x_Year_y_Temperature_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>14.867167</td>\n",
       "      <td>0.308167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>14.991833</td>\n",
       "      <td>0.312083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>15.006500</td>\n",
       "      <td>0.316417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>14.955167</td>\n",
       "      <td>0.283833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>14.991000</td>\n",
       "      <td>0.276417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  LandAndOceanAverageTemperature  \\\n",
       "0  1850                       14.867167   \n",
       "1  1851                       14.991833   \n",
       "2  1852                       15.006500   \n",
       "3  1853                       14.955167   \n",
       "4  1854                       14.991000   \n",
       "\n",
       "   LandAndOceanAverageTemperatureUncertainty  \n",
       "0                                   0.308167  \n",
       "1                                   0.312083  \n",
       "2                                   0.316417  \n",
       "3                                   0.283833  \n",
       "4                                   0.276417  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Data/temp_data-AllYears.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let X be world population and y be global average yearly temperature, then Reshape X and y\n",
    "X = dataset['Year'].values.reshape(-1,1)\n",
    "y = dataset['LandAndOceanAverageTemperature'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the train - test data to be 80% train, 20% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the algorithm using a linear regression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The y intercept is:   [5.03304052]\n",
      "The slope is:   [[0.00527395]]\n"
     ]
    }
   ],
   "source": [
    "#To retrieve the intercept:\n",
    "print(\"The y intercept is:  \", regressor.intercept_)\n",
    "#For retrieving the slope:\n",
    "print(\"The slope is:  \", regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.854417</td>\n",
       "      <td>15.649500</td>\n",
       "      <td>-0.204917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.344167</td>\n",
       "      <td>15.496555</td>\n",
       "      <td>0.152388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.345000</td>\n",
       "      <td>15.280323</td>\n",
       "      <td>-0.064677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.954667</td>\n",
       "      <td>15.079913</td>\n",
       "      <td>0.125246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.600333</td>\n",
       "      <td>15.575664</td>\n",
       "      <td>-0.024669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.810417</td>\n",
       "      <td>15.074639</td>\n",
       "      <td>0.264222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.758250</td>\n",
       "      <td>14.826764</td>\n",
       "      <td>0.068514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.021083</td>\n",
       "      <td>14.816216</td>\n",
       "      <td>-0.204868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.340667</td>\n",
       "      <td>15.501829</td>\n",
       "      <td>0.161162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.776000</td>\n",
       "      <td>15.111557</td>\n",
       "      <td>0.335557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.264667</td>\n",
       "      <td>15.401624</td>\n",
       "      <td>0.136957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.441583</td>\n",
       "      <td>15.522925</td>\n",
       "      <td>0.081341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.139500</td>\n",
       "      <td>15.317241</td>\n",
       "      <td>0.177741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.261167</td>\n",
       "      <td>15.306693</td>\n",
