXKlbrzzjt7/L0nnnhCq1atyn59ySWX6Mc//rF++9vfynEcrVy5svctByKOBfYAAADuKmoc1bdv3077+uyxxx7q27dvwd/58MMPtXTpUn3ve9/TSy+9pLfffls7d+7UiBEjJEk1NTW6/vrrdfrpp5fRfCC6WGAPAADgroLBz6OPPipJ+ud//mfNmTNHp556qmzb1oMPPqh/+Zd/KfgP//jHP9a8efP07rvvSpK2bt2qysrK7OuVlZXasmVLyQ0utIDJL5WVg4NuAjqgP8xCf5iDvjAL/WEW+sMs9IdZotwfBYOfO+64o9PXy5cvz/5/Y2Nj3t+79957tc8++2jUqFF64IEHJEmZTKbT3kCO4/RqryCqvaEj+sMs9Ic56Auz0B/e6U2lNPrDLPSHWcLeH2VVe+sa/KRSKTmOo4qKioJv+vDDD6uhoUFTp07VRx99pJaWFlmWpYaGhuzPvP/++6qqqirmbwAAAOjGlP3QALfFvfy5l4oqeNDY2Kizzz5bI0aM0GGHHaYZM2YUTFlbvny51q5dq9WrV+uCCy7Qcccdp7q6OvXt21fPPfecJGn16tU69thj3fkrAABA7KSkbOAj7d4OYPFtT4tasgiz9qC+dlm9ZtetV+2yejW1tMm2ixq2owdFfYoLFy7UiBEjtGnTJm3atElHHHGErrzyypLfbMmSJaqrq9PEiRPV0tKiGTNmlPxvAAAQJradUFPzTqUsS46diP0AxrYTcuyEK59Hof3QgLAiqPdWUdXe3njjDV133XXZry+44AJNnjy5qDeoqalRTU2NJOnQQw/Vfffd14tmAgAQPp+mZdUbkZYVdCqN22lq7fuhdQyAsvuhpQmAEE5scu6toh63pFIptba2Zr/esWNHr4oVAAAQJyY9wTUhlcbtz4P90Mzh5oxe3LHJubeKuj5MmjRJM2fOVE1NjSzL0v33368JEyZ43TYAAELNpCe4+QKPujmj5deQyu3Pg/3QzEDhCXe1B/VdP8+kpHTQjYuAoq41c+fO1fDhw/WHP/xBmUxGNTU1OuWUU7xuGwAAoeZ2WlY5aWsmBGJepKml0xlZ+mRAk3YYHAbAhMA6SgjqvVXU9e7MM8/Ur371K02bNs3r9gAAEBluPsEt9+m6CetjeKIdTSYE1lFDUO+doo7J5uZmtbS0aMCAAV63BwCAyGh/grvkwmO1szVV1hPccp+umxB48ES7PEEXrMin1MC6698R5Ob1iJ+igp/+/ftr7NixOuSQQzoFQDfffLNnDQMAIArS6Ywqhw5Uw87msp7glvt03ZTAgyfavWPyuppSAutcf8cVs47UHv2Sgf8diIcer5evvPKKxo0bp6OPPlrDhw/3o00AAKALN9LWCDzCy+R1NaUE1rn+jkW/fMqIvwPxUDD4uf/++3XNNdfogAMO0JtvvqklS5bomGOO8attALowNeUBgPdMSFvrqtA1ieuVu0xfV1NsYG363+ElzgkzFDzO7rjjDq1Zs0Z77723nn/+eS1dupTgBwiIySkPALxnStpau0LXJEm+XK/iNJg0oWCFG6Lyd5SKe7g5etyBau+995YkHX744WpqavK8QQByM2mzRADBSKczstIZJR1HVjoT6KCp0DXJj+uVCZu2+ikqG7rm+juumHVk6P6OUnEPN0fBY82yOmdf2rbtaWMA5Bf2VIE4PaEF4qDQNan9/3O95tb1yuQ1MF4wbeavt3L9HXvtOUCNjduCbpqnwn4Pj5KSPu+uwRAA/4Q5VYDpfiB6Cl6TPvl/L69XcRxMRqVgRde/I5GI/vgyzPfwqCk4N/zyyy/rq1/9ava/9q8PP/xwffWrX/WrjQAU7pQHpvuB6Cl0TfLjetU+mOyoY/AFmCTM9/CosRzHyRtuvv322wV/+bOf/azrDepJY+O2QDfDqqwcrIaG5sDeH53FrT9MTx3L1x8py9LsuvXdvn9r7Tgl81+CUIa4nRumi2p/BFntrZwZ5aj2R1jFpT9Mv4e3C3t/JBKWhg0blPf1ggFnEMENgPzCmvLAdD8QTYWuSV5fr6KyBgbxEdZ7eNREsyQKAKMw3Q/ACyZVvwMQDow9AHiOJ7QAAMAEBD8AfMF0PwAACBppbwAAAABigeAHAAAAQCwQ/AAAAACIBYIfAIAxbDshx04oZVly7IRsm9sUUCrOIyA/Ch4ABgvLhmiIlqCOu3I2rQSwW6nnEfcZxA3BD5BH0DcEBoIIQpDHXUrKvq8kbW3aocW3Pa26OaNlefrOQHSUch5xn0EcMQ8K5NB+Q6hdVq/ZdetVu6xeTS1tvqYO5LuBpXxrAeIoyOMunXGy79tua9MOpTOOD+8OREMp51HY7jOk88ENHDVADibcEBgIIghBHnd2wlLVkP6dvlc1pL/sBPM+QLFKOY/CdJ8x4aEkooEjBsjBhBsCA0EEIcjjLilp/syR2fdvT8EhPxtR5cVMRinnUZjuMyY8lEQ0cE8Bcmi/IXQMgLI3hLQ/AVD7DaxrLnZSUtqXFiAM3F6bFuRxl05nNGRAhermjGbxNSLPq/U2pZxHYbrPFHooyWAWpeB4AXIw4YbAQLC7oItQmKbQ4Km3gj7u0umMLH1yc0o7xg3AALd4WeCj2PMo6PO9FCY8lEQ0EPwAOZhyQ2Ag+CmqEnVXaPBUDo47hFWYHpCYMpMRlvPdhIeSiAaCHyCPsNwQ4oIyyN2ZsDYN/gjToD4oYXtAwkxGaUx5KInwo+ABgFBgoN9dmBYro/eoclWcsC2Ip8BH6dLpjKx0RknHkZXOEPigV7hyAggFBvrdMXiKh7AN6oMStgckHWcybq0dp7o5o42dpQKihHskgFAg37s70kDiwZS1IaYLYxoZ6dWA/7huAggFBvq5MXgyh1frcsI4qA8CD0gAFIPgB/AQi5TdxUDfG12P0z62pV1ph+O2BF4utmdQXxwekAAoBsEPjBX2wCFslYeiJuzHj1+6HqdHfmlvTR9/qOo4bkvi9Z4tDOqLwwMSAD2h4AGMFIXqRixSDk4Ujh+/dD1Ox339gGzgI3HcFsvrxfZUuQIAdzASgJHCGjjYdkKOnVDKsuRIGjK4X6fXTa48ZKJOn6edKDp4CevxE4Sug/bBAypCVTHLFFQjBIBwIO0NRgpjdaNcaW4XTj9ctz/0ol5+s0kSi5RLUU7aYP7jR5JlkTbUQdfF9M0tbSyu7wXW5fSMVFQAJmDmB0YK41PUXLMN1939vKaP/4Ik9mApVTmzN/mOn7cbmkmD66LrXkHrn9ms2ojtHdTbGcRSsGdLYaSiAjBFmO9niLAwPkXNN9vw2crBurV2HE86S1TO7F+u46d9Fq7933FrMXrY5VpM38e2IrO43s/CIyy2z8/LghAAUAqCHxgpjNWN8u/FIVlph8FQicrZ26Tr8ZNIWPrpHc9l0w8l89Mo/dR10N6WVq8G8SamNTHoNkMYU5kBRBPzzTBW2KobdU0fCipdyI8UHz+U+3l2On4kNTXv7PS66WmUYWNqWpPXVdhQnHypqMlENK5X8EdU7m8IFg9cAJeYMFsVpb2F3Pw8w5hGGTamzrCUM4MI9+Q6By+fOVI721K68j+eDP31Ct6L0v0NwbIcxwnV1b+xcZsyAT6xq6wcrIaG5sDeH53RH505dkK1y+q7DfTq5oyW5cPNweT+MDEly0t+90XKsjS7bn23799aO07JAG8zpgyYTD43/NL1HLRtS5fesDGQ6xX9YZZi+iPo+1uchP38SCQsDRs2KO/rzPwAEUJefX4sRveWqTMsJszIYreu5+CujMX1CkXj/ga3kCwJREgYS4QjGkxZ85ZL2NYPxkWUr1e51qawXqU8UT5e4C8T