{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>date</th>\n",
       "      <th>newcastle</th>\n",
       "      <th>HBA</th>\n",
       "      <th>ICI_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>Dec-23</td>\n",
       "      <td>146.25</td>\n",
       "      <td>117.38</td>\n",
       "      <td>118.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Nov-23</td>\n",
       "      <td>132.15</td>\n",
       "      <td>139.80</td>\n",
       "      <td>118.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Oct-23</td>\n",
       "      <td>121.10</td>\n",
       "      <td>123.96</td>\n",
       "      <td>121.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Sep-23</td>\n",
       "      <td>160.01</td>\n",
       "      <td>133.13</td>\n",
       "      <td>116.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Aug-23</td>\n",
       "      <td>156.00</td>\n",
       "      <td>179.90</td>\n",
       "      <td>114.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2012</td>\n",
       "      <td>Apr-12</td>\n",
       "      <td>100.75</td>\n",
       "      <td>105.61</td>\n",
       "      <td>106.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2012</td>\n",
       "      <td>Mar-12</td>\n",
       "      <td>107.00</td>\n",
       "      <td>112.87</td>\n",
       "      <td>111.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2012</td>\n",
       "      <td>Feb-12</td>\n",
       "      <td>112.10</td>\n",
       "      <td>111.58</td>\n",
       "      <td>116.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2012</td>\n",
       "      <td>Jan-12</td>\n",
       "      <td>117.45</td>\n",
       "      <td>109.29</td>\n",
       "      <td>115.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2011</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>112.25</td>\n",
       "      <td>112.67</td>\n",
       "      <td>113.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year    date  newcastle     HBA   ICI_1\n",
       "0    2023  Dec-23     146.25  117.38  118.48\n",
       "1    2023  Nov-23     132.15  139.80  118.75\n",
       "2    2023  Oct-23     121.10  123.96  121.70\n",
       "3    2023  Sep-23     160.01  133.13  116.50\n",
       "4    2023  Aug-23     156.00  179.90  114.57\n",
       "..    ...     ...        ...     ...     ...\n",
       "140  2012  Apr-12     100.75  105.61  106.26\n",
       "141  2012  Mar-12     107.00  112.87  111.01\n",
       "142  2012  Feb-12     112.10  111.58  116.55\n",
       "143  2012  Jan-12     117.45  109.29  115.64\n",
       "144  2011  Dec-11     112.25  112.67  113.00\n",
       "\n",
       "[145 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../coal-price-data/coal_price_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.newcastle\n",
    "y = df.ICI_1\n",
    "\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 0.600533935403765\n",
      "intercept: 33.65381401159914\n",
      "r: 0.9606500704209069\n",
      "p: 1.9310655623962052e-81\n",
      "std_err: 0.01452032511898455\n"
     ]
    }
   ],
   "source": [
    "print(f\"slope: {slope}\")\n",
    "print(f\"intercept: {intercept}\")\n",
    "print(f\"r: {r}\")\n",