       "      <td>0.045526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.073333</td>\n",
       "      <td>15.058817</td>\n",
       "      <td>-0.014516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.598000</td>\n",
       "      <td>15.533473</td>\n",
       "      <td>-0.064527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15.713833</td>\n",
       "      <td>15.565116</td>\n",
       "      <td>-0.148717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.131083</td>\n",
       "      <td>15.454363</td>\n",
       "      <td>0.323280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.969000</td>\n",
       "      <td>15.027174</td>\n",
       "      <td>0.058174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15.330750</td>\n",
       "      <td>15.290871</td>\n",
       "      <td>-0.039879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15.238667</td>\n",
       "      <td>15.449089</td>\n",
       "      <td>0.210423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.963250</td>\n",
       "      <td>14.963886</td>\n",
       "      <td>0.000636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.031917</td>\n",
       "      <td>15.085187</td>\n",
       "      <td>0.053270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.292917</td>\n",
       "      <td>15.433268</td>\n",
       "      <td>0.140351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14.909417</td>\n",
       "      <td>14.926969</td>\n",
       "      <td>0.017552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15.826583</td>\n",
       "      <td>15.596760</td>\n",
       "      <td>-0.229823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15.106000</td>\n",
       "      <td>15.348884</td>\n",
       "      <td>0.242884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14.881500</td>\n",
       "      <td>14.832037</td>\n",
       "      <td>-0.049463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.027833</td>\n",
       "      <td>15.174844</td>\n",
       "      <td>0.147011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.816000</td>\n",
       "      <td>14.984982</td>\n",
       "      <td>0.168982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14.914750</td>\n",
       "      <td>15.021900</td>\n",
       "      <td>0.107150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15.262750</td>\n",
       "      <td>15.296145</td>\n",
       "      <td>0.033395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>15.829167</td>\n",
       "      <td>15.591486</td>\n",
       "      <td>-0.237681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>14.819083</td>\n",
       "      <td>15.106283</td>\n",
       "      <td>0.287199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual  Predicted     Error\n",
       "0   15.854417  15.649500 -0.204917\n",
       "1   15.344167  15.496555  0.152388\n",
       "2   15.345000  15.280323 -0.064677\n",
       "3   14.954667  15.079913  0.125246\n",
       "4   15.600333  15.575664 -0.024669\n",
       "5   14.810417  15.074639  0.264222\n",
       "6   14.758250  14.826764  0.068514\n",
       "7   15.021083  14.816216 -0.204868\n",
       "8   15.340667  15.501829  0.161162\n",
       "9   14.776000  15.111557  0.335557\n",
       "10  15.264667  15.401624  0.136957\n",
       "11  15.441583  15.522925  0.081341\n",
       "12  15.139500  15.317241  0.177741\n",
       "13  15.261167  15.306693  0.045526\n",
       "14  15.073333  15.058817 -0.014516\n",
       "15  15.598000  15.533473 -0.064527\n",
       "16  15.713833  15.565116 -0.148717\n",
       "17  15.131083  15.454363  0.323280\n",
       "18  14.969000  15.027174  0.058174\n",
       "19  15.330750  15.290871 -0.039879\n",
       "20  15.238667  15.449089  0.210423\n",
       "21  14.963250  14.963886  0.000636\n",
       "22  15.031917  15.085187  0.053270\n",
       "23  15.292917  15.433268  0.140351\n",
       "24  14.909417  14.926969  0.017552\n",