7ksAXMLaFgSFGRaUKqrXq1yplleec5Ta2jK6ivUqvRbV4wX+Y81PicKeBxk19Ed3Qa5toT/MQV+Yxav+CPtatqDa7+X5kWttyoKzj9RN97/AepU8iu2PsB/vYRH2+wdrfoCYYW0LEA+mFHPoragOZHOtTenXJ8l6FRdwf4MbSDgFACCE8pUXTwXbrKKYui+UG3KtTdm5K8V6FcAQ4b/KAAAQQ2HewDXMgVtPchX/2HvoAF1uaEEQIG447yImqmkEAIDOTC0vXowoly3OV/xDFTYFQQADMPMTIVFOIwAAdGZyefGeRL1sca7y6pRcB8wQhmskipQvjaBuzmhF43YCxBMzusglzOXFKVsMICgEPxESVBoBAzPAO2Gv6AVvhbX6VZgDNwDhRj5UhASRRkCqHeCtKC8MR7yRBgYgCIxQIySI/G8GZoC3wlzRCwAA05D2FiFBpBFEuWIPYIIwV/QCAMA0zPxEjN9pBFGv2OM3207IsRNKWZYcO0H6IDyd0eV4AwDEDQ/nUZZyKvZQKKEzFrYjF69mdDneAABxRPCDsvR2YMbAqztKlSMfLyp6cbwBAOKIHAeUrTepdhRK6I6F7fATxxtMQgomAL8w84NAUCihOxa2w08cbzAFmQAA/MSjlV7gCVX5KJTQXRClyhFfHG8wBZkAAPzEfa5EmYzDEyoXlFMoIarY8Rx+4ngzT1yLwJAJAMBPXFdK9NH2Vl8XCUf1ZsjAKzcvFrYD+XC8mSPOqV+kYALwE8FPidpSGd+eUEX9ZsjACwB2S0m669GXdPbUL2vwgAo1t7Tprkdf0uyTvhz56ntkAgDwE8FPiSqSCd+eUFGKtveiOmMGIKIsqfqYA3X9Pc9nA4ALvn244nCxJxMAgJ9YqV+iPQb29W2RMKVoe6d9xqx2Wb1m161X7bJ6NbW0UZgCgLkcKxv4SLuv9dff87zkxCD6Ue+2TACA3mDmp0SJhOXbEyryoHuHGTMAYZPJ5E6pzmQc2QG1CQCiiEfhveDXEypK0fYOM2YAwiaRp/x/grs0ALiKcbTByIPuHWbM4DbWkMFrLPoHAH8Q/BiOimilYxABN0W96iLMwMMuAPAHwU8MRPGpdfvftLWpRbITnf4mBhFwE2vI4BcedgGA9wh+AuJXQOLGU2vTgqdi/iYGEXALu88DABAdLKUMgJ+lmPM9tU4Z2NZilfs3AaWw8yxEtxPM+wAAEDYEPwHwc/BebuUzEwMNqrnBT1RdBAAgOrh/B8DPNJpyK5+ZmPJDNTf4iTVkKJZpKcIAgO6Y+QmAn2k05T61NjHlhyfx8Fv73l59PznuWzOOHDsRaPonzGJiijAAoDvLcZxQPSpvbNymTIDpTZWVg9XQ0FzWv+F36dxynkaaWua3/W+SZUmOwxNWQ7hxfpjK1HMhnyj3RdByXVNTkmqX1Xebka6bM1pWOkN/GIb+MAv9YZaw90ciYWnYsEF5X+dheQD8TqMpp/KZX20tNUBr/5vaT1CquZktCulAlLyGlD8IHtgvaVyKMACgO67JAQlTKWav2xq2J+ooTVT618T1b/Bf/iD4aNYiAkAIkIyMwJlYUQ7uiUr/mrj+Df7LFwTLcliLCAAhwHUZgeOJerRFpX/bC210ncFKSkbP3MJd+apNyhFVAQEgBDwde/z85z/XI488IkkaM2aMLr30Um3atEl1dXVqbW3VCSecoHnz5nnZBIQApaujLSr9S8lrSD0EwT6kCId97RwABM2z4GfTpk3auHGjVq1aJcuydPbZZ2vt2rVasmSJ7rjjDu2zzz4699xz9fjjj2vMmDFeNQMh4McTdQYNwYnSjEmY1urBG0EFwVFZOwcAQfMs+KmsrNRll12mPn36SJIOPPBAvfHGGzrggAO03377SZKqq6u1bt06gp+Y83owwaAhWMyYIGqCCIJNqzbIAyUAYeVZ8HPwwQdn//+NN97QI488on/7t39TZWVl9vtVVVXasmVLSf9uobrdfqmsHBx0E9BBT/3R1LxTi2+r7zZoWHLhsaocOtCPJsYK54c5/OqLTMbRR9tb1ZbKqCKZ0B4D+ypBIYhuyumPrU0teQotWL6fc5mMo83vfaxFv3wq+0DpillH6oDhnwlVv3OtMgv9YZYo94fn641fffVVnXvuubr00ktl27beeOON7GuO48iySrtQRmGTU7inmP5IWVbOQcPO1pQadtKXbuL8MIdffcHManHK7g87kafQguP7OefYiWzgI+2+ni765VPZDV3DgGuVWegPs4S9P3ra5NTTUtfPPfecZs6cqR/84Ac6+eSTNXz4cDU0NGRfb2hoUFVVlZdNAChRDHgoKqXM/WbbCTl2QinLkmMnZNuFb8fta+dMKKVdqIIjAJjOs+vmu+++q7lz52rp0qUaNWqUJOkrX/mKXn/9dW3evFn77ruv1q5dq2nTpnnVBEBStBbcA6aJSilzP/VmtsyktXNRqeAIIJ48uzf94he/UGtrq66++urs96ZPn66rr75a559/vlpbWzVmzBhNnDjRqyYAkswaNJiGRcuQyjsOGAiXrrfFC0ypNsgDJQBhZjmOE6q7E2t+0BH90XterNWgP8xRbF+Uexyw5qc4HfsjZVmaXbe+28/cWjtOyZDcksP+4IRrlVnoD7OEvT8CXfMDwFys1YBU/nHQcWb11tpxqpszmsCnB1FYh5hOZ2SlM0o6jqx0hv4GEBoEP0BMsWgZkjvHAQPh0phUvAAA4oZrLRBTrNWAxHEQBNYhAkBwmPkBYoqnz5A4DoLCbBkABIP7GxBTPH2GxHEAAIgXgh8gxkwpnVuMsFeXMlmYjgMAAMpB8IPQ6TgIbmreKdtOMAiOOMopAwAAN7DmB6HSPgiuXVav2XXr9cPr/ltNLW2ybQ7lKKMsNwAAcAMjRoQKg+B4oiw3AABwA8EPQoVBcDyVuimkbSfk2AmlLEuOnWBmEAAASCL4QchEYWd0lK6UcsxdUyNrl9WTGgkAACRR8CDSolgdq30Q3HXhe1KiQlWElVKOOV9qZN2c0SJEBgAg3gh+Iiqq1bG6DoL79U3KaUuH+m9CcYotx1woNZILHgAA8UYeSERFuTBAx53RhwzuR+CDTkiNBAAA+RD8RBSFARBXpawPAgAA8cJ4IKLan353DICyT7/TBECIrlLWBwEAgHhh5ieiePqNOOuYGmmlMwQ+AABAEjM/kcXTbwBei2JFSQBAtBH8RFix1bEQXgw+EZSoVpQEAEQbaW9ASLGZJ4LUm4qStp2QYyeUsiw5doJjFQDgO+48QEhFuZw5zFdqRUmCdQCACbjrACFFOXMEqdT9lAjWAQAmIPgBQorNPBGkUitKEqwDAExAwQMgpNoHn10XnCclilvAc6VWlGTvMQCACQh+gJCKSzlzKtqZq5SKkgTrAAATEPwAIRb1cuaUU46OnoJ1glwAgB9Y8wOUgdK93mKRfLSk0xlZ6YySjiMrnekU+FAJDgDgB+4sQC+5NWAjgMqPRfLxQJALAPALoyygl9wYsPHEuzAq2sUDQS4AwC+MsBB5Xs2suDFg44l3YaWWU0Y4EeQCAPzCGAKR5uWCeTdK9xYKoDg541PRLu6oBAcA8AszP4g0L2dW3JiV4Il3z/Itkkd0dAxyb60dp7o5o6noBwDwBA+XQ4IysL3j5cyKG7MSPPFGnBS6jkW9bDsAwAwEPyHAXie95/Wu8uUO2EjrQlxwHQMAmIC0txBgUXzvhWHBPGldiIKeCotwHQMAmMCkMSDyYFF87zGzAnivmFkdrmMAABMw8xMCcVoU70VZamZW4DY2pu2smFmdOF3HAADmivcdOyTCkLrlBjb8RBhwnHZXzJ5XcbmOAQDMxn0nBOKSupXv6XHdnNHi2TBMwXHaXTGFReJyHQMAmC2+jypDJg6pW8U8PY470q2CF4bj1LYTamreqbRlybFtOcmEp8dLsbM6cbiOBYVrAwAUh5kfGMPrstRh15tSwewP5T7Tj9NPj5P67HFywbcP15o/vKbTxh/qSWlpZnWCRRlxACgej4ZgDNYEFFZqqWDWpnjD9OM013Fy/T3Pa9zXD/CktHT7jEPrJzNffRMWszo+o4w4ABTPlPs1wNPjHpRaKpi1Kd4w/TjNd5wMHlDhemlpZhzMQBlxACgej4BhFNYE5FdqqeAwrE0JK5OP03zHSXNLm+ulpZlxMANlxAGgeAQ/QEiUmm7FgCiech0nF3z7cK1/ZrPr6XkE2GYwPRUTAEzCtREIiVLTrdoHRF1TkpKS0n42HL5qP06WXHisWlvTSiQsyXI0+6Qvu56eZ3rxh7gwPRUTAExC8AOESDqdkaVPTty0UzCIYUAUX+l0RpVDB6phZ3OnSNftoJcA2xylXBsAIM4IfoAIY0AELxFgAwDChuDHAOzFAiCsCLABAGFC8BMwSsUCAAAA/qDaW8AoFQsAAAD4g+AnYJSKBQAAAPxB8BMw9mIBAAAA/EHwEzA2pwMAAAD8wRg7YJSKBQAAAPxB8GMASsUiaijfDgAATETwA8BVXpdvJ7CKBvoRABAEgh8ArspXvr1uzmiVW8aDfbGigX4EAASFggcAXOVG+XbbTsixE0pZlhw7IdvefaliX6xooB8BAEFh5geAq9rLt3cMgLLl29M9B0CFZgVaCwRWXMzCo1CATD8CALzEzA8AV5Vbvr3QrAD7YkUD/QgACAoP2QC4qtzy7YVmBfomLM2fObLbrFBSokpiiLQHyPQjAMBvBD8AXFdO+fZCaXNB7otFdTL3sL8ZACAopL0BMEpPaXPpdEZWOqOk48hKZ3wLfJpa2lS7rF6z69ardlm9mlrasoUYULog+hEAAGZ+ABjFxFkBL8t3xwmzZwCAoBH8ADBOOWlznrSH6mRlY28fAIAJyNkAgB5Qnax87O0DADABwQ8A9KDc8t1wZ/NbAADKxb0bAHpg4jqksCl381sAANzAzA8AFIHqZOVh9gwAYALuOwAAzzF7BgAwAcEPAMAXplXxAwDED2lvAAAAAGKB4AcAECq2nZBjJ5SyLDl2QrbNrQwAUBzS3gBEim0nlJJYVxJRbJYKACgHj8sAREb7wLh2Wb1m161X7bJ6NbW0MTMQIWyWCgAoByMCoESk3JiLgXH0sVkqAKAcpL0BJSDlxmyFBsZc7KKBzVIBAOXgkTVQAmYWzNY+MO4oOzBGJLBZKgCgHNwvgBIws2C29oFx15m5pMSeMhHBZqkAgHIwXgNKQMqN2RgYxwObpQIAeou0N6AEpNyYL53OyEpnlHQcWekMgQ8AAMhizAaUgJkFAACA8CL4AUpEyg0AAEA4kfYGAAgN9tkCAJSDmR8AQCiwzxYAoFyBPDJbs2aNJk2apPHjx2vFihVBNAEAEDLsswUAKJfvMz9btmzR0qVL9cADD6hPnz6aPn26jjzySB100EF+NwUAECLsswUAKJfvMz+bNm3SUUcdpT333FMDBgzQhAkTtG7dOr+bAQAImfZ9tjrK7rMFAEARfH9YtnXrVlVWVma/rqqq0gsvvFD07w8bNsiLZpWksnJw0E1AB/SHWegPc0StLzIZR1fMOlKLfvlUds3PFbOO1F57DlAiBAFQ1Poj7OgPs9AfZolyf/ge/GQyGVnWpzcpx3E6fd2TxsZtymQcL5pWlMrKwWpoaA7s/dEZ/WEW+sMcUe2LPfolu+2z1di4Lehm9Siq/RFW9IdZ6A+zhL0/Egmr4GSJ72lvw4cPV0NDQ/brhoYGVVVV+d0MAEAIpdMZWemMko4jK52