    "print(f\"p: {p}\")\n",
    "print(f\"std_err: {std_err}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UyNHK1PdeBCKVNbLhzrR4b27EqrSNXl0l6+2zlStXwmQyYfz48dDr9baf999/HwAQFhaGnTt3YtKkSRg4cCCefPJJZGVlYevWrbbXCAkJwbZt2xASEgKDwYA5c+bgwQcfxMsvvyxXt4iIyF8IAeyaIA1EPWcADwjFBSIgOKbFy0n222et6dmzJ/Lz89t8nd69e2P79u2eahYREQUChRdTOxIM0+Ll5BeF1kRERB6l8GJqZ6zT4p1V76jQOAtNydPi5cRQREREgaP+GrBOBXzz303Hhr0J3F8NhNjvcKA01mnxAOyCUaBMi5cTQxEREQWG0s/sZ5fde1FRs8tcEejT4uXkN4s3EhERtYujlal7zgDG/kO+NnlZIE+LlxNDERERKVcAFlO7ytm0eF9v/xFIGIqIiEiZTvxWWjvUqTOQVREQtUPtJcf2H4GENUVERKQsDoup3wJ+eiXoA5Fc238ECoYiIiJSDqfF1AvkaY+faGv7D6Bx+48GS+vrAwY7hiIiIvJ/tpWp05uO9cxS7MrUnubO9h/kHGuKiIjIvwVxMbWruP2HZzAUERGR/yr8DXDs+abHLKZ2iNt/eAZDERER+Z/6a/a1Q8PeCvraIWes238YTTUO64pUaFzckdt/tI41RURE5F9YTO02bv/hGQxFRETkH1hM3SHc/qPjePuMiIjkx2Jqj+D2Hx3DUERERPKyK6buAmRdZjF1Oznb/oPaxlBERETyYDE1+RmGIiIi8r3Sz6S1Q0BjMTVrh0hGDEVEROQ7QgC704Cy3U3Het4HjN0oX5uIfsRQREREvnGlGPioj/TYxH1A3Bh52kPUAkMREdGPGiyCs3a8hcXUpAAMRUREAPIKS7Fsa5FkU029NgJLM5O5vktHsJiaFISLNxJR0MsrLEX22iN2u4wbTTXIXnsEeYWlMrVM4bgyNSkMQxERBbUGi8CyrUUO94uyHlu2tQgNFkdXkENCADvvbLEy9X1cmZr8Hm+fEVFQO1hcYTdC1JwAUGqqwcHiCi6I5woWU5OCMRQRUVArr3IeiNpzXVBjMTUpHEMREQW1+OiIti9y47qgxGJqChAMRUQU1EYmxUKvjYDRVOOwrkiFxl3GRybF+rppysCVqSmAsNCaiIJaiFqFpZnJABoDUHPWx0szk7leUUuOiql7/YTF1KRoDEVEFPQyUvRYOWcodFrpLTKdNgIr5wzlOkUtXSkG1quB8j1NxybuA27/u2xNIvIE3j4jIkJjMJqYrOOK1m0p/DVw7IWmx526APdVAOpQ+dpE5CEMRUREPwpRqzjt3hlHxdTD/wjclCNPe4i8gKGIiIhad/FTYE+G9BiLqSkAMRQREZFjQgC77pLWDvX6CWuHKGAxFBERkT2uTE1BiKGIiIikWExNQYqhiIiIGrGYmoIcQxERETkppi4FInXytIdIBgxFRETBjMXURDYMRUREwcphMfUXQNxt8rSHSGYMRUREwahlMXWoBsj6D4upKagxFBERBZP6q8DfO0uPDX8buOmX8rSHyI8wFBERBQsWUxO1iqGIiCjQCQHsuhMoz2861uunwO3vy9cmIj/EUEREFMhYTE3kMoYiIqJAdfwV4PiLTY9ZTE3UKoYiIqJAw2JqonZhKCIiCiQspiZqN4YiIvILDRaBg8UVKK+qQXx0BEYmxSJErZK7WcrBYmqiDmMoIiLZ5RWWYtnWIpSaamzH9NoILM1MRkaKXsaWKQSLqYk8Qi13A4gouOUVliJ77RFJIAIAo6kG2WuPIK+wVKaWKcTxV6SBKFQDzLzOQETUDgxFRCSbBovAsq1FEA7OWY8t21qEBoujK4Jc/VVgnUo6u2z428BPTJxdRtRODEVEJJuDxRV2I0TNCQClphocLK7wXaOU4GKe/eyye0s5u4yog1hTRESyKa9yHojac13AEwLYNR4o39t0jMXURB7DUEREsomPjvDodQHtyr+Bj/pKj7GYmsijePuMiGQzMikWem0EnE28V6FxFtrIpFhfNsv/HH9FGohYTE3kFQxFRCSbELUKSzOTAcAuGFkfL81MDt71ilhMTeRTDEVEJKuMFD1WzhkKnVZ6i0ynjcDKOUODd50iFlMT+ZysoSg3NxcjRoxAdHQ04uPjMX36dJw8eVJyTU1NDXJyctCtWzd06dIFWVlZKCsrk1xTUlKCqVOnIioqCvHx8Xj66adRX1/vy64QUQdkpOixb/FdWP/IaLw5cwjWPzIa+xbfFZyBSAhg5x3AnslNx3r9FHhAcKsOIi+TtdA6Pz8fOTk5GDFiBOrr6/Hcc89h0qRJKCoqQufOjf+HtGjRInz88cfYuHEjtFotFixYgBkzZuCLL74AADQ0NGDq1KnQ6XTYv38/SktL8eCDDyI0NBS//e1v5eweEbkhRK2CoW83uZshL4fF1PuBOIM87SEKMiohhN+sinbp0iXEx8cjPz8f48aNg8lkQlxcHNatW4f77rsPAPDdd99h0KBBKCgowOjRo/HJJ5/g7rvvxsWLF5GQkAAAWLVqFRYvXoxLly4hLCyszfc1m83QarUwmUzQaDRe7SMRkUPHX5HWDoVqgaxLrB0iaoWnv7/9qqbIZDIBAGJjG2eaHD58GHV1dUhLS7NdM3DgQPTq1QsFBQUAgIKCAgwePNgWiAAgPT0dZrMZJ06ccPg+tbW1MJvNkh+iYNNgESg4cxlbjl5AwZnLXDVaLo6KqUe8A/ykkoGIyMf8Zp0ii8WChQsXYsyYMUhJSQEAGI1GhIWFISYmRnJtQkICjEaj7Zrmgch63nrOkdzcXCxbtszDPSBSDm7A6icu5klrh4DGYmrWDhHJwm9GinJyclBYWIgNGzZ4/b2WLFkCk8lk+zl37pzX35PIX3ADVj/gsJj6fhZTE8nML0aKFixYgG3btmHv3r3o0aOH7bhOp8P169dRWVkpGS0qKyuDTqezXXPw4EHJ61lnp1mvaSk8PBzh4eEe7gWR/2trA1YVGjdgnZisC961gbyNxdREfkvWkSIhBBYsWIBNmzZh9+7dSEpKkpwfNmwYQkNDsWvXLtuxkydPoqSkBAZD4z8gBoMBx48fR3l5ue2aHTt2QKPRIDk52TcdIVIIbsAqs+Mvt1iZWvvjytQMRET+QNaRopycHKxbtw5btmxBdHS0rQZIq9UiMjISWq0W8+bNwxNPPIHY2FhoNBo89thjMBgMGD16NABg0qRJSE5Oxs9+9jO89tprMBqNeP7555GTk8PRIKIWuAGrTOqv2i/EOOIdoH+2PO0hIodkDUUrV64EAIwfP15yfPXq1XjooYcAAG+88QbUajWysrJQW1uL9PR0vPPOO7ZrQ0JCsG3bNmRnZ8NgMKBz586YO3cuXn75ZV91g0gxuAGrDC5+AuyZIj3GYmoiv+RX6xTJhesUUbBosAjc/upuGE01DuuKVGjcXmPf4rtYU9RR1mLqS/9sOtbrfuB2708mIQoWAb1OERF5Fzdg9ZEr/wbWq6WBaOJ+BiIiP8dQRBRkuAGrl9kVU8ewmJpIIfxiSj4R+VZGih4Tk3U4WFyB8qoaxEdHYGRSLEeIOoLF1ESKx1BEFKS4AasHsZiaKCAwFBERtZcQwM5xwKV9TcdYTE2kWAxFRETtwZWpiQIOQxERkbuOvwwcX9r0ODQGyCrnrvZECsdQRETkKhZTEwU0hiIiIlewmJoo4DEUERG1xlExde+ZwJj18rWJiLyCoYhIYRosgusL+QqLqYmCCkMRkYLkFZZi2dYilJqadrHXayOwNDOZK1F7GoupiYIOt/kgUoi8wlJkrz0iCUQAYDTVIHvtEeQVlsrUsgBTfxVYp5IGohHvAD/5gYGIKMAxFBEpQINFYNnWIoc721uPLdtahAaLoyvIZRc/sZ9ddq+Rs8uIggRDEZECHCyusBshak4AKDXV4GBxhe8aFUiEAHaMlc4u6z0LeEAAkQnytYuIfIo1RUQKUF7lPBC15zpqhsXURPQjjhQRKUB8dIRHr6MfHV8mDURhXYGZ1xmIiIKUx0LRmTNncNddd3nq5YiomZFJsdBrI+Bs4r0KjbPQRibF+rJZymUrpn6p6diId4D7KlhMTRTEPBaKrly5gvz8fE+9HBE1E6JWYWlmMgDYBSPr46WZyVyvyBUXtrOYmogccrmmaMWKFa2ev3DhQocbQ0TOZaTosXLOULt1inRcp8g1DlemngWMWSdfm4jIr6iEEC7N4VWr1dDr9QgLC3N4/vr16zAajWhoaPBoA33BbDZDq9XCZDJBo9HI3RyiVnFF63aoOgNs7Sc9xmJqIsXz9Pe3yyNFvXv3xquvvoqf/vSnDs8fPXoUw4YN63CDiKh1IWoVDH27yd0M5Ti+TFo7FBYLzDCydoiI7LhcUzRs2DAcPnzY6XmVSgUXB52IiLzPYTH1SuC+ywxEROSQyyNFL7/8Mq5ever0fHJyMoqLiz3SKCKiDrmwHcifKj12r5ELMRJRq1wORcnJya2eDw0NRe/evTvcICKidnNYTP0AMOY9+dpERIrBFa2JgkTAF2g7KqaeVAB0Hy1Pe9oQ8J8HkQIxFBEFgbzCUrup/PpAmsp/7CWgcFnTYz8vpg74z4NIobjNB1GAyyssRfbaI3YbyhpNNcheewR5haUytcwD6qsbi6mbByI/L6YO6M+DSOEYiogCWINFYNnWIjiaF2o9tmxrERosCpw5emE78Pcu0mP3GoH+8+VpjwsC+vMgCgAMRUQB7GBxhd2IRHMCQKmpBgeLK3zXqI4SAthxu3R2We8HgAeE388uC8jPgyiAeGybD6vHH3+83Y0hIs8qr3L+Bdye62SnsGLqlgLu8yAKMC6HojfeeKPNa1QqFUMRkR+Jj47w6HWyOvaSooqpHQmoz4MoALkcirgwI5HyjEyKhV4bAaOpxmEdiwqNG8qOTIr1ddNcV19tXzs0YhXQ/xfytKcDAuLzIApgrCkiCmAhahWWZjYuvNpyBRzr46WZyf67Po7TYmrlBSIgAD4PogDncijavXs3kpOTYTab7c6ZTCbcfPPN2Lt3r0cbR0Qdl5Gix8o5Q6HTSm/J6LQRWDlnqH+ui6PgYuq2KPLzIAoSKuHiLq733HMP7rzzTixatMjh+RUrVuDzzz/Hpk2bPNpAXzCbzdBqtTCZTNBoNHI3h8grFLOCssKLqV2lmM+DyI95+vvb5VDUu3dv5OXlYdCgQQ7Pf/fdd5g0aRJKSko63ChfYygi8hPHXlJ8MTUR+Y6nv79dLrQuKytDaKjzf5g6deqES5cudbhBRBSEAqiYmoiUy+WaohtuuAGFhYVOzx87dgx6Pe+FE5GbLnwcUMXURKRcLoeiKVOm4IUXXkBNjf2iYteuXcPSpUtx9913e7RxRBTAhAA+GwPkN/t3I0CKqYlImVyuKSorK8PQoUMREhKCBQsWYMCAAQAaa4nefvttNDQ04MiRI0hIUN4/ZqwpIvKxICmmJiLvkq2mKCEhAfv370d2djaWLFkCa5ZSqVRIT0/H22+/rchAREQ+dmwpUPhy02MWUxORn3A5FAGNM9C2b9+OH374AadPn4YQAv3790fXrl291T4iChSOiqlH/g/Q71F52kNE1IJbociqa9euGDFihKfbQkSB6sLH0tohAJhRBkTEy9MeIiIHXA5FM2bMcOm6Dz/8sN2NIaIAY12Z+j/7m471fgAY8558bSIicsLlUKTVar3ZDiIKNA6Lqb8Euo+Spz1ERG1wORStXr3am+0gokDSspg6vBtwb6nTYmpueUFE/qBdNUVESsQvXh9oRzF1XmEplm0tQqmpaQ00vTYCSzOTuTkqEfkUQxEFBW988TJktdCOYuq8wlJkrz2CloulGU01yF57hLvGE5FPMRRRwPPGFy9HN5oRAtgxBvhPQdOxG2cDt61t9WkNFoFlW4vsPhcAEABUAJZtLcLEZF1wh00i8hmXt/kgUqK2vniBxi/eBotLC7sDaApZzQMR0BSy8gpL299gpak6DaxXSwPRpC/bDEQAcLC4wu7vsDkBoNRUg4PFFR5oKBFR2xiKKKB5+ovXGyFLsY69CGzt3/Q4vBsw87rLs8vKq5x/Lu25joioo3j7jAKap7943QlZhr7dXHpNxfHQytTx0REevY6IqKMYiiigefqLN+hHNzy4MvXIpFjotREwmmocjrypAOi0jQXsRES+wNtnFNCsX7zOynRVaCyQdvWLN2hHN4QAPjVIA9GNs4EHRLu36ghRq7A0MxkA7D4f6+OlmckssiYin2EoooDm6S9eT4csRbAWU1/+sumYi8XUbclI0WPlnKHQaaUhUqeN4HR8IvI5lRAiCCpCW2c2m6HVamEymaDRaORuDnmBJ6fQW2efAZDc9rEGpY58mfvd2kfHXgQKX2l6HN79x5WpPXvn3e/6TUSK4Onvb4YiMBQFC09+8XpjnSK/WvvIQ8XURETexFDkBQxF1B6eDlmOFpj0xOiT2zxYTE1E5E2e/v6WtaZo7969yMzMRGJiIlQqFTZv3iw5/9BDD0GlUkl+MjIyJNdUVFRg9uzZ0Gg0iImJwbx583DlyhUf9oKCVYhaBUPfbpg25AYY+nZrdyDym7WPvFBMTUSkJLKGourqatxyyy14++23nV6TkZGB0tJS28/69esl52fPno0TJ05gx44d2LZtG/bu3YtHH+UQPymHX6zs7LCY+oBHiqmJiJRC1nWKJk+ejMmTJ7d6TXh4OHQ6ncNz3377LfLy8nDo0CEMHz4cAPDWW29hypQpeP3115GYmOjxNhN5muxrH/momJqIyN/5/ZT8PXv2ID4+HgMGDEB2djYuX75sO1dQUICYmBhbIAKAtLQ0qNVqHDhwwOlr1tbWwmw2S36I5CLb2kf11cA6lTQQjfwfIOsSAxERBSW/DkUZGRn461//il27duHVV19Ffn4+Jk+ejIaGBgCA0WhEfLy01qFTp06IjY2F0Wh0+rq5ubnQarW2n549e3q1H0StkWXtowvb7GeXzSjz+uyyBotAwZnL2HL0AgrOXA6OPeKISDH8+n8HZ86cafvz4MGDkZqair59+2LPnj2YMGFCu193yZIleOKJJ2yPzWYzgxHJxrrAZPbaI1DB8dpHHlvZWQjgs9uktUM3zgFu+1vHX7sNfrXkABGRA349UtRSnz590L17d5w+fRoAoNPpUF5eLrmmvr4eFRUVTuuQgMY6JY1GI/khkpNPVnZ2Wkztm0CUvfaIXUG50VSD7LVHkFdY6vU2EBG1xa9Hilo6f/48Ll++DL2+8QvCYDCgsrIShw8fxrBhwwAAu3fvhsViwahRo+RsKpHbMlL0mJis887KznbF1HHAvRd9UjvU1pIDKjQuOTAxWcdVrIlIVrKGoitXrthGfQCguLgYR48eRWxsLGJjY7Fs2TJkZWVBp9PhzJkzeOaZZ9CvXz+kp6cDAAYNGoSMjAw88sgjWLVqFerq6rBgwQLMnDmTM89IkaxrH3mMw5Wp/wT0e8Rz79EGd5Yc8GjfiYjcJGso+uqrr3DnnXfaHlvrfObOnYuVK1fi2LFjePfdd1FZWYnExERMmjQJr7zyCsLDw23Pee+997BgwQJMmDABarUaWVlZWLFihc/7QuR3LmwD8jOlx2RYmVr2JQeIiFwkaygaP348Wttl5NNPP23zNWJjY7Fu3TpPNotI2RwWU/8MuO2vsjRHtiUHiIjcpKiaIiJqQ9VpYGt/6bFJB4DuI+VpD5qWHDCaahzWFanQWFDu0SUHiIjaQVGzz4ioFd+8IA1E4XHAzDpZAxHQtOQAALu1mDy+5AARUQcwFBEpnXVl6hO/bjo28k9AVrnfrEztkyUHiIg6yD/+xSSi9vGTYmpXeHXJASIiD2AoIlIiPyumdpXHlxwgIvIghiIipTGfArbdJD0mczE1EVEgYCgiUpJvXpDWDkXEA9Mv+E3tEBGRkvFfUiIl8IOVqYmIAh1DEZG/O78V2HuP9JifFlMTESkZQxGRv3JUTJ30IGB4V742EREFMIYiIn/kqJg6/SDQbYQ87SEiCgJcvJHI33zzgjQQRcQ3rkzNQERE5FUcKSLyF3VXgI3R0mMj/xfo91/ytIeIKMgwFBH5AxZTExHJjqGISE5CAJ8ZgMsHmo6xmJqISBYMRURyYTE1EZFfYaE1kRy+eZ7F1EREfoYjRRQwGizC/3dgZzE1EZHfYiiigJBXWIplW4tQaqqxHdNrI7A0MxkZKXoZW9ZMO4upFRH2iIgCAEMRKV5eYSmy1x6BaHHcaKpB9tojWDlnqLzBSAjgs9HA5YNNx1wsplZE2CMiChCsKSJFa7AILNtaZBeIANiOLdtahAaLoyt8wHwKWK+WBqL0gy4Houy1RySBCGgKe3mFpZ5uLRFRUGMoIkU7WFxhFxqaEwBKTTU4WFzhu0ZZdaCY2u/DHhFRAOLtM1K08irngag913mEB4qp3Ql7hr7d2tlQIiJqjqGIFC0+OsKj13XY+Y+AvdOkx2aUAxFxbr2MX4Y9IqIAx9tnpGgjk2Kh10bA2VwsFRoLk0cmxXq3IUIAn46SBqKkB4EHhNuBCPDDsEdEFAQYikjRQtQqLM1MBgC7YGR9vDQz2btT2DtQTO2M34Q9IqIgwlBEipeRosfKOUOh00pHTXTaCO9Px/fSytR+EfaIiIKMSggR9NNXzGYztFotTCYTNBqN3M0hJ9paxNCnixw6KqYe9Weg7zyPvg3XKSIics7T398MRWAoUgK/CgceKqZ2FVe0JiJyjKHICxiK/JuzFautscBnK1Z3YGVqIiLyPE9/f3NKPvm1thYxVKFxEcOJyTq3Rk/cHn0x/wvYNkB6LP0Q0G24y+9JRET+jaGI/FpHFzF0FH52FBnduxX3qQG4/GXT44gEYPp5QM3/fIiIAgn/VSe/1pFFDB3VIcVEhaLyap3dtQ43j62tAP4hDVqWEf8LdX/XV6YmIiLl4JR88mvtXcTQ2WaqjgIR4GA/sW9ftwtEY779P4z5IIkbsRIRBSiOFJGs2qrtsS5iaDTVOKwrUqFxPaLmixi2VofUmsZbcdcQskH6/wqm+s64pej9Hx/UYP7aI5g35kakJevarEXizDEiIuVgKCLZuDLN3rqIYfbaI1ABkqDjbBHDtuqQnLk16jts6veU5Nh/nX0BO82j7K79yxdn8ZcvzrZai+RXywgQEVGbePuMZOHs9pa1tievsBQNFoGCM5dRW2/BwrT+SNCES651tmJ1ezZJ/bDvk3aBqN+xzQ4DkbP2NudK/4iIyL9wpIh8zpVp9s9+eBwvfVQEo7kpVOg0EViUdhNu7B7V6q0odzZJ1YZU4ZubZ0mOnYmZiwl7f+LS81suCwAAX/77Mp79x3GPLyNARETexZEi8jlXptlXXq2TBCIAKDPX4A87/4XwTmoY+nZzGija2kzV6tG4f9gFoj0DD6J8wP9zpRuS9paaavDH3adw+6u7MfvPB1B5zXFBd/PrDxZXuPU+RETkXQxF5HPtub0FOJgh5kTbm6kKnE29G8/pV9uOmxu6IC/1IsYPHeFyqGrpjZ2n3Kplau/fAxEReQdDEfmcO7e3WnJ1lCUjRY+Vc4ZCp5W+V1rcv3E2NVNy7Lub/obOs812xd2AfajypI78PRARkeexpoh8zjoS054ZYlaujLJkpOgxMVlnmxJ/1/fTEF19WHrRzOsYqA51+NyVc4bazR7zBEfLCBARkfwYisgnWq7X89+TB2HBhq/b/XqujrKEqFUw9FAB/+ghPdE/Bxjxx1af2zxU7Sgy4v++OOtwWQB31kNytowAERHJj6GIvM7Rej2xne1HZ1zh9ihL0WvA0cXSY9POAp17u/T0ELUKhr7dYOjbDSOTYu36odNGYOaIXnhj579cej0d1ykiIvJbDEXkVdb1elqOplRUO5+d5YxboyxCAOtblMyFxgA/+cHt97VqeTvOuiwAAGw4VOJ01W2gcc+1t2cNxehWZs0REZG8WGhNXtPe7TaccbZYo51LBfaBaNyWDgUiK+vI0bQhN9iWBWhrtpsKwPIZgzGmf3cGIiIiP8aRIvKa9m63ATTdJnv9vlvwn+pa1/cN+3Q0cPmA9NjM64CDYmpPclaYzdtlRETKwVBEXtPedXia3yYb07+7a0+qrbDb1R43LQCGv9WuNrSHs9trHB0iIlIGhiLyGldniMV2DkNF9XXbY7dHVxwWU38PdO7lalM9xnp7jYiIlIehiDym5bT7Yb27Qq+NcFqAbL1Flv/0nTj8/Q/uj644KqYO6wrcx+0ziIjIfQxF5BGOpt3rtRG45xY9/rS32OH6PkDjLbKwH/cyc8ulAmDHbdJj47YAPe5x+pSWoY23toiIqDmGIuowZ9PujaYa/GlvMR4dl4SPvil1uwDZaYhpRzG1s9DGImgiIrJiKCKb9oyktDbtXqBxROijb0rdvkXmKMQM6Hodn/acIb3wpseA4StabWNroS177RHXpvkTEVHAYygiAO0fSWlr2r11A9e/FZzFQ2OSXLpd5SjE/CLuAyzRr5Fe6EIxtSuhbdnWIkxM1vFWGhFRkOPijWQLIS3DjXUkJa+w1OlzXZ12/8rH3+L2V3e3+lqAoxAjcDb1bkkgMjdEo2GmxaXZZa6GtoPFLM4mIgp2DEVBrq2RFKBxJKXB4nhdalen3QNth6wGi8CaL4ptIWZo1Lc4m5opuWZe8QtIPbHe5RDjamhr75pKREQUOHj7LMi5M5LiaIbYyKTYVqfdt3wtZ7erWt6+29xvEYZEnZI8v9+xzaj/8VfW1RDjamhzJ9wREVFg4khRkOvoSEpr+3454uh2VfPbd9qQKpxNvVsSiFb/JxM3HttmC0SA6yHGGtqctU2Fxtop68auREQUvBiKgpwnRlKs+37ptK6PtlhDVvPbd7+I+wDf3DxLct1t3/4fll38heRY16hQl0NMW5u1Ao1rJbHImoiIZA1Fe/fuRWZmJhITE6FSqbB582bJeSEEXnzxRej1ekRGRiItLQ2nTklvqVRUVGD27NnQaDSIiYnBvHnzcOXKFR/2QtnaGkkBgNjOoTCaa1Bw5rLT2qKMFD32Lb4LL0wd5NL7WkNW4+27a3bF1JX1XXDjsW24WBdv99y2btM5apuj0KbTRnA6PhER2cgaiqqrq3HLLbfg7bffdnj+tddew4oVK7Bq1SocOHAAnTt3Rnp6Ompqmm7lzJ49GydOnMCOHTuwbds27N27F48++qivuqB4rtz+qqiuw6L3j2LW/37Z6gyyELUKD41JQkxU6zvSxzQb6an+T5HDYuohRRucPr/yap3bs8WsoW39I6Px5swhWP/IaOxbfBcDERER2aiEEO7+j7dXqFQqbNq0CdOnTwfQOEqUmJiIJ598Ek899RQAwGQyISEhAWvWrMHMmTPx7bffIjk5GYcOHcLw4cMBAHl5eZgyZQrOnz+PxMREh+9VW1uL2tpa22Oz2YyePXvCZDJBo9F4t6N+ytE6RY5Yg9PbDwyFNjIUBf/+DwQAbXgoKmuuQwUV1hScRXVtg9PX6BwegmNL0xFy7DmgaLnkXPNi6ta8OXMIpg25oc3riIgocJnNZmi1Wo99f/vt7LPi4mIYjUakpaXZjmm1WowaNQoFBQWYOXMmCgoKEBMTYwtEAJCWlga1Wo0DBw7g3nvvdfjaubm5WLZsmdf7oCQZKXpMTNbhYHEFjKZreOXjbyU711tZE3TOOvsVol1WV4WQDdJByv9nnI23ymc5eYI9zhYjIiJP89tCa6PRCABISEiQHE9ISLCdMxqNiI+X1px06tQJsbGxtmscWbJkCUwmk+3n3LlzHm69MoWoVTD07QadNtJhIGquvYFokqYAJ1J+Kjl264n3XA5EnC1GRETe4rcjRd4UHh6O8PBwuZvht7yzkKHA1n4LMTjqjO3I3yvS8Mz5hW6/EmeLERGRN/htKNLpdACAsrIy6PVNxbBlZWUYMmSI7Zry8nLJ8+rr61FRUWF7PrnP07em+oSfx+4B8yXH7j71BxRe6+f2a2nbKOImIiJqL7+9fZaUlASdToddu3bZjpnNZhw4cAAGgwEAYDAYUFlZicOHD9uu2b17NywWC0aNGuXzNgcKV6bpu+oZ3RpJIDLWxaLPsS3tCkQAYLpa1+Z+bERERO0h60jRlStXcPr0advj4uJiHD16FLGxsejVqxcWLlyIX//61+jfvz+SkpLwwgsvIDEx0TZDbdCgQcjIyMAjjzyCVatWoa6uDgsWLMDMmTOdzjyjtlmn6WevPdLu1+isvmpXO/T0uV9h4w8TO9Q27mxPRETeImso+uqrr3DnnXfaHj/xxBMAgLlz52LNmjV45plnUF1djUcffRSVlZW4/fbbkZeXh4iIpts77733HhYsWIAJEyZArVYjKysLK1as8HlfApE2MhSV1+rcft4kTQH+dONvJMduPfEefmjQeqRdbe3HRkRE1B5+s06RnDy9zoHSWfcic/8Xw76Y+v2KiVh8/le2x2oV4GRRbLdxrSIiouDm6e9vv60pInk034vMHX3Cz+NsaqYkEN196g+SQAQAcw29oYLzfch+PuZGt7cKISIi8gS/nX1G8mjci8y9KfnP6Nbgl/Ef2B4b62Jx27erYUGI3bWTbtZjVJ9udqtn67QRWJqZjIwUPRosAn/eVwyjqcZhOFP9eD3XKiIiIk9iKCKbBovAF6f/4/L17hZT6zThGJkUixC1yrZ6dnlVDeKjI2zHAWmhtwrShSK5sz0REXkLQ1GQabAI21Ye/7lyHZXXrkMFoJNajQ2HzsFodm2UqD3F1C/dc7Mk+LRWJG3d2b61ESUiIiJPYigKIq5u+tq6toupW4qJCsXyGYPdDjLN92NzNKJERETkSQxFQaL9M8qatGdl6pzxffHEpAHtDjJtjSgRERF5CkNREGjvjLLmWhZTX7zeHbd/9xeHxdTN3d4/jiM7RESkCAxFQaA9M8qsHBVTP3VuIT74Ia3V53GGGBERKQ1DURBo76737V2ZmjPEiIhIiRiKgoD7ixy6X0zdHGeIERGREjEUBQHrrveu3ELrG34OuwZkS461VUzdXGznUOQ/fSfCOnGxdCIiUhZ+cwUB62KIbd3IWqxbIwlEF693R59jW1wORABQUV2Hw9//0M6WEhERyYehKEhkpOjx9gO3OgxGndVXcTb1bmQ3m1321LmFuO27NW3OLnOkvTVMREREcuLtsyDStXO43bT89hZTt4YbtRIRkRIxFAUR6RYeHSumdoTT8ImISMkYigKcda+z8qoaHPm+AoDjYuqpp/6AE27UDrXk6jT85u3hth1ERORPGIoCSMvAcbmqFi9uLURFdZ3tmsW6NZLaIVdXpm6LK9PwHe29puf0fSIi8hMMRQGirc1e27syNQB0jQpFeCc1jOZa2zG9NgIzR/TCjd2jXBrxcbb3mtFUg+y1R7ByzlAGIyIikhVDUQBoa7PXjhRTqwDkzhjcod3qW9t7Tfz4Hsu2FmFiso630oiISDYMRQrX+mavAtv6L0RKZFMx9frLk7DkwuMuvbY2shNezUq1jeC0d7f6tvZeEwBKTTU4WFzR7vcgIiLqKIYihfvyzGWHgcMTxdTvzB6GMf26d7iNrq5bxPWNiIhITgxFCpZXWIrF/zhud/xZ3WrMj/+H7XF7iqn12giM7uOZURtX1y3i+kZERCQnhiKF2n6sFL9cd0RyrCPF1Fbe2OHeuvea0VTj8DYf1zciIiJ/wG0+FGj7sYvIWS8NROma/XaB6NYT77kViIDGcOLpmWDWvdcA2G0z4o0QRkRE1B4cKVKYvMJS/HLd182OCHzc/1e4OfLftiPuFFMDQFSYGr+5NxU6jfcWU8xI0WPlnKF2ywa4sr4RERGRLzAUKUiDReDZD5tqiDy1MvXV6xboNBFen/mVkaLv0NR+IiIib2IoUpAv/30ZlVcbV6f2RDF1c76a+RWiVnHaPRER+SWGIgXZf/o/Doupnzy3CP/4YUKHXpszv4iIKNgxFClItx+24UTK05JjQ06sQ2WDxulztJGdoFKpYLpax5lfRERErWAoUoCGBgtqtg7Bz0VTPVFbxdRTUhIwe9SNGN23G3YUGZG99ghUgCQYceYXERFRE4YiP5d/aC/uOHUHOjc71lYx9cIJ/bFw4k22x5z5RURE1DaGIj/25T9+jjtqV9seu1JM3TUqFI9N6G93nDO/iIiIWsdQJLMGi7APKg1VwEYtRje7ztVi6t9MH+w06HDmFxERkXMMRTLKKyy1u6U1U3cIy+OXSa5rq5i6uac++Aanyq9gwV39OApERETkBpUQwtGkpKBiNpuh1WphMpmg0bgWPjoqr7AU2WuPNCt87vjK1M3FRIVi+YzBrBciIqKA5envb+59JoMGi8CyrUW2QNQ3/BzOpmZKAtHUU39odyACgMqrdZi/9gjyCks72FoiIqLgwFAkg4PFFbZbZs/q/k+yVceF63Hoc2yL21t1OLNsaxEaLEE/GEhERNQm1hTJoLyqBl3UV1HohZWpWyo11eBgcQULrImIiNrAUOQjzWeZdS77CIUpv5Scd6eY2l2+2teMiIhIyRiKfKBpltk1bO//OJIji23nOlJM7Srua0ZERNQ2hiIvs84y6xN+DmdTsyXn2lqZuqO4rxkREZHrGIq8yDrLbLHu/zA//kPb8QvX4zD2uz/DghCoVYC7ddARnVSoqW/9SdzXjIiIyD0MRV50+NT3KOidJjnWspjaIoDoiE6oqql3+XXbCkQA9zUjIiJyF0ORF/UrmiF57KyY2p1A1JaYqFC8PWsoRvftxhEiIiIiN3CdIi+6FjMOQGMx9Y3HtnltdllzlVfroFarGIiIiIjcxJEiL9Ld8QZGL78bRnOtT993Z5GR6xIRERG5iSNFXtJgEfjj7tMwX6vz2GuOvLGrS9dtOnqBq1gTERG5iSNFXpBXWIpnPzyOyqueC0QAMLpPN5y+VI2K6uutXldRXcdVrImIiNzEkSIPyyssxfy1RzweiADg71+dwz23uDabjKtYExERuYehyIOs6xJ5i9Fci55do1y6lqtYExERuYehyIMOFleg1OTdEZrYLuHQa50HHhUAPVexJiIichtDkQf54paVTtO4KKMKTatWW3EVayIiovZjKPIgb96yaj4ClJGix8o5Q6FrMWKk00Zg5ZyhXMWaiIioHTj7zINGJsVCr43w+C00RyNAGSl6TEzW4WBxBcqrahAf3RiYOEJERETUPgxFHhSiVmFpZjLmrz3i0dd1to9ZiFrFafdEREQewttnHpaRosc7DwyFykMDNovS+mPf4rt4S4yIiMjLGIq8YEqqHm/PurXDr6MCsOHQuY43iIiIiNrEUOQlU1ITkX5zQodeQwAoNdXgYHGFZxpFRERETvl1KHrppZegUqkkPwMHDrSdr6mpQU5ODrp164YuXbogKysLZWVlMrZYqn98F4+8DlenJiIi8j6/DkUAcPPNN6O0tNT2s2/fPtu5RYsWYevWrdi4cSPy8/Nx8eJFzJgxQ8bWShn6dPfI63B1aiIiIu/z+9lnnTp1gk6nsztuMpnwl7/8BevWrcNdd90FAFi9ejUGDRqEL7/8EqNHj/Z1U+2M7tsNMVGhre6DpkLjbTJn53RcnZqIiMgn/H6k6NSpU0hMTESfPn0we/ZslJSUAAAOHz6Muro6pKWl2a4dOHAgevXqhYKCglZfs7a2FmazWfLjDSFqFZbPGNzqNY+OS+Lq1ERERH7Ar0PRqFGjsGbNGuTl5WHlypUoLi7G2LFjUVVVBaPRiLCwMMTExEiek5CQAKPR2Orr5ubmQqvV2n569uzptT5kpOixas5Q6DThkuM6TThWzRmKJVOSuTo1ERGRH1AJIZzdvfE7lZWV6N27N37/+98jMjISDz/8MGprayXXjBw5EnfeeSdeffVVp69TW1sreZ7ZbEbPnj1hMpmg0Wi80vYGi2h19em2zhMREZGU2WyGVqv12Pe339cUNRcTE4ObbroJp0+fxsSJE3H9+nVUVlZKRovKysoc1iA1Fx4ejvDw8Fav8bS2Vp/m6tRERETy8uvbZy1duXIFZ86cgV6vx7BhwxAaGopdu3bZzp88eRIlJSUwGAwytpKIiIiUyK9Hip566ilkZmaid+/euHjxIpYuXYqQkBDMmjULWq0W8+bNwxNPPIHY2FhoNBo89thjMBgMfjHzjIiIiJTFr0PR+fPnMWvWLFy+fBlxcXG4/fbb8eWXXyIuLg4A8MYbb0CtViMrKwu1tbVIT0/HO++8I3OriYiISIkUVWjtLZ4u1CIiIiLv8/T3t6JqioiIiIi8haGIiIiICAxFRERERAAYioiIiIgAMBQRERERAfDzKfm+Yp2A562NYYmIiMjzrN/bnppIz1AEoKqqCgC8ujEsEREReUdVVRW0Wm2HX4frFAGwWCy4ePEioqOjoVJ5fhNW64az586dC+h1kIKln0Dw9DVY+gkET1+DpZ8A+xqIWvZTCIGqqiokJiZCre54RRBHigCo1Wr06NHD6++j0WgC+pfVKlj6CQRPX4Oln0Dw9DVY+gmwr4GoeT89MUJkxUJrIiIiIjAUEREREQFgKPKJ8PBwLF26FOHh4XI3xauCpZ9A8PQ1WPoJBE9fg6WfAPsaiLzdTxZaExEREYEjRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFDkMS+99BJUKpXkZ+DAgbbzNTU1yMnJQbdu3dClSxdkZWWhrKxMxha7bu/evcjMzERiYiJUKhU2b94sOS+EwIsvvgi9Xo/IyEikpaXh1KlTkmsqKiowe/ZsaDQaxMTEYN68ebhy5YoPe9G2tvr50EMP2X3GGRkZkmuU0M/c3FyMGDEC0dHRiI+Px/Tp03Hy5EnJNa78vpaUlGDq1KmIiopCfHw8nn76adTX1/uyK21ypa/jx4+3+1znz58vucbf+7py5UqkpqbaFrQzGAz45JNPbOcD5fME2u5rIHyejixfvhwqlQoLFy60HQukz7U5R3312ecqyCOWLl0qbr75ZlFaWmr7uXTpku38/PnzRc+ePcWuXbvEV199JUaPHi1uu+02GVvsuu3bt4v//u//Fh9++KEAIDZt2iQ5v3z5cqHVasXmzZvFN998I+655x6RlJQkrl27ZrsmIyND3HLLLeLLL78U//znP0W/fv3ErFmzfNyT1rXVz7lz54qMjAzJZ1xRUSG5Rgn9TE9PF6tXrxaFhYXi6NGjYsqUKaJXr17iypUrtmva+n2tr68XKSkpIi0tTXz99ddi+/btonv37mLJkiVydMkpV/p6xx13iEceeUTyuZpMJtt5JfT1o48+Eh9//LH417/+JU6ePCmee+45ERoaKgoLC4UQgfN5CtF2XwPh82zp4MGD4sYbbxSpqaniV7/6le14IH2uVs766qvPlaHIQ5YuXSpuueUWh+cqKytFaGio2Lhxo+3Yt99+KwCIgoICH7XQM1qGBYvFInQ6nfjd735nO1ZZWSnCw8PF+vXrhRBCFBUVCQDi0KFDtms++eQToVKpxIULF3zWdnc4C0XTpk1z+hwl9lMIIcrLywUAkZ+fL4Rw7fd1+/btQq1WC6PRaLtm5cqVQqPRiNraWt92wA0t+ypE4z+2zf/xbUmpfe3atav485//HNCfp5W1r0IE3udZVVUl+vfvL3bs2CHpWyB+rs76KoTvPlfePvOgU6dOITExEX369MHs2bNRUlICADh8+DDq6uqQlpZmu3bgwIHo1asXCgoK5GquRxQXF8NoNEr6ptVqMWrUKFvfCgoKEBMTg+HDh9uuSUtLg1qtxoEDB3ze5o7Ys2cP4uPjMWDAAGRnZ+Py5cu2c0rtp8lkAgDExsYCcO33taCgAIMHD0ZCQoLtmvT0dJjNZpw4ccKHrXdPy75avffee+jevTtSUlKwZMkSXL161XZOaX1taGjAhg0bUF1dDYPBENCfZ8u+WgXS55mTk4OpU6dKPj8gMP87ddZXK198rtwQ1kNGjRqFNWvWYMCAASgtLcWyZcswduxYFBYWwmg0IiwsDDExMZLnJCQkwGg0ytNgD7G2v/kvovWx9ZzRaER8fLzkfKdOnRAbG6uo/mdkZGDGjBlISkrCmTNn8Nxzz2Hy5MkoKChASEiIIvtpsViwcOFCjBkzBikpKQDg0u+r0Wh0+Jlbz/kjR30FgAceeAC9e/dGYmIijh07hsWLF+PkyZP48MMPASinr8ePH4fBYEBNTQ26dOmCTZs2ITk5GUePHg24z9NZX4HA+TwBYMOGDThy5AgOHTpkdy7Q/jttra+A7z5XhiIPmTx5su3PqampGDVqFHr37o2///3viIyMlLFl5CkzZ860/Xnw4MFITU1F3759sWfPHkyYMEHGlrVfTk4OCgsLsW/fPrmb4nXO+vroo4/a/jx48GDo9XpMmDABZ86cQd++fX3dzHYbMGAAjh49CpPJhA8++ABz585Ffn6+3M3yCmd9TU5ODpjP89y5c/jVr36FHTt2ICIiQu7meJUrffXV58rbZ14SExODm266CadPn4ZOp8P169dRWVkpuaasrAw6nU6eBnqItf0tZzw075tOp0N5ebnkfH19PSoqKhTd/z59+qB79+44ffo0AOX1c8GCBdi2bRs+//xz9OjRw3bcld9XnU7n8DO3nvM3zvrqyKhRowBA8rkqoa9hYWHo168fhg0bhtzcXNxyyy148803A/LzdNZXR5T6eR4+fBjl5eUYOnQoOnXqhE6dOiE/Px8rVqxAp06dkJCQEDCfa1t9bWhosHuOtz5XhiIvuXLlCs6cOQO9Xo9hw4YhNDQUu3btsp0/efIkSkpKJPfBlSgpKQk6nU7SN7PZjAMHDtj6ZjAYUFlZicOHD9uu2b17NywWi+0XW4nOnz+Py5cvQ6/XA1BOP4UQWLBgATZt2oTdu3cjKSlJct6V31eDwYDjx49LQuCOHTug0WhstzH8QVt9deTo0aMAIPlcldDXliwWC2prawPq83TG2ldHlPp5TpgwAcePH8fRo0dtP8OHD8fs2bNtfw6Uz7WtvoaEhNg9x2ufq/v14eTIk08+Kfbs2SOKi4vFF198IdLS0kT37t1FeXm5EKJx6mSvXr3E7t27xVdffSUMBoMwGAwyt9o1VVVV4uuvvxZff/21ACB+//vfi6+//lp8//33QojGKfkxMTFiy5Yt4tixY2LatGkOp+Tfeuut4sCBA2Lfvn2if//+fjdVvbV+VlVViaeeekoUFBSI4uJisXPnTjF06FDRv39/UVNTY3sNJfQzOztbaLVasWfPHsn01qtXr9quaev31Tr9ddKkSeLo0aMiLy9PxMXF+d1U37b6evr0afHyyy+Lr776ShQXF4stW7aIPn36iHHjxtleQwl9ffbZZ0V+fr4oLi4Wx44dE88++6xQqVTis88+E0IEzucpROt9DZTP05mWM7AC6XNtqXlfffm5MhR5yP333y/0er0ICwsTN9xwg7j//vvF6dOnbeevXbsmfvnLX4quXbuKqKgoce+994rS0lIZW+y6zz//XACw+5k7d64QonFa/gsvvCASEhJEeHi4mDBhgjh58qTkNS5fvixmzZolunTpIjQajXj44YdFVVWVDL1xrrV+Xr16VUyaNEnExcWJ0NBQ0bt3b/HII49Ipn8KoYx+OuojALF69WrbNa78vp49e1ZMnjxZREZGiu7du4snn3xS1NXV+bg3rWurryUlJWLcuHEiNjZWhIeHi379+omnn35asv6JEP7f15///Oeid+/eIiwsTMTFxYkJEybYApEQgfN5CtF6XwPl83SmZSgKpM+1peZ99eXnqhJCCNfHlYiIiIgCE2uKiIiIiMBQRERERASAoYiIiIgIAEMREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFBEREREBYCgiIuqQhx56CNOnT5e7GUTkAQxFROQVDz30EFQqFZYvXy45vnnzZqhUKpla1X5nz56FSqWybURJRIGHoYiIvCYiIgKvvvoqfvjhB7mbQkTUJoYiIvKatLQ06HQ65ObmOr1m3759GDt2LCIjI9GzZ088/vjjqK6uBgD88Y9/REpKiu1a6yjTqlWrJO/x/PPP2x5v3boVI0aMQEREBLp37457773Xdu5vf/sbhg8fjujoaOh0OjzwwAMoLy+3nf/hhx8we/ZsxMXFITIyEv3798fq1asBAElJSQCAW2+9FSqVCuPHj3fYH4vFgtzcXCQlJSEyMhK33HILPvjgAzf+1ohILgxFROQ1ISEh+O1vf4u33noL58+ftzt/5swZZGRkICsrC8eOHcP777+Pffv2YcGCBQCAO+64A0VFRbh06RIAID8/H927d8eePXsAAHV1dSgoKLAFlI8//hj33nsvpkyZgq+//hq7du3CyJEjbe9XV1eHV155Bd988w02b96Ms2fP4qGHHrKdf+GFF1BUVIRPPvkE3377LVauXInu3bsDAA4ePAgA2LlzJ0pLS/Hhhx867HNubi7++te/YtWqVThx4gQWLVqEOXPmID8/v0N/l0TkA4KIyAvmzp0rpk2bJoQQYvTo0eLnP/+5EEKITZs2Ces/PfPmzROPPvqo5Hn//Oc/hVqtFteuXRMWi0V069ZNbNy4UQghxJAhQ0Rubq7Q6XRCCCH27dsnQkNDRXV1tRBCCIPBIGbPnu1yGw8dOiQAiKqqKiGEEJmZmeLhhx92eG1xcbEAIL7++mun/aypqRFRUVFi//79kmvmzZsnZs2a5XK7iEgeHCkiIq979dVX8e677+Lbb7+VHP/mm2+wZs0adOnSxfaTnp4Oi8WC4uJiqFQqjBs3Dnv27EFlZSWKiorwy1/+ErW1tfjuu++Qn5+PESNGICoqCgBw9OhRTJgwwWk7Dh8+jMzMTPTq1QvR0dG44447AAAlJSUAgOzsbGzYsAFDhgzBM888g/3797vVz9OnT+Pq1auYOHGipE9//etfcebMGbdei4h8r5PcDSCiwDdu3Dikp6djyZIlkttVV65cwS9+8Qs8/vjjds/p1asXAGD8+PH405/+hH/+85+49dZbodFobEEpPz/fFmwAIDIy0mkbqqurkZ6ejvT0dLz33nuIi4tDSUkJ0tPTcf36dQDA5MmT8f3332P79u3YsWMHJkyYgJycHLz++usu9fPKlSsAGm/j3XDDDZJz4eHhLr0GEcmHoYiIfGL58uUYMmQIBgwYYDs2dOhQFBUVoV+/fk6fd8cdd2DhwoXYuHGjrXZo/Pjx2LlzJ7744gs8+eSTtmtTU1Oxa9cuPPzww3av89133+Hy5ctYvnw5evbsCQD46quv7K6Li4vD3LlzMXfuXIwdOxZPP/00Xn/9dYSFhQEAGhoanLY1OTkZ4eHhKCkpkYQ1IlIGhiIi8onBgwdj9uzZWLFihe3Y4sWLMXr0aCxYsAD/9V//hc6dO6OoqAg7duzAH//4RwCNQadr165Yt24dtm3bBqAxFD311FNQqVQYM2aM7fWWLl2KCRMmoG/fvpg5cybq6+uxfft2LF68GL169UJYWBjeeustzJ8/H4WFhXjllVckbXzxxRcxbNgw3HzzzaitrcW2bdswaNAgAEB8fDwiIyORl5eHHj16ICIiAlqtVvL86OhoPPXUU1i0aBEsFgtuv/12mEwmfPHFF9BoNJg7d65X/m6JyDNYU0REPvPyyy/DYrHYHqempiI/Px//+te/MHbsWNx666148cUXkZiYaLtGpVJh7NixUKlUuP32223P02g0GD58ODp37my7dvz48di4cSM++ugjDBkyBHfddZdt1lhcXBzWrFmDjRs3Ijk5GcuXL7e7LRYWFoYlS5YgNTUV48aNQ0hICDZs2AAA6NSpE1asWIH/+Z//QWJiIqZNm+awj6+88gpeeOEF5ObmYtCgQcjIyMDHH39sm9JPRP5LJYQQcjeCiIiISG4cKSIiIiICQxERERERAIYiIiIiIgAMRUREREQAGIqIiIiIADAUEREREQFgKCIiIiICwFBEREREBIChiIiIiAgAQxERERERAIYiIiIiIgDA/wfaBA6fb5vNOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def myfunc(x):\n",
    "    return slope * x + intercept\n",
    "\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel, color=\"orange\")\n",
    "plt.xlabel(\"Newcastle\")\n",
    "plt.ylabel(\"ICI 1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}