       "25  15.826583  15.596760 -0.229823\n",
       "26  15.106000  15.348884  0.242884\n",
       "27  14.881500  14.832037 -0.049463\n",
       "28  15.027833  15.174844  0.147011\n",
       "29  14.816000  14.984982  0.168982\n",
       "30  14.914750  15.021900  0.107150\n",
       "31  15.262750  15.296145  0.033395\n",
       "32  15.829167  15.591486 -0.237681\n",
       "33  14.819083  15.106283  0.287199"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare actual to predicted values\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten(), \"Error\": y_pred.flatten() - y_test.flatten()})\n",
    "# pd.DataFrame({\"Predicted\": predictions, \"Actual\": y, \"Error\": predictions - y})[[\"Predicted\", \"Actual\", \"Error\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart to show comparison between actual and predicted\n",
    "df1 = df.head(23)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our straight line with the test data\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Temperature (Celsius)\")\n",
    "plt.title(\"Scatter Plot of Global Average Temperature over Time \\n\")\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/Year_to_Temp_scatter.png', bbox_inches='tight')\n",
    "#fig1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.13596173454789362\n",
      "Mean Squared Error: 0.027025730486877658\n",
      "Root Mean Squared Error: 0.16439504398514468\n"
     ]
    }
   ],
   "source": [
    "# Calculate margin or error\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max X values (Year) 1850 2015\n"
     ]
    }
   ],
   "source": [
    "x_min = X.min()\n",
    "x_max = X.max()\n",
    "print(\"Min/Max X values (Year)\", x_min, x_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max Y values (Temperature) 14.740083333333333 16.058583333333335\n"
     ]
    }
   ],
   "source": [
    "# Get y (temperature min and max actuals)\n",
    "y_min_actual = y.min()\n",
    "y_max_actual = y.max()\n",
    "print(\"Min/Max Y values (Temperature)\", y_min_actual, y_max_actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 14.740083333333333\n",
      "Calculated Min Value: 13.038924169999994\n",
      "Actual Max Value: 16.058583333333335\n",
      "Calculated Max Value: 15.952543669999997\n"
     ]
    }
   ],
   "source": [
    "#The y intercept is:   [-19.62893083]\n",
    "#The slope is:   [[0.0176583]]\n",
    "y_min = -19.62893083 + 0.0176583 * x_min\n",
    "y_max = -19.62893083 + 0.0176583 * x_max\n",
    "print(f\"Actual Min Value: {y_min_actual}\")\n",
    "print(f\"Calculated Min Value: {y_min}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Calculated Max Value: {y_max}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 14.740083333333333\n",
      "Predicted Min Value: [[14.78984586]]\n",
      "Actual Max Value: 16.058583333333335\n",
      "Predicted Max Value: [[15.66004742]]\n"
     ]
    }
   ],
   "source": [
    "# Predict calculated values\n",
    "y_min_predicted = regressor.predict([[x_min]])\n",
    "y_max_predicted = regressor.predict([[x_max]])\n",
    "print(f\"Actual Min Value: {y_min_actual}\")\n",
    "print(f\"Predicted Min Value: {y_min_predicted}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Predicted Max Value: {y_max_predicted}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55JLfRSbBokVue9wwoTEMwyhDvvsuv+1REprQiMhgEZkpIp+lbD9DRL4QkYkickOGY/f2ab4WkYvDstEwDKNc6dAhv+1REmaNZgiwd/IGEdkNOBDYRlW3Am5MPUhEKoC7gH2ALYGjRGTLEO00DMMoO/r3h2bNVt3WrJnbHjdCExpVfQeYm7L5VOB6VV3q08xMc2hX4GtV/VZVlwGP4MTJMAzD8PTqBYMGQWUliLjPQYPiFwgApffRbA7sIiIfisjbIrJjmjQbAtOS1qf7bWkRkT4iUi0i1bNmzSqyuYZhGPGlVy+YMgVqatxnHEUGSi80DYFWwJ+AC4DHRERS0qSuA2imE6rqIFWtUtWqtm1DnVLBMAwjntTURG1BVkotNNOBp9TxEVADtEmTpn3S+kbAjBLZZxiGUT4sXAgXXgj77AOa8X08ckotNM8AfwUQkc2BxsDslDRjgM1EZGMRaQwcCTxXUisNwzBizLBh0Hu9EUxZcysYMICvl7aHpUujNisjYYY3Dwc+ADqJyHQROREYDGziQ54fAY5XVRWRdiIyAkBVVwCnA68AnwOPqerEsOw0DMOII5mGl3nqzhk0Of5whszsySKasQvvsO2Y/zLsySZRmpsV0RhXt/KlqqpKbSpnwzDizrBhrgf/d9+5fi/9+6/qyE8dXgZgzaYreePwe+n04L9prEu5mssYwAUspzHgos6mTMnfFhEZq6pVtbui7NhYZ4ZhGCUkyBhlqcPLbMs4Bi3uw47/G8Or/I3TuJtv+MMq543jiAAJbAgawzCMEhJkjLKEaDRnAQM4n2qqqGQqRzOMvXhlNZGBeI4IkMCExjAMoxZkG6o/sU8EGjZ0n1Onpj/P1Kku7WmnuXPtx/NMZCvO5ybu50S2YDLDOZp0PUDiOiJAAms6MwzDKJBszWCw6r6VK3Ofb+pUePae73mUMzmEp/iMrdiZUbzPzlmPi+uIAAlMaAzDMAokVzNY6r5sNGAlp3E3/bmERiznX1zLTZzHchpTUQFNm8KCBasfV1kZb5EBExrDMIyCKdZQ/dvzMQM5hR2p5mX24jTu5v/Y5Lf9K1em7ybTuHG8m8wSmI/GMAyjQDI54NdZx/lZctGcBdzEuYxhR9ozjSMZzj68tIrIJFi+fPXj11or/rUZMKExDMMomHRD9TdqBPPn5/bJHCTPMoktOZdbGEQftmAyj3Ik6Yd7TM/c1PHxY4oJjWEYRoGkG6q/RQtYtizzMRsxjac4mKf1IObRkp14n9O4h19oiQjsvvvv56uoyJ5/nEOakzGhMQzDqAWpQ/VnqmVUsIKzuJVJbMlevMKF/IcujGVMxU6IQOvWrsntjTdc+oceyj4oc9xDmpMxoTEMwyiATP1n0tUydmAsH9KNWzmHd9mFrZjIAC5kBY2oqXGisngxzJnjBmFOhEmvs076vCsq4h/SnIwJjWEYRp4k+s9MnbqqMAwbtqrfZi1+5VbO4iO6spHM4DAeoycvMoWNfztXhw6Zw6Qh/XTN//tf+YgMmNAYhmHkTbb+M716waCBSp+2TzOJLTmDO/hmj768PXAyI5odRrKzP9H8lSkceu7c8pmuORs2erNhGEaeNGiQfp4xEaiZ8h2ccQY89xxss41Thm7dgMyjNnfsmH5omkJHZM6HUozebDUawzCMACT7ZNL1kalgBf1a3gxbbgmvvQYDBkB19W8iA6sHDiRqJunCpMvJ2Z8LExrDMOo92QbGTOxP9smk9pGpYgxjZUcu+/k86NEDJk2C8893nWoCkC5MuhybyDJhQmMYRr0mm2M/QTqfDEDLBr9yB2fwId3YrOVM3jnzCTpOeJ4GG1emFaxsZKrt1AVsrDPDMOo1mRz7xx/vvvfqlc5ZrxzCk9xWcxYbyg9w+ulcuugabr2jxW++m3QTmtVXrEZjGEa9JlPE18qVv9dskvvGVDKF59mfJziMeY3WhdGjGdbtdm4d3GK1AIHUCc3qKyY0hmHUa7IN45IQiv79oUXT5ZzHjUxkK3rwFhc3uonx/x0DXbtyySXpo9Ag3lMslwoTGsMw6h3Jzv8FC9xw+5n47jvoteloprSt4kYu4HV2Z88NJ7H1A+dy9HENf0uTiXIZjyxMTGgMw6hXpDr/E8O+pAtZbsEv3Nf4n9Ts1J2F383hlLZPMf+hZ3l/eodV/C6ZxESk7oQo1wYTGsMwyprU0OTTTsscqjxsmHPypzr/ly+HVq2S+7Ioh/I4k9mC3kvv5XbOpDOfM2jWwfzjBKFNm1XPn64fjAj07WuBAACoaigLMBiYCXyWtO1K4HtgnF/2zXDsFGCCT1MdNM8uXbqoYRj1h6FDVZs1U3V1kvRLs2YuXa60Ii7Nzu2+1RfZRxV0XMUO2oUxgc9fWenOU1np1suBfMrYQpfQhqARkb8AC4AHVfWPftuVwAJVvTHHsVOAKlWdnU+eNgSNYdQv2rRxTV+5qKx0n+mGeUmwaYflfH3azdCvnxse+ZpraHT2P1kRoBdIKYaKCYuyHoJGVd8BymT+N8Mwyo1hw4KJDDhnfTaHfY81PmCMdoGLL4a99nI9+886iw0rg3U1tMiy7EThozldRD4VkcEi0ipDGgVeFZGxItIn28lEpI+IVItI9axZs4pvrWEYsSSf/ikdOqR32LfkZ+6VvryxbGda6c/wzDPw9NPQvj0A++7rfC1Bzm9kptRCcw+wKbAd8ANwU4Z0O6vqDsA+wD99M1xaVHWQqlapalXbtm2LbrBhxI1c43LVF4LWIhKDU67qsFeO4BE+pzMn6X3cqmezlUxi2IIDAXdP27SBe+5ZvX9Mw5RKTl0a/DI0wnQAAR1JCgYIui8l3ZXA+UHys2AAo66TzqGdcEbXNyor0zvnmzfP7JQfOlT1z+2+0ZfYSxV0jFTp9oxd5V6eemr2oIHWrcvT6Z8JShAMUNIajYhskLR6MPBZmjTNRWStxHdgz3TpDKM+km3CrXwot1pRuhDmBQtWT9esGQwcmGFwymXL6PXddbw7dyv2Xut9rmh1O910NJ+ww2/HL1rkRk1ON4Bmgrlz6+7gl6ERloIBw3HNY8uB6cCJwEO4sOVPgeeADXzadsAI/30TYLxfJgKXBM3TajRGXUck/Vu2SPBzxLVWlCk8OEgIc6KmkfEaRo1S3Worl/CQQ1SnT894L3MtlZWluR+lghLUaEI9eakXExqjrpFa+LZuXfvCL1OTU+o5shX8ie2tW7slSDNStn4m2cQvk72B7sHcuaonn6wKuqB1Bz2h7XM572VFReY84iDIxcaExoTGqKcMHZq+IGzUSLVx48ILv6FDMxeiiVpRpryD+C8y2ZKrFpVN/ILWPFap1dXUqA4bprruuqoVFTpx3/O1bdP5ge5lpmvMWmMqY0xoTGjyolx7JhurkqupqFBndK7zJs6VLU22t/1sNYtctahsTYJ512i++kp1jz3cxq5dVT/5JOM5Mt3L+vRfMqExoQlMXNvdjfwJUrBWVro373wKw2znzbeZKnDNwpOtVpIt3yDil7D/9D5L9YaW1+hi1tBfpIUO3+Uu3bjDirxtrW/EQmhwfV7OAK4DLgf+DqwdtmGFLPVZaIK2uxvxp1Anda4Xi1yFfW3yLrRGk7A7XXNV8vWk1jBSRfbGA9/RSdJZFfRRDtMN+L5gW+sbpRCajOHNInKMiIwF+gGtgKnAr8AewFsicr+IbFSU0Dej1mTqvGZDY8SbdGG76YarD0Ji+uFMoxZnOm9l5e8hurXt4Z6p82K60Y0TLFoEI0a4sOLKStcTv7LSrSfs6tXLhRI/9JBbv/de9/noPXOZssdJnPfsX2iqi9iXFzmCx/iBdgXbaoRAJgUCzgKaZ9lfBfwtbCXMZ7Eajb2xlRNBw3YLXYKMWpxaC6qNTRUVq9eoUiPUcjVjZWsCXNW2Gu3FQzqTtrqyQYX+hwu1GQvystealR3EoemsnJb6LDSnnrp6s4f5aOJNMfwhQZqGMuWTThhUVxWHIM7/ZKFIPU+qaAVpmsv03CauYzO+0JHsrgr6Ad10rw3G530v7QXsd2IhNDjfTAugIfAK8BNwdNiGFbLUV6HJ9Ic+9dSoLTOyUQx/SG2WII7wfGo4yYX30KH5iVQQIViDJXopV+li1tCfWVtP4R4VVv42j0xQO+0FbFXiIjTj/OdBuJ79bYDxYRtWyFJfhcaazUpHMcNeg76FpxOk1q3di0Su5qhstYigz0e2vjfpCu8ghX6ik2cusUnc51cveUu/bthJFXQ4R+j6zFjtOjIFDMDvolfXQ5ULIS5C85n/HISfETMhPnFb6qvQFGNYEiM3QUPIg4pR0LDdbGHMhYpVvrNCZsunEJsS4pApbcLe1szS+/mHKuistTbWA9d4yWonRSYuQjMAN6jleKCRr9F8GLZhhSz1VWisRlMagtznfPszZeqFn3jrz1WI5tP8lioo+diaT9ogNiWPQpDej1OjxzFEZ9Fal9FQr+Vi7dR+Yb3qSFkqYiE0zg7WBRr672sCG4ZtWCFLfRUa66xZGoLUHAsV/UIL0HxqNKnnzNfWIDYG9c0kjk/XtLU