diff --git a/Notebooks/Cobify.ipynb b/Notebooks/Cobify.ipynb
new file mode 100644
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--- /dev/null
+++ b/Notebooks/Cobify.ipynb
@@ -0,0 +1,2180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "22443281",
+   "metadata": {},
+   "source": [
+    "# Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91577208",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set_context('poster')\n",
+    "sns.set(rc={'figure.figsize': (7, 4)})\n",
+    "sns.set_style('whitegrid')\n",
+    "import statsmodels.formula.api as smf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c36c72df",
+   "metadata": {},
+   "source": [
+    "# Databases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d883f91",
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>specials</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>refill liters</th>\n",
+       "      <th>refill gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "      <td>4,2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11,2</td>\n",
+       "      <td>5,5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12,9</td>\n",
+       "      <td>3,9</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18,5</td>\n",
+       "      <td>4,5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>21,5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3,7</td>\n",
+       "      <td>39</td>\n",
+       "      <td>24,5</td>\n",
+       "      <td>18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>16,1</td>\n",
+       "      <td>4,3</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3,8</td>\n",
+       "      <td>45</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>15,4</td>\n",
+       "      <td>4,6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>14,7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>25</td>\n",
+       "      <td>25</td>\n",
+       "      <td>30</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>388 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    distance consume  speed temp_inside  temp_outside specials gas_type  AC  \\\n",
+       "0         28       5     26        21,5            12      NaN      E10   0   \n",
+       "1         12     4,2     30        21,5            13      NaN      E10   0   \n",
+       "2       11,2     5,5     38        21,5            15      NaN      E10   0   \n",
+       "3       12,9     3,9     36        21,5            14      NaN      E10   0   \n",
+       "4       18,5     4,5     46        21,5            15      NaN      E10   0   \n",
+       "..       ...     ...    ...         ...           ...      ...      ...  ..   \n",
+       "383       16     3,7     39        24,5            18      NaN     SP98   0   \n",
+       "384     16,1     4,3     38          25            31       AC     SP98   1   \n",
+       "385       16     3,8     45          25            19      NaN     SP98   0   \n",
+       "386     15,4     4,6     42          25            31       AC     SP98   1   \n",
+       "387     14,7       5     25          25            30       AC     SP98   1   \n",
+       "\n",
+       "     rain  sun refill liters refill gas  \n",
+       "0       0    0            45        E10  \n",
+       "1       0    0           NaN        NaN  \n",
+       "2       0    0           NaN        NaN  \n",
+       "3       0    0           NaN        NaN  \n",
+       "4       0    0           NaN        NaN  \n",
+       "..    ...  ...           ...        ...  \n",
+       "383     0    0           NaN        NaN  \n",
+       "384     0    0           NaN        NaN  \n",
+       "385     0    0           NaN        NaN  \n",
+       "386     0    0           NaN        NaN  \n",
+       "387     0    0           NaN        NaN  \n",
+       "\n",
+       "[388 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../data/measurements.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7514b33f",
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>specials</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>refill liters</th>\n",
+       "      <th>refill gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>39</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>16.1</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>45</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>15.4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>14.7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>388 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     distance  consume  speed  temp_inside  temp_outside specials gas_type  \\\n",
+       "0        28.0      5.0     26         21.5            12      NaN      E10   \n",
+       "1        12.0      4.2     30         21.5            13      NaN      E10   \n",
+       "2        11.2      5.5     38         21.5            15      NaN      E10   \n",
+       "3        12.9      3.9     36         21.5            14      NaN      E10   \n",
+       "4        18.5      4.5     46         21.5            15      NaN      E10   \n",
+       "..        ...      ...    ...          ...           ...      ...      ...   \n",
+       "383      16.0      3.7     39         24.5            18      NaN     SP98   \n",
+       "384      16.1      4.3     38         25.0            31       AC     SP98   \n",
+       "385      16.0      3.8     45         25.0            19      NaN     SP98   \n",
+       "386      15.4      4.6     42         25.0            31       AC     SP98   \n",
+       "387      14.7      5.0     25         25.0            30       AC     SP98   \n",
+       "\n",
+       "     AC  rain  sun  refill liters refill gas  \n",
+       "0     0     0    0           45.0        E10  \n",
+       "1     0     0    0            NaN        NaN  \n",
+       "2     0     0    0            NaN        NaN  \n",
+       "3     0     0    0            NaN        NaN  \n",
+       "4     0     0    0            NaN        NaN  \n",
+       "..   ..   ...  ...            ...        ...  \n",
+       "383   0     0    0            NaN        NaN  \n",
+       "384   1     0    0            NaN        NaN  \n",
+       "385   0     0    0            NaN        NaN  \n",
+       "386   1     0    0            NaN        NaN  \n",
+       "387   1     0    0            NaN        NaN  \n",
+       "\n",
+       "[388 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../data/measurements.csv\", decimal=\",\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1c6a35a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "\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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>specials</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>refill liters</th>\n",
+       "      <th>refill gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>39</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>16.1</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>45</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>15.4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>14.7</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>SP98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>388 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     distance  consume  speed  temp_inside  temp_outside specials gas_type  \\\n",
+       "0        28.0      5.0     26         21.5            12      NaN      E10   \n",
+       "1        12.0      4.2     30         21.5            13      NaN      E10   \n",
+       "2        11.2      5.5     38         21.5            15      NaN      E10   \n",
+       "3        12.9      3.9     36         21.5            14      NaN      E10   \n",
+       "4        18.5      4.5     46         21.5            15      NaN      E10   \n",
+       "..        ...      ...    ...          ...           ...      ...      ...   \n",
+       "383      16.0      3.7     39         24.5            18      NaN     SP98   \n",
+       "384      16.1      4.3     38         25.0            31       AC     SP98   \n",
+       "385      16.0      3.8     45         25.0            19      NaN     SP98   \n",
+       "386      15.4      4.6     42         25.0            31       AC     SP98   \n",
+       "387      14.7      5.0     25         25.0            30       AC     SP98   \n",
+       "\n",
+       "     AC  rain  sun  refill liters refill gas  \n",
+       "0     0     0    0           45.0        E10  \n",
+       "1     0     0    0            NaN        NaN  \n",
+       "2     0     0    0            NaN        NaN  \n",
+       "3     0     0    0            NaN        NaN  \n",
+       "4     0     0    0            NaN        NaN  \n",
+       "..   ..   ...  ...            ...        ...  \n",
+       "383   0     0    0            NaN        NaN  \n",
+       "384   1     0    0            NaN        NaN  \n",
+       "385   0     0    0            NaN        NaN  \n",
+       "386   1     0    0            NaN        NaN  \n",
+       "387   1     0    0            NaN        NaN  \n",
+       "\n",
+       "[388 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_excel = pd.read_excel(\"../data/measurements2.xlsx\")\n",
+    "df_excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df8f39cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(388, 388)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if they are exaclty the same:\n",
+    "duplicated = pd.concat([df, df_excel])\n",
+    "duplicated.duplicated().sum(), len(df) # The data in both databses is the same"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dde8de1",
+   "metadata": {},
+   "source": [
+    "# EDA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9577a9a0",
+   "metadata": {},
+   "source": [
+    "**Variables**:\n",
+    "According to Kaggle, the units it which these variables are measured are:\n",
+    "\n",
+    "- distance: km\n",
+    "- consume: l/100km\n",
+    "- speed: average speed (km/h)\n",
+    "- temp_inside: ºC\n",
+    "- temp_outside: ºC\n",
+    "- specials: if something happened (rain, AC, AC+rain...)\n",
+    "- AC: 0/1, if air conditioning was on or not\n",
+    "- rain: 0/1, if it was raining\n",
+    "- sun: 0/1, if it was sunny\n",
+    "- refill litters: l\n",
+    "- refill gas: type of gas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "78c57cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(388, 12)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "72397d43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 388 entries, 0 to 387\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   distance       388 non-null    float64\n",
+      " 1   consume        388 non-null    float64\n",
+      " 2   speed          388 non-null    int64  \n",
+      " 3   temp_inside    376 non-null    float64\n",
+      " 4   temp_outside   388 non-null    int64  \n",
+      " 5   specials       93 non-null     object \n",
+      " 6   gas_type       388 non-null    object \n",
+      " 7   AC             388 non-null    int64  \n",
+      " 8   rain           388 non-null    int64  \n",
+      " 9   sun            388 non-null    int64  \n",
+      " 10  refill liters  13 non-null     float64\n",
+      " 11  refill gas     13 non-null     object \n",
+      "dtypes: float64(4), int64(5), object(3)\n",
+      "memory usage: 36.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cfdf0acb",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>percentage_miss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>distance</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>consume</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>speed</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_inside</th>\n",
+       "      <td>3.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp_outside</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>specials</th>\n",