hyhsAoCS+Bz/f+MY39MQTT+iDDz7Qjh079Oijj+rYY4/1uxkAAAAAYsb3tLe9995b8+bN04wZM9TW1qZTTjlFhx12mN/NAAAAABAzgVQHra6uVnV1dRBvDQAAACCmAtnkFAAAAAD8RvADAAAAIBYIfgAAAADEAsEPAAAAgFgg+AEAAAAQCwQ/AAAAAGKB4AcAAABALBD8AAAAAIgFgh8AAAAAsUDwAwAAACAWCH4AAAAAxEIy6AaUKpGwgm6CEW3Ap+gPs9Af5qAvzEJ/mIX+MAv9YZYw90dPbbccx3F8agsAAAAABIa0NwAAAACxQPADAAAAIBYIfgAAAADEAsEPAAAAgFgg+AEAAAAQCwQ/AAAAAGKB4AcAAABALBD8AAAAAIgFgh8AAAAAsUDwAwAAACAWCH7yWLNmjSZNmqTx48drxYoV3V5/8cUXVVNTowkTJujyyy9XKpUKoJXx8fOf/1yTJ0/W5MmTde211+Z8fezYsZo6daqmTp2as8/gnjPOOEOTJ0/Oft5//OMfO72+adMmVVdXa/z48Vq6dGlArYy+e++9N9sHU6dO1de+9jUtXLiw089wbvhj27ZtmjJliv7xj39IKu4ceOedd/Sd73xHEydO1Hnnnaft27f72eTI6toX99xzj6ZMmaLq6mrV1tZq165d3X5n1apVOvroo7PnCdct93Ttj9raWo0fPz77WT/22GPdfocxlnc69sfjjz/e6R5y1FFH6dxzz+32O5E7Pxx089577zljx451mpqanO3btzvV1dXOq6++2ulnJk+e7Dz//POO4zhObW2ts2LFiiCaGgv19fXOt7/9bae1tdXZtWuXM2PGDOfRRx/t9DPnnnuu8z//8z8BtTBeMpmMc/TRRzttbW05X9+xY4czfKqh7QAACmVJREFUZswY580333Ta2tqcWbNmOb///e99bmX8vPLKK87xxx/vNDY2dvo+54b3/vd//9eZMmWK86Uvfcl56623ij4HZs+e7axdu9ZxHMf5+c9/7lx77bV+Nz1yuvbF3//+d+f44493mpubnUwm41x66aXO8uXLu/3ewoULnTVr1vjf4Ijr2h+O4zhTpkxxtmzZUvD3GGN5I1d/tNu6daszbtw45/XXX+/2e1E7P5j5yWHTpk066qijtOeee2rAgAGaMGGC1q1bl3397bff1s6dOzVixAhJUk1NTafX4a7Kykpddtll6tOnjyoqKnTggQfqnXfe6fQzf/7zn3XLLbeourpaCxcuVGtra0Ctjb6///3vkqRZs2bpxBNP1K9//etOr7/wwgs64IADtN9++ymZTKq6uprzwwdXXnml5s2bp6FDh3b6PueG91auXKkFCxaoqqpKUnHnQFtbm5555hlNmDBBEvcRt3Ttiz59+mjBggUaNGiQLMvSF77whW73D0n605/+pFWrVqm6ulo//OEP9dFHH/nd9Ejq2h87duzQO++8o/nz56u6ulrXX3+9MplMp99hjOWdrv3R0bXXXqvp06frc5/7XLfXonZ+EPzksHXrVlVWVma/rqqq0pYtW/K+XllZ2el1uOvggw/OXgTfeOMNPfLIIxozZkz29e3bt+uLX/yiLrnkEq1atUoff/yxli1bFlRzI+/jjz/WqFGjdOONN+q2227T3Xffrfr6+uzrPZ0/cN+mTZu0c+dOnXDCCZ2+z7nhj6uuukpHHHFE9utizoGmpiYNGjRIyWRSEvcRt3Tti89+9rMaPXq0JOmDDz7QihUrNG7cuG6/V1lZqTlz5ug3v/mN9tlnn27po+idrv3x/vvv66ijjtLixYu1cuVKPfvss7rvvvs6/Q5jLO907Y92b7zxhp5++mnNmDEj5+9F7fwg+Mkhk8nIsqzs147jdPq6p9fhjVdffVWzZs3SpZde2unJxMCBA/Uf//