5k/UNeqiCjqK7bsWE1e6zUTxiITTA0emWsA0rZKmvQqNav4bMiIogBXOQTpHFpFBnfTZbCy3Qg9qSqYNmq6aLdfzBV+gSGutcWurJDFRhpdXQQyYuQnNP0vIAruPmU2EbVshSn4XGCJ9cNcdcb/PZerrXRoRSz5WruSpBtvG/CiGbvyV1hOfUtD14QyezuSroh5sdrevxo9XQS0QshGa1A9woAc+GbVghiwmNETa1nTMl09hdxSxI85kGoFGj1dM1blyYLfnUkBJp2zBTh3CcKujXbKJ/4xUL1S8xcRWahsDnYRtWyGJCE39K3cQXdn7FGoiymE1DmUQvXXBBMea3SZCPz6eyQ432ZrDOZh1dRkO9hn9rExZlrRFak3A4xEJogKeBp/zyDPAVMCBswwpZTGjiTamDFsLML1u0WKFLMZ3d2eaUSb7+YvlpguanqqqTJumPW/xFFfQd/qxb8tlvaXPdI2tCKz5xEZrdk5ZdgY5hG1XoYkJTfErRQTEsJ28YUVWJdGGMUVbs+xDk+vMdniaf+7FaDWrxYtXLLnPtda1a6Qcn/Vc7dliZ1XdTyuelvhILoSmnpVyFplTNO8WYKCvIG2Wm/ErdsTSf/II4+vMd/6tRo2A94BM2Fft3D3L9+Qy4mYlAgj5ypOof/uB2HHOM6k8/pT1XEBG3MOfiEqnQAG/7z5+BuUnLz8DcsA0rZClHoQm7Oak25y+kBpJt3LVivD0HJVsEWD7zpQSdeCvTcbnOn7wUm3yCAvK5V6lkFbSffnLCAk5oRo7Meb5cNRur0RSXqIWmgf+sSLeEbVghS7kITZAmgtoUvkHevoOcv5AaSLYQ12zzzYctrrnyKcZUwtnOn0uswig8i9GbP0jtId39EVbqhevcp9qqlavaXXaZazoLyX6jcGLRdAZ0BBr7738GTgNahG1YIUs5CE2ho8yG4T/I9actpEaTrZkoV5NTsQrbQmpO2fqUBBWZXEPG5OUwLxK1HcssyG+S+sx1ZqKOavBnt7LLLqqTJoVuv1E4cRGacX6Ms02Bb4E7gBfCNqyQpRyEJt9Q2Hz7XRRy/kwU8kZZm1DfdPOZFFLI5Pt2XmgEWUVFYQVgHAvP2tYehg5V3bz9Ir2GS3QpjXTJmuuo3n+/6sqV4Rpu1Jq4CM3H/vMC4Ez//ZOwDStkCUtoilkwFDIHST7t5/meP1fTSL7XPnRo4fOs1GZwymTyeTsvNIIs30I4bsKSjlrZ+corqptu6m7OccepzpwZkpVGsYmL0HwEHAZ8Cmzit30W4LjBwMzktMCVwPe+ljQuMe1AmmP3Br4AvgYuDnoxtRWadH+0YrYT5xqipLY1ANXi1WhqU+ikm+0z34K7mE05mX6z2vweYUfulQ0//qh69NHuwjbbTPX116O2yMiTuAjN1sDdwDF+fWPgkgDH/QXYIY3QnJ/juArgG2AToDFueoItg1xMbYQmXeHYqJFqgwaFF3jJhNH3orZv6MUYEj7b9QYJSMgkZLUNhc4llLX5PfL57Uvdd6hkrFypOnCgasuWbsyaK67I29lvxINYCE2tTu4CCfIVmp2AV5LW/wX8K8Lt344AACAASURBVEh+hQpNIc09+cby56ppVFY6sQv6hp1rjpNcx2ebcbDQwjFT4V5MX0/YAQO1Eec49B0qCRMmqHbv7i6kRw/VyZOjtsioBVGHN38CfJxpCXTy9EIzxTfDDQZapTnmUOC/SevHAndmyaMPUA1Ud+jQoaAbXUihk2+BF7TAyXciq0KiqHIV+oUUjvl0eEwVoXTNlWFGZwUR4kz3Lt8aYJ2q0SxcqHrxxaoNG7qbMWSIak1N1FYZtSRqodk02xLo5KsLzXq+aawB0B8YnOaYw9IIzR1B8iu0RlNbf0I28u18lq/o5dv8FWQQxWwilak5qlidOxs1ci0xQQv5fMnVZCayev65Rg/Ot7NnWfpoXnpJdeON3QX07q06a1bUFhlFIjZNZ8BGwG7++xpA84DHrSI0QfZF0XSWT+Ge2hcjmy8gV6GWyUGdr+8gU/+QdLYVOixJOgFItr+YnTvDrAFkyzNxPfkGQuS69nKJOkvLjBmqRxzhLqhTJ9U334zaIqPIxEJogBN8c9k3fn1z4LVAJ1+9RrNB0vdzgEfSHNMQ119n46RggK2C5FcbH00hI8fmelvNVZPJ5mNJHJvsS8llX5C3/nyGJUkuHDPVhGrTCTOfmmRtgwBy1SyhcAGoU81jCVauVL3nHtW113ZvGP36qS5ZErVVRgjERWjG+QL/k6RtEwIcNxz4AVgOTAdOBB4CJngfzXMJ4QHaASOSjt0X+BIXfZYzwi2x1CbqLFdBlK7WkKuAKfa0vkHEJlezTKFNOYX0jUlM2Ztvc1uhhXama8s29E0xRKHONI8l+PRT1T/9yV3IX/+q+sUXUVtkhEhchGa0//zEf1YEEZoolmJ02MxWaKS+Led6Ow7STFNb2wopNAtpysnXd1RRkb6ATw0QCNJElzwKciZ7s/WJyVXrKoYolHXzWIIFC1QvvNDdsDZtVB980Jz99YC4CM1NwIXA58BuwBPAdWEbVshSiNBkinoK0nEzyNt8sQdSDNLJMIzQ2XyvP5sYp44AkO3+t26d3TdUiG2ptpSlKBSbF19U7djR3ZQTT1SdPTtqi4wSERehqQBOxc20+Yz/3iBswwpZ8hWafJo8Cg2BzhZKW6gg5CpY8xGwoG/imUKOs9lQjD4kQcQq129TmyHw6zzff6962GHuhnTurPr221FbZJSYqMOb2wCd0mzvDLQO27BClnyFJh8nbiE+ikSBGoazuBh9TTINFZMaVFBIbS7fPiT5dniEYGky1SzL2odSDFasUL3zTtUWLVTXWEP1mmtUly6N2iojAqIWmocTIc0p2/cBhoZtWCFLvkKTzxt3bTp1huksLtQ3EGQ0hITg5Ou0z3dEgHTpck2WlkiTrcaSHMCRKszF6pdTlnzyiWrXru5G7LGH6ldfRW2RESFRC83ELPtyDqoZxRJmjaYQP0ByYRY3Z3FQ8Sgk7DuZINedyZZck6XlY1OdiwwrhAULVM8/3ynwuuu6izdnf70naqH5opB9US5h+mgS6ZMLzeTQ3datVZs3z68QjpJ8mgKzBR8UQzSz2ZLLz5VqZyZBq5N9XfLh+edVO3RwF33yyapz5kRtkRETohaaEcBeabbvCbwctmGFLMWKOiuUcirMChnqJiwBzdU8lo+9mWyqk4NbBmH6dNVDDnEXu9VWqu++G7VFRsyIWmi2AL4C/usjzU4F7vfbtgjbsEKWUs2wma/jOo6FWT5NgYlrDKvpL5u/KJufK0iTWYJyegkoCitWqN5+u+paa6k2aaJ67bXm7DfSEnl4M9AEOBm4zS99gKZhG1XoUgqhydbclqkwyzZXfZSk9leJsukvXQRcOj9LUGFMd631xkfz8ceqVVXuIvfcU/Xrr6O2yIgxkQtNuS2lEJp8R+ott0ItyqCF2gQOBKlBxi0go+jMn696zjlutr711lMdPtyc/UZOSiE04vJZHRF5E3gMeFZVZyRtbwh0B44HRqnqA2lPEAFVVVVaXV0dah4NGrjiLBURqKmBYcPg+ONh5crV01RWwpQpoZpX5xk2DPr0gUWLMqepl/f5uefg9NNh2jTo2xeuuw5atozaKqMMEJGxqloVZh4NsuzrCTQCnhaR6SLyqYh8hRtZ+R/APXESmVLRoUP27b16OcFJx3ffhWNTfaJXLxg0yIkJOIFPplkz6N+/9HZFxrRpcPDBcOCBsPba8N57cM89JjJGrMgoNKq6SFVvV9VuuMnOegI7qWoHVf2HqoZbdYgp/fu7wiyZ1MItlxgZtaNXL1djUYWHHnKiI+I+Bw1y++s8K1fCbbfBllvCK6/A9dfDxx9D9+5RW2YYq5GtRvMbqrpUVaep6uywDYo7yW/UmQq3IGJkFIeE6NTUuM96ITJjx0K3bnD22fDnP8PEiXDRRdCoUdSWGUZaAgmNsSq5CrcgYmQYeTN/vhOXrl3h++/h0UdhxAjYeOOoLTOMrDSM2oC6Sq9eJixGEXnmGefsnzEDTj3VVY/ND2OUCYFqNCKykYjs5r+vISLNwzXLMAzARZAceKBz+LduDR98AHfdZSJjlBU5hUZETsBNu/xfv6kSeDZMowyj3rNiBdx8s3P2v/YaDBgA1dXON2MYZUaQprMzga7AhwCq+qWIrBuqVYZRnxkzxnUWGjcOevaEO++Ejh2jtsowCiZI09kSVV2WWBGRCkCypDcMoxB+/RXOPNPVWn76CZ54Ap5/3kTGKHuCCM17InIh0MT7aR4FXgjXLMOoR6jCk09C586u9vLPf8Lnn8Mhh6zeI9UwypAgQnMhMB+YDJwFvA5cEqZRhlFvmDoVDjgADj0U1l0XRo+GO+5wvfwNo46Q1Ufjm8kGq+rxwD2lMckw6gErVrie/Zdf7tZvusk1mzW0HgdG3SPrU62qK0VkAxFppKrLS2WUYdRpPvwQTjkFxo+H/fd3NZjE4G2GUQcJ0nT2LfCuiPxLRM5MLLkOEpHBIjJTRD5Ls+98EVERaZPh2JUiMs4vzwWw0TDizy+/OP/LTjvB7Nnw1FPw7LMmMkadJ0g9fRYwEmjml6AMAe4EHkzeKCLtgb8B2cYyXqyq2+WRl2HEF1UXQXbWWS6a7Mwz4eqrYa21orbMMEpCTqFR1csKObGqviMiHdPsugUXYGCdPo26z//9n6vFvPQS7LCDmzemKtSpPwwjduQUGhEZCaw21Zeq7plvZiJyAPC9qo6X7GGbTUSkGlgBXK+qz2Q5Zx/cFNN0sHH4jbiwfDnccgtceSVUVLjvp59uzn6jXhLkqb806XsT4BBgab4ZiUgzXFh0EIHqoKozRGQT4A0RmaCq36RLqKqDgEHgZtjM1y7DKDoffOCc/RMmwEEHwe23Q/v2UVtlGJERpOnsw5RNb4vI2wXktSmwMZCozWwEfCwiXVX1x5Q8Z/jPb0XkLWB7IK3QGEZsmDcP/vUvGDgQNtwQnn7aCY1h1HOCNJ21SFptAHQBNsg3I1WdAPw2RpqITAGqUidTE5FWwCJVXeqj0nYGbsg3P8MoGarw2GPO2T9rlpszpl8/c/YbhidI09lEnI9GcD6T/wNOznWQiAwHegBtRGQ6cIWq3p8hbRXQV1VPAjoDA0WkBids16vqpAB2Gkbp+fZbOO00N51yly5uIrIddojaKsOIFUGEZpPUzpoiEqTJ7agc+zsmfa8GTvLf3we2DmCXYUTH8uWuN3+/fs7Bf/vtTnAqKqK2zDBiR5AOm6k+GoCPim2IYZQN770H22/v/DH77usGwDzjDBMZw8hAxpqJn3NmA6CpiGzN71MDtCC/jpuGUTf4+We4+GIYNAg6dHB9YvbfP2qrDCP2ZGsC6wmcgIsOuztp+3ygoE6chlGWqMLw4XDOOTBnDpx3nusfs+aaUVtmGGVBRqFR1QeAB0TkcFV9rIQ2GUZ8+OYbOPVUGDkSunZ1Tv/tbHQkw8iHIE79x0RkL2ArXIfNxPZrwzTMMCJl2TIYMACuuQYaNXITkvXta34YwyiAIP1o7gZaAn8BHsCNDDA6ZLsMIzrefdf17P/8czch2W23Qbt2UVtlGGVLkKizP6vq0cAcP8BmN5zfxjDqFnPnwsknw1/+AosWwQsvwOOPm8gYRi0JIjRLEp8isr5f7xiaRYZRalRh6FDYYgt44AG44AKYOBF69ozaMsOoEwTpsDlCRFoCNwLjgJXA/0K1yjBKxVdfOWf/669Dt27O6b/ttlFbZRh1iqxCIyINgJdUdR7wuIi8ADRV1bklsc4wwmLpUrjhBujfH5o0gbvvhj59zNlvGCGQVWhUtUZEbgP+5NcXA4tLYZhhhMbbb7sIssmT4Ygj3FwxG+Q9TqxhGAEJ4qMZKSIHhm6JYYTNnDlwwgnQowcsWeIGwHzkERMZwwiZID6a04G1RWQprjYjgKrqOqFaZhjFQhUeesj16J83Dy66CC6/HJrZSEqGUQqCCE2b0K0wjLD44gvn7H/zTdhpJzcp2dY2OLhhlJKcTWequhI4DLjIf98AsDE4jHizdKkbwn+bbeDjj+Hee2HUKBMZw4iAnEIjIncCuwHH+k2LgHvDNMowasVbbzmBufJKOOQQ5/Q/5RRoEMQlaRhGsQnyz+uuqqfgO2760ObGoVplGIUwezb07g277QYrVrgBMB9+GNZfP2rLDKNeE0Rolvv+NAogIq2BmlCtMox8UHU9+jt1gmHD4N//hs8+gz33jNoywzAIJjR3AU8CbUWkHzAK+E+oVhlGUCZPdjWYE06Azp1h3DjXCbNp06gtMwzDE2SagAdFZCywh990mKp+Fq5ZhpGDJUvg2mvh+uuheXO47z4nNuaHMYzYESS8GaACWI5rPrN/shEtr7/uQpa/+gp69YKbboL11ovaKsMwMhAk6uwSYDjQDjc9wMMi8q+wDTOM1Zg1C447DvbYw/llRo50oy6byBhGrAlSozkG6KKqiwBEpD8wFrguTMMM4zdqan4fvn/BArj0UufwNz+MYZQFQZrBprKqIDUEvg1ychEZLCIzRWQ1n46InC8iKiJpRx4QkeNF5Cu/HB8kP6MOMmmSG5vspJPgj3+E8ePh6qtNZAyjjAgiNIuAiSLyXxG5D5gAzBORm0Xk5hzHDgH2Tt0oIu2BvwHfpTtIRNYBrsDN5tkVuEJEWgWw1agrLF7sai7bbecmIbv/ftcRs3PnqC0zDCNPgjSdveiXBKODnlxV3xGRjml23QJcCDyb4dC9gJGJeW9EZCROsIYHzdsoY0aOdM7+b75xPpkbb4S2baO2yjCMAgkS3nx/MTMUkQOA71V1vIhkSrYhMC1pfbrflu58fYA+AB06dCiipUbJ+eknOPdc15t/s81cdNlf/xq1VYZh1JIgUWd7i8gY72uZKyI/i0hBM2yKSDPgEuDyXEnTbNN0CVV1kKpWqWpVW3vrLU9qalw/mC22gCeegCuugE8/NZExjDpCkKazO4HDcb6Z2g49symwMZCozWwEfCwiXVX1x6R004EeSesbAW/VMm8jjnz2mZvt8r33YNdd3SjLW2wRtVWGYRSRIMEA04FxqrpcVVcmlkIyU9UJqrquqnZU1Y7+3DukiAzAK8CeItLKBwHs6bcZdYVFi1yI8vbbu2FkHnjAzRljImMYdY4gNZoLgedF5C1gaWKjqt6e60ARGY6rmbQRkenAFZl8PiJSBfRV1ZNUda6IXA2M8buvSgQGGHWAV16B006Db791oy0PGABtbH49w6irBBGafrjhZ1qSZ9OZqh6VY3/HpO/VwElJ64OBwfnkZ8ScH3+Ec86BRx5xIy2/+abrI2MYRp0miNCsq6pdQrfEqLvU1MCgQXDxxa5/TL9+cNFFsMYaUVtmGEYJCOKjeV1ELPzHKIwJE+DPf3b9Yrp0ceuXX24iYxj1iCBCczLwmogsqG14s1GPWLjQ1Vp22MGNsvzgg/Daa7D55lFbZhhGiQnSdGZeWiM/XnrJOfunTHFzxNxwA7RuHbVVhmFERM4ajQ9lPgy4yH/fANgubMOMMmTGDDj8cNh3X2jSBN5+241RZiJjGPWaICMD3AnsBhzrNy0C7g3TKKPMWLkS7r7bDXj53HNudOVx4+Avf4naMsMwYkCQprPuqrqDiHwC4Pu4NA7ZLqNcGD8eTjkFPvzQTUh2991unDLDMAxPkGCA5SLSAD/WmIi0pvZD0RjlzsKFbiKyLl1cx8uhQ+HVV01kDMNYjYxCIyKJ2s5dwJNAWxHpB4wC/lMC24y48sILsOWWbvj+E05wQ8j06gWZR+M2DKMek63p7CPcOGQPishYYA/cqMqHqepqM2Ya9YDvv4ezzoInn3RC8+67ro+MYRhGFrIJzW+vp6o6EZgYvjlGLEk4+y+5BJYvh2uvhfPOg8bmqjMMIzfZhKatiJybaaeq5prG2agLfPIJ9OkD1dWw555OcDbdNGqrDMMoI7IFA1QAawJrZViMusyCBa7WUlUF06a5WS9fftlExjCMvMlWo/lBVa8qmSVGfHjuOTj9dCcwp5wC110HrVpFbZVhGGVKthqNhRDVN6ZPh7//HQ48ENZe2816ee+9JjKGYdSKbEKze8msMKJl5Uq4/XbXs//ll+H66+Hjj6F796gtMwyjDpCx6cxmtKwnjB3rmsfGjoW994a77oJNNonaKsMw6hBBRgYw6iLz58PZZ0PXrq5/zKOPwogRJjKGYRSdIGOdGXWNZ56BM85wAtO3r+sX07Jl1FYZhlFHsRpNfWLaNDjoIDj4YFhnHXj/fdcvxkTGMIwQMaGpD6xYAbfc4pz9r77qJiKrroY//SlqywzDqAdY01ldp7ra9ez/5BM3Idldd0HHjlFbZRhGPcJqNHWVX3+FM8+Ebt3gxx/h8cfdqMsmMoZhlBir0dQ1VOHpp52z/4cf4LTToH9/1wHTMAwjAkKr0YjIYBGZKSKfJW27WkQ+FZFxIvKqiLTLcOxKn2aciDwXlo11jqlT4YAD4JBDoG1bGD0a7rzTRMYwjEgJs+lsCLB3yrYBqrqNqm4HvABcnuHYxaq6nV8OCNHGusGKFXDTTW6OmDfecN+rq10fGcMwjIgJrelMVd8RkY4p235NWm2Onx7aqAUffuh69o8fD/vt52owlZVRW2UYhvEbJQ8GEJH+IjIN6EXmGk0TEakWkdEiclCO8/XxaatnzZpVdHtjyy+/uBGWd9oJZs92s14+95yJjGEYsaPkQqOql6hqe2AYcHqGZB1UtQo4GrhVRDJOgqKqg1S1SlWr2rZtG4LFMUPVRZB17uw6W55xBkya5EZdFhtw2zCM+BFlePPDwCHpdqjqDP/5LfAWsH3pzIoxU6a45rHDD4f113fNZrfdBi1aRG2ZYRhGRkoqNCKyWdLqAcDkNGlaicga/nsbYGdgUmksjCnLl8OAAc7Z//bbcPPN8NFHsOOOUVtmGIaRk9CCAURkONADaCMi04ErgH1FpBNQA0wF+vq0VUBfVT0J6AwMFJEanBBer6r1V2g++MA5+ydMcBOS3XEHtG8ftVWGYRiBCTPq7Kg0m+/PkLYaOMl/fx/YOiy7yoZ58+Df/3YzXG64oeuEeVDWuAjDMIxYYkPQxA1VNzdM584wcCCcdZZz9pvIGIZRptgQNHHi22/hn/900yl36QIvvgg77BC1VYZhGLXCajRxYPlyuP562GorGDXKRZJ9+KGJjGEYdQKr0UTN++87Z/9nn7m+MLfdBhttFLVVhmEYRcNqNFHx889OYHbe2fXyf/ZZ17vfRMYwjDqGCU2pUYXhw2GLLeD+++Hcc52z/wAbO9QwjLqJNZ2Vkm++cfPDvPqq62z58suwvQ16YBhG3cZqNKVg2TK49lr44x9dB8w77nCfJjKGYdQDrEYTNqNGOV/MpElw6KFw662uA6ZhGEY9wWo0YTF3Lpx8MuyyCyxcCC+84EZdNpExDKOeYUJTbFRh6FDn7H/gAbjgApg4EXr2jNoywzCMSLCms2Ly1Vdw6qnw+uvQrRuMHAnbbhu1VYZhGJFiNZpisHQpXHMNbL01jBnjJiR77z0TGcMwDKxGU3veecc5+ydPdhOS3XorbLBB1FYZhmHEBqvRFMqcOXDiibDrrrBkCYwY4UZdNpExDMNYBROafFGFBx90zv4HH4SLLnLO/n32idoywzCMWGJNZ/nw5ZfO2f/GG7DTTm6+mK1tjjbDMIxsWI0mCEuXQr9+TlTGjnWzXo4aZSJjGIYRAKvR5OKtt6BvX/jiCzjqKLj5Zlh//aitMgzDKBusRpOJ2bOhd2/YbTc3VtnLL8PDD5vIGIZh5IkJTSqqMGSIc/YPGwb/+peblGyvvaK2zDAMoyyxprNkJk92zWRvv+0mJBs40E2vbBiGYRSM1WjA9YO54grYZhsYPx4GDXIdMU1kDMMwak2oQiMig0Vkpoh8lrTtahH5VETGicirItIuw7HHi8hXfjk+NCN/+cUJzFVXuZ79kye7UZcbmAYbhmEUg7BL0yHA3inbBqjqNqq6HfACcHnqQSKyDnAF0A3oClwhIq1CsXDtteHgg92sl0OHwnrrhZKNYRhGfSVUH42qviMiHVO2/Zq02hzQNIfuBYxU1bkAIjISJ1jDQzH0P/8J5bSGYRhGRMEAItIfOA74BdgtTZINgWlJ69P9tnTn6gP0AejQoUNxDTUMwzBqTSSOCFW9RFXbA8OA09MkkXSHZTjXIFWtUtWqtm3bFtNMwzAMowhE7fF+GDgkzfbpQPuk9Y2AGSWxyDAMwygqJRcaEdksafUAYHKaZK8Ae4pIKx8EsKffZhiGYZQZofpoRGQ40ANoIyLTcZFk+4pIJ6AGmAr09WmrgL6qepKqzhWRq4Ex/lRXJQIDDMMwjPJCVNO6PsqSqqoqra6ujtoMwzCMskFExqpqVZh5RO2jMQzDMOo4JjSGYRhGqNSppjMRmYXz+xRCG2B2Ec0pFWZ3aTG7S4vZHT6Vqhpq35A6JTS1QUSqw26nDAOzu7SY3aXF7K4bWNOZYRiGESomNIZhGEaomND8zqCoDSgQs7u0mN2lxeyuA5iPxjAMwwgVq9EYhmEYoWJCYxiGYYSKCY1hGIYRKiY0hlEgIpJu3qSyo5yuo5xsTSAizURkjajtiBITmhREZAsR2UVE1hCRhn5b7B9uEdlfRC6M2o58Kef7DVxdDramIiI7iMiBIlIpIs1UVUUk9mVBOd5zEfk7MBR4SUT2E5FNorYpCiKZyjmu+IfieuD/cMNHjBWRIX7aAtGYhuiJyJ7AVUBZCU0Z3++9cPf7glQb42w3gIgcANwIjAMOBuaLyLWq+oOINFDVmmgtTE853nMR2Rj3fPcCOgH7AF1E5FlVHRepcSUm9m8xpcK/TR8CnKiqewGPA+sBF4jIOnF8kAFE5E+4KbH7qupIEWkpIhuLyFqJGkIcKeP7vS0wEOinqq/5yfm297WD5r52EOc37n1whfXhwO3Az8CtIrJ+jEVmW1y/lHK75y2A6ao6RlWHAg/gXu73F5HKaE0rLSY0v9MAWBvYGkBVnwGe8/uOjnHTwkxgEbCJiLQBngXuBP4H9I5x27BQnvd7NvAtbjK/7YDngSuBm4BLRKRFjEWyAqgAOgOo6sfAfbhZbi8QkaYRmpeNmcA3lNk9V9XxwDwROcOvV+Oe8Q7AFlHaVmri+mcuGd5H0FZVlwH/AfYWkd397vdxTQy7ELN7JSJ7icjeqvotsDeuWWECMFRVewJP46bAbhWhmashIo1FpImqLgduAPYSkT387jjf74Yi0lRVvwdOBg4DngT+p6oH4moHHYDYvan6t/8NVXUl7iXkSBE5zO+eDrwErIsT/tggIn8TkRNV9QfK5J6LSA8ROVxEjvWbHgQqReQIAFUdA3wAnCYijaKys9TE6s9cakRkH+BuXBUXYDwwAjhKRPZQx6NAa2C7iMxcDS+ETwB3ikhHVf0cJzb9VPU+AFV9CGgObBSdpasiIocADwPPecfud7ga2OEi8rcY3+99gSHAsyKyu6p+A/QBrki63+8ATYD1IzM0Dd63cQ/QEkBVP8XVBv4hIof7ez7a7986MkNT8C8fjwB3iEgnf89PIsb3XER2A4bjxO9sEbkZ+ALng+wqIuf5pIuBBbhafb0gtm34YSMiPYFrgZP9Q4yq/ioiD+ME+DwR2Qr4FdgQ9+YXOb7Q6w8cA3QBtgGm+Gv4JindoUA74mP35sA1wAlAR9wb6kvAlz7JOSKyJfG73/vgHLoX4AqQoSLS0zc7TU1KdwiwKa4ZKhaIyH64Z7y3qk5MOPtV9Rnv0rhKRDoB84E/EBPb/X+zP9ATV7vdW0S+UdVpuAiuRLrY3HPvI9oHuEFVbxGRO3E+mWNxLyl/APr4/+8GQC/filIvqJdCIyLNcA/Az6r6kYi0AE7HNR08oap3i8inwKk4/8fRqvpjdBY7vAOxD3Cmqr4rIl2Ac/ndt5F44I8HLgIOjYPdnlbAT6r6AfCBiEwFegM1OPu/AE4jXvd7TVzhcamqvuK3bYjzcXyclO4E3P3+uy8MI8f7uPYHmqjqx/5aLvN+vKe92HyN+x+sDRwZB9tFZD1cE9nZqjpaRLYGDgfu8PvFO///gbvnh8TBbm/Tx0APEVlPVX8SkRNxInOqqp4DjBCRzYC5qjonSntLTb0dVFNE/gD8E9e01BHXZKbAccBJqvqGd55qXKJxfJvu2qo6O2nbK8Czqnq3X28A7ATMVtUvorE0PSLyIPAC8JSqrhCRnYFLgdtV9aUY3u8GwB+BKcACVa0RkauA1qr6z6R0m+Ps/ioaS9Pjn5dbgCpcM80TwBJctN89vpkyVvhoxOaq+kvStpeBT1X1wqRtmwFEfc9FpD0uWKEBzs91LU5cRqnqYv9S+y5wjao+HZmhEVOvajQi8ldcFXaFqg4WkbtwzSKPq+oNPs1sXPv1W955Gjne7s2B5ap6v9/WUFVX4AqPTom0vpB+LxJDUxCRrkAjYJl3gr4FdAdmish7qvqeiDyCu9+v+QCByPF2Nwbm+8ihRAFYgwu42MFvRUwsUwAABoFJREFUOwoXvvpuVLamknTPl/oop9NFZCjwpaoO8GnmAL1E5KmY3fM1gIW+WRIffLEYF6RzhIi0UtWfRaQiaoHx9vX0tr0PrIVrXRgOnO12ywR1/ZNeB+pNM1k66o3Q+Lb2Abi3jQNEZL6qPi4ipwKp1difY/RWnWz3fiKySFWHe5EBeAP4t7ipYx+Jys5UknxgLwIdRGSmqp4rIv8CDgLaAw/hapFLcIV45KTY3V5EFqtqn6T7/Quw0Edt9QMOjMjU1chi+zGyap+qxrhnPo73fCNv9yleZMA1q24PHImriUX6AuibpzfCvaSeDnyOa67+ENeacA/Oh4qIfA8cDfw3EmPjgqrW+QUXffUK0NOvn4778bukpDsWqAa2itrmLHYfBVSlpDsF1x9ijaht9vY0wzn6d/frHXAF261J9/khXA1nArB91DZnsXsmMDgpzQHAcuAjoHPUNuewfVay7X77P/wz/seobQ56z/32Q/x/oRm+yT9iuytwnUg3TNgDnIMLENkAF4hzIHA50Clqe6Ne6lN48w8A4jp8nY97CO4QkSf99m2B/YB/qOrEyKxcnVS7DwJuT9jteQu4RFWXlt68tAgukuknAFX9DhiMix66WlUfUtVjgTNwBcwn0Zm6Cuns/h/QTURu8mk+B0YBx6sLK48L6WwfQpLtPsKsO+4Z/ywiO1MJcs8BXgOOUtVF6kv1KBCRP4jIjrhw8LVx0WMKoKq3APfiIubmqOqzqnqVxsxXGgV1Wmi8kxZVXYjrCNgb9xbymKoeoardcU0MhwITgT6qOiEqexPkYffRPt0XqjozKnsTpNg9CXhZRA4TkYFAU2BfYAMRaeXTTSgTu/cHWvjoxGm4aL5YiEwetq+JC38/K4bPeDa7E/1/flHVuZEZzG/h4k/hxorrhxv66TTfHJxgOLCCeu6TSaXOCo1/KMZ5ZzOqeiuuHfVO4NWkpG8BFaq6QpMiXaIiT7tj8zAn2f0ogKpeifMtdQLmAeeoG8WgLa6TXSzIw+51gbVUdYnGJDQ1T9tb+Gd8UVT2JsjT7lgMiyMi3XECc7yq7orzc3XF1RBPFZFLfSRrD1ywSMuobI0jdTK8WUSa44aqeAr3IDRW1aP9vuOBq4FDcb3PTwWOUNUvM5yuZNQhu9dQ1aPSpDsG6AscpEkh2lFRrnZD+dpexnZ3BzZX1SF+vS0wRFV7ihv6/1JcUEtXXNNk5LXGOFEnhQZARNrhepk3wbWbLlXVXn7fZbhOdy2Ai2PUXl2X7F6WJJINgT1w47H10RgNkV6udkP52l6Odovr49Vc3eghFTiH//PAvupCmCuB70npA2Q46qzQJCMirXE+juWqeqR/A2kBTNIYDwNRB+xerC609o/AJsBHGoMe/5koV7uhfG0vR7u9GDbBdZTe3de+dsGNZrA4+9H1k3ohNADiht4YAOyMi3TZTVVjMZ5WNuqA3d1xvsBdVXVGtFblplzthvK1vYztHoKLCt0TN56cNZdloM4GA6Ti23k/xdUI/l4OhTXUCbvXxtkd+4IDytduKF/by81ucTTG1WJ64caJM5HJQn0aGaAVLrx2z3J6KMzu0lKudkP52l5udvt+M8tE5GpgjMZgOJy4U2+azgDETbi1JGo78sXsLi3lajeUr+3laLeIG0k6ajvKgXolNIZhGEbpqTc+GsMwDCMaTGgMwzCMUDGhMQzDMELFhMYwDMMIFRMawygQ359ilLjJ6RLbDhc39bBhGB6LOjOMWuCHTHkcNwNkBW5ah71V9ZtanDMxTbdh1AlMaAyjlojIDcBC3Iyo81X1aj/a9j9xw8m/D5yuqjUiMgg3jHxT4FFVvcqfYzowENgbNxPp4xFcimGEQr0ZGcAwQqQf8DFufqAqX8s5GOiuqiu8uBwJPIwbdXuuH5jxTRF5QlUn+fMsVNWdo7gAwwgTExrDqCWqutBP4rVAVZeKyB7AjkC1iICrvUzzyY8SkRNx/712wJa4GSYBHi2t5YZRGkxoDKM41PgF3Cjbg1X1suQEIrIZcBbQVVXnichQVp1tdGFJLDWMEmNRZ4ZRfF4DDvfD3yMirUWkA24E7vnAryKyAbBXhDYaRsmwGo1hFBlVnSAi/YDXRKQBsBw3LXE1rpnsM+Bb4L3orDSM0mFRZ4ZhGEaoWNOZYRiGESomNIZhGEaomNAYhmEYoWJCYxiGYYSKCY1hGIYRKiY0hmEYRqiY0BiGYRih8v8fgQKqp60uPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, c='blue')\n",
    "plt.plot([x_min, x_max], [y_min, y_max], c='red')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Temperature (Celsius)\")\n",
    "plt.title(\"Linear Regression of Global Average Temperature over Time \\n\")\n",
    "fig2 = plt.gcf()\n",
    "fig2.savefig('images/Year_to_Temp_correlation.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.savefig('images/Year_to_Temp_correlation.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plugging in the Numbers:\n",
    "#### Average Global Temperature = slope * Year + y-intercept\n",
    "#### slope = 0.0176583\n",
    "#### y-intercept = -19.62893083\n",
    "#### Global Temp. Above 1.5 degrees C from 1850 average global temperature = 16.37\n",
    "#### Year = 16.37 - (-19.62893083))/0.0176583\n",
    "#### Year = 2038.641\n",
    "#### Mean Squared Error: 0.027025730486877658"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
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