+       "      <td>76.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas_type</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AC</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rain</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sun</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>refill liters</th>\n",
+       "      <td>96.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>refill gas</th>\n",
+       "      <td>96.65</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               percentage_miss\n",
+       "distance                  0.00\n",
+       "consume                   0.00\n",
+       "speed                     0.00\n",
+       "temp_inside               3.09\n",
+       "temp_outside              0.00\n",
+       "specials                 76.03\n",
+       "gas_type                  0.00\n",
+       "AC                        0.00\n",
+       "rain                      0.00\n",
+       "sun                       0.00\n",
+       "refill liters            96.65\n",
+       "refill gas               96.65"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame({\"percentage_miss\": round(df.isna().sum()/len(df)*100, 2)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0b547e0",
+   "metadata": {},
+   "source": [
+    "## NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b69a7d16",
+   "metadata": {},
+   "source": [
+    "Knowing what \"special\" means and its amount of miss, let's drop it.\n",
+    "Having this amount of miss let's also drop refill liters and refill gas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "13b615e2",
+   "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>distance</th>\n",
+       "      <th>consume</th>\n",
+       "      <th>speed</th>\n",
+       "      <th>temp_inside</th>\n",
+       "      <th>temp_outside</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>38</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>18.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distance  consume  speed  temp_inside  temp_outside gas_type  AC  rain  sun\n",
+       "0      28.0      5.0     26         21.5            12      E10   0     0    0\n",
+       "1      12.0      4.2     30         21.5            13      E10   0     0    0\n",
+       "2      11.2      5.5     38         21.5            15      E10   0     0    0\n",
+       "3      12.9      3.9     36         21.5            14      E10   0     0    0\n",
+       "4      18.5      4.5     46         21.5            15      E10   0     0    0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = df.drop(columns=[\"specials\", \"refill liters\", \"refill gas\"])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4d21444",
+   "metadata": {},
+   "source": [
+    "To fill the NaN in temp_inside, let's check it's metrics and distribution "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "229448f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    376.000000\n",
+       "mean      21.929521\n",
+       "std        1.010455\n",
+       "min       19.000000\n",
+       "25%       21.500000\n",
+       "50%       22.000000\n",
+       "75%       22.500000\n",
+       "max       25.500000\n",
+       "Name: temp_inside, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.temp_inside.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4d0eecf7",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data[\"temp_inside\"], bins=14)\n",
+    "plt.axvline(data.temp_inside.mean(), c=\"r\", label=\"Average temp\", linestyle=\"--\")\n",
+    "plt.axvline(data.temp_inside.median(), c=\"b\", label=\"Median temp\", linestyle=\"--\");\n",
+    "plt.axvline(data.temp_inside.mode()[0], c=\"g\", label=\"Median temp\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f64f8c88",
+   "metadata": {},
+   "source": [
+    "Any of the three central metrics would do, but I prefer mean or median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "eea467f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"temp_inside\"].fillna(data[\"temp_inside\"].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fb5d35f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SP98    228\n",
+       "E10     160\n",
+       "Name: gas_type, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"gas_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1d172266",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "distance        0\n",
+       "consume         0\n",
+       "speed           0\n",
+       "temp_inside     0\n",
+       "temp_outside    0\n",
+       "gas_type        0\n",
+       "AC              0\n",
+       "rain            0\n",
+       "sun             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18cfb28b",
+   "metadata": {},
+   "source": [
+    "## Features useful for finding the best gas type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "385d494c",
+   "metadata": {},
+   "source": [
+    "Taking into consideration the meaning of each variable, these are the ones included in the analysis:\n",
+    "- total_consumed: distance * consume\n",
+    "- speed\n",
+    "- diff_temp: temp_inside - temp_outside\n",
+    "- AC\n",
+    "- sun\n",
+    "- rain\n",
+    "\n",
+    "Price is an important variable to take into account to know which gas type is economically more efficient, and not only know which consumes more. But E10 can be SP98 and SP95, so it's hard for me to know the price that I would be looking for, and also prices fluctuate amogst cities or even petrol stations. I decided to have it as an additional possible future evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5212216d",
+   "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>speed</th>\n",
+       "      <th>gas_type</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>total_consumed</th>\n",
+       "      <th>diff_temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>26</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>9.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.40</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61.60</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>36</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.31</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>46</td>\n",
+       "      <td>E10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>83.25</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   speed gas_type  AC  rain  sun  total_consumed  diff_temp\n",
+       "0     26      E10   0     0    0          140.00        9.5\n",
+       "1     30      E10   0     0    0           50.40        8.5\n",
+       "2     38      E10   0     0    0           61.60        6.5\n",
+       "3     36      E10   0     0    0           50.31        7.5\n",
+       "4     46      E10   0     0    0           83.25        6.5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"total_consumed\"] = data[\"distance\"] * data[\"consume\"]\n",
+    "data[\"diff_temp\"] = data[\"temp_inside\"] - data[\"temp_outside\"]\n",
+    "data.drop(columns=[\"distance\", \"consume\", \"temp_inside\", \"temp_outside\"], inplace=True)\n",
+    "data.to_csv(\"../data/df_clean.csv\", index=False)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "985a767f",
+   "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>speed</th>\n",
+       "      <th>AC</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>sun</th>\n",
+       "      <th>total_consumed</th>\n",
+       "      <th>diff_temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>388.000000</td>\n",
+       "      <td>388.000000</td>\n",
+       "      <td>388.000000</td>\n",
+       "      <td>388.000000</td>\n",
+       "      <td>388.000000</td>\n",
+       "      <td>388.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>41.927835</td>\n",
+       "      <td>0.077320</td>\n",
+       "      <td>0.123711</td>\n",
+       "      <td>0.082474</td>\n",
+       "      <td>93.529433</td>\n",
+       "      <td>10.571274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13.598524</td>\n",
+       "      <td>0.267443</td>\n",
+       "      <td>0.329677</td>\n",
+       "      <td>0.275441</td>\n",
+       "      <td>111.596700</td>\n",
+       "      <td>6.698575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>-8.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>32.750000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>55.165000</td>\n",
+       "      <td>6.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>40.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>68.060000</td>\n",
+       "      <td>11.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>91.650000</td>\n",
+       "      <td>14.625000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1145.330000</td>\n",
+       "      <td>26.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            speed          AC        rain         sun  total_consumed  \\\n",
+       "count  388.000000  388.000000  388.000000  388.000000      388.000000   \n",
+       "mean    41.927835    0.077320    0.123711    0.082474       93.529433   \n",
+       "std     13.598524    0.267443    0.329677    0.275441      111.596700   \n",
+       "min     14.000000    0.000000    0.000000    0.000000       12.000000   \n",
+       "25%     32.750000    0.000000    0.000000    0.000000       55.165000   \n",
+       "50%     40.500000    0.000000    0.000000    0.000000       68.060000   \n",
+       "75%     50.000000    0.000000    0.000000    0.000000       91.650000   \n",
+       "max     90.000000    1.000000    1.000000    1.000000     1145.330000   \n",
+       "\n",
+       "        diff_temp  \n",
+       "count  388.000000  \n",
+       "mean    10.571274  \n",
+       "std      6.698575  \n",
+       "min     -8.000000  \n",
+       "25%      6.375000  \n",
+       "50%     11.500000  \n",
+       "75%     14.625000  \n",
+       "max     26.000000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88783b5f",
+   "metadata": {},
+   "source": [
+    "## Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bc2d6b46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = data[\"gas_type\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "926f6bf0",
+   "metadata": {},
+   "source": [
+    "**Most of the trips used SP98.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a42b6d00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data.speed);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e82c7920",
+   "metadata": {},
+   "source": [
+    "**Speed in the normal range of urban trips.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dedb4ea8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = data.AC);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a8add5",
+   "metadata": {},
+   "source": [
+    "**Most of the trips were done without AC.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6cab561b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = data.rain);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba35f7de",
+   "metadata": {},
+   "source": [
+    "**Most of the trips were done without rain.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f2c58ef5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = data.sun);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bf55100",
+   "metadata": {},
+   "source": [
+    "**Most of the trips were done without too much sun.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b1f58bac",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data.total_consumed);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5111e433",
+   "metadata": {},
+   "source": [
+    "**Total gas consumed seems to show extreme maximum values.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bdba1643",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data.diff_temp);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef322dce",
+   "metadata": {},
+   "source": [
+    "**Most of the values are positive, meaning that usually the temperatue inside the car is higher than ouside.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d901a624",