EfOvDAA5VMJjVr1iw9/vjjwTU04g4//HBde+21Gjx4sIYOHapTTjml0+fN+eG/u+++W2eddVa373NuBKOYcyDX9zhPvLNlyxadeeaZmjZtmo488shur99444362te+JsuydPbZZ+sPf/hDAK2Mvv3220833nijqqqq1L9/f51xxhndrkncQ/x3zz336PTTT1efPn1yvh6184PgJ4fhw4eroaEh+3VDQ0OnKcKur7///vs5pxDhnueee04zZ87UD37wA5188smdXnvnnXc6PTlyHCf7NBXue/bZZ/XEE09kv+76efd0/sBdu3bt0jPPPKPjjjuu22ucG8Eo5hwYOnSompublU6n8/4M3PHaa69p+vTpOvnkkzV37txurzc3N+u2227Lfu04jmzb9rGF8fHyyy/rt7/9bfbrXNckxlj+W79+vSZNmpTztSieHwQ/OXzjG9/QE088oQ8++EA7duzQo48+qmOPPTb7+mc/+1n17dtXzz33nCRp9erVnV6Hu959913NnTtXS5Ys0eTJk7u93q9fP/30pz/VW2+9JcdxtGLFCh1//PEBtDQempubde2116q1tVXbtm3TqlWrOn3eX/nKV/T6669r8+bNSqfTWrt2LeeHh15++WV97nOf04ABA7q9xrkRjGLOgYqKCh1xxBF6+OGHJUkPPvgg54kHtm3bpu9+97u68MILNWvWrJw/M2DAAP3nf/5ntmrlr3/9a84TjziOo8WLF+ujjz5SW1ub7rnnnm6fNWMsf33wwQfauXOn9ttvv5yvR/H8IPjJYe+999a8efM0Y8YMnXTSSZoyZYoOO+wwnXPOOfrTn/4kSVqyZInq6uo0ceJEtbS05M2TRPl+8YtfqLW1VVdffXW2zOJdd92V7Y+hQ4dq4cKFOu+88zRx4kQ5jpMzBQjuGDt2rMaMGaOTTjpJ06ZN07Rp03T44Ydr6tSp2rJli/r27aurr75a559/viZNmqTPf/7zmjhxYtDNjqy33npLw4cP7/Q9zo1gFToHLr/8cq1fv16StGDBAq1cuVKTJk3Ss88+q4suuijIZkfSfffdp/fff1/Lly/P3j+uu+46SZ/2hW3b+tnPfqYrr7xSJ5xwgv7yl7/okksuCbjl0XTooYdq9uzZOu200zR58mR98Ytf1JQpUySJMVZA/vGPf3S7h0jRPj8sx3GcoBsBAAAAAF5j5gcAAABALBD8AAAAAIgFgh8AAAAAsUDwAwAAACAWCH4AAAAAxAK73QEAfHHIIYfoC1/4ghKJzs/dbrzxRu27776eve9dd92l5uZmzZ4927P3AACEA6WuAQC+OOSQQ/TEE09o6NChQTcFABBTpL0BAAK3atUqfetb39L27dvV0tKiE044QQ8++KAkacOGDTr11FN10kknafr06Xr++eclSTfccIO++93vqrq6Wj/84Q+VSqVUV1enCRMmaNKkSbr88su1a9cu3XDDDVq4cKEk6c4779SJJ56oadOm6fTTT9ff/vY3SdKWLVs0d+5c1dTUqLq6WjfffHMwHwQAwFOkvQEAfHPmmWd2Snvbd999deONN+rkk0/Wxo0b9dOf/lS7du3SEUccoZNOOklvvPGGli5dqttvv11DhgzRq6++qrPOOkuPPvqoJOntt9/W2rVrlUwmdfvtt+svf/mLVq9erT59+ujiiy/Www8/nH2vdDqtxYsXa8OGDaqqqtKDDz6o5557TgcddJAuueQSzZw5U8cdd5xaW1t1zjnnaP/999ekSZN8/4wAAN4h+AEA+OZXv/pV3rS3n/zkJ5o6dar69eunBx54QJJUX1+vrVu3aubMmdmfsyxLb775piRpxIgRSiZ338o2bdqU/X1J+tnPfiZp9wyRJNm2rYkTJ2r69On65je/qaOPPlpjxoxRS0uLnnnmGX300Ue67rrrJEktLS166aWXCH4AIGIIfgAARmhsbFRra6t27dqlrVu3ar/99lMmk9GoUaOygYwkvfvuu6qqqtJjjz2mAQMGZL/fHgS1e//995XJZDp9b8mSJXrllVe0adMm3XrrrVq9erWuuuoqOY6ju+++W/3795ckffDBB+rbt6+Hfy0AIAis+QEABK6trU0XX3yxLrzwQn3/+9/XvHnz1NbWplGjRqm+vl6vvfaaJOnxxx/XiSeeqJ07d3b7N0aNGqW1a9dq165dymQyuvLKK/XQQw9lX//ggw80ZswY7bnnnpo5c6Yuuugi/elPf9KgQYM0YsQILV++XJL08ccf67TTTtP69ev9+eMBAL5h5gcA4Juua34k6eKLL9aTTz6pvfbaS6eeeqok6Xe/+52WLl2qSy+9VAsXLtTFF18sx3GUTCZ10003aeDAgd3+7enTp+vtt99WTU2NHMfRyJEjdcYZZ+imm26SJA0dOlTnnXeeZs6cqX79+sm2bS1atEjS7hmhf//3f1d1dbV27dqlKVOm6MQTT/T40wAA+I1S1wAAAABigbQ3AAAAALFA8AMAAAAgFgh+AAAAAMQCwQ8AAACAWCD4AQAAABALBD8AAAAAYoHgBwAAAEAs/H943WewWBmhfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(ncols=1, figsize=(14, 8))\n",
    "ax1 = sns.scatterplot(x=\"Exercise\", y=\"Probability\", data=df, label=\"Age: All\", ax=ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the plot is done using Seaborn and its `scatterplot` function. This works closely together with Pandas DataFrames, where we simply write the strings of the dataframe columns and provide the dataframe via the `data` keyword. \n",
    "\n",
    "## Questions: \n",
    "\n",
    "- What can you conclude from the following plot? \n",
    "\n",
    "***\n",
    "\n",
    "- In this example we have more data that we haven't used yet; the age of the patient. How would you include this information in the plot and does this alter your above conclusion? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# insert code here\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "\n",
    "- ___Hint___: _look up the `hue` keyword in the `scatterplot` function_\n",
    "\n",
    "***\n",
    "\n",
    "### Optional question:\n",
    "\n",
    "When finished, you can take at the generating function located in `Simpsons_paradox_generate_data.py` and see if it makes sense.\n"
   ]
  }
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