+   "metadata": {},
+   "source": [
+    "### Relatonship between total consumed for each gas type and  every variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1528effd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QopGQxE4IIYQQopGQxE4IIYQQopGQxE4IIYQQopGQxE4IIYQQopGQxE4IIYQQopGQ5U7EUY0cOZJVq1ZVWb5s2TKSk5NDX3/++edMmTKF77//Puy8/fv38/TTT7N69WqMRiPDhg3jwQcflIWOhRBCiFokid0pIBBU2LyriOIyNwk2I51aJ6JRq459YS25+OKLmTBhwuF4AgE8Hg8Gg4HExMTQ8a+++ooJEyaQkpISdr3P5+PWW2+lVatWzJ49m7179zJhwgQMBgP33nvvSXsOIYQQorGTxK6BW74xh//O+40i++GdHhJjjdw+tCtndWtyUmIwGo1hrXKHdp4wGo2o1WocDgcTJ07k66+/pm3btpSXl4ddv3DhQnJycvj444+x2Wy0b9+eoqIi/u///o8777wTvV5/Up5DCCGEaOxkjF0DtnxjDs/OXB2W1AEU2d08O3M1yzfm1FNk4fbv34/dbueTTz7h/PPPjyhfs2YNnTt3xmazhY717dsXh8PB1q1bT2aoQgghRKMmiV0DFQgq/Hfeb0c95+3PfycQVE5SRFXLzMzk7bffpmPHjlHLc3NzSUtLCzt2qLs2J6dhJKdCCCFEYyBdsQ3U5l1FES11f1VY6mLzriK6tk2q01gWLFjAwoULQ18rSmUyee655zJ58uRjXu92u8Na6wAMBgMAHo+nFiMVQgghTm+S2DVQxWVHT+qO97wTMXjwYB5++OHQ14cmT8THx1freqPRiNfrDTt2KKEzm821F6gQQghxmmtwXbFTp05l5MiRYce2bNnCiBEj6NGjB4MGDWL69Olh5cFgkFdeeYUBAwbQvXt3Ro8ezZ49e47rHg1Ngs1Yq+edCIvFQosWLcL+NG/ePGxCxdGkpaWRn58fduzQ16mpqbUerxBCCHG6alCJ3YwZM3jllVfCjpWUlHDLLbfQsmVL5s6dy9ixY5kyZQpz584NnTN16lRmz57NM888w5w5c1CpVNx2222hVqLq3KOh6dQ6kcTYoydtSXEmOrVOPOo5DUFWVhabN2/G4XCEjq1YsQKLxUJmZmY9RiaEEEI0Lg0iscvLy2PMmDFMmTKFVq1ahZV99NFH6PV6Jk6cSJs2bRg+fDg333wzb7/9NgBer5d33nmHsWPHMnDgQDIzM5k8eTJ5eXksWrSoWvdoiDRqFbcP7XrUc267sstJWc/O7XZTUFAQ9qewsJCCgoJqjZE7//zzSU5O5v7772fr1q189913TJ48mdGjR8tSJ0IIIUQtahCJ3aZNm4iNjWX+/Pl07949rGzNmjVkZWWh1R4eDti3b192795NUVERW7duxel00rdv31C5zWajU6dOrF69ulr3aKjO6taEx0ZlRbTcJcWZeGxU1klbx+7rr7+mf//+oT8DBw7kggsuYODAgXz33XfHvN5gMDBt2jSCwSDXXnstTz31FDfeeCN33333SYheCCGEOH00iMkTgwcPZvDgwVHLcnNzad++fdixI5fKyM3NBSA9PT3inIMHD1brHkfunlBdiqJQUVERtczj8RAMBgkEAgQCgeO+95H6dE6lV8cUNu8uoqTMQ7zNQKdWlTtPnOi9q2PGjBkRxxRFCe08oVKFx3H33Xdz9913R8TWtGnTiBZSRVFq5RkCgQDBYBCXy0UwGDzh+1WHy+UK+/t0c7o/P0gdgNQBSB2A1AGcnDpQFAWV6ti9dA0isTsat9sd0V135FIZhyox2jl2u71a96gJn8/Hli1bqizXarW1upRHuwwrZFgrX9vrwVdrd665hrJUicfjwe/3s2vXrpP+2tnZ2Sf9NRuS0/35QeoApA5A6gCkDqDu66A6w5cafGJ3rKUyjMbKbkqv1xv696FzTCZTte5REzqdjrZt20Yt83g85OTkYDAYwmJqLP7aYtcQaLVamjdvHkrY65rL5SI7O5uWLVuG3menk9P9+UHqAKQOQOoApA7g5NTBjh07qnVeg0/sjrVUht/vDx1r3rx52DmHZlzWxXIbKpWqyqRQrVajVqvRaDRoNJoa3b8hO9R9qlKpGsTzaTQa1Go1JpPppCfSJpPptF6L73R/fpA6AKkDkDo49DP4dK4DqNv3QXUbUhrE5ImjycrKYu3atWFjsVasWEGrVq1ITEwkMzMTq9XKypUrQ+VlZWVs3ryZXr16VeseQgghhDg+QZ8HX2ke5P5Ba0sQjbuMoM977AtFnWrwid3w4cNxOBxMmDCBHTt28OmnnzJz5kzuuOMOoLK/ecSIEbz44ossXryYrVu38sADD5CWlsaQIUOqdQ8hhBBCVF/A7cSx8Qf2vXkvuf/7F0Uf/5uctx/Auflngp7oEwvFydHgu2ITExOZNm0akyZNYtiwYSQnJzN+/HiGDRsWOufee+/F7/fz+OOP43a7ycrKYvr06aFBhtW5hxBCCCGqx1e4j8Jv/ht2TPF7KfjiNfQpzTGkt6mnyESDS+yee+65iGPdunVjzpw5VV6j0WgYN24c48aNq/KcY91DCCGEEMcW9LooWVb1zk2lv8wn+bJ7UOtkAfr60OC7YoUQQgjRcCg+L357fpXl/pKDKH4Za1dfJLETQgghRLWp9MajdrUamrRHpT85S0+JSJLYiWNasGAB1113HT179qRnz55ce+21fPLJJ6HykSNH0qFDh9CfzMxMzjjjDK699tqwLccUReH999/n4osvpkePHlx33XUsX7487LUKCgp48MEH6dOnD3369OG+++4L7S4ihBCi/ql1BmL7DgVVlBRCrcXW62LUGt1Jj0tUksROHNUnn3zCP//5T4YPH86nn37K3LlzGTp0KC+++CJTp04NnXfxxRezbNkyli1bxo8//sjcuXNp164dY8eO5bfffgPg7bff5vnnn+emm25i3rx5XHbZZdx5552sWrUqdJ8HHniAgwcP8u677/Luu++Sm5sre8oKIUQDo4tPI+2Gf6KxJYWOaWNTSL/pSbTxKfUYmWhwkydEJCUYwL1vCwFHCRprPMZmHVGpT87CwB9++CFXX3011157behYixYtOHDgAO+99x5jx44FKnf3SE5ODrv2ySef5KuvvuKLL76ga9euTJs2jb/97W+MGDECgJYtW7Jt2zZef/11evfuTVlZGatXr+aNN96gU6dOANx+++3cfffdlJSUEB8ff1KeWQghxNGpdXrMrbqRcfOz+J1leL1eDLZ4DHHJx75Y1ClJ7Bo459ZfKPz2HQLlRaFjmphEki4YjSWzb52/vlqtZt26ddjtdmJjY0PHb775Zq655pqjXqvVVr699Ho9xcXF2O12srKyws7p2LEj8+bNIxAIYDAYMJvNzJs3j969ewPw+eef07Jly7DXFkII0TBoYxLwaoz8sWULHZOa1nc4AumKbdCcW38hb+4LYUkdQKC8iLy5L+Dc+kudx3DbbbexZcsWzjnnHG6//Xb++9//snHjRqxWKy1btqzyupKSEv7973/j8XgYMmQIsbGx6PV6Dh48GHbegQMH8Pl8lJWVYTAYmDRpEqtWraJXr15kZWWxYcMG3n77bdRqeasKIYQQxyItdg2UEgxQ+O07Rz2ncNE7mNtn1Wm37IUXXsicOXN4//33WbZsGUuXLgUqu2MnTZoUaoFbsGABCxcuBCr3kvV4PGRkZDBp0iS6desGwJVXXhnqZu3WrRsrV65k7tzKtZC8Xi+KorBt2zZ69uzJmDFjCAQCTJ48mXvuuYf//e9/WK3WOntOIYQQojGQxK6Bcu/bEtFS91eBsiLc+7ZgatGlTmPp1q0bL7zwAoqisH37dn744Qfee+897rjjDhYtWgTA4MGDefjhh4HK7lur1RoxJu7RRx/F4/Fw4403AtCuXTtuu+02XnjhBWJiYvjyyy/58MMPWbJkSSiJe/PNNzn33HOZO3cuo0aNqtPnFEIIIU510r/VQAUcJbV6Xk3k5uby9NNPk5eXB4BKpaJDhw6MGTOGN998E6fTyerVqwGwWCy0aNGCFi1a0KxZs6gTHaxWKy+88ALr169n6dKlfP7555hMJpKTkzGbzaxdu5ZWrVqFtczFxsbSqlUrsrOz6+w5hRBCiMZCErsGSmOt3gzQ6p5XE3q9njlz5jB//vyIskPJV1JSUkRZVSZMmMBHH32EwWAIzaD95ptvGDBgAADp6ens2bMHj8cTusblcrF//35atGhxIo8ihBBCnBakK7aBMjbriCYm8ajdsRpbIsZmHesshoSEBMaMGcPLL7+Mw+Hgoosuwmq1sn37dqZOnUqfPn3o1atXte+Xnp7OlClTaNasGRkZGbz77rts3ryZZ555BoChQ4cyffp07r//fu677z4AXn75ZfR6PVdddVWdPKMQQgjRmEhi10Cp1BqSLhhN3twXqjwnacjoOl/P7v7776dly5Z89NFHzJo1C7fbTVpaGkOGDDnuhYPvvPNOKioqGDduHE6nkx49evD++++HWuNSUlL48MMPeeGFFxg1ahRqtZpevXrxv//9D5vNVhePJ4QQQjQqktg1YJbMvqQOHxe5jp0tkaQhJ2cdO6hsSRs6dGjo60AggNvtxmg0AvD+++9X6z5arZbx48czfvz4Ks9p06YNb7755gnFK4QQQpyuJLFr4CyZfTG3z6q3nSeEEEI0bEowiErW+hR/ksTuFKBSa+p8SRMhhBCnDiUYwG8vwLl1Je4D2zCktsLauT/a2GRUGvnVfjqT774QQghxivHk7uLgB0+i+CpXEajYtpKSnz8h/YZ/Sq/OaU7aboUQQohTiL+8mPxPXwoldSEBP3mf/qdO1zcVDZ8kdkIIIcQpJFBRht+eH7UsWFGG31F6cgMSDYokdnVIUZT6DuG0IPUshDitBANHLw/4T04cokGSxK4O6HQ6ACoqKuo5ktPDoXo+VO9CCNGYqc02VAZz1DKVRocmJuEkRyQaEpk8UQc0Gg1xcXHk51c2lZvNZlQqVT1HVXsCgUBo2y+Npv4G6CqKQkVFBfn5+cTFxdVrLEIIcbJorfEknj+Kwi/fiCiLH3g9GkvcyQ9KNBiS2NWRtLQ0gFBy15gEg0H8fj9arRZ1A1g7KS4uLlTfQgjR2Kk0WiyZ/dDFplD8wyy8hQfQxacRP/B6jE07oNbp6ztEUY8ksasjKpWK9PR0UlJS8Pl89R1OrXK5XOzatYvmzZtjMpnqNRadTictdUKI047GaMHUqhtpqS1R/D5UGi0aS2x9hyUaAEns6phGo2l0iUcwGATAYDCEthUTQghx8mnMso+2CFf//WhCCCGEEKJWSGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFISGInhBBCCNFInBKJnc/nY/LkyQwaNIiePXty4403sm7dulD5li1bGDFiBD169GDQoEFMnz497PpgMMgrr7zCgAED6N69O6NHj2bPnj0n+zGEEEIIIerUKZHYvfHGG8ydO5dnnnmGefPm0bp1a2677Tby8vIoKSnhlltuoWXLlsydO5exY8cyZcoU5s6dG7p+6tSpzJ49m2eeeYY5c+agUqm47bbb8Hq99fhUQgghhBC165RI7BYvXsxll11G//79adGiBY8++igOh4MNGzbw0UcfodfrmThxIm3atGH48OHcfPPNvP322wB4vV7eeecdxo4dy8CBA8nMzGTy5Mnk5eWxaNGien4yIYQQQojac0okdnFxcSxZsoT9+/cTCASYM2cOer2ejh07smbNGrKystBqtaHz+/bty+7duykqKmLr1q04nU769u0bKrfZbHTq1InVq1fXx+MIIYQQQtQJ7bFPqfTYY48d142fffbZ4w6mKhMmTOCBBx7gvPPOQ6PRoFarmTJlCs2bNyc3N5f27duHnZ+SkgJATk4Oubm5AKSnp0ecc/DgwRrHpCgKFRUVNb7+VOZyucL+Ph2d7nVwuj8/SB2A1AFIHYDUAZycOlAUBZVKdczzqp3YrVy5Muzr/Px8/H4/TZo0ITk5mdLSUvbt24deryczM/P4Iz6KnTt3YrPZeP3110lNTeXjjz/mkUce4YMPPsDtdqPX68PONxgMAHg8nlAlRzvHbrfXOCafz8eWLVtqfH1jkJ2dXd8h1LvTvQ5O9+cHqQOQOgCpA5A6gLqvg7/mMtFUO7H7/vvvQ/9esGABL774Iq+++irdunULHd+xYwf33HMPF1988XGGWrUDBw4wbtw4ZsyYQa9evQDo2rUrO3bs4NVXX8VoNEZMgvB4PACYzWaMRiNQOdbu0L8PnWMymWocl06no23btjW+/lTmcrnIzs6mZcuWJ1SHp7LTvQ5O9+cHqQOQOgCpA5A6gJNTBzt27KjWedVO7I40efJkHnroobCkDqBt27bcd999PPvss4waNaomt46wceNGfD4fXbt2DTvevXt3fvzxR5o0aUJ+fn5Y2aGvU1NT8fv9oWPNmzcPO+dEWhZVKhVms7nG1zcGJpNJ6uA0r4PT/flB6gCkDkDqAKQOoG7roDrdsFDDyRMlJSXExMRELdNqtbU69uzQ2Lht27aFHd++fTstWrQgKyuLtWvXEggEQmUrVqygVatWJCYmkpmZidVqDetKLisrY/PmzaEWQCGEEEKIxqBGiV2PHj147bXXKCkpCTuen5/Pq6++Sp8+fWolOIBu3brRq1cvHnnkEX755Reys7N5+eWXWbFiBbfffjvDhw/H4XAwYcIEduzYwaeffsrMmTO54447gMr+6BEjRvDiiy+yePFitm7dygMPPEBaWhpDhgyptTiFEEIIIepbjbpiH3nkEUaOHMngwYPp2bMn8fHxFBUVsX79emJjY3njjTdqLUC1Ws3UqVN5+eWXeeyxx7Db7bRv354ZM2bQo0cPAKZNm8akSZMYNmwYycnJjB8/nmHDhoXuce+99+L3+3n88cdxu91kZWUxffr0ag1CFEIIIYQ4VdQoscvMzOSLL75gxowZrFu3jgMHDhAfH8/o0aMZNWoUcXFxtRpkbGwsTz75JE8++WTU8m7dujFnzpwqr9doNIwbN45x48bValxCCCGEEA1JjRI7qJyY8Mgjj9RmLEIIIYRoxIJeFwGnHW/BPlRqDbqkpmgssah1hvoOrdGocWLn9Xr55JNPWL58OQUFBfz73/9m1apVdO7cOWK2rBBCCCFObwFXOWUbFlOyZBYowcqDGi1JF92OtWM/1IbTe0ZtbanR5Ini4mKGDx/OpEmT2LNnDxs3bsTtdrN06VJGjhzJ+vXraztOIYQQQpzCvHnZlHz//uGkDiDgp/DLqfhKcusvsEamRond//3f/+F0Ovnqq6/47LPPUBQFgClTptC1a1deeeWVWg1SCCGEEKeugLuCkmVzqyy3r/qCoN93EiNqvGqU2C1ZsoT77ruPFi1ahC2YZzAYGD16NJs2baq1AIUQQghxalP8XvxlhVWW+0pyUfzeKstF9dUosfN4PFXOfNVoNPh8knULIYQQopLaYMLQpOptOI1NM1HrjFWWi+qrUWLXtWtXPvzww6hlCxYsoEuXLicUlBBCCCEaD7XOQNxZw0AVmXaotHpsPYeg0mjqIbLGp0aJ3X333cfPP//MlVdeyZQpU1CpVHzxxRfceeedfPPNN9xzzz21HacQQgghTmG6+HTSb3oSbVzq4WNJTUkf+S+0scn1GFnjUqPlTnr16sW7777Lf/7zH6ZNm4aiKMyYMYNOnTrx1ltv0bdv39qOUwghhBCnMLVOj6lFF5qMmkSwohxUKtTmGLSWuPoOrVGp8Tp2WVlZzJ49G7fbjd1ux2q1YrFYajM2IYQQQjQyWms8WOPrO4xGq8aJHYDD4aCsrAwAu92O3W4PlTVp0uTEIhNCCCGEEMelRond1q1bGTduHDt27KjynC1bttQ4KCGEEEIIcfxqlNg98cQTlJSUMH78+CqXPRFCCCGEECdXjRK77du389xzz3HRRRfVdjxCCCGEEKKGarTcSbNmzQgGg8c+UQghhBBCnDQ1SuwefPBBpkyZwqpVq/B4PLUdkxBCCCGEqIEadcW2atUKRVEYNWpU1HKVSsXmzZtPKDAhhBBCCHF8apTYPfbYY5SUlHDttdeSnCyrRQshhBBCNAQ1Suw2b97Ms88+yyWXXFLb8QghhBBCiBqq0Ri7lJQUTCZTbccihBBCCCFOQI0Su9tvv52XX36Z7OzsWg5HCCGEEELUVI26YhcuXMiBAwe4+OKLsdlsWK3WsHKVSsV3331XKwEKIYQQQojqqVFil5yczJAhQ2o7FiGEEEIIcQJqlNg9++yztR2HEEIIIYQ4QTUaYyeEEEIIIRqeGrXYZWZmolKpjnrOli1bahSQEEIIIYSomRoldvfcc09EYud0Olm3bh179+7l4YcfrpXghBBCCCFE9dUosRs7dmyVZY888gi///47w4cPr3FQQgghhBDi+NX6GLuhQ4fy1Vdf1fZthRBCCCHEMdR6YpednY3f76/t2wohhBBCiGOoUVfsa6+9FnEsGAxy8OBBvvrqKwYPHnzCgQkhhBBCiONTa4kdgNVqZciQITz22GMnFJQQQgghhDh+NUrstm7dWttxCCGEEEKIE1RrY+wKCgrYtGkTgUCgtm4phBBCCCGOQ40SO6fTyWOPPcb7778PwFdffcW5557L1VdfzWWXXcbBgwdrNUghhBBCCHFsNUrsXnzxRRYuXEh8fDwA//nPf8jMzOS1115Dq9Xy4osv1mqQQgghhBDi2Go0xm7x4sU8+uijXHbZZWzZsoUDBw4wfvx4zjvvPPx+P08++WRtxymEEEIIIY6hRi12paWltG7dGoAffvgBrVbL2WefDUBsbCwej6f2IvzTvHnzuOSSS+jatSuXXnopX3/9dahsy5YtjBgxgh49ejBo0CCmT58edm0wGOSVV15hwIABdO/endGjR7Nnz55aj1EIIYQQJ18wGKzvEBqMGiV2GRkZbNu2DYBvv/2WHj16YLVaAVi6dClNmzatvQiBzz//nH/84x9cd911fPHFF1xyySU8+OCDrF+/npKSEm655RZatmzJ3LlzGTt2LFOmTGHu3Lmh66dOncrs2bN55plnmDNnDiqVittuuw2v11urcQohxKkoEAhSVOqisNSFo0J+LopTg9cX4EC+g1nfbOHZmWuYt3QHuUVOgkGlvkOrVzXqir3xxht57rnn+OCDD9i9ezcvvfQSULmH7Hfffcfjjz9eawEqisKUKVMYNWoUo0aNAuCee+5h3bp1rFq1ilWrVqHX65k4cSJarZY2bdqwZ88e3n77bYYPH47X6+Wdd95h3LhxDBw4EIDJkyczYMAAFi1axKWXXlprsQohxKmmyO7i6xXZfLlsNxVuH13bJnHLZZ1plhqDXqep7/CEiMofCPL7ziL+Nf0XAn8mcr/8fpAPF27juXv60zojtp4jrD81arEbOXIkzz33HL179+all17ikksuAUCr1TJx4kRuuummWgtw165dHDhwgMsvvzzs+PTp07njjjtYs2YNWVlZaLWHc9S+ffuye/duioqK2Lp1K06nk759+4bKbTYbnTp1YvXq1bUWpxBCnGqKy9w8O3M1cxZtx+HyEVTg1z8KeWjKj+zNLa/v8ISoUnGZm+ffXx1K6g5xefy8OGstpeW1PyTsVFGjFjuASy+9NKK1a/LkyScc0F9lZ2cDUFFRwa233srmzZtp2rQpd911F4MHDyY3N5f27duHXZOSkgJATk4Oubm5AKSnp0eccyLLsiiKQkVFRY2vP5W5XK6wv09Hp3sdnO7PD42jDvbnOdm2pyTieCCoMG3+bzwy4gwMOlWV1zeGOjhRUgf1Uwf5xRVUuKPvS78vr5xShxu95uStq3sy6kBRFFSqqv8/HlLjxG737t0sXbqUioqKiEGLKpWKe+65p6a3DuNwOAB45JFH+Pvf/87DDz/MwoULufvuu3n33Xdxu93o9fqwawwGAwAejydUydHOsdvtNY7L5/OxZcuWGl/fGBxKuk9np3sdnO7PD6duHcTExLBys7vK8k27iiktr6A4b+8x73Wq1kFtkjo4eXWgVqvx6VKOeo7L5WFLzo6TEs+R6roO/prLRFOjxG7evHk89thjKEr0AYq1mdjpdDoAbr31VoYNGwZAx44d2bx5M++++y5GozFiEsShWblmsxmj0QiA1+sN/fvQOSaT6YTiatu2bY2vP5W5XC6ys7Np2bLlCdXhqex0r4PT/fnh1K8DtVpN3N6qkzaDXoPZZCK1Y8cqzznV66A2SB3UTx2UOAOo1aqoEyVsFj0JsWZapFX93q1tJ6MOduyoXqJao8TujTfe4KyzzuKZZ54hLS2tWk2DNZWWlgYQ0d3atm1bfvjhBzIyMsjPzw8rO/R1amoqfr8/dKx58+Zh52RmZtY4LpVKhdlsrvH1jYHJZJI6OM3r4HR/fji166Bf13Te+zp6z8OFfVqQEGtCpz32BIpTuQ5qi9TBya0DlcbPtee1Y/ai7RFldwzrSlK8BY267nKTqtRlHVQ316rR5ImcnBzGjBlDenp6nSZ1AJ06dcJisfDrr7+GHd++fTvNmzcnKyuLtWvXhu1Ru2LFClq1akViYiKZmZlYrVZWrlwZKi8rK2Pz5s306tWrTmMXQoiGLCHWyJ1XdYs43iI9hmGD2lYrqROiPpgMWi4f0JrHRmXRMt2GyaClU6sEnr37bM7smFovSV1DUaMWu1atWp20/WCNRiNjxozh9ddfJzU1lW7duvHll1/y888/M2PGDNq2bcu0adOYMGECY8aMYePGjcycOZOnnnoKqOyPHjFiBC+++CIJCQlkZGTwwgsvkJaWxpAhQ07KMwghRENkNuo498ymdG2TxE8b9lNS7qFf13RapttIjD09uxXFqcNmMXBWtyZ0bp2Izx/EoNcQYz72GLTGrkaJ3UMPPcTTTz9NRkYGPXr0CE1WqCt33303JpOJyZMnk5eXR5s2bXj11Vfp06cPANOmTWPSpEkMGzaM5ORkxo8fHxqPB3Dvvffi9/t5/PHHcbvdZGVlMX369GoNQhRCiMbMbNTRPE3HTRedvPFIQtSmWGvd5iCnmholdpMmTaKoqIibb745arlKpWLz5s0nEleEW265hVtuuSVqWbdu3ZgzZ06V12o0GsaNG8e4ceNqNSYhhBBCiIakRondFVdcUdtxCCGEEEKIE1SjxO7vf/97bcchhBBCCCFOUI0XKPZ6vXz66aesXLmSsrIy4uPj6dWrF8OGDavzMXdCCCGEECJSjRK7srIy/va3v7F161aaNGlCcnIyu3fv5osvvmDWrFl8+OGHxMTE1HasQgghhBDiKGq0jt1//vMfcnNz+eCDD/j++++ZM2cO33//PR988AFFRUVMmTKltuMUQgghhBDHUKPEbvHixdx///0RC/z26tWLe++9l2+//bZWghNCCCGEENVXo65Yp9NJs2bNopY1a9aM0tLSE4lJCCGEEMdQaHexP8/B3twyMlKstEizkRQnC0uf7mqU2LVu3ZolS5Zw9tlnR5QtXryYFi1anHBgQgghhIgup8DB428up6DUFToWZzUw6a6zaJ5mq8fIRH2rUWJ366238uCDD+L1ern88stJSkqisLCQBQsW8PHHHzNx4sRaDlMIIYQQAHaHh/97f01YUgdQ6vDwzDureO7v/UmwGespOlHfapTYXXLJJWRnZ/Pmm2/y8ccfA6AoCnq9nnvuuYfrrruuVoMUQgghRCW7w8POA/aoZQeLnNgdHknsTmM1Xsfu7rvvZsSIEWzYsAG73U5sbCw9evTAZpMmYCGEEKKueHyBo5ZXuP0nKRLRENVoVizA/Pnzef755znnnHO4/PLLMZvNjBo1ikWLFtVmfEIIIYQ4QoxZj1YT/de3WgXxNtkk4HRWo8Tu008/Zfz48bhch/v3ExMTadq0Kffdd58kd0IIIUQdiY8xMHRgm6hl5/duQZxVErvTWY0Su3feeYcxY8bw0ksvhY61atWKV199lVtuuYWpU6fWWoBCCCGEOMyg13LlwNbcclknrCYdAGajlhsu6MCIizMxG3X1HKGoTzUaY7dv3z769+8ftax///7MmjXrhIISQgghRNXirEauPKcNA3pm4PUG0evUxNuMVXbRitNHjd4BKSkpbNy4MWrZ5s2biY+PP6GghBBCCHF0Go2a5DgzGSlWkuPNktQJoIYtdkOHDuWNN97AYrFw/vnnk5CQQHFxMd999x2vvfYaf/vb32o7TiGEEEIIcQw1SuzuuOMOdu7cydNPP80zzzwTOq4oChdddBFjx46ttQCFEEIIIUT11Cix02q1vPTSS9x9992sWbOG0tJSYmJiOPPMM8nMzKztGIUQQgghRDXUeIFigLZt29K2bdsqywOBAF26dOGTTz6hc+fOJ/JSQgghhBC1wucP4vMHMOg0aBrZ2MQTSuyqQ1GUun4JIYQQQohjqnD7yC2qYMGyXeQUOOjYMoEL+rQgJaHxTD6p88ROCCGEEKK+eX0BVm7K5aUP14WObd5dzIKfdvHvu8+mQ4uEeoyu9jSO9FQIIYQQ4ihKyt289tGGiONef5CXZ6+npNx98oOqA5LYCSGEEKLRyyl04vUHo5btz3dQ5vSe5IjqhiR2QgghhGj0AoGjj/lvLFMCZIydEEIIIRq9pilWNGoVgWBkBpccb8Jm1uHx+il3+VABcVbDKTljVhI7IYQQQjR6cVYDf7u0I+8u2Bx2XK2Csdf0wB9QmDp3I7/8fhC9VsOFfVtwUb+WJMWZ6inimpHETgghhBCNntGgZUjvFrRtGsecRdvJL6mgbdM4rj2/PSaDlnv/swSn2w9ABX7mfLed5b/l8K/bzzqlkrs6TexUKhVZWVlYLJa6fBkhhBBCiGOKMevp1jaZNhlxeH0BTAYtKhX8d97voaTuSPvyHGzNLqZ/j4x6iLZmqp3YzZs377huPHToUNRqNe+///7xxiSEEEIIUWcsJh0Wkw6AIruLVZtyqzx3ybr99O2SjlZ7aoy3q3Zi9+ijj1b7piqViqFDh9YkHiGEEEKIk0atUmHQa6osNxu1qE+NnA44jsRu8eLFdRmHEEIIIcRJF2s1cMnZrXh3waao5Rf3a4n6FMrsqp3YZWRUv39Z9ocVQgghxKlArVYxsGcGP/+aw/a9JWFlF/drSUaytZ4iq5kaT5748ssvWbVqFT6fL5TIKYpCRUUFGzZs4Mcff6y1IIUQQggh6kpirIkJN2exO6eM79fsxaDXcmHflqQnmbFZDPUd3nGpUWL32muv8dprrxETE4Pf70en06HVaikuLkatVnPNNdfUdpxCCCGEEHUmIdZEQqyJnh1SUKtV9R1OjdWo0/izzz7jiiuuYNWqVdx8882ce+65LF++nE8++YS4uDjatWtX23EKIYQQQtS5Uzmpgxomdnl5eVx55ZWoVCo6d+7M+vXrAejSpQt33nknH3/8ca0GKYQQQgghjq1GiZ3ZbEalqsxoW7Zsyf79+3G73QB07NiR/fv3116EQgghhBCiWmqU2HXt2pXPPvsMgObNm6PRaFi+fDkAO3fuRK/X116Ef7F792569uzJp59+Gjq2ZcsWRowYQY8ePRg0aBDTp08PuyYYDPLKK68wYMAAunfvzujRo9mzZ0+dxSiEEEIIUR9qlNjdeeedfP3119x5553o9XquuOIKHn30UcaOHcvzzz9P//79aztOAHw+Hw8//DAVFRWhYyUlJdxyyy20bNmSuXPnMnbsWKZMmcLcuXND50ydOpXZs2fzzDPPMGfOHFQqFbfddhter7dO4hRCCCGEqA81mhWblZXFJ598wrZt2wB44oknUKvVrFu3josuuojHHnusVoM85NVXX43Yd/ajjz5Cr9czceJEtFotbdq0Yc+ePbz99tsMHz4cr9fLO++8w7hx4xg4cCAAkydPZsCAASxatIhLL720TmIVQgghhDjZapTY5eTk0KZNGzIzMwEwGAw8/fTTAHg8HjZt2sQZZ5xRe1ECq1evZs6cOcybN49BgwaFjq9Zs4asrCy02sOP0rdvX9566y2Kioo4cOAATqeTvn37hsptNhudOnVi9erVNU7sDq3ZdzpyuVxhf5+OTvc6ON2fH6QOQOoApA5A6gBOTh0oihKa33A0NUrszjvvPObMmUO3bt0iyjZu3MiYMWP49ddfa3LrqMrKyhg/fjyPP/446enpYWW5ubm0b98+7FhKSgpQmYDm5lZu7PvX61JSUjh48GCNY/L5fGzZsqXG1zcG2dnZ9R1CvTvd6+B0f36QOgCpA5A6AKkDqPs6qM4chmonds8//zylpaVAZdY4depU4uPjI87bsmULMTEx1Y+yGiZOnEiPHj24/PLLI8rcbnfEgxoMlatEezyeUPYc7Ry73V7jmHQ6HW3btq3x9acyl8tFdnY2LVu2xGQy1Xc49eJ0r4PT/flB6gCkDkDqAKQO4OTUwY4dO6p1XrUTuzZt2jB16lQAVCoVv//+e0SypNFoiImJqdUxdvPmzWPNmjUsWLAgarnRaIyYBOHxeIDKZVmMRiMAXq839O9D55xI5atUKsxmc42vbwxMJpPUwWleB6f784PUAUgdgNQBSB1A3dZBdbph4TgSu6uvvpqrr74agMGDBzN16tTQGLu6NHfuXIqKisLG1QE8+eSTTJ8+nSZNmpCfnx9Wdujr1NRU/H5/6Fjz5s3DzjkZ8QshhBBCnCw1GmP3/fffh/69c+dOysvLiY+Pp0WLFrUW2CEvvvhiaPHjQy644ALuvfdeLrnkEr788ktmz55NIBBAo9EAsGLFClq1akViYiIxMTFYrVZWrlwZSuzKysrYvHkzI0aMqPV4hRBCCCHqS40SO4AvvviC559/nsLCwtCxpKQkHnroIYYOHVobsQGVrW7RJCYmkpGRwfDhw5k2bRoTJkxgzJgxbNy4kZkzZ/LUU08BlWPrRowYwYsvvkhCQgIZGRm88MILpKWlMWTIkFqLUwghhBCivtW4xW7cuHH07duXBx98kKSkJPLz85k/fz6PPfYYcXFxEV2ndSUxMZFp06YxadIkhg0bRnJyMuPHj2fYsGGhc+699178fj+PP/44brebrKwspk+fXqc7ZAghhGgc3B4/Lo8fvU6DxaSr73CEOKoaJXZvvPEGF110EZMnTw47Pnz4cB544AHeeuutOk3sDi2MfEi3bt2YM2dOledrNBrGjRvHuHHj6iwmIYQQjYvH6yenwMmc77azY38pKQkmrju/A60zYokxS8OAaJhqtKXY9u3bw1rEjjRs2DC2bt16QkEJIYQQ9W3z7mLun/wDP2/MIa+4gt92FPH4m8v5duUe3B5/fYcnRFQ1Suzi4+NDa9r9VUlJiXRxCiGEOKUV2V28+tEGgkpk2ftfbaHU4Tn5QQlRDTVK7Pr168err75KTk5O2PEDBw7w+uuvc/bZZ9dKcEIIIUR9cFT4KCiNvj1UIKiwP99xkiMSonpqNMbuwQcfZPjw4Vx00UX06NGD5ORkCgoK2LBhAzabjYceeqi24xRCCCFOmmOtBatRV2+xWCFOthq12GVnZzN79mxGjhyJ2+3m999/x+12M3LkSD777DM2bNhQy2EKIYQQJ0+MRU/TFGvUMr1WTZPk6GVC1Lcatdj97W9/Y86cOVFnmf7yyy889thjXHrppSccnBBCCFEf4mOMPHDDGTz2+jK8/mBY2dhrexAfY6inyIQ4umondo888ggHDx4EQFEUJk6ciNUa+YklOzubpKSk2otQCCGEqAetM2J5ddy5LFq1ly27i2mSbOGys1uTnmRBr9PUd3hCRFXtxO7CCy/k3XffDTumKOHThTQaDT169OCmm26qneiEEEKIeqLVqGmSZGXEhZl4fAH0Wg1abY1GMAlx0lQ7sRs8eDCDBw8GYOTIkUycOJE2bdrUWWBCCCFEQ6DRqDFrJKETp4YajbF7//33azsOIYQQQghxguQjiBBCCCFEIyGJnRBCCCFEIyGJnRBCCCFEIyGJnRBCCCFEIyGJnRBCCCFEI1GjWbFCCCHEqcju8ODzB1GrVcTHGFAda1NYIU4xktgJIYRo9JwuHzv3l/LuF5vZdaCUxDgT1wxuT7+uacTFGOs7PCFqjSR2QgghGjVFUVi/PZ/n31sTOlZQ4mLq3F/Zub+Umy/rjNWsq8cIa8bj9VNS7sHl8WPSa4mLMWA0yK/10528A4QQQjRqRXY3//3st6hlC1fuYeigNqdcYldS5uajxdv5ZsUe/IEgGrWKQWc2ZeTFHUmMNdV3eKIeyeQJIYQQjZrT5aOk3FNl+d7c8pMYzYlzuX188M0Wvli2G38gCEAgqLB49T7e+uw3HBXeeo5Q1CdpsRNCCNGoaTRHnyBhMtber0JFUcgvcfH7zkI27SqiRbqN3p3SSI43otVoauU1Sh1evlu9L2rZit8OMuqSTljN+lp5rboQqCgj4HagQoXaZEVjiqnvkBoVSeyEEEI0arEWA5kt49maXRJRZtBryEiy1tpr7c0r57HXl1Fe4Qsdm/nlZp66vR+dWiag0Zx4R5nD5SUYVKosL3V4yEipvWeqLUrAjyc/m8Kv3sKbuwsAQ9NMki6+A31yU1Qq6USsDVKLQgghGrUYi577rutJrDW8FUujVvHYqCwSbIZaeZ1Sh4cXP1gbltQB+PxB/v3uKorL3LXyOqZjTJCwmBrmeEFfSR4HZz4eSuoAPPu3kvPeBPylBfUYWeMiLXZCCCEavaYpMbx030A27S7i1z8KaZZqpV+XdJLiTGi1tdNFWubwkH2wLGqZw+WjoNRFcrw5anl5hRe7w0OZ04vVpCPWaiDWGj3htFn0dGmTyO87iyLKWqbbiLM2vG7YoN9L6crPUQK+iDLFU0H5bz8Q3/9qVOra+V6cziSxE0IIcVpISTCTkmDm3DOb1cn9/YGqu0cB3N5A1OOFpS5e/WgD67blh461ax7HIyOzSE2ITARtFgMPXH8Gk2asZNeBw4lk0xQr/7i5d4Ncly/orsC9Z3OV5a7dG4ntfRkao+UkRtU4SWInhBBC1IIYsx6rSYfDFdkqpVZBemJkkuZ0+/jvvN/CkjqAP/aW8tzMVUy8rV/UlruUBDMTb+tHsd1NfkkFSXEmEmNNJNgaXlIHoNLq0Fhi8ZccjFqusSag0jbMLuRTjYyxE0IIIWpBgs3ArVd0jlp2+YA2URM0e7mHX36Pnuzs2G+npLzqcXnxMUbaNI2jX9cmtGsW32CTOgCN0ULc2VdVWR7X93LU2obXhXwqkhY7IYQQohZoNGr6dkknLsbIzC83sye3jJR4M9cNaU9WxzTMxsgWqQqPH+UoPbh2R+NZk87YpB22PldQtnL+EUdVxA8egS6hSb3F1dhIYieEEELUEqtZT6+OqbRrFofPX7kjRPxRWtIsRh1qFVS1eklcTO3M2G0INGYb8f2vxtbjfNz7tqBSazA2y0RjiUVtiD6pRBw/SeyEEEKIWlbVjNa/iovRc07Ppvywbn9EWadWCcRV8z6nCo3RgsZoQZ+UUd+hNFqS2AkhhBD1xGTQcctlnQgGFX769UCoW7ZH+2TuvbZntRNEIQ6RxE4IIYSoRwmxJu65pjs3XZSJw+XDbNQSZzU06G3BRMMliZ0QQghRz8xGXdTJFUIcL0nshBBCCHHc/IEAxXYPJWVuDLFNKXMF0RuCaGthP1xRc5LYCXEac7p82B0eXB4/ZqOO+BgDxmPsQynEifD6AwSDCka9vM9OZRVuH6s35/H6J7/i8vgBsBi13Hd9T3p2SJHvbz2SmhfiNFVYWsEbczeyekseilK5Ifr5vZtx44UdG8RCpwG3k0BFGYrXjdpoRmONlwVMG5DyCi+l5X6sCc0odwcxGhXUalWV59sdHvbllbNg2W4q3D4G9MigZ4cUkuNMJzFqUVsOFDh5cdbasGNOt59nZ65myoODaNUktp4iE5LYCXEasjs8vDhrHZt2Hd5EPBBUWPjLXlQqFbde3qVeW+589gIKv3oL1671AKi0emy9LyW29+VoLfILo74dKHDwypz1bN5dDFSutXb70C6c0SEViylynFiZ08OHC7fy1fLs0LEN2wtITTDz77vOJiXKfqii4XJ7/Xy8eFvUMkWBz3/cyd3Du6PXaU5yZAJOkS3FSktLeeKJJzjnnHM444wzuOGGG1izZk2ofMuWLYwYMYIePXowaNAgpk+fHnZ9MBjklVdeYcCAAXTv3p3Ro0ezZ8+ek/0YQjQYpQ5PWFJ3pEUr91Li8JzkiA7zO0rJ+/j/QkkdgOL3Yl/+GWVrviYYiNyHU5w8BSUVPPb6slBSB1Ba7uH/3l/Ltr0lUa/JK3aFJXWHj1fw2dIdeH2BugpX1AG3J8D+fGeV5Xtzy/HI97TenBKJ3YMPPsivv/7KSy+9xCeffELnzp259dZb2blzJyUlJdxyyy20bNmSuXPnMnbsWKZMmcLcuXND10+dOpXZs2fzzDPPMGfOHFQqFbfddhteb+PZqkWI41Fkr3r/yUBQoSLKJuYni9+ejzdvV9Qy+6oFBMqKo5adqpSAH5+9AE/uLrwF+wg47fUd0lFt3VNMSXn0xP/dBZuwR/lQsGTt3irvt3j1Psqc8rP4VGI0aGiRFlNleasmNgx6aa2rLw2+K3bPnj38/PPP/O9//+OMM84AYMKECfz444988cUXGI1G9Ho9EydORKvV0qZNG/bs2cPbb7/N8OHD8Xq9vPPOO4wbN46BAwcCMHnyZAYMGMCiRYu49NJL6/PxhKgXR1vNXqUCU312wxblVFmmeN0EPVW3FJxqAq5yHL8tpXjp/1C8lcm2PrUVKUPvR5/UtJ6ji+63HdFbegGyD5ZFbX3z+oJVXuMPVF0mGiajXss157Xn5405EfvcqtUqrjinDXqtJHb1pcG32MXHx/Pf//6XLl26hI6pVCoURcFut7NmzRqysrLQag//Iurbty+7d++mqKiIrVu34nQ66du3b6jcZrPRqVMnVq9efVKfRYi65vH5KXN6jtm1FR9joHkVn7j7dkkjth73p9RY46ouVGtRaRvPWl8VOzdQtOjdUFIH4M3bzcEPnsBnL6jHyKqWkWKtsizBZow6gWJgz6qT1H5d07FGGZcnGrYmyRYeHZWFzXJ4QlOsVc/jt/QhLdFSj5GJBt9iZ7PZQi1th3z99dfs3buX/v37M3nyZNq3bx9WnpKSAkBOTg65ubkApKenR5xz8ODBGselKAoVFRU1vv5U5nK5wv4+HTW0OvD5Ib/Uw2dLd3KgwEHrjFiuHNCaRJuOaEtKGbQw4ebeTJqxir255aHjXdsmMeaKLqiCPioqqu6Orcvn18QkoolJIFAe2eVq7dgPtMYG8X/vROtA43VS8sOHUcsCTjvunB349VaUvzaJ1LOsjinM+EKFPxAZ1/DBbTHrifj+pCea6N4uiV//KAw7bjZquWFIe/C78DidBN0O1DoDisFKUHtqbKXV0H4WnCwVXoWd+0q5fWhXdNrKHzIeb4A/9hXTuomVgL7qGdKN0cl4HyiKgkp17Hpt8IndX61du5Z//OMfnHfeeQwePJhnn30WvT58CQSDofIHgsfjCVVytHPs9pqPZfH5fGzZsqXG1zcG2dnZ9R1CvWsIdWC2xHCgTM+UOb+Gju06YOf7NfuYMOpMrGp71B82Wq2Wh67tiE/RYnd4SbAZUAU9HNiznWCwet1jdfH8rZqkkHL5WAq+nIr/iFYrU8tuxPS6mP2FdkrsVY/ZOtmOtw4O9Ti0TU/Eb8+v8jz3/m0UqxNwOBwnGGHtMltieGzUmbzwwXrc3sMtw4POyKBbKyubN2+OuEalUnHb5e35fXcqXy3fi8vjp1fHZC7p1wJz0E75iu9xrPka5c+JMYammdguvINdeSX4/f6T9mwnoiH8LDhZDAYDRZ4YPlr8R9Ty1k1iMCvF+Hyn30Snun4f/DWXieaUSuy+++47Hn74Ybp3785LL70EgNFojJgE4fFUDt41m80YjZXrcXm93tC/D51jMtV8/SSdTkfbtm1rfP2pzOVykZ2dTcuWLU+oDk9lDakOyiqCPDHzh4jjwaDC63N/54WxZ2M1Hn3URbOUQ10nZtJT4o/5mnX9/GqTkYTzRqFSqQi4ytFYE1ACPjS2JNISTKQ1aVLrr3m8jqcOVCoV5a4AdqcPl9tPQqwRvc6NxhJHwFka9RpDSguaN2/e4FrsoHJJi1cfGsSBAgflTjetMuKwWXQYtCpSEjpWed3gBCt9OqcTVBTMBg1alULFuu8pXzk/7DzP/q2UfvYCmTc8SUDfsJdCaUg/C04Wj1/hvZnrqiz/asVeHr6hB6fTMLuT8T7YsWNHtc47ZRK7Dz74gEmTJjFkyBBefPHFUNaalpZGfn74p95DX6empoY+7eXn59O8efOwczIzM2scj0qlwmxu2D9w6prJZJI6aAB1sCevKKzl5EjFZW4crgApCVWPizoRdfH8Hq+P7AIN2fkxdEjRojHE4PXr2bDfQ79EA01sDes9d6w6UBSF7INlPP3OSgpKKltOVSoYdUlHzjvrKooXvRNxjUpnxNSiM7oGnChYLGAzq9mxI5/k2JRqvw+OfCSfvQD7L59HPc9XnINSXoi5Wc1/Tp9MDeFnwcnic3hwuqtujXO6/Kg1Wszm029B8bp8H1SnGxZOkcTuww8/5Omnn2bkyJH84x//QK0+3PqQlZXF7NmzCQQCaDSVHw9WrFhBq1atSExMJCYmBqvVysqVK0OJXVlZGZs3b2bEiBH18jxCnFwNr8XnaIrsHp5+ZyV2R+QSGNv2V3DnVV3xBxTKnB40GjU2i57E2IabABWWupjwxs+UHzFmUVFgxpdb6PX3M7GdeRFl674FpbL7W2OJJfXax9DaEusr5ONyIt1tis+D4ql6vKS36ADGUySxO51YTTr6dE4LG597pLO6pmM2yoSY+tLgE7vdu3fz73//myFDhnDHHXdQVHR4qr3RaGT48OFMmzaNCRMmMGbMGDZu3MjMmTN56qmngMr+6BEjRvDiiy+SkJBARkYGL7zwAmlpaQwZMqS+HkuIWpMYa8Kg1+CJ0moXH2PAZjk1BqEfcrDQGTWpA1i5OZcbL+rA+FeXhfanTE0wM35kL9pkxKJpgJuP78qxhyV1R3r0nY289dB1NOt9GX57ASqDCY01Hm1MAirVyXkWl8dPsd3Nqs252J0ezuyQSkaK9aRsK6fS6lFp9Sj+6N9vXVxqnccgjp9Go+bCPi34ZkV2xHs7zmpgQI+Mo24vJ+pWg0/sFi5ciM/nY9GiRSxatCisbNiwYTz33HNMmzaNSZMmMWzYMJKTkxk/fjzDhg0LnXfvvffi9/t5/PHHcbvdZGVlMX369GoNQhSioYuzGbhneHde+l/4mBe1WsX9159BYmz97/t6PIrLq148ORhUcHsCoaQOKncvmPDGz7z68LkNcpmF/XlVT35wVPhw+NXYktLRJaRXeV5dcXn8/LzxAK/M2RBaj2zu9zto2zSWCaP7kFTDllB/eTGKzw1qLRprXJV7/GqsccT0OJ+yNV9FKYuvlzoR1ZOSYObFe89h1sJtLN94AFBxTs8Mrh/SQbaIq2cNPrG78847ufPOO496Trdu3ZgzZ06V5RqNhnHjxjFu3LjaDk+IeqfXaujTJY2X7h/I3O//qFzupGkswwa2JT3JUu1xGdXlqPBSUu7HFNeUUmcAtcZfq/vKtkizVVlmMWqjtgS4vQF++f0gQwfW74Qmu8NDQYmLzbuLiLHo6dgygU6tE6o8P85qIEan4CvJJeAoRaUzoLHY0MacnG7YYrs7LKk7ZMd+O/N/3MXIizPRHccI+IDLgWv3RooXv4e/rACVVo+1+7nEnzU8ateyWqsn7qxhBBwlOLeuCB3XxqWSdgp1R5+OVCoVTZKtjL2mO3+7pAOuCheJcRZirJLU1bcGn9gJIY7NbNTRrlkc99/QE483gFGvwaCv/f/e+SUVvPbxr6zfVjlBSaNWMaRPC268oAPxtdR1lxhrpGPLeLZkR+47Onxwe75dWbnPs1ajJhgMEvwzKdmaXYJyTvXWeaoLxXYXk2evZ8P2w0u0aNQqHh5xJudnNeO71fsirpl0S1e8a+ZRsGoBBCu70jW2JNKueQR9ass67479ZdPBiKTukG9WZHPFgNYkxVWv1U5RFFy7NpA/b/LhY34v5WsX4s3NJvXq8WijLD6tjUkg6dI7iR90A4GyItQmKxpLHNqYqhPihk4JBPA7SlB8blRa/VFbLU91RoOWYEDN/t17jzojWpw8ktgJ0YgY9VqMdZDQwZ8bvb+3mm17S0PHAkGFb1Zko9WouPnSTkdNJpVgACUYQKXRHTX5Sow18dBNZzLrm638tOEA/oCC1aTj2vPbk5FsJTevmJdv64zea0el0VHkMzBzSQ5tmsbWW1IXCARZuHJPWFIHlfXzwvtrePXhc8k+WMaO/ZVrZ+q1au4Y1hVb0e8U/zIv/JqyQnI+eJKmY/6DLi6lTuMurWLPV6jspvX6A2zJLmZrdjGJsUY6tIgn0WZEG6UVL+AooWjxzKj38hzYhr+sMGpiB6AxWtEYrZCYUaPnaEgCTjvlG5dQuvxTgm4nKq2emJ5DiOs3DG3MsZcSEuJESWInhKiWknJ3WFJ3pG9W7OHKc9qQlhj5IyXodeO3F1C2fhG+4oOYWnTGktkPbVxylS1SqQkW7r66O9cP6YDHF8Bs0JIUZyLvYD5NUnbh/ewlfMHKcXaxJivjLrgXXZO0WnvWY/E77aidpbRLNKFx26nwB5n/466o5wYVWLetgKfvOIviMjdeX5AYi454jZuDMz6Oeo3iqcC9fyu6uBSCPi9KwIfaYKr1FrwzM1OZt3Rn1LLMFvFs2JbPG5/+Fjqm16p5YkxfOrdKiEjugl5X1N1CDvEV56C1JRJwlkHQh9pkQxMTj1rTeGZPBv0+7Gu/ofSnj0LHFL+XstVf4i8rJPnSu9CYom/lJ0RtkcROnDKCPg8BZynewv0Q8KNPbo7GEovaYMbrD1BS5sHl8WPUa4iLMVSr5crvtKMEfKjU2ipbE2pDqcOD3x9Aq6mM7VR0aA22aPyBIBWeyB0Cgn4vFTvXkf/pS2jjUtCYbdhXzqdk2Sc0+dszGFJbVnlPo15Lk+Tw9fdiyndTsPKT8NdwOXB98X8k3j4ZqHp8Xm3xFeeQ9+l/8OZlVx5Qa7H1vpS7L83i/z7ZHvWagpIKrGY91iPW9fKVOgk4IrubD/HmZeNJbk7Jj3MIVJRhaXcmlk5no41NqbWWyeZpVlo3sbErpyzsuFoFN1zQgVc+2hAekz/IU9N+4Y1HBpOaED5RRaXRgVoT6lI+kj6tFbq4FHLeexx/SeU2jyqdkYRzb8LaZUCjSXYCjhLsK+ZFLavYtpLAoBsbzbOKhksSO3FKCHpcOLevpODLNyDwZwKhUhPX/2qM3S/gk2UHmb9sFx5vAK1GxblnNuOmizKrXN8s4HLg3reF4h9m4Ss8gC4+lbgB12Fu3R2NufaSgzKnh63ZxezZV4hVF8ThV9OiaTIdWyZg1vgIOO14DmwD1BiatkNriUNtaJiDj482hk6tApM+WvdcKfZVX5J6zXj8pQX4ywrRn3EBqDUULZ5J6pX3o7HEVuv1A057WEtIeKEf55YV6M8eXq171ZS/rIicDyYSKD+87BJBP2W/fE7bs82c2SGdtdsKI67r0T454phKo0VjSyJQFnk+gC4xg7y5L4QSIc/+rZT+Mp8mo/6NPrHqnTcC7gqCFWUoQR9qvRlNTEKViWCCzcQ/b+3LZz/sYOHKPXi8ATq0iGf05Z2Zt3QnRfbIGco+f5Dte0sjEjuNJRZr5/44flsacU38wBs4+L9nULyHPxwoPjdF305HG5uMpX1Wlc/TEPjLi1H83srvmSUelSb6hJKg21nl0i0AfnsB+qSmdRWmEIAkduIU4SvNo2D+q+EHlSClP32ELbEVP6wvDa3j5g8oLFq1F7vDy/039CTmL6ufKwE/jk0/UbRw2uH7Fx+k4POXiet/DXH9hqHWH71VTaVSodUe/b+P1xdgx+5cMjTlZNi/IlhyAHVCBjS/FGdJEN/OpZQuO7L1SUX8oBuwnXFBg/xUnxhrJCPZyoGCyOU7+nfPiNoS6S8rJK7vFeTPm1K5/MWftHGpJF18OwG3o9qJnRLw4y/Nq7Lcm7sbRQnW6YQDb9GB8KTuCJ61C7jy3MciErv0RAutm0Q+ozYmgYSB11Ow4LWIMrXJisZoCSV1hwRd5RQtfo+UK+9FE+UDgK80j8KF03DtWA8oaKzxJJ5/M6bWPdGYoi8FkxRn4ubLOjFsUBuCQTAaNDjdflb8drCqaqC4LDLhU+sMxA+8EW/eHrz52aHjhibt8ZfmhyV1Yff6YRaGjPZoq/k+OJkCFeVU7FxH8Q8fEigrRG20YMu6FNsZF0Zt4Vfpjj5BQt0A/1+LxqfhreYpxF8oAT9la76ustyzeh4XnxE5yHzV5lzsjsjB4X5HCcVLPoh6r9Lln1a5dydU7iKwYXsBi1bn4NYkUu4KVnmuw+kmzbULz6dP4tmxCl/RATx/rML/7WSsroN/SeoAFEp++LCyq7kGAq5yvIUHcOfswFuUQ8DtPOY1Xl+A/OIK9uWVU1BSQSBw+Hk8Xj+FdhdFdhf+QJAEm5Enx/SheWr4L6czOiQz+vLOmAyRY6XUBjNF37+P7uwRKFc9h+uK59Bc/SzqdmdRuuJzjudHkEqrP2prh6FZxzqfReor2FtlWdDtoHWamYw/u4/VahXn9Mjg6TvPIrGKmaXmtmcSP/BGVEfMmNQlZpB27T8o/uHDqNe4dqwl6KpMrt1eP/4/v2f+siIOznoK1451HNptJOAoIX/eZNz7txz1uXRaDXExRmJj9NgsBvRaNSnxVc+G7dA8+iQAXWwSaTc8TpO/TSLh/JtJGfYQqdc+gudg1Xtc+goPQKDhbRavBAI4Nv1EwfxXQq2qQbeT0p8+omjRuwRckR9wNOZYjC26RL2fxpZ0Ss/0FacOabETDZ4S8OP7S8vFkfxlhSS2jt41UljqpmlKeCISrChH8VaxCG4wgL+8GF185Ir3B/IdPP7WzxSWHr42wWbkmTvPollq5CdxnbcM+9LIfUAtmWdRvuqLKp/HvmIehtRWqPXVXz7EZy+g4IvXcWcfGuiuwpzZh6QLbq3yl0mx3cXcJTtY+MsePL4AFqOWq89rx/lZzXF5Any8eDvLN+ag1aoZ0rsFl5zVkvQkK8/cdRbFdjfFdiepiVbibaaIVtFDAu4KVOffz3++OMCO/ZsrI1PBgK5tuenMThCMHJdXFY05hriBN5E3+18RZSq9EXO7M6u8NuAsw19WiGvv76gNFkzNO6Gxxh9XHUNl0lUVlcGM2WLm2bvPpsLjR6tWYbMaMB1ljT+N2UZsn8uxdhlAoKKscmkMsw3nthX4ig5Ev0gJ4vcH+Pbn3azcdJBYq4HL+7cmWVtRZYtm8XczMaS3jdrKVO70klPo4Iufd1Pu9HJWtyZkdUzh1iu68OzM1RHnt28eR2pi1cMFtNZ4tNb4sK3A9KmtgR+inq+LTwN1w/tV5HcUU7L0f1HLnJuXET/gWjSm8DGgGpOV5MvuJnf2pLDvn8YSS9p1/5DETpwUDe9/kxB/odLpMTbvjHvP71HLtalt2FUQfdkGmyXKjDvN0d/2Km3kNaXlHp57b3VYUgeVXVKT3l3Fs3efHTEGTe0pj7oPptpkwe+setC831GCEvAB1Us6AhVl5M97Gc/+rUccVajY+gtFag1Jl9wZ0W1XXuHlrc9+Y/kR3W1Ot5+ZX27B5fZTXOYOW3ftk+//YPnGHCbddTZJcSYMmiD2/F0k2eKPutG3UxvHs3O2si/v8J6SigI/bszDbGjCzU0MHM/qXoXaVIyDb8f78wcE/6xbbXw62vPuolSxEm1xEL+jhMIv36Bix9rDB1Vqki+7B0tmH9T66u+uoEtIR2OJi9qqG5t1KVprHPEaLcezqIVap0cdlxK2tImxSfsqzze27MKiDYW8/fXhWbg/rN3Pjee3pn/Py/GtXxBxja84B8Uf+X+kvMLLZ0t38PHiP0LH1m7NJznOxLN3n80/bs5i+vxN5BVXoNOqOa9XM64b0oH4mONLiC3tzqRkyQdRx5/FD7y+Ticu1VTQ7Qy9x6LxFeegT4pM9HVxqaTfNBF/aT7ewr1o41LRJWagsyXVZbhChEhXrGjwVCo1MV3PQaWL8stEpUafNZRv10a2VKQnWUiMNeEvL8aTl40nbzf+siI0ltgqtyrSWGLRWiN/LdudHrIPlkW5Ag4UOCiN0uWrUkf/7+UrPICxSbuoZQCmFl2OK9kIOO1/SeoOc25ZQdBpjzhud3jCkrojff7jLrI6RS4dklPo5LcdlV1SaiA5zsaxtoMs8enDkrojfbculzL/0ccyuipcOOx23B4fdoeHFz/ewpu/Wii/4J9ohj6F5qqnye5xFxPnHmTp+sgWLkUJ4vj9p/CkDkAJUrDgVfxl0cfLVUVRFJIvvwdt/BH1o1Jj7TIQQ3obAhXRn/V4BTwVWDr1jziu0hnQnzWCj36K7K7/8Ltd+Nr0hyjd0WqjpXLG6l8U2d1hSd0hBaUu5ny3nTMzU/i/sQN469HzePOR87htaNdqL1h8JK0tifSbJqI5MoFTa4k/5zpMLbse9/1OBtUxFhRWG61VlmljEjA2y8TW8wLMrbpLUidOKmmxE6cEbWwyTf72DAULXsWb/+fOA3GpJF1yJ964dHp3KqdfWzOJVg3l7iBLt5bzt4sy0Rfv5MDnU0JjZDSWWJIuuYvUqx/lwMzHwlrUVFo9qVePRxNlEdFDEzOqEq1cZ41DbbKGxkMd4ty2irTrJ+DYtCyiBUOlM2LrOQRVFa2KQb+PgKOkcpkMlQqtNZ5gVd3KAEowaqtD/lGWLvH4AgSr2I7Abi/DnZtN+dqv0RTn4MzogLrHeehik6PGnF9adWz+QBB3FT2xFWVlBEpzKV/zJTiKUKd3ILbHecSYNaz/o4j1f0QmZDv3lxEMKmFbjlXOyo1swTrEseknEgbeUGX5X7l2bcC+4nNizxqKNiYRxe9FrTdSsXM9eZ+8QNM7p+B3lFZOFFBrKpfj0R09efX6ApSUuyl3etFpNdiselQFezE2bY+peSfKf/uBQEU5xmaZxGRdxpMfZWN3RJ95uX6Xgz5prfAeDF+bztbrUrSWyPf18o05Vca1ZO1+brggk+SjjLWrLpVGiyGjHRm3/B8BZymK34cmJh6NOfa4u8OrSwn6UYIK6igt8NWhMcdgbN4J997NEWVqUwy62MiZzkI0BJLYiVOCSq3BkNaKtBufJOgqByWI2mhFG5OAoaKM27qWU/rj2wTKCkk227i171Asmubs//BfYQOzA047eR8/T8bo52k65iUqdq3Hc2A7+tRWWNqdidaWFHUAfoxFj0atIhCMTHjUKoi1Rn6618bEk3zZWPI+eR6UIyZZKEHQaCsT1a/exJtb2aVmaNKWpIvvRFvFbgMBdwUV21ZSuPBtFF9lC6HKYCb5kruwdDwL55bl0WoOVZTZkzHmo/+y02kj66B3h0TOii8gZ/qTHBqc7967mbLVX5I+4l8YMyJbIVMTo8/EhMotwUwGLUowQMDlQKVWozHF4HI6cW5ZTvl3bx8+ed8WXOu/5r5h/+SxYnfUNfU6ZFgi95FVgpUL4lbBb4++1EhVlIAPf1kBRd/8GZtKHfreWjqdjb80n6JF7+Ir3AdqLdbOZxN/zvVV7iBR5vSw8Jc9zFm0HY+v8sNBs9QYxt/YD80XE1GCAawd+6HSm/Hm7aag2MGWPZEtsIf41PqI1l5zuyxsZ1wQdYkOr6/qDyyBYBCFKvYbqwGVSo3Wlljn+78GnHa8RQcoW/sNiteNtctAjM0yj/t1NaYYki+9m4OznsJfdnhHEZXeSNp1/0Bjk/FyomGSxE6cMLfHT3G5m827ivH4/HRunUSCzYDNUvsL8WotsXDEsgjBgI/yjUsoXvze4WMVZZR8/x6+ov3Yep4fZUatQsnPc/H2vYXZv8cTVHqjKlXR3xiga5sANkvkf4v4mMoB6vN+jFyl/4K+LYi1Rj6rSq3B1KorGWNepGzVV3gL9qBPaUFs1qVoYpPwFR8kpvt5aP9ce81fXoKiKFS1eaevcD8FX4QvjaF4Ksj/7CXSbvgnFX+siWgBNHfojdpko6CkgpJyD2qVirgYAwkxRpLjTVETpC6tE/kjyg4T1/ZPxbngCfjLL3vF76Vg/iukj/xXRDd2YqyJZqkxUbtjh5/bhgSNk9LlX+PcshyVVoct61L0aW0o/z5y0oni8+BbOo3r+t/MawvCd3kw6jVktY1BUcL3ilXpjRibZVY5PvNoEy6iMf11xuOfSZ3GloS109nk/u9pQvUT9OP4bSmeA9tJH/GvqAPnV2/O472vwmes7ssrZ8J/V/J/o+7DM3s8pcs/C5Xp41vSpU0qv++M3oXcq2MqyX3uwVe4n0BFGfq0Vmit8VWuzdi3Szpzl0SfsXpGhxQsxuNv7SosdbF9bwnrtuWTlmimX9cmJMUa62Tv4r8KOMsoXjKL8l8Xh45V7FiLLqkZ6Tc8jvY4u0R1Cek0GTUJb8Fe3Dk70Cc2wdCkHVpbYp3PwBaipiSxawT85cX4inJw79+KNjYFY7MOaK2JqI6xzlptqHD7+HH9Ad6Y+ytHNmade2ZTRl/embjjHGR9vALlJZT8GH3RWsevS0i77h9Rl0rx5mWz5Y+D/PTr4a6oZRtyGHFxJhf3a4nT5WfH/lIURaFt0zjiYgwMH9wWm1XP3CU7cLp8mI1arjynDRef1RJzFb8A1ToDhpQWJF40BsXnQaUzoNbq8BbuJ2fGPyJmhao0OjJueyliAdqgx0XJz39dHuUQBcemZdj6XE7FlhVoYhLwl+ajS2pGwpDRrNhawlvzficx1kgwqFDm9DB+ZBZP3tqXx99aHrZfaNMUK2Ov7cF/5/0W9gomg5YEyiivYvFVX3FOZZfzXxK7BJuRJ27tw/+9v4Y/9pVW1omq8v1xdZ9EDr43IWwbquLFM4kfeGPU3Qug8vuWdaEVk0GL68+dLlITzDw0rDXxwWJUqmZh52uMVhIGj6isayV8aRqtLRljRtWTFKJRW+KxdD4H56Yfw47beg6h9Oe5/DXphco1Er35eyISu+IyN7O+iT42sszpZVeZnu4X30HJTx9VdsU270RCxzMY3cbGo68vx+sPf56+ndOw6oOhdQF1wQAqvfGoC26nJZrJ6pjK6i3hY1QNeg23XN4Zi+n4ErvcIif/eOPnsA8M73+1hUdHZXFmZip6XfTZ67XFV5ITltSFjhfuo2zDYuLPvrrKxYWrcqiV0dymZ22FKUSdksTuFOcrzSd39jNhU+tVGh1pNzyOsWlmlWO1aktecQWvf/JrxPEla/fTvV0y52U1r9PXD7odYQvfhlMIVNhRafURLVmauDTyyyIHeDmcXhav3seMLzaFElWVCq4f0oHL+rfiqkFtGXRGU9weP36fm9TEGMzmYyevaq0O/hzrE/R7sa9cEHWpDyXgo2ztNyScNzJsD82g34O/OAe12UZM98EY0tsC4N63BcfGJfiK9mM8+wZ2Ws9mT245bbvE0q5ZPPscfvJzC3n2hhZwcDOotShpHVm8YTcXDMhk8v3nsD/fSW6Rk+ZpMaQlmkmwmXjg+h4ozlK8jlJUag36mDg09gMcbWqAokRf0y8t0cKTY/pid3hweQLEmHUkWLU4fvpf5N6iKnWom7kqRi28NKo1TpUFjVqFsWwf6vUfoL/8nqjnF6sTMV75D4LL368cn6lSo2/XG864Grti4Xg66HQx8SQMvglT6+7Yf/mcgLMUY9NMLJl9q1waA6Bi5/qIxMDnD1JQWvVYx50Hyuh/6fmY2/UCJYhKb8Lh8vPN0p1MGN2H71btZdOuImwWPYN7NSPWqidG46PwqzdxbP4ZggG08ekkXTAaY7PMqDuaxMUY+fu1PVi9OZfPf9yFo8LLmZkpDB/cjvSjdKNHfUa3j2mf/xbRChxU4P/eX8Mbj5xHWqKFgNuJEgygMVpQRZnQUVOKolC2blGV5eXrF2HrOUSWHBGNniR2p7Cg10Xx4vci1rtSAj5y5/ybpre/XOXYntqgVqtZ+Et2leVzl+zgzMzUOt0bVXWMDcQ11gSSLrot9EtN8fuwr/0apdtlLPwkvJXCYtKR2TKRWQu3cudV3ULdqw6XjwU/7aJz60S6t0smOd6My+Vi+/bdpCZWPTOuKorXjfvAtirL3fu2oHjccMQ4OD86DG2zsLbuSumKz//cj1KFqU0PUoY9RMX+bUz/Ziffrz3cAhlr1fPUmL70Yx3uuV+GvcaQPleTeyCBHl1bkhRnBg4PBA96XGj2raPwm/+GJn5obEmkXTehyr1ANTGJEWt6HSnWagjrrvaVFeHc/FPEeYHy4sp1zY4Yu3YkXUITFL2V7YUeftx8AJNezcW9Mmh28f3obLEoikLAUYKiBFFrDbgw8Ppnm8kvcXHN2bfQop8OBTWLt5Xx7bRN3He9nv7dq16bLppiv5mPt8XRuu0t2Ewadhd4GOKqnHkarGJR6Gjju7QaFQk2Y9RdHABaptsqx6UdkYi4HOV8u2ofS9fn0L9HBsPPbYvL42fx6r3cPDiD4rmvh/088JccJHfOJNJufBJzq25RXyfBZuTCvi3p0zmNQFDBatLVqNu0zOll9ebo6+j5Awrb9xYTU7qd0mWfoHhdmDv0JabboNr7GaUoKL6qt/NS/D6itagK0dhIYncKCzjtOLetjFqm+Dx4C/bUaWIXCCpHnV1ZWu4mEKx6Z4aqlDs92J1ePN4AOp0Gm1kX1qUbcDshGKzcdslsQ5/WCm/u7oj7WDqdDSiV2wH9udm62mQl8YJb+b3CEjGzsF/XdHYdsHPThZms/P0AXZsaUKtUbN7vZvjgtuzYV0JmMgSK9+PJ20uLuDQ0HgeYj29vV5VGh9aWjK9gH7rEDEx//sJ17dqAr/hg5Sbvf9mayB3UENPtXHJnPnZEC6WCa+d6PDl/kDpiEj9+uT7sGrvDy8tzNvDIWZGJi3flJ2QM74zT7YsYC+kt3Ef+Zy+FHQuUFVK64jPiB95AyV937VCpSb70LrxaG3kFDnYfsKPTaWiRFkO8zYghSvebP6AA0ddKcW79hdi+V0Rupq7WEH/RHbz1zV6WrDu83Meyjblc0Kc5Iy5og/qPZZSu+IyAoxRDemu0l4xn8+5i/AGFV+eHj8sDWL0597gSu2K7myf/uyJiW7XdeW7u7HkRjhVzIy9SqTG3y8Lp9lHm8OD1BzEbtcRbdFwzqAVvzY9M8s1GLZnNI7tQ1WoNWo0Kjy/A4tV7OdTpGBdjIFVTWuWixkWL3kV/08Sjbtt1osMmAoEgQQXirAbO792c5mkxOFw+lq7dz7a9JZSXllG8aga+P3dW8ebvoXzdNzQZ9e/KZP4EqdRqrN0G4dwabRIRmDP7ojbKll6i8ZPE7hSmBPxRWzUOCTiqnj1XG9Qq6N0ptcpP6Z1aJRx11f1o8oorWPn7QT79YQdFdjcWk44L+7bg4n4tSdJ7cO3ZVDlmLujH2nUQ5sw+pFz5AAdnPUHAURq6j8poJe6sqzjwzviwFqagy0HB56/Q6pqnsBi1OI9YbyPWYuDMjinoPHauTd1J8PcfQVHo0O4sPJYEWqYlU/Dpc9C0K8GYNNT7d1D+0/9IHfYQ+uTwsV1HrTeDibizh2PtMoBgRTnO7atApcLW6xLUBjO6pIyIJTIsehXFy76J2u0cdDko/f1nOrdqwq87wrs2sw+W4YnvGDWFUmevwpCahiM/D19pPvrEDLS2ROy/zI8at/P3H7FcPZ60G5+kdNknlRuap7Uivv81+CzJfLRoG/N+3Bma+6HVqLnv+p707ZKG1m3HV5pPoLwIXWIT1OY4LB37RR3/WP7rYtJveR5Di67Yl8+tbMVLb0tcv6vYmK9iybr1Edd8u3Iv53VNwLT0f6ElbDw5Owju3YTNYqiyVSw57viS8pxCR9S9ctdsK6Do7L7ENt+Ke++mwwUqNSlD78eptvLah2tZvTkPRQGbRc/Lt3ejpyWPoWdlMP+XHIJ/9v0nxRl59Op2mPM2QvKAsNexWStbGH84IrEFaJpsRZ0bfbweVG6FdrQubr/TTrCiDCXgQ22KQWuNP+5hHGajjkv6taRHhxTm/7iTz3/cSXyMgSF9WnDxWS1pZvHgXxn+syLgtFPy81ySLhxzzGVhqsOQ1gpDk3Z4csLX5lMbLcT3G4r6GHu5CtEYSGJ3ClPrTWhiEiLHKf3JkN66Tl9fURTOyEwlMdZIx1YJ9O6UhkatYmt2CUvX7eOmizpiNuoI+r0oHhcqXeRSDEdyuLz8sHYfHxwxoNzp8vHpkh0Ulbq4qV0Jru/eDJV5Du7Evvor0kf+iyY3P483bxeenJ3ok5thaN6pssUn6iB8BeXXBVzS+wI+/vHw7goWo4Y4dQWBJZPxFh+xvteaeei2/4z+6n9RdNbfmbN0D/vynTRJbM51Z/dGs3k1KWdaoy5sDJVbnvlK8gg4StBYE9DFp6CNSaBo0bt4j9hD0539G4amHUi58oGIe6h8LrzZv0UcPyS491c6ZbTDajHSLMnMzoPloYRbpTOiu/IJHJhRqcASsKPZ+h0xmX3Ife8fodZMAF1SUxIvuBXX7l+jrn9XvuYbUq95hIShD1JWXIgtMQWD1cbajTl8tjR8xrA/EOR/C7fSu4mfAx9NCnufGjI6kHzpXVT8sQa/vSDsOlPrHgTKCrC0z8KQ3pagz4vGaMbuVnh17g9V1sF3vxYwashoDMnNUKnVBN0VOHas5Yq+PZnxbWSLLsDAM6reexYqt9uq8PjQqNXEWvTsOnD4w5LNosdk0FJc5sbnD/L4zE3MGH8vCZ4S3Hs2oTbZMLXohEtt4bE3VoUlhGVOL4UlTgwLX+HCbhcy5NaBlHpUGHRqzO58lB9extehNxCe2Bn1WkZc3JHNu4vCWssdLh/6uGS8BjO2HudhbNYJJRhA8booW/ct3sL9VS6Y7S3YR/68yaH1IVV6E/GDbiSmywA0f25a7w8E0ahVYTOO/yreZiSrcxr/mv5LKLnPL3Ex65ut9O2STsf+sbij7Anr3LSMhHOur1FiF3CWEfR7K5fKscajjUkgdfh4HFt+rlzuxOfB0qEPsX0uRxsXuU2gEI2RJHanME1MAonnjSJ/3uSIMmPLbmhi6na9KICUeDP/vutsPl2yg6mf/IrXH+SMDin8686zSI834C3YR+kv8/Hk/IE2Npm4s69Cn9wcjTFyYHZpuafKpReWrj/A1b27oLUlY2rbE5VKjWvPJnyF+3D8uoS4s4aii+2NpX1voHLNN8/ByKVJDvHl72XY9c2IT4hjS3YRrTPiOPfMpng3LcFXHLloqyo+nS17y3npo8PLZhSWutm4s5h7Lu/AOW43WiuhjcEPjTfzluSSn1fEgXIthc4EklwaWgZLMZXuDkvqDvHs34Z732Z0cQPDX1+rRW2quhvJ0LIrQzsmU7FzHSqXHbokor3kTJbtcrO7OMBb8/aHZpHaLHoevHYE/PZFWFIHlUuqlP48F1uvi/+c5RlOl9KcACrsXg0+QzLlHhVelYc5320PnWMyaAkEgnj9QW6/sBkFf0nqADwHtlH6y3ySr7wfd/ZGKnasQ6UzYO10VmW9FeZgzdSiMx8et6e4XPj80VuojXo1o8/LwLtpCbnfzSDodqBLakr8wBu4KDaWTftSWL0lP3S+WgX3XteTZJsWd8EBnNtXEijNx9iqG4b0dqisCew+aOfteb+zbU8Jeq2aywe0JrNFAm2axnLN4PZUeHzYHV6aplg5WOjk4++341KbiWmSFLazyNYdhVFb+bILPXRNbYl340LYuBDrn+MKD6U+hhbRd2RITTDz0r39CThK8DrL0Oj06KxxmDR+LMlNsK9agH3lF4CCxhpH3FnDUZtjUNQaKnb9imv3r2hiErC0PRNFrSHngydQAn4snc5GrTfiObiT4m+no41NoSK5Exu2F7BqUy7JcSYu6NuC1ARz5Qc2nweNu4y2afFo/G7sDg3TPv896mo9v/x+kOv6JqDWaCHwl0lDR0kWqxJwV+DN3UnR4pl4c3ejscYT128Ylk5no7UlENv7Uqyd+4OioDbF1HiRYiFORZLYncJUqsrB88lXjaPk+/fwl+ah0hmJ6XkBcX0vP+p4mtpSWOpi4rRfOFh4eND4mi15/LazkMljz0K/fgG2HudhO/MCQIVz+yq8ubuJ6T44YsV5l9sXSj7+SqtRY7UY0fe/GufWFSjBADHdB6ONSaB05QJiepyH4vcQ9LpQ64yozTb0yc2q3GpLl5CO2Wrh8gHJXD6gsmWzorwM9x/Loj9ojyt563/R7zV94W56dMog8Ov3lK1fBIpCTM/zMbXqzsFSP2VeHU10JTRR5YI2DUWTTvn6qmfvla37FnPbM0KtJVC5bEdcv6GVix3/haFJO8wtu6GU5qDT6/FXBNHp9ahL9zGoZSoPvrczrF7LnF7+NXMdL48ZApuWYmrRCY01Hn9JHu59m3Hv+Z34AdegjUtBY7SCSoWvNJ+yNd+g6Xk56/8o5vMfd5FfUkHrjFiGntOGGy5oz5rf9jG8byoGTzFotJQRg0nlwekoxZzZD1vP81CpNQTcLuy/fI7jtx+wdjobXUpL4jL7owbcO1djX/E5GaP+HfGcMRYd/bqms/iIPWwPee6WLpR//w6uHetCx3yF+8mf+wLJV9zLnVedycW5DrZkF2MyaOncOpEm8Xq82b9S+Nl/QkMaHBsWobElkXLDRP41fQPlTi82ix6fP8jcJTt49u6zuX5IB17+37qwbvzOrRN5YnRf4qNMFNq+tzJ5Tk+0cNN5LYk1qfl1TwWfrcil+xU3wud/PusRwyo0iU3xWdMor/Bid3jx+gNYTToSbEZUHgfKlqXYf5yN8ueuI4b0Npguu4eSZR+HvecDjlKKvp1O6tXjKfzqLVx/rD78Pli7kPizr8LWcwj61JY4t67EX16MtVN/dANvoEibwuNTfqTkiOVwvvh5N3cP78aATnFU/PQBjt+XQdCPPqUF8UNupU2aKWoSC7A1p4Lu8WmhMXaHWDsPOOqHlr9SFAV39kby5r5wxHOWULToHdwHd5B0wWg0f3YnC3E6ksTuFGf3aPh0u4UOPe8hOUaDN6Dimz8cXOLU0uIoPyuVYKBys3mvG5VWX62tj/5KpVKxaXdRWFJ3iMcb4OMlu7glqx95nzxPsKIMlVZPTPfBGDP7EXDaIxI7o67qBT8fuqot3h/fpWzX4fFV7uzf0CU3J2Xo/ZSu+IzytQtRAr7KvTu7DiS292WUb1gcdRxiXP9r0Pxl+QdFqXqWrUMxh/0iP5LbG6C4sISKL14//Pw5fxB76xvog25sP7yA74gWK+u5I3AebVKJEkTx+QiqXQR9btR6E2q9EX1qK2y9r8CX1AZNYnNQqfAf3E5Cs1YEnCUUfPF62KxMjTWe5Cvu5ZYhzZn0v/BFcINBhT/yvQy44XGcW1fgL83H0LQDcWdfhWvPJoJeFyU/fkSgvHIhXF1SM5KvvI9fdjt4afbh5W1yiyr45beDPHFrH0b1UCgtycGhj0cTVKEv24ElKZnYm5/FsXkZeZ+8iOJzV7auDLiW2LOG4bQ2Y/OuAvbuKgUlSLdm3Wg2cgDa2MiJAwadlmvPb88vv+fSKj2GM1pZcfsUVm4voYk1wMEjkrojFS/5gPhr2xCrcnNBBx0KKvYW5qO1xJL/+eSI90egrJDS797lXyNHoA16UJXlotKZKFfHolErvPThuogPIJt2FbFk7X5aZ1R+mPL5g6jVKjRqFSkJZl64tSvNrB6cG74i4LRzRcuuXDOmFz9uc9Dv2scp+/7PSQUaLYbM/vi6XkGBR8/0j1eyeXfle8ds1HLP1T3oofmD4u9mhL2+5+BOcmc/Q+L5N5Mf5cNM8fcfYDvzQlx/rEFttKD4vehik0ClJuipIP/T/4TOde1cj/Hsm5ixwxeW1B3y5qcb6ZzSFf/GH0LHvPl7yJv1JDcNe4Lf95RHHdNotcVEzFrVWOOI6TvsuMa+BRzFFH47PWqZ8/cfiT97eNiHIiFON5LYncK8vgCffP8HC5ZFjh9avqmAyfef8+dSFuECFWU4tq6gZOlsghVloNFi7TKQhHOuO65tdxRU/BRl4/VD1m4r4JoO1srXoHKHgrK13+ArzSPxvJsjzrdq/XRsGc+W7PDuweQ4E60Mpbh3RQ6a9xXspWLbKrwHd1UmdQBKEMfGJWhjU0gd/jAFC14LjRdT6QwkXjAafXLk+noGiwVTjwuj7lIQsVXVX6iirEmnVStUfPdaRDdkxfbVmDv0wZsXfdyXtetAggEP3pz9KD43Kp0RbUwiHpcTbbcL0bpK0QdKQAFPajNUOiOFX72FNi6VmG7norHG4bcXUr7hO4oXv0fHS8ZGvEbXVnH0iLNzcNbh1ip2rKVszdek3/BP8he8FkrqoHKB19wPn6LtVU+jUoVvjhFU4PVPNvL4zWfy9CdrKCytHDPXqomNp0ekUr50Bq4/1oTODzhKKPr6LRLOvwV1Qluy2sbQrfAAaq0ebbyJPcUe1BonMQY1gQo7Qa8bjcmKxhJHeqKFGQ+eScUfawiWbgOthisu64gqGAC1NuragIHyYvSKB938f1Lx53qGGentUMyXRXYL/snS7ky027/+s2W18mFNBjPxVz5Ex+Y21v0ROa71u1V7uGJAK4rKPPy+oxCrRUePdsmc2dyAe/MK8j47vM6da9cGNCvnM+iGiXy6QaFFz3tomWrBH1SYt+ogXcs0zPhiddg6dxVuP1qPnZJVH0aNOVBeTNBTEXXcra84B21aW1RXP8fBUi9Woxad3os2RkfB/Fci7uVLbs+aL6IPZQgqsHmfg86JGX+ZhasQXDWboX2v5Z1v94Rdo1GraN8sFnSj0P7+NUGvC1r0wtP0TH7Y7uKCPsox/4+FXt9dUeW4YgBP7m70SUcfOylEYyaJ3SmstLxyn8mqyvbnOyMSOyUYwLF5OUULj9iHM+DH8etifMU5pA4fV+0uXBVH33PUbNRClFmcrp3rUQbdGHHcog3w94symDjbHfYLrV+nZNTbv6rydRy/LyW23zCs3QahNphR/F4cm36i9KePaHrXa2TcNrlyLJkSRGONR2ONjzrmRqMCfXo79C264d2zMazMqnYTH2OI2oJhs+ixBsv467Bwjb+ics/Qv/Ac2EbcWcPQJaTjKz4YVqZLaoqpZVfsP39K+e8/QsBf2dLZ83xMva5EW56HffUXFP+xBlRqLB16Y+5/NfEDriHocWFfuQC/PR9dYgaxfS7HX5qPVokcsD5iYBOcC/8V0Vql+NwUfPE68QOugWCgcv0/lQp/WSH2VV9i2r+Kbm2b8usf4VtaFZS6yC3xUFh6+Pu9J7ccbcAVltQdqXTZR6S37ELRdzMxNu2AP+Cn/McPyeh/HYZgE3I/eg3PgT9bn9QaYnqcT2zfofj2b0FntuIu9FYmvVotntzdJA4eQdFfWrIqqVBrdej6XkcwqQ2qYAB98R9VrjlnaNKOgNtB+fpvw+vGU0Hx3Ge5ZdikqImd1x+krMLLhDd+Dh3T69TMeqAbpVEWLw447ZQseZ9Lzx1N2d7taNatQ2WMZWjPsyhXqaIuXpxg1YQl3BExFOxDG5sSkfiojRZyXXru/+/hCTi9O6VxV5vsqPcJUuXOdgC4vcGos2a9B7bTsXf4eoYqFdw3tC3Goj+Ytc1ArPVKjHoVv25zsvGr34mPMZDVKY3E2KonVoXd7xizdTXG45vpLERjI4ndKczjC0RsK3SkvGInRy48C4e24Iq+Qr5n3xYC5UXVT+xUChf0TOK7KGOeAC7tlYJq25yoZb6iHArVKfy0YT8eX4C+XdJpGavGuO0b/nVlXw54zOzKc5EeZ6BrCwveZVU/pxIMoo1NpmD+FAKOUtRGCzE9zsfa8Wz89jzMrbpXdjtFu1ZR8PoD6DQaFI8L54aFxF50F57c3fg3LUZRFLQdB2FOa8KD18bx1Iz1f67BVkmtVnH/Ve1hzbTI+vFXvbyE4vOSdPEduLJ/w7ltFajA0qEvlg69KVk+D+fG7w+f6/cS1Meg8ZST+8nzhxMSJYhzy3I01nhUGk3YMiW+ogMUfvUm8QNvQKUO/2+u06qxUY43yqxXqGzd0cWlkvfZf0IJgi4xg8Tzb6Zi90Z6tI6LSOyAiBmTaYlmgsX7I847JOh2ogT8GNJbU7F9VWUC2/UcDFYbBZ88Fz4WKxigfN1CrF3OoWztQjw5hydrlK3+ktg+V2Bu3xu1KYagK3x/DFPr7gQ0Rl7eEM+2vbvQatSMOL8zl7aJ/uPP2m1Q2P6sYQJ+NDkbadcsMbRF2iEJNmPE1nK9Oqbi2h3+IeFIrl0bSBjkpujbVw5/MFj3JYmDRnFZn3S+WBme+Ds9CkaTNbRw9F/p4lKo2LE24ri558W8vSyPc89sRqsmNircPvJLXHhc0deh1Jbuo1UTG7tzyqKWd25uxbc+cpKR2mShabyO+4e2Yf0eFykxGgZk2tBu+gptXC827S7iQEF4Qm13eiv3SK4mtTkGY4suUVvWVVo9uqTqLz0kRGMkuxifwox6DRZj1bl5s9TIcSZBr6vKXwpQ+Ym/2hSFhIp9DO8f2e3RtU0ivZsE8Vaxw4LaEscjr//ErIXb+OT7HTz8yk+Ul5Riad8b9a+fk7b8Rc4p+IjWv76Od9kHWLsOqjIMS2Zf7CsXhNaxC7qd2H/5nIrdG6qcGez3B8kpdPDhwq38+93VzPhyMwfLgqAzUfrxMygGC9rBd6I/706ISaT4k2dIO/A9z9/Tn0vOakm3tklc1LcF/7q9HxmaYnwHt0e8htZkrdxB4S90yc0IOIrJ/fh51AYLyZfeRfIld6HS6isXnd64JOIac7szKd+wKGork6lVtz9nQUYqXTEPtU5X2Xr6p/gYAxb90bu9/I6SsFYfX9EB8udNJqZzf2yWyPFQTZIsFNnDkwSNSnXMQfEBpx37inl48/fgyfmD4iWzCFTYIwbYAxhbdKHijzVhSd0h9pXzUfxeTH/Ztksbm0Js78vJLbCzbW9p5bMFgqzcVkTQ78XU9oyIe2nMNgJlhVXGrHXkEmeNHI961aC2aP5SrWqVKmI7uzBKMOoY0PIfZnJx97iI7skftpRjzboi6q1UeiPGll3D1nMEMLXLwt60H5f0b0MwGGTBsl2s2ZJPx5YJmP+cRR4R1vr53H5JazRRukfP6dEEU/7vh4c+HCGmxxDKl8yk/ea3GWn6gfMd81HmPoZ/50qCcU0jkjqA7m0Tsao8ePL34NiyHNfezfjLCqtM9jRGK0mX3IHmr5Mj1JrKHgfZMkyc5qTF7hSWYDMyfHA73vtqS0RZ0xQraQmRXRIqra7K7ZqgcjBzdSmoiIm1cJ51K2eP6cWybeW4vEHOah9D04wkfN+/Ef01LLFoLHGUV4TvBGAx68md+TJxZ19FbN8rCFbYUZtiUAJBtHEpUXeY0MQkVnZdRllU17npZ+LOHh41hu37Svjnm4c3Ul+3LZ/5P+7k8Zv7kWpcS/lHT0ZcE9dvGHtKy8gvcdE0xUrhn7sQPD2yI3EpLfHlZ/+lgoJYe5yH4y8zYM1te+E68AepVz1M2dqvKf5zJwdz+16Y2/T8s4s2vDVEjRK1heL/27vz8KjKs/Hj33NmX5PJvrAvgQAhhiXsu6AC4sYiCCJuVVx+2lbRqoXWt7VvAdEKatsXi1VUXChUBKyKKMoOQmQnQFgCSUhCMklmMuvz+2MgMMzEaouKmedzXblazzmZnHNnktw8y32j0SI87ka/n8Lrxl1bywPjr0BVQnXI6r1+/OZgo+3BVLM96h9t4ffiOrQdt+gedlynVbljTBcW/jP8/gJCoIlLiTqKBmBqnRtezBfQmOMipqfPsWT3iVqC5Zy6fRtxDBiHPqlZaF1daitUvYnTK+YTGPaLsGuLTjnxBx1YO/XHkNqamh2fEKirwpDeFq09GX1Ky4a6bhczNu9Eis+EqoTWm8VZ9dw4uB1Ol5fquvC4bdlbyv1Du8Jn0dfFGTLa4b+o5Mw5avFO2jVLbdhVC9A8LY5TjitwdD6Jd/dnnFv/p7HEo7vqYU77baRNfBJfRTFBdx26pEwC8S04cTrIH17d1PB+P33GTeG7VWRMyyGpTTfqD4dvPAm4qkmyKMx+cACLV+9j39EzxFsNDO/VggFdkjAcOU3FRb9HjM2zseUO5fTR3fjKTzQk54reSNqEJ3hpbWQhc71W5dGx7alc8Sfqi86PbGoscaTd/BT61FZRa+fpEzLIvO0P1Bfvx120C11COpasnmjsSd97f2xJutzJn4CfMI1GZXh+Szy+AP/4tLDhl3aXtok8dHMeCVHWrGgscVg69qZub2TbHdVoRZfw7dsrCSHQp7TEWlpEor6Gm1pVghBoLAK1PojoMRL/mdKwBdaq0UrSqOlUlZVgM+vC2nopqgZDRjvOrH0DUFD0htCU5ah7qfryXeL73oSv/AS1e7+EgB9zux7Y8q7k1JtPAwJ9aiu08akEas/gKT4ACALV5XDRRomKajdzFm+LmMYOBAVz39zBsz+7H9/fwzcc2PKGE/S4ibcqbN0b/gfqd0sOsPDnMwgcLwgtthfi7PV1mDLbozFYcH71L4THhWIwY2iWhbltHqXv/pFg/fnRU9f+zdQf30fKmAcpeet3XNjX0l9xImoTd4LBULL+DQwGAzVn+3hqNCp9ctJRjHbi+o2lel3kVHnCoIk4t62O+lqe4oP0HTyII93SOHXGS1aGmeG5iZxx13Pyot3RHm+A6oCR5Bt+Ttl7sxs6QgBoHenE9bmesgtKVgAE3DWNjrioWkPUosnnBOtrqVr/D7ynDqGabNQd2NyQ4O0rD98k4fb4OeQ0kLRpKRqzHcegm1GNFnzlxVR88irxA8ZH3BuEkt7T+mYIUcdjU/MJBIN4fUE+2nSUiup60hPDv0ceb4CiapWk7L54Th4M1YozmPGWHaWucBsJV95G+eq/RnwdAAOeiPIpHVo6ePLP6xk3oB8Dxl2NWleBojdR5jHyyooT5HdWuTZ+P9Wb/omqM+KvqcRy5595+5OCqMs2Zv59N4seuQtxYCvBXR8SrK9F2yIX0eUaNh2up1MbG+2bO+iTk47b48frC2LUadAkZZI2/nE8pUcI1tdhyGiPCAZQVC1xfW9AYzDjKTuK1hZa0xr0+5k2sgP+AHz59SmCQUHLNBuPTc7FveHtsKQOQiO5p974DZl3zGl0GYU2LglrXBLWTv2inpekWCUTu5+4eJuB8cOyGJ7fglqXD4NeQ5zVgM0cvXyAqjeRMGwqvjMleEvOj5ipRgtpE59Ca/9u0xiKVo8ClLz1P2HH02+ZRdk/XyBhyC2oWj3eymK0tkRUo4XKTxdDn1up94SPFgU89cT3uZ6K2jP4KoobanShqBDwU7Z0DoZm2cT1HA2qSt3+zaEdk0YLSVffhbfsKL7Tx9G3ysHRfxxVG5ejNlII+XQjPW5r3T6q/Hpa3vwEnlOHQQgM6W1xH9mJ68RBTqW3j/icZIcZpzCTfsWV6Nr2AsBks+GpOMnp12dhbnsFyVffDeeKszqa4Sr4JCypOyfoclJ/Yh+m1jlha7Oqv1xK/IBxnF7+XPgniGCoUKwljkBdZAs5XWIm+0p8vPdpEV3aJhEMCv7+wW6euycXodORNGo6zu0f4q8qRZ/cgvi+N1JffBDPiehT6FpHGoFNbzHOrEUkJaCeOUH9ku2kXP0gY4e0Y+naQs52xqLO5cWHngO1Njrc8lv8JYX4q8rQp7VGl9yKsqWzIxI14atH+OrRxqXgry4LO+c5VYipVVdcBzZHvTdLh15Urn0Df9X5xFvR6tEMmMbb70SuB5uztJAX7nkEz2d/o3xlqKOJojcS3+d6nKZMjFfei3f94oZd3YaMdliG38srn4dKm6xcXxT2er+6LZ83PowcPX/h/SM8f/ckfCWHqd7yAUGXE0PzbDKn/p76kwfxNTIyaG3fg3u6t+TGIe2oc/tIT7KgKAqqqrLk8+Ms+TxUDNrnDzSs+xzSXYev/ARBl5MgofsO+APsK4o+KugPBPno6yqKTqXQJetOzHqFncdcHFlRzGO35ZPiMJMYZ6Le50erqsTbDOh1GoLmDgRqzxDw1hPwuNDYk9HaHJR/9ArufRtDm1rsiQQ9daGpYVVD83te4IEJeUwd3YlAUGA26LD4qzi+a23Uewu6a/BVFjea2EmSFJ1M7JoAvU5DaoKF1G+Zk+nikkib8AR+52m8pUVo7Unok5qhsSeiRFkT9k38VaVUrnkt4rircDuG9LaUf/AiqtGCxpZI0O0kUFuFxhpPhc+Exxee2NULPXX/WkjCkMmhsivBACgahBBYc4fiS2hDfau+fHbQiS8QJD93EjYVEoZNpWzpnLAkQdHoSL7uwahr7IKBaG3GLjwfpOS9Oejik0FROLPubVBVUqbO5svVJxk7oBnNEvScPONjx5Eabh3VCY0IsGHHUT7cEkogRvTIIKuFA1v+tdRs/AeuC3aGJk57Fteh6DXXAFyHvsLaaUD4onuNJlSIuEMvXPs3hV3vrTxFyo2/pOTNp8PWc6lGC7aRDzLr74cpO+OmtPIYAFqNgqit5Mya10MlUnIGobHG46sqo2LtG8T3HNnInSnYrhjGqcWzEN7zibFqtuNISyfZpfDUHb0pO+NCp1WxmfV4PV5MNjsLvzhOVmoGyQltOHysjsQaL13aXIHv9LGIr1L52RLSxj9G2fLnw9baBdy1OAaMw314R8S6NV1iJpq0djiumU7t5uWhnrSZHbH1GMmXhzxh7bfOcdZ5cXpU0gZOJL7vjQifB8VgQlF11HiC/K3AzJjBjxGn84Gq5auTXg6vr+T6we2wmvV8vPkYbo+fFmk2xg3NIsmqYdrwVvzfh0UcL61Boyr0z0nhrhEtcG5YTs2O81PyvspT1O3+gvQpv0VjS4zY6Wpo0RmdI40kq4mkC0beT59xMSA3g0/P9oq9sJ6eokD3DsnULQ5/fygiEFGiJuxraeCWq7JZu/04Ryo99M1ryS3p9oYR/4Q4Y8TnqHojakI6flM8J4qKaOnIAE8N7n2hry189eGlUIIBPCcPYu2cGtY/2lvua7TkDIC/uvG1jpIkRScTuxiltcajtcaHtT76rlRVwbk9egcF51cfkTr20VCf1NPHGhb9q2Y7KeOf5M//iFwc//oXp7l75L14y49TueY1/GdK0FjisHUbganLUL44YiOwt5yeba0gBPuOncGWlYT46JXIkZ+Aj/JVfyb99jn466oI+rwggqg6AzajgsWko84duY5Mp1VJsGoJpLTAe7aRuC65BdYr76K4VuW+wXG4Nr6Hv+AE2QkZXHvtjQRsMGfJTnYdOT8qsm1fGZ1axfPIpDHEmezUbPoHQZcT1WzHYNCjMVojyqOcozFaMHTogy2hFdScRthSqBJWTq9/H2urrsT1GEndwS0oqgZz+x7Un9iPp/QoaZN+jaf4IN6KExhSW6NvkcOCf5VEJDVajUpdTWi00F9VGkpcL+A7U0r8wAlUrXunYQ2VotWTMHwafr2N9Ftm4T68A391GfrU1uibZbO7DLKbm1n8USEHimtQgV4dE7h2cAesqqB/l1T8KLi8AVq1sGI3a7C1vQr33g34neG9YvUprVD0RuLyr0VjiUP4PKgGM76qMoTBTtLk31Oz7g3qD+9A0eqx5A7D3G0kR2t0zF96mvvH3EqcSWHfSQ+L/28vv/1ZX3LanuLrQ+HJ0z3Xd0KzawWn9nyKuW03VJMF7+kTeIr3o01uybTRT/HBhmPsPFiO2ahlcLdmdG6tQ6MoOGwGpt/UFZ1WQ1VtPToNmI58jnH/Wn418Dr8traoBNEc2YS2RkfFjsifE+H3UvHxq6TfMpOKjxdRX7QL1WQlrsdIrDmD0EZd76pw09D27D92JmLq+84xXTDhps4X/v02eM7QPSuJrfujJ0lXtE8kPcnCxBEdo57/JkIIXK7Qz54IBrhw+cDFAlE2/qg6A6rRGnX0Gohab1KSpG8mEzvpP6aIIIHa6IVChddN+aqXSZ80k0BtFd7Tx9DGpaBLzERrT+T/3ZzChp3FJBr9aDVQUgN9cpvhLQqvsReoq6Zq3TsEA4KruvTGvW4pnmU7AUG3jPYkdb6Lk1F2UAIEPfXgdeM+sZe6fRshGMDcvieOFp25d1Qb5rwbOd1465UtseKC6x4lUF+LgoJqMCH0FuL2b6Tyw/kN1/qrT1N/ZCfxV92DSR+5/m1PURUFhacZ0HMkxg69IRhAUTVojEasXQdTf3xP1Pu25gxhbUEpr6w+is2sx1lXxO3Dm9PdX4fWnkDl52+iavWIYBDPqULie19HzZ71iKCfut2fo7E6cJ44gEno6J7dgc8uKiJd7w0QMCc2uonGueUDMqb9AXOrrngrT6JqdGjjkhFaHQfPKCz+8CQ3D+yGI1PPvrI6lr99lPtv6or2oz8yrVk2oncOIhhALfwCpcTP3M99dO+YyoCuqSiKgj8QYPOechyG3SRedQfeksO4Dn0V6hWb3RcUhYDLiZKRTcDtJBgQaIzxkJlC1dZV6Lpfh2XEfVh8blAUhMGG0WKijbmWP9yUjHPjIvw1lXRsls2f7hqJId7Ao1N6UFLhYtu+MqxmHT06pmLX+yj/6+cIv5e6/RvDYmBIa82KTUeoqPYxtEdzPN4AqzYU0SzFitWso6SijsxkK0KERsxPlFbRPlhJbeUp+CQ0rRsE1NTWeGyN7wz2HN+LompIue6h0CiooqKxxKGomqjXW0xa1mw9yuNTe3K4uJqCQ+XYLQb65KTj8QZQ9Vqa/ex5and/SaC2EnNWT3TJqdw+KokDJzbjrAsf6Zw0tBV2w3fv1RqNajChdaTjPxN984uxWYeIYxpbAo4B46j46G8R5/QprdDGJUcclyTpm8nELkYFfR4CtVUE6+tQ9AY0Zvt3bsMTRMXSoVfYNOOFDKlt0Jjt6OJTI36pJ+o8XN1e4K+pQgT8XJGejEbj5uTaxRjS2xLsei1BUzyqz42650PMzTtQ9s5vwnZX+k4ePDvao2DI6kUgewRedOjVIJrCz7FmtqVy7eKwArmug1sxZGbRc9ht/H5qZ9764hTHSutITzIzvl8q6bV7UXRpOAN6TlbrCQYhM8VIcqCGyrWvRH3O6jWLGDdyFluijIh8VlBGTvtUqt16yqvcJMfrSTVo0VgdWLL7RmxisXYZiMZkpaCohnpvgPqzU551PoE970pK3vyfiB2rpacOkTbhSao1DuKa5xD0uFCNVo47oUNCPCN6teRfm86v41IVqPDoaN7tKmq2rYq4Z1Pfm6l0BdGWHkOv0xL0e3GWnaTS1JKvj1UwbXQXvjpQRtnealql27l5eEfsNiPWMQ/i3PIBdWteRNHoMOcNx5yewf+7ys1r66tY/OE+giJUpueP0zrjWr2SmppKDJkdMLbohAj4qdq4HH9VKZbOA1F0evxeL4o1iWDZKgJ1TgJD7kejaIlzmIH4hnsOelzU7Pjo7Mabs++P08dwFawh/danic9oT7zNSMdW59criGAA3cSnOPXm02FTy4aMLBIGT2SUz8Sq9UWs2Xocs1HLxBEdyGmbhKKENkW89dF+qmo8dG6TyM3DOxBvTqX+SEF4YiOCEa3zwimgKGiMFoiyHvRiZqOO/rnNmPnXDQzq1owBuZnU1ftYuPxrurRNot3QLPRmBwmDJoR9XnLwDHOm57O+oJhthTU4rFpG5WeQkWzFEn9pekprrQ6Srr6Tkjf/h4tH7swdekXdFKOoGqydByACfs58+V5og42iYm7fg8QRtzcyailJ0jeRid1lRghBRXU99V4/Oo1KnM2AUX9pv03+2iqqNy6neuvKhvUtxuadSB7zALr4lH/7+aqqYjKZEEJgbJUTdY0QGi2OgRNQ9ZE7c33OCuqLCij/cOH5P6iqFseAcTiu+wWbS3S8seoE5VWVWE067rhmAt3LdkctmUEwgHHkz/n4mJH3Fx+n3htAq1G5rn8/bjLXR+164Ck+gK/0MElHCrg3qwXBHumotWUENy7BMuaXbDhYx6urtpHV3AEKFB4/w3NTWze6I1P46rEpkedsZh03DWnPbxZu5mjJ+XsfO7g1E/Is2PKGY+9xzdnNAArmDr1CvWENZu4cnYpBq+F4uZvWqSaG9mhJ7cY3opch8dbjPrQdQ/fr2bjPQ9GpAB1aqHRunYi+8iDX9c5gUF4m+4+dQatRad88niRzkLj0a9DaHDi3rSZQU4kuIZ243tehZnbi3gUFdGntoFc7K4GgYM2uKgoO7eJXt+SQFG/kivbJVNfWkxgfWlyfYDcCoY058X1vDCUrljhcB7fh+cezTO46gpt79cLjVzAqXhJEOeVn33ue4v14Lqp3KPxefHljERVHEbUVKF2v41idgfKTAUa3iUyUAnXVnFkbWXhbBHyUr3yZtIm/jii8fW4XdrO7n8V7+vjZHbSt0dqT0FrjSQOmjOzIDYPbolFVbBfU7xuY14zOrR1UVTlJTorDbg2N2GZM/g31xQeo2/NlqA9q7jAUjTbqOlQI1SBUjdao5xqTmWLld/f24/OvTrDk4wPEWfXcNrozLdLsWBvpBGO0O7DpPIzOz2BEd9BoFISiw2C1otVcunKmxmYdyZj6P1R8/Hc8Jw+iscQR1/s6rJ37ozFH9v+F0E79uPzRWDr1C/2jRKdHNcdF9HKWJOnbkYndZaSmzsuWvaW8+sEeKp31aDUKA/OaMeWabJLiv127nX8nGPDh3Laa6k3hdd/qj++hZMnvSL9lFtqLC3+edfqMm4PHz7D7cDmpCWbikwMkWa2kTXoK/5kyCPoRwdAIhWqyNlqcNlB7htMrXgyfBgz6OfPZm8Tf+Bhvri1raE1V6/ZRVOKkk3tH1NdyVZ5mdUVr/vF5UcMxfyCIz+endse/on4OQM1XH5F8/UO4D+/EW/I1usRMDFffzQm3jqo6F/fc0JXt+8sICrhjTA5KlMTtQiLKppMbh7Rn0YrdYUkdQLwhSMBdQ23Bp7gOfYUhvR0gcG5bjblDLyyd+mGorWKceQPBTimozhLU/cdxHY9s7n6O++hu0vpcz/D8lg3HAq4aSta+jnCWk5A7kn4tugIBggdXYslsyalPF6O1J+IYMB7VaCVQU0H1lg9QCz5jyrCJvLC8kC++Pr/DtFOreFqa60iwaDDrTRxxlZCZ6MBsPp9oqVod6gUjM7rEUPkcz/YVsH0FWsAP1HXuj7ldN2ovaCR/IVPOYBZvLWfTLicWk4U6dymj+rXmqt7NoyYinlOHaGx9l7e0KFSUO0pHFUXVoItLQRcX/R80Wo2GeFv0aVGTXqGo4iTpKedfV2tPxGrvg6Vjr4aNSIH6OuL7j6Pqi3fCPl81WkgccXtotO47Sk0wc+OQ9ozs2wqtRsXwLf7xZzUZwGQgsrTypaPqjRibdSRt/OMIvyc0tWyN/7ebshSNFp2cdpWkS0ImdpcJIQRb95Uy783zuyX9AcGarcc5VuLk13f2xmH7pimdbydQe4bqTe9HPecrP4G/+nTUxO7k6Voef/FLKp3ne4H+bcVeZt2ZT1ttNRWr/twwaqfoTTgGjMfQLEiV30DRKSf7j1aSkWwlt10igYK1jRbUdW9ayqieE1j00fmpQ6c7COb4qNfXJ2bx/j8jm5XrVBD+xrYnhM4FaquoLfgUbVwydfs34vV6qUgaSOHxqrCiz59sOcbcu3IwWOIJ1FVFvJZqslIrIhPv1hl2Xv0gch1dQNESqCsPrfuDsCK9dbvXYW6bh6Iz4NnzecNxY1xCaOdqZWTZDggValY04aM1Iugn4KohUFdNYP2bwNkRLVWD2urnBGoqCNRUnK35F67/CDO1I1rx+e7QKN/VeQlkx7mwuEpQNJ3AF6C+PrIPcOR9OUi+9n7Klj0X9j13F31Nxi2/wXVwW8RIrDatHWpiS6aNdnDDoHZ4fAHMBi0JcQa0muhJ1r91aZaRffsvd0EiozFaiMsfhalNbqhDSl0V5jZ5WHMGom0kofw2NKqCxRS9rNGPTWO2Ad9taYckSZeGTOwuE5XV9SxaEX0xfeGJakorXZcksRNeD8LX+B9kX8VJjJlZYcdqXV7mv7MjLKmD0MjY7xZtZe7klmFTscLrpmr9e+hunstTL6wLa2Z+Xb/mjPJEX1wN4K8qIyUrPEHZuOc0428ZBnu/iLjeFdDgD0QmidsPO7mydx+4qLPBOZbsPhhSW5Ny3UP4ayvRWOKpNyTgLqxm/deR9/fiyqPMGn0/le89E96tQVFJue4h6hNSmTzCwyfbT4GAIXlpmA3Rf7wUEaRmZ2TbsHNqCj7F2LJL2LHaPV+SMOhm6o9Ff4/E9xqNqgv/I68aLZja5IaKJl/49bU6gp7IHYoXClaX0qvkfXpc0Q016EfsW0rA70N39d3fmDBfTKM3YWqdS+Ydc6gt+BRfVSmGzCwsHXrhMSZgGvsbfAX/InBkK6pWj9JpGPp2+agWByaDDpPhm4svn2NIb9voZhB9ams0xh83ydCYbJiaZ2NIa4MI+FH1xkY3SEiSJP03YiaxCwaDzJ8/n3feeQen00n37t2ZOXMmLVu2/Pef/ANwe/0RidOFDp2opmPL/74HoqIzoGh0UddqAWijrLGrrqmPKBVxjtvjp9StJcloCetjqs0bwwtL94YldQBFZS6CbdvC4R1RX0+T3IJj5Z6Ir/H5YR9XDZpE9UWtmUzm6Otwjpx0Um3vhD0xM7yeFqCxOrDlDkVjtqEx29CnhEoqeF0e1m6L3iv3ULGTd3c7mHzns9Tu+AhvyRG0Sc2I6341OkcaZp2esVdmMzw/9FpxdjOnypxR64cpIhi2WP9iwuOKOB+oqcBfe4a4XmMumkZXSBg6uWHK80KqVk9crzHU7voc4TsfU+GtR2Nt/L2kaPXoHGlo7Ul4ty0lqNFh7dQXc7seeEoOY2qdA/5/P1p3jsZkRWOyYhh+G0G/D/Vspww9EBRpBPPH4+s4AqGqmOMTsJr1mI3fLqFr+BqWeBKG3BKxlk3R6kkadS8aS/T1XT80VWcA3fc5GSpJUqyLmcTuxRdf5K233uKZZ54hNTWV2bNnc9ddd7FixQr0+h9/OkOnVdFq1KijTwCJUYqE/ic0lnisuUOp2f5h5DlbIrr41IjjPl/jBUQBat1+knUGuCCx86V1ZveKyHIiBYUV+Ifmo2x/P2pzdF2PG1m5OLJ8ycqtZYx6cDDNsnvjLvoa4fdhbt0Vt85OTtvEqInn/31SzDO3PoF712fU7FyDEAGsnfpj7341Okfkc6qKisvT+LNuP1jJ2OHZJA69FY+7luJTZVitSRjOjpRpNCoJjvMjQzadn35dUvji6/AOCjuPuejVujs01ou0dW7U6dGaHZ+QPvm32PKupP74fhRVxdisAxpLPKoh+hpMnSOVjNueoeJfr4R6zSoq5qweod6anfpTtydyFDSuz/VUrf8HilZH0og7EMEArsJtlC6dS+adc75zEesLqRe1P4uzGoizGiDlv9uZqRpM2K64EkOzjlRvXI7fWY6xRSfiul8tS2ZIkhRTYiKx83q9vPLKKzzyyCMMGjQIgHnz5jFgwAA++ugjRo0a9SPfIcRbDQzt0Yx/bYqsxG/Ua2idcWlKEqg6PY7+YwnUVeO6oHaX1pFG2vjHQx0fLmI2KMTbDFTVeCLOAbRINobaBl3A30hzByHgxQ+LeeLmmVR9ML+hNIRqtofagiVkoNOGryMzGbQ8NbUbVrsdjS4R/QWjU3rgwbFdePrV7Ry7YJNCUryRn4/PwRAXh6H/Tdi6DQ/1sTXbG20SbjJo6dc1g/1Ho7df6pOThs2kQ9GoBFUdVU4n6ZmN99a1GDRM6W3DpNew5qsSAkGBVqOQYNES33Ugnq8/bmhXdY7GEo8tZxDOzSvCjit6Iyk3/LyhvZI+yghdNIqqwZDSktSxjxCsD20A0ZhsqAYTicNvQxuXjHPrKoSvHtVkw9F/HJbsPnhOFlK+6mVqC0JTxtr4VNIn/fqyXuCuMVkxNe+IIbUVIuBD1ZtkQ3hJkmJOTPzW27dvH3V1dfTu3bvhmN1up1OnTmzZsuWySOwMei0TR3TkaElNWGJhMmiZdVdvki7RiB2A1pZA8qh7CQyZRKCmEtVoRWONb3Q3rMOkMG14K+YtjRyBG5yXgf7kDrwXrW0yBusa7e6wp6iKOkt3Mm59moDLCcFgaFrUloBFCGZP782R4jMcPF5Fs2QLHVonkxRvQqOLPj2XmmTjt7d353RlLcVlNaQ4zKSl2EmMtzT8YW/s2S6kqgr9cjNY/vkhKqrDpxrtFj0jerVC8x1KQ2htCSTYKxmbsIXrb++F269g0gTRHN2EhjQyp/6eM1+8Q+2eL1FQsHQegKP/TWjtSTS7ex51+zbhOXUIQ7MOWNr3QPtf9MzUGK1oLiqrobU6cAyagL37VQi/D0WnR2t1hIooZ/UgM302AbcTRdGgmm3fKoaXg1DduEv38yJJkvRTEhOJXUlJCQDp6elhx1NSUjh1qvGF/N/kwlY6l4pZD4/d2p2Kag+Hi6tJsBtpmWbDZlLxeL79mqZvRwWTA0wOAkAA8DbyPIrGwBUt9Dw5sSN/X1PMsdIa4qx6buibwYBOCVj9Rsp22BtGnxStnjitj1uv6chLS7+OeL2BeZkYdQpe1QDW0AhQAMAdekaLWUdOViq5WcmgaAgEg3j9Abz+xuNtMulo1TyR1s0TEYTWVNZ7feD99gv9AWxGhWem9+Mfaw+xdvsJgkIwIDeDccPaYzcpDd9zt9sd9r+NUeIzSMy/hvqiAoynDmFIb4ux10gCJjtCgH3oNOIG3gyA0JnxKSo+jxcMdox5V2HqpiCEwBcM4quPPmL6X9NZ4GzOHPY1tCawnZ/ivfD98W2fvymTMZAxABkDkDGAHyYGQggU5d9v8VeEaKw1dNOxfPlyHn30Ufbu3Yuqnh9xefTRRykrK2PRokXf6fW+/vprvN7I9WGXiqqq6PV6AoEAPt93S0y+L4lxVpIM4KyoJKCzoAR92PRBvJZkyp0u0uJMaP31oX6RRhuna+oROjPHygO88a+DlFa6sJp0XNu/Ff1ykigvib5J4XKgKApWexw6Q2i9nM9TS62ziv/mR8VkMqHX6/F6vTH9y0+SJEn6z+n1enJycr7xmpgYsTMaQ9MyXq+34f8DeDweTKb/rPCvTqejXbt2l+T+fioURSEhLgXh81BT50LYE7Do9ViibLBMP7skMDNNIaddEj6/QKNRsBk1gCDZkf2D3vt/xwoZaWFH3G43RUVFtGrV6j9+D/2Uxfrzg4wByBiAjAHIGMAPE4PCwsJvdV1MJHbnpmDLyspo0aJFw/GysjI6duz4H72moiiYGym10aSZTLhcLo4XFZOdlPatYtCUf85NJlNsvg/OivXnBxkDkDEAGQOQMYDvNwbfZhoW4NI1CbyMdezYEavVyqZNmxqOOZ1O9uzZQ48ePX7EO5MkSZIkSbp0YmLETq/XM3nyZObMmUNCQgKZmZnMnj2btLQ0hg8f/mPfniRJkiRJ0iURE4kdwIMPPojf7+fJJ5+kvr6enj17snDhwsuiOLEkSZIkSdKlEDOJnUaj4ZFHHuGRRx75sW9FkiRJkiTpexETa+wkSZIkSZJigUzsJEmSJEmSmgiZ2EmSJEmSJDURMdF54lLbvn07QoiY3XghhMDn86HT6b51XZ2mJtZjEOvPDzIGIGMAMgYgYwA/TAy8Xi+KotCtW7dvvC5mNk9cSrH6xj1HUZSYTWrPifUYxPrzg4wByBiAjAHIGMAPEwNFUWSvWEmSJEmSpFgi19hJkiRJkiQ1ETKxkyRJkiRJaiJkYidJkiRJktREyMROkiRJkiSpiZCJnSRJkiRJUhMhEztJkiRJkqQmQiZ2kiRJkiRJTYRM7CRJkiRJkpoImdhJkiRJkiQ1ETKxkyRJkiRJaiJkYidJkiRJktREyMROkiRJkiSpiZCJndSoqqoqfv3rXzNw4EC6devGxIkT2bp1a8P5vXv3MnnyZK644goGDx7MwoULf8S7/X5UVFTwyCOP0Lt3b/Ly8rj77rspLCxsOB8LMTjnyJEj5OXlsXTp0oZjsfD8xcXFdOjQIeLjnXfeAWIjBgDLli1j5MiR5OTkMGrUKFatWtVwrqnHYNOmTVHfAx06dGDYsGFA048BgM/nY968eQwePJi8vDwmTZrE9u3bG87HQgzq6up4+umnGTRoEN27d2f69OkcO3as4fxlEQMhSY2YNm2aGDNmjNiyZYs4dOiQePrpp0XXrl1FYWGhqKysFL169RJPPPGEKCwsFO+++67IyckR77777o9925fUuHHjxIQJE0RBQYEoLCwUDzzwgOjXr59wuVwxEwMhhPB6veLGG28UWVlZ4r333hNCiJh5/k8++UTk5OSI0tJSUVZW1vDhdrtjJgbLli0T2dnZYtGiRaKoqEjMnz9fdOzYUWzfvj0mYuDxeMK+92VlZeKLL74QnTp1Em+//XZMxEAIIZ5//nnRr18/sW7dOlFUVCSeeOIJ0a1bN1FSUhIzMbjzzjvFgAEDxJo1a0RhYaF48sknRd++fUVlZeVlEwOZ2ElRFRUViaysLLFt27aGY8FgUAwfPlw899xz4uWXXxYDBgwQPp+v4fzcuXPFVVdd9WPc7veisrJSPPzww+LAgQMNx/bu3SuysrLEzp07YyIG58ydO1dMmTIlLLGLled/6aWXxJgxY6Kei4UYBINBMWTIEPGHP/wh7Pjtt98uXn755ZiIwcW8Xq8YNWqUeOihh4QQsfE+EEKIMWPGiGeeeabhv2tqakRWVpZYvXp1TMTg3O//tWvXNhwLBAJixIgRYv78+ZdNDORUrBSVw+HgL3/5C126dGk4pigKQgiqq6vZunUrPXv2RKvVNpzv3bs3R44coaKi4se45UvO4XDw7LPP0r59ewDKy8tZuHAhaWlptGvXLiZiALBlyxaWLFnC//7v/4Ydj5Xn379/P+3atYt6LhZicPjwYYqLi7n22mvDji9cuJCf/exnMRGDiy1evJhTp07x+OOPA7HxPgCIj4/n008/5cSJEwQCAZYsWYJeryc7OzsmYnDkyBEAevTo0XBMVVU6duzIli1bLpsYyMROisputzNo0CD0en3DsVWrVnHs2DH69+9PSUkJaWlpYZ+TkpICwMmTJ3/Qe/0hPPXUU/Tr14/Vq1fzu9/9DrPZHBMxcDqdPProozz55JOkp6eHnYuF5wc4cOAAFRUVTJo0ib59+zJx4kTWrVsHxEYMioqKAHC5XNxxxx306dOHcePGsWbNGiA2YnAhj8fDyy+/zNSpUxueM1Zi8MQTT6DVahk2bBg5OTnMmzeP5557jhYtWsREDJKTk4HQ9/tCxcXFVFRUXDYxkImd9K1s27aNX/3qVwwbNoyhQ4dSX18flvQBGAwGIPSLr6mZOnUq7733HmPGjOG+++5j9+7dMRGDWbNmccUVV0SM1gAx8fxer5eioiJqa2t56KGH+Mtf/kJOTg533XUXGzZsiIkY1NbWAjBjxgxGjx7NK6+8Qr9+/Zg+fXrMxOBCy5cvx+PxMGXKlIZjsRKDQ4cOYbfbWbBgAUuWLOHGG29kxowZ7Nu3LyZikJubS9u2bZk5cyanTp3C6/WyaNEi9u7di9frvWxioP33l0ix7uOPP+aXv/wlubm5PPvsswAYjUa8Xm/YdefeuGaz+Qe/x+/buam4p59+mh07dvD66683+RgsW7aMrVu38v7770c939SfH0Cv17Nlyxa0Wm3DL+wuXbpw6NAhFi5cGBMx0Ol0ANxxxx3ccMMNAGRnZ7Nnzx7+9re/xUQMLrRs2TJGjBiBw+FoOBYLMSguLuaRRx5h0aJFDVOROTk5FBYW8sILL8REDHQ6HQsWLOCxxx5j8ODBaLVaBg8ezNixY9m1axder/eyiIEcsZO+0euvv84DDzzAwIED+etf/4rRaAQgLS2NsrKysGvP/XdqauoPfp/fh4qKClasWEEgEGg4pqoqbdu2paysrMnH4L333qOioqKhtEFeXh4AM2fOZNSoUU3++c8xm80R/wrPysqitLQ0JmJwbmopKysr7Hi7du04ceJETMTgnMrKSr766itGjhwZdjwWYlBQUIDP5yMnJyfseG5uLkVFRTERA4DWrVuzZMkSNm/ezIYNG1iwYAFVVVW0atXqsomBTOykRr3xxhs8/fTT3HLLLTz33HNhf9x69uzJtm3bwpKeDRs20Lp1axITE3+M273kysrK+MUvfsHmzZsbjvl8Pvbs2UPbtm2bfAzmzJnDypUrWbZsWcMHwIMPPshf/vKXJv/8APv27SMvLy+sfiPArl27aNeuXUzEoFOnTlgsFnbu3Bl2/MCBA7Ro0SImYnDO9u3bURSF/Pz8sOOxEINza2z3798fdvzAgQO0bNkyJmJQW1vL5MmT2bVrF3Fxcdjtdmpqali/fj0DBgy4fGLwg+7BlX4yDh8+LDp37izuu+++iPpNTqdTlJeXi549e4oZM2aIgwcPivfee0/k5OSIpUuX/ti3fskEg0Fx++23i6uuukps2bJF7N+/Xzz88MOiZ8+eori4OCZicLELy53EwvMHAgExbtw4MXr0aLFlyxZRWFgofv/734suXbqIffv2xUQMhBBiwYIFIi8vT7z//vvi6NGj4sUXXxQdO3YUGzdujJkYCCHECy+8IEaMGBFxPBZiEAgExKRJk8TVV18tNmzYII4cOSLmzZsnsrOzxVdffRUTMRBCiMmTJ4uJEyeKffv2ib1794pJkyaJMWPGCJ/Pd9nEQCZ2UlQvvfSSyMrKivoxY8YMIYQQO3fuFOPHjxddunQRQ4YMEa+99tqPfNeXntPpFDNnzhT9+vUTXbt2FbfffntYXbtYiMGFLkzshIiN56+oqBCPP/646Nevn8jJyRETJkwQW7ZsaTgfCzEQQohXXnlFDB06VHTu3FmMGTNGfPTRRw3nYiUGM2fOFOPHj496LhZiUFVVJWbNmiUGDx4s8vLyxIQJE8SmTZsazsdCDEpLS8UDDzwgevToIfLz88WMGTNERUVFw/nLIQaKEEL8cOODkiRJkiRJ0vdFrrGTJEmSJElqImRiJ0mSJEmS1ETIxE6SJEmSJKmJkImdJEmSJElSEyETO0mSJEmSpCZCJnaSJEmSJElNhEzsJEmSJEmSmgiZ2EmSJEmSJDURMrGTJEn6iVq6dCkdOnTgxIkTP/atSJJ0mZCJnSRJkiRJUhMhEztJkiRJkqQmQiZ2kiRJjdi9ezdTp06le/fu5OXlcdttt7Fz504AHnvsMaZMmcK7777LkCFDyMvL49Zbb2XPnj1hr3Hy5El+/vOfk5+fT25uLlOnTo24xuPx8Mc//pFBgwbRpUsXrr32WlauXBl2TTAY5MUXX2Tw4MHk5uYyffp0qqurv98ASJL0kyMTO0mSpChqa2u58847cTgc/OlPf2LevHm43W7uuOMOampqANi7dy/z5s3j/vvvZ/bs2VRVVTFlyhRKS0sBqKys5Oabb2b37t089dRTzJ07l2AwyC233MKhQ4cAEEJw33338dZbbzFt2jReeukl8vLyePjhh1m2bFnD/cyePZsFCxZw0003MX/+fBwOB3Pnzv3B4yJJ0uVN+2PfgCRJ0uWosLCQyspKpkyZQvfu3QFo06YNb731FrW1tQDU1NTw0ksv0bNnTwC6du3KlVdeyaJFi5gxYwavvvoqVVVVvPnmm2RmZgIwcOBARo4cyfPPP8+f/vQn1q9fz7p165g3bx4jR44EYMCAAbjdbubMmcPo0aNxuVy89tpr3HrrrTzwwAMN15SWlrJu3bofOjSSJF3G5IidJElSFO3btychIYF7772XmTNnsmbNGpKTk3n00UdJT08HICMjoyGpA0hJSSEvL49t27YBsGHDBrKzs0lNTcXv9+P3+1FVlYEDB7J+/fqGaxRFYdCgQQ3X+P1+hg4dyunTpzl48CA7duzA5/MxbNiwsHu85pprfqBoSJL0UyFH7CRJkqKwWCwsXryYl156iZUrV/LWW29hMpkYM2YMTzzxBBBK5C6WmJjI7t27AaiqquLo0aN07tw56tdwu91UVVUhhKBbt25RrykrK8PpdAKQkJAQdi45Ofk/fj5JkpommdhJkiQ1ok2bNsyePZtAIEBBQQHLly/nzTffpFmzZkAocbtYeXk5iYmJANhsNvLz83n00Uejvr5er8dms2E2m/n73/8e9ZqWLVtSUFAAQEVFBW3atGk4F+3rS5IU2+RUrCRJUhSrV6+md+/enD59Go1GQ15eHrNmzcJut1NSUgLAsWPHKCwsbPic0tJSduzYQZ8+fQDIz8/nyJEjtG7dmpycnIaPf/7zn7zzzjtoNBry8/NxuVwIIcKuOXjwIAsWLMDv95OXl4fRaGT16tVh9/jpp5/+cAGRJOknQY7YSZIkRdGtWzeCwSD33Xcfd999NxaLhVWrVlFTU8OIESNYtmwZQgimT5/OQw89hEajYf78+djtdqZMmQLAbbfdxvLly7ntttu4/fbbcTgcrFy5krfffpvHH38cgEGDBtGzZ0+mT5/O9OnTadu2LQUFBbzwwgv079+/Yfp1+vTpPPfcc5hMJnr37s1nn30mEztJkiIoQgjxY9+EJEnS5aigoIDnn3+eXbt24Xa7ad++Pffccw/Dhw/nscceY/Pmzdx1110sWLAAt9tN3759mTFjRsNULYRG9ebOncuGDRvweDy0atWKKVOmMHbs2IZrXC4Xzz//PKtXr6aiooLU1FRGjRrFfffdh8FgaLjutdde49VXX6W0tJS8vDyuueYaZs2axSeffBL2NSVJil0ysZMkSfoPnEvs1qxZ82PfiiRJUgO5xk6SJEmSJKmJkImdJEmSJElSEyGnYiVJkiRJkpoIOWInSZIkSZLURMjETpIkSZIkqYmQiZ0kSZIkSVITIRM7SZIkSZKkJkImdpIkSZIkSU2ETOwkSZIkSZKaCJnYSZIkSZIkNREysZMkSZIkSWoi/j/UUvtm7QFaxAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data = data, x = \"speed\", y =\"total_consumed\", hue = \"gas_type\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9949841b",
+   "metadata": {},
+   "source": [
+    "**Except for extreme values (infrequent high speeds), the total consumed seems lower for SP98 for every value of speed.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "76369c65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SCgoKHFOkgxDcAACAoUVFRemFF14o0+7j42PT9l5eXiosLLRqux7YbN1HVSG4AQAAQ6tTp46aN29u9/bBwcE6duyYVVtmZqYkqVGjRhWqzdG4xg0AANRqERERSk5O1uXLly1tBw4cUJ06dRQeHu7EysoiuAEAAEPLz8/XhQsXbvhjyzVqffr0UWBgoCZOnKiUlBTt3btXixYt0ogRI6rVo0AkpkoBAMAvhATVNdQxP/zwQ3344Yc37Fu4cKH69+//q9t7enpq5cqVmjVrlh5++GHVrVtXw4YN07hx4+yuqbIQ3AAAgEVpqdnuNxg44tguLqZybbN+/fpyrR8XF6e4uLgy7c2bN9fq1avLtS9nYKoUAABYlDc41ZRjGwXBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGITTg1t2drZmzJihXr16qUuXLho6dKiSkpIs/UePHlVMTIw6deqku+++W6tWrbLavrS0VEuWLFFkZKQ6duyoESNGKC0trapPAwAAoNI5PbhNmjRJ//nPf7Rw4UJt3bpVt99+u0aOHKkTJ07o0qVLevLJJ3Xrrbdq27ZtiouL0+LFi7Vt2zbL9gkJCdq0aZNmz56tzZs3y2QyafTo0SosLHTiWQEAYEzm0lJDHvuDDz7QI488os6dO6tz584aPHiwNm3aZOmPjY1V69atLT/h4eHq0qWLHn74Ye3du/enGsxmrV+/Xvfff786deqkRx55RJ9//rnVsS5cuKBJkyape/fu6t69u5599lmlp6fbXXt5OPVdpWlpafrXv/6ld999V126dJEkTZ8+Xfv379euXbvk5eUlDw8PzZw5U25ubmrZsqXS0tK0YsUKDR48WIWFhVq9erUmT56s3r17S5IWLVqkyMhIJSYm/uZLZQEAgDWTi4tSd61QXtb5Kj2ud4PGCh0w2q5tt27dqtmzZ+ull15SRESEzGazDhw4oDlz5ujixYt65plnJEn333+/pk+fLulaQLty5YpWrlypuLg4vffee2rfvr1WrFihJUuWaOrUqerZs6c+/fRTjR07VitXrlS3bt0kSc8995xKSkq0Zs0aSdKsWbM0btw4bd++3QGfxK9zanCrX7++/vznP6tdu3aWNpPJJLPZrJycHH333XeKiIiQm9tPZfbo0UNvv/22srKydPbsWV25ckU9evSw9Pv7+6tt27Y6dOgQwQ0AADvkZZ1XXsYpZ5dhs40bN2rIkCF6+OGHLW0tWrRQenq6/vKXv1iCm5eXlwIDA622feWVV7R7927t2rVL7du318qVK/XYY48pJiZGknTrrbfq+++/V3x8vLp166bc3FwdOnRIy5YtU9u2bSVJTz31lMaNG6dLly6pfv36lXquTp0q9ff3V+/eveXh4WFp+/DDD3Xq1Cn17NlT6enpCg4OttomKChIknTu3DnLsGTjxo3LrHP+fNX+pgAAAJzDxcVFX331lXJycqzaR48erc2bN//qttcHhzw8PPTDDz8oJydHERERVuu0adNGX375pUpKSuTp6SkfHx+9//77unz5si5fvqy//vWvuvXWW1W3bl3HntiN6q30I5TDl19+qZdeekn33nuvoqKi9MYbb1iFOkny9PSUJBUUFCgvL0+SbrjOL//yystsNuvq1asV2gcAwHZms9lqme/gylNQUKDS0lKVlJSopKTEqs/V1dVJVV3zy3psMXLkSE2aNEm9evVSt27ddMcdd6h79+5q37696tSpo5KSEpnNZpnNZqv9Z2dnKz4+XgUFBbr33nvl6+srDw8PnTt3zmq9M2fOqKioSNnZ2apXr55ee+01zZ49W127dpXJZFJgYKDWrVtXZv8/P6fS0lLl5eWp9AbX8ZnNZplMJpvOtdoEt7179+qFF15Qx44dtXDhQknXhjR/eZNBQUGBJMnHx0deXl6SpMLCQsvy9XW8vb0rVE9RUZGOHj1aoX0AAGxXVFRktcx3cOVyc3Oz/D/1OhcXlwr//7OiCgsLbxhufk2vXr20bt06vfvuuzpw4ID2798vSWrevLleeeUVderUSaWlpdq1a5f27Nkj6dpTKQoKCtS4cWPNmDFDt912m4qKitS/f38tW7ZMLVq0ULt27ZSUlGS5KfLHH3+Up6enkpOT1aFDBz3++OMqKSlRfHy8nnnmGa1Zs0Z16tQpU19BQYGKi4v1f//3fzc9h18OQt1MtQhu77zzjubMmaO+fftqwYIFluKDg4OVmZlpte71Pzdq1EjFxcWWtmbNmlmtEx4eXqGa3N3dFRYWVqF9AABs5+7ubrXcpk0bJ1ZTsxUUFOjcuXPy9PS0GvioDmwNML90xx136I477pDZbNZ///tf7d+/X+vXr1dcXJz+/ve/y8XFRffcc48mTZok6VpI9fPzU7169az289JLL+nVV1/VyJEjJUlhYWEaNWqU/vSnP6lBgwb66KOPtGXLFu3bt88S0tq1a6c+ffpo9+7dio2NvWF9bm5uatasmWXm8OeOHz9u83k6Pbht3LhRr732mmJjY/XSSy/JxeWny+4iIiK0adMmlZSUWIZuDxw4oNDQUDVo0EB+fn7y9fXVwYMHLcEtNzdXycnJlosK7WUymeTj41OhfQAAbPfzqSK+gyuXi4uLXFxc5Orq6vSp0V8qbz3p6elasWKFnnrqKTVq1EjStWvS2rRpoz59+uiBBx7QV199JZPJJF9fX7Vo0eJX9+fv768FCxZozpw5ys3NVWBgoDZs2KDAwED5+fnp66+/VmhoqPz9/S3bBAQEKDQ0VGlpaTes39XV1TKaeaOgbOs0qeTkmxNSU1P1+uuvq2/fvhozZoyysrJ04cIFXbhwQT/++KMGDx6sy5cva/r06Tp+/Li2b9+udevWacyYMZKupfKYmBgtWLBA+/btU0pKip577jkFBwerb9++zjw1AABQBTw8PLR582bt3LmzTJ+vr68kqWHDhjbvb/r06Xrvvffk6elpuQP173//uyIjIyVduyEyLS3Napo5Ly9PZ86cUfPmzStyKjZx6ojbnj17VFRUpMTERCUmJlr1RUdHa+7cuVq5cqXmzJmj6OhoBQYGasqUKYqOjrasN2HCBBUXF+vll19Wfn6+IiIitGrVKruHWgEAgHEEBARo1KhRevPNN3X58mX169dPvr6+On78uBISEtS9e3d17drV5v01btxYixcvVtOmTRUSEqI1a9YoOTlZs2fPliQNHDhQq1at0sSJE/Xss89Kkt588015eHho0KBBlXKOP+fU4DZ27FiNHTv2V9fp0KHDr97K6+rqqsmTJ2vy5MmOLg8AgFrJu0Hj316pGh1z4sSJuvXWW/Xee+9pw4YNys/PV+PGjfXAAw9YZulsNXbsWF29elWTJ0/WlStX1KlTJ61fv94ymhYUFKSNGzdq/vz5evzxx+Xi4qKuXbvq3XfftZo+rSwm88/vv4Yk6fDhw5Kk9u3bO7kSAKg9hg0bpoyMDEnXbkDbuHGjkyuqufLz85WamqrQ0NAy11yZS0tlcnHOlVTOPHZl+rXPWypf7qh5nw4AALCbM4NTTQxtjsYnBAAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AANRSPFiiajjycya4AQBQy1x/L+zVq1edXEntcP1z/vn7eO3l9HeVAgCAquXq6qp69eopMzNTkuTj41Ou92XCNmazWVevXlVmZqbq1avnkPfCEtwAAKiFgoODJckS3lB56tWrZ/m8K4rgBgBALWQymdS4cWMFBQWpqKjI2eXUWO7u7g4ZabuO4AYAQC3m6urq0GCBysXNCQAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCDcbF1x2rRp5drxG2+8Ue5iAAAAcHM2B7eDBw9a/TkzM1PFxcW65ZZbFBgYqOzsbJ0+fVoeHh4KDw93eKEAAAC1nc3B7aOPPrIsf/DBB1qwYIHeeustdejQwdJ+/PhxjR8/Xvfff79jqwQAAIB917gtWrRIzz//vFVok6SwsDA9++yzWrlypUOKAwAAwE/sCm6XLl2Sn5/fDfvc3Nx09erVChUFAACAsuwKbp06ddLSpUt16dIlq/bMzEy99dZb6t69u0OKAwAAwE9svsbt51588UXFxsYqKipKnTt3Vv369ZWVlaWvv/5adevW1bJlyxxdJwAAQK1n14hbeHi4du3apUcffVRXrlzRd999p/z8fI0YMUI7d+5UkyZNHF0nAABArWfXiJskNWrUSC+++KIjawEAAMCvsDu4FRYWauvWrfr888914cIFvf766/riiy90++23l7nbFAAAABVn11TpDz/8oMGDB2vOnDlKS0vTt99+q/z8fH3yySeKjY3V119/7eg6AQAAaj27gtv//u//6sqVK9q9e7d27Nghs9ksSVq8eLHat2+vJUuWOLRIAAAA2BncPv74Yz377LNq3ry5TCaTpd3T01MjRozQkSNHHFYgAAAArrEruBUUFKhevXo37HN1dVVRUVFFagIAAMAN2BXc2rdvr40bN96w74MPPlC7du0qVBQAAADKsuuu0meffVZPPPGEHnroIfXu3Vsmk0m7du3SW2+9pc8++4x3lQIAAFQCu0bcunbtqjVr1sjb21srV66U2WzW2rVrdeHCBb399tvq0aOHo+sEAACo9ex+jltERIQ2bdqk/Px85eTkyNfXV3Xq1HFkbQAAAPgZu4ObJF2+fFm5ubmSpJycHOXk5Fj6brnllopVBgAAACt2BbeUlBRNnjxZx48fv+k6R48etbsoAAAAlGVXcJsxY4YuXbqkKVOm3PSxIAAAAHAsu4LbsWPHNHfuXPXr18/R9SghIUEHDhzQ+vXrLW3Tpk3T9u3brdZr1KiR9u/fL0kqLS3V0qVLtWXLFuXm5uqOO+7QK6+8oubNmzu8PgAAAGex667Spk2bqrS01NG1aO3atTd8Xdb333+vsWPH6rPPPrP8vP/++5b+hIQEbdq0SbNnz9bmzZtlMpk0evRoFRYWOrxGAAAAZ7EruE2aNEmLFy/WF198oYKCggoXkZGRoVGjRmnx4sUKDQ216ispKdHx48fVvn17BQYGWn4CAgIkSYWFhVq9erXi4uLUu3dvhYeHa9GiRcrIyFBiYmKFawMAAKgu7JoqDQ0Nldls1uOPP37DfpPJpOTkZJv3d+TIEdWtW1c7d+5UfHy8zp49a+k7efKkCgoK1LJlyxtum5KSoitXrlg9O87f319t27bVoUOH1L9/f5vrAAAAqM7sCm7Tpk3TpUuX9PDDDyswMLDCRURFRSkqKuqGfceOHZPJZNK6deu0f/9+ubi4qHfv3po4caL8/PyUnp4uSWrcuLHVdkFBQTp//nyFawMAAKgu7ApuycnJeuONN/TAAw84up4y/vvf/8rFxUUhISFavny50tLSNG/ePB07dkzr1q1TXl6eJMnDw8NqO09PT6vnypWX2WzW1atXK1Q7AMB2ZrPZapnvYNQWZrNZJpPJpnXtCm5BQUHy9va2Z9Nyi4uL0xNPPCF/f39JUqtWrRQYGKhHHnlEhw8flpeXl6Rr17pdX5akgoKCCtVYVFTEs+gAoAoVFRVZLfMdjNrklwNQN2NXcHvqqaf05ptvKjQ0VLfeeqs9u7CZyWSyhLbrWrVqJUlKT0+3TJFmZmaqWbNmlnUyMzMVHh5u93Hd3d0VFhZm9/YAgPJxd3e3Wm7Tpo0TqwGqzq+90OCX7Apue/bs0dmzZ3X//ffL399fvr6+Vv0mk0l79+61Z9dlPP/888rOztaqVassbYcPH5YkhYWFqWnTpvL19dXBgwctwS03N1fJycmKiYmx+7gmk0k+Pj4VKx4AYLOfTxXxHYzaxNZpUsnO4BYYGKi+ffvas2m5DRgwQE8//bSWLVum/v37KzU1Va+++qoGDBhgudM0JiZGCxYsUEBAgEJCQjR//nwFBwdXWY0AAABVwa7g9sYbbzi6jpu65557tHjxYi1fvlzLly+Xn5+fHnzwQU2cONGyzoQJE1RcXKyXX35Z+fn5ioiI0KpVq2yeLwYAADACk/nnt/FA0k9Tse3bt3dyJQBQewwbNkwZGRmSrr3WcOPGjU6uCKga5ckddo24hYeH/+Z8LHcDAQAAOJZdwW38+PFlgtuVK1f01Vdf6dSpU3rhhRccUhwAAAB+Yldwi4uLu2nfiy++qO+++06DBw+2uygAAACUZddL5n/NwIEDtXv3bkfvFgAAoNZzeHA7efKkiouLHb1bAACAWs+uqdKlS5eWaSstLdX58+e1e/fum74wHgAAAPZzWHCTJF9fX/Xt21fTpk2rUFEAAAAoy67glpKS4ug6AAAA8Bscdo3bhQsXdOTIEZWUlDhqlwAAAPgZu4LblStXNG3aNK1fv16StHv3bt1zzz0aMmSIBgwYoPPnzzu0SAAAANgZ3BYsWKA9e/aofv36kqQ//elPCg8P19KlS+Xm5qYFCxY4tEgAAADYeY3bvn37NHXqVA0YMEBHjx7V2bNnNWXKFN17770qLi7WK6+84ug6AQAAaj27Rtyys7PVokULSdI///lPubm56a677pIk1a1bVwUFBY6rEAAAAJLsDG4hISH6/vvvJUn/+Mc/1KlTJ/n6+kqSPvnkEzVp0sRxFQIAAECSncFt2LBhmjt3ru6//34dPXpUw4YNk3TtHaZr167Vo48+6tAiAQAAYOc1brGxsQoICNAXX3yhuLg4PfDAA9d25uammTNn6pFHHnFokQAAALAzuElS//791b9/f6u2RYsWVbggAAAA3JjdwS01NVWffPKJrl69qtLSUqs+k8mk8ePHV7g4AAAA/MSu4Pb+++9r2rRpMpvNN+wnuAEAADieXcFt2bJl+t3vfqfZs2crODhYJpPJ0XUBAADgF+y6q/TcuXMaNWqUGjduTGgDAACoInYFt9DQUN5HCgAAUMXsCm7PP/+8EhISdPDgQd6SAAAAUEXsusZtzpw5ysrK0hNPPHHDfpPJpOTk5IrUBQD4DaWlZrm4cLmKUfD3BUewK7j94Q9/cHQdAIBycnExKf7df+lsZo6zS3GIS7l5VssvLd7txGocKySorsYPvcvZZaAGsCu4PfPMM46uAwBgh7OZOTp59pKzy3CI4pJSq+Wacl6AI9n9AN7CwkJt375dBw8eVG5ururXr6+uXbsqOjpanp6ejqwRAAAAsjO45ebm6rHHHlNKSopuueUWBQYGKjU1Vbt27dKGDRu0ceNG+fn5ObpWAACAWs2uu0r/9Kc/KT09Xe+8844++ugjbd68WR999JHeeecdZWVlafHixY6uEwAAoNazK7jt27dPEydOVNeuXa3au3btqgkTJugf//iHQ4oDAADAT+wKbleuXFHTpk1v2Ne0aVNlZ2dXpCYAAADcgF3BrUWLFvr4449v2Ldv3z41b968QkUBAACgLLtuThg5cqQmTZqkwsJCPfjgg2rYsKEuXryoDz74QFu2bNHMmTMdXCYAAADsCm4PPPCATp48qeXLl2vLli2SJLPZLA8PD40fP16PPPKIQ4sEAABABZ7jNm7cOMXExOibb75RTk6O6tatq06dOsnf39+R9cGATp8+raVLl0q69rDmm10PCQAAyseua9wkaefOnZo3b5569eqlBx98UD4+Pnr88ceVmJjoyPpgQPHx8UpKSlJSUpISEhKcXQ4AADWGXcFt+/btmjJlivLyfnqvXIMGDdSkSRM9++yzhLda7tSpU5bltLQ0J1YCAEDNYldwW716tUaNGqWFCxda2kJDQ/XWW2/pySefZJQFAACgEtgV3E6fPq2ePXvesK9nz55KTU2tUFEAAAAoy67gFhQUpG+//faGfcnJyapfv36FigIAAEBZdt1VOnDgQC1btkx16tRRnz59FBAQoB9++EF79+7V0qVL9dhjjzm6TgAAgFrPruA2ZswYnThxQq+99ppmz55taTebzerXr5/i4uIcViAAAACusSu4ubm5aeHChRo3bpySkpKUnZ0tPz8/3XHHHQoPD3d0jQAAAFAFHsArSWFhYQoLC7tpf0lJidq1a6etW7fq9ttvr8ihAFQSHpgMAMZh9wN4bWU2myv7EAAqgAcmA4BxVHpwA1C98cBkADAOghsAAIBBENwAAAAMguAGAABgENUuuCUkJCg2Ntaq7ejRo4qJiVGnTp109913a9WqVVb9paWlWrJkiSIjI9WxY0eNGDGCa3UAAECNU62C29q1a7VkyRKrtkuXLunJJ5/Urbfeqm3btikuLk6LFy/Wtm3bLOskJCRo06ZNmj17tjZv3iyTyaTRo0ersLCwqk8BAACg0lToOW6OkpGRoenTp+vLL79UaGioVd97770nDw8PzZw5U25ubmrZsqXS0tK0YsUKDR48WIWFhVq9erUmT56s3r17S5IWLVqkyMhIJSYmqn///s44JQAAAIer1BE3k8mkiIgI1alT51fXO3LkiOrWraudO3eqY8eOVn1JSUmKiIiQm9tPGbNHjx5KTU1VVlaWUlJSdOXKFfXo0cPS7+/vr7Zt2+rQoUOOPSEAAAAnsnnE7f333y/XjgcOHCgXFxetX7/+N9eNiopSVFTUDfvS09PVqlUrq7agoCBJ0rlz55Seni5Jaty4cZl1zp8/X66aAQAAqjObg9vUqVNt3qnJZNLAgQPtqaeM/Px8eXh4WLV5enpKkgoKCpSXlydJN1wnJyfH7uOazWZdvXrV7u3Lw2QyVclxqsrP35ZhNpstf0c1SU16I8gv/76q6t89KsZkMsnb29vZZaCc8vLyatT3BxzDbDbbnAVsDm779u2zu6CK8PLyKnOTQUFBgSTJx8dHXl5ekqTCwkLL8vV1KvKlVlRUpKNHj9q9va3c3d3Vtu3tcnNzrfRjVZWf/+Orif9zKS4uUXLyERUVFTm7FIf4+XlU1b97VJy3t7fatm3r7DJQTqmpqTXyl1lU3C8HoG7G5uAWEhJi88Ed+dtEcHCwMjMzrdqu/7lRo0YqLi62tDVr1sxqnfDwcLuP6+7urrCwMLu3t5XJZJKbm6vi3/2XzmbaP0JYnVzKzbNafmnxbidW41ghQXU1fuhduu2222rMb83u7u5Wy23atHFiNbBVTRupry1CQ0NrzHcHHOf48eM2r2v3XaV/+9vf9MUXX6ioqMjyj/D6NMs333yj/fv327trKxEREdq0aZNKSkrk6nptVOrAgQMKDQ1VgwYN5OfnJ19fXx08eNAS3HJzc5WcnKyYmBi7j2symeTj4+OQc7DF2cwcnTx7qcqOV5mKS0qtlmvKef1cTRpF/OUIaVX+uwdqm5r03QHHKc8vYnYFt6VLl2rp0qXy8/NTcXGx3N3d5ebmph9++EEuLi76n//5H3t2e0ODBw/WypUrNX36dI0aNUrffvut1q1bp1mzZkm6NrQYExOjBQsWKCAgQCEhIZo/f76Cg4PVt29fh9UBAADgbHY9DmTHjh36wx/+oC+++EJPPPGE7rnnHn3++efaunWr6tWrp9tuu81hBTZo0EArV65UamqqoqOjtXTpUk2ZMkXR0dGWdSZMmKAhQ4bo5Zdf1tChQ+Xq6qpVq1bZPF8MAABgBHaNuGVkZOihhx6SyWTS7bffrr/97W+SpHbt2mns2LHasmWL3dOUc+fOLdPWoUMHbd68+abbuLq6avLkyZo8ebJdxwQAADACu0bcfHx8LPOxt956q86cOaP8/HxJUps2bXTmzBnHVQgAAABJdga39u3ba8eOHZKkZs2aydXVVZ9//rkk6cSJE0xRAgAAVAK7pkrHjh2rJ598Uj/++KOWL1+uP/zhD5o6daq6d++uzz77TH369HF0nQAAALWeXcEtIiJCW7du1ffffy9JmjFjhlxcXPTVV1+pX79+mjZtmkOLBAAAgJ3B7dy5c2rZsqXlAbeenp567bXXJF17Y8GRI0fUpUsXx1UJAAAA+65xu/fee2/6Wpxvv/1WTz75ZIWKAgAAQFk2j7jNmzdP2dnZkq69ISEhIUH169cvs97Ro0fl5+fnsAKB6qSun5fMpaUyudj1Ow+chL8zADWFzcGtZcuWSkhIkHTt1QzfffddmbtHXV1d5efnxzVuqLHqeHnI5OKi1F0rlJd13tnlOETR5Wyr5eR1rzqvmErg3aCxQgeMdnYZAOAQNge3IUOGaMiQIZKkqKgoJSQkVOgl7oCR5WWdV17GKWeX4RDmkhKr5ZpyXgAq3+nTp7V06VJJ0jPPPKOmTZs6uaKaz665g48++sgS2k6cOKFvvvlGaWlpDi0MAABUb/Hx8UpKSlJSUpJlVg6Vy667SiVp165dmjdvni5evGhpa9iwoZ5//nkNHDjQEbUBAIBq7NSpn0boGcCpGnYFt48++kiTJ09Wjx49NGnSJDVs2FCZmZnauXOnpk2bpnr16unuu+92cKkwCjevuiosvPz/l+s5txgAAGoQu4LbsmXL1K9fPy1atMiqffDgwXruuef09ttvE9xqMb+mPfTj6X///+XuTq4GAICaw65r3I4dO6bo6Ogb9kVHRyslJaVCRcHY3Lzqqv5t96n+bffJzauus8sBAKDGsCu41a9f3/JMt1+6dOkSL5kHAACoBHYFtzvvvFNvvfWWzp07Z9V+9uxZxcfH66677nJIcQAAAPiJXde4TZo0SYMHD1a/fv3UqVMnBQYG6sKFC/rmm2/k7++v559/3tF1AgAA1Hp2jbidPHlSmzZtUmxsrPLz8/Xdd98pPz9fsbGx2rFjh7755hsHlwkAAAC7Rtwee+wxbd68WZMnTy7T9+9//1vTpk1T//79K1wcAAAAfmJzcHvxxRd1/vy1dzOazWbNnDlTvr6+ZdY7efKkGjZs6LgKAQAAIKkcU6X33XefzGazzGazpe36n6//uLi4qFOnTnrjjTcqpVgAAIDazOYRt6ioKEVFRUmSYmNjNXPmTLVs2bLSCgMAAIA1u65xW79+vaPrAAAAwG+w665SAAAAVD2CGwAAgEEQ3AAAqGR1/bxkLi11dhkop+r4d2bXNW4Aao4gX3ddyi+2LANwvDpeHjK5uCh11wrlZZ13djkOU3Q522o5ed2rzivGwbwbNFbogNHOLqMMghtQyz0YHiDz0R8sywAqT17WeeVlnHJ2GQ5jLimxWq5J51ZdEdyAWi6ojodGdQ12dhkAABtwjRsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAADsEuTrfsNlVB7eVQoAAOzyYHiAzEd/sCyj8hHcAACAXYLqeGhU12Bnl1GrMFUKAABgEAQ3AAAAgyC4AQAAGATBDQBQLbh51f3Zcj3nFQJUYwQ3AEC14Ne0hzz8Q+ThHyK/pt2dXQ5QLXFXKQCgWnDzqqv6t93n7DKAao0RNwAAAIMguAEAABiEIYLb2bNn1bp16zI/W7ZskSQdPXpUMTEx6tSpk+6++26tWrXKyRUDAAA4niGucfv+++/l6empvXv3ymQyWdr9/Px06dIlPfnkk+rTp49mzZqlb775RrNmzVK9evU0ePBgJ1YNAADgWIYIbseOHVNoaKiCgoLK9K1bt04eHh6aOXOm3Nzc1LJlS6WlpWnFihUENwAAUKMYYqr0+++/V1hY2A37kpKSFBERITe3nzJojx49lJqaqqysrKoqEQAAoNIZZsQtMDBQw4YN08mTJ9W8eXONGzdOkZGRSk9PV6tWrazWvz4yd+7cOTVo0MCuY5rNZl29erXCtf8Wk8kkb2/vSj8OUNvl5eXJbDY7uwyH4bsDqBpV8d1hNputLgX7NdU+uBUWFurkyZPy9vbWlClT5OPjo507d2r06NFas2aN8vPz5eHhYbWNp6enJKmgoMDu4xYVFeno0aMVqt0W3t7eatu2baUfB6jtUlNTlZeX5+wyHIbvDqBqVNV3xy+zzM1U++Dm4eGhQ4cOyc3NzXJS7dq104kTJ7Rq1Sp5eXmpsLDQapvrgc3Hx8fu47q7u990etaRbE3YAComNDS0xo24Aah8VfHdcfz4cZvXrfbBTbpxAGvVqpU+++wzBQcHKzMz06rv+p8bNWpk9zFNJlOFgh+A6oVpRQD2qIrvjvL8Ilbtb05ISUlR586dlZSUZNX+3XffKSwsTBEREfryyy9VUlJi6Ttw4IBCQ0Ptvr4NAACgOqr2wa1Vq1a67bbbNGvWLCUlJenEiRN644039M0332js2LEaPHiwLl++rOnTp+v48ePavn271q1bpzFjxji7dAAAAIeq9lOlLi4uWr58uRYsWKCJEycqNzdXbdu21Zo1a9S6dWtJ0sqVKzVnzhxFR0crMDBQU6ZMUXR0tJMrBwAAcKxqH9wkKSAgQK+//vpN+zt06KDNmzdXYUUAAABVr9pPlQIAAOAaghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMAADCIGhHcSktLtWTJEkVGRqpjx44aMWKE0tLSnF0WAACAQ9WI4JaQkKBNmzZp9uzZ2rx5s0wmk0aPHq3CwkJnlwYAAOAwhg9uhYWFWr16teLi4tS7d2+Fh4dr0aJFysjIUGJiorPLAwAAcBjDB7eUlBRduXJFPXr0sLT5+/urbdu2OnTokBMrAwAAcCw3ZxdQUenp6ZKkxo0bW7UHBQXp/Pnzdu2zqKhIZrNZ3377bYXrs4XJZFL/boEqKW1QJceD/Tzc3XT48GEVh/eRqVWJs8uBDQpcXHX48GGZzWZnl+JwfHcYB98dxlOV3x1FRUUymUw2rWv44JaXlydJ8vDwsGr39PRUTk6OXfu8/uHZ+iE6gr+vV5UdCxXn5uPn7BJQTlX533NV4rvDWPjuMJ6q+O4wmUy1J7h5eV370iosLLQsS1JBQYG8vb3t2mfnzp0dUhsAAIAjGf4at+tTpJmZmVbtmZmZCg4OdkZJAAAAlcLwwS08PFy+vr46ePCgpS03N1fJycnq2rWrEysDAABwLMNPlXp4eCgmJkYLFixQQECAQkJCNH/+fAUHB6tv377OLg8AAMBhDB/cJGnChAkqLi7Wyy+/rPz8fEVERGjVqlVlblgAAAAwMpO5Jt4jDwAAUAMZ/ho3AACA2oLgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AAAAGQXADbFRaWqolS5YoMjJSHTt21IgRI5SWlubssgAYSEJCgmJjY51dBgyM4AbYKCEhQZs2bdLs2bO1efNmmUwmjR49WoWFhc4uDYABrF27VkuWLHF2GTA4ghtgg8LCQq1evVpxcXHq3bu3wsPDtWjRImVkZCgxMdHZ5QGoxjIyMjRq1CgtXrxYoaGhzi4HBkdwA2yQkpKiK1euqEePHpY2f39/tW3bVocOHXJiZQCquyNHjqhu3brauXOnOnbs6OxyYHA14l2lQGVLT0+XJDVu3NiqPSgoSOfPn3dGSQAMIioqSlFRUc4uAzUEI26ADfLy8iRJHh4eVu2enp4qKChwRkkAgFqI4AbYwMvLS5LK3IhQUFAgb29vZ5QEAKiFCG6ADa5PkWZmZlq1Z2ZmKjg42BklAQBqIYIbYIPw8HD5+vrq4MGDlrbc3FwlJyera9euTqwMAFCbcHMCYAMPDw/FxMRowYIFCggIUEhIiObPn6/g4GD17dvX2eUBAGoJghtgowkTJqi4uFgvv/yy8vPzFRERoVWrVpW5YQEAgMpiMpvNZmcXAQAAgN/GNW4AAAAGQXADAAAwCIIbAACAQRDcAAAADILgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AUE5TpkxR69at9ec///mm66SmpmrmzJnq06ePOnTooLvvvlvPPfecUlJSqrBSADUNb04AgHK4fPmyevbsqaZNmyovL0+JiYkymUxW6yQmJmry5Mm67bbb9Oijj6pJkyZKT0/X+vXr9f333ys+Pl69evVy0hkAMDKCGwCUw+bNmzV79mytXLlSjz32mFauXKnIyEhL/6lTp/TQQw+pZ8+eevPNN+Xq6mrpy8/P17Bhw5Senq6PPvpIXl5ezjgFAAbGVCkAlMO2bdvUvXt3de/eXaGhodq0aZNV//r161VYWKiXX37ZKrRJkpeXl1588UUNGTJEubm5VVk2gBqC4AYANjpx4oT+85//KDo6WpI0aNAgffzxx8rIyLCs8+mnn6pt27Zq1KjRDffRvXt3TZo0SUFBQVVSM4CaheAGADbaunWr/P391adPH0nSwIEDJUlbtmyxrJORkaEmTZo4ozwAtQDBDQBsUFxcrJ07d6pPnz4qKChQbm6uvLy81L17d23ZskUlJSWSJJPJZFkGAEdzc3YBAGAE//znP3Xx4kVt375d27dvL9P/8ccfq0+fPgoJCdG5c+duup/i4mL98MMPTJUCsAsjbgBgg61btyokJER/+ctfyvzUq1fPcpNCz549lZycrAsXLtxwP59++qkiIyP1t7/9rSrLB1BDENwA4DdcvHhRn376qfr372+5o/TnPw888ID+9a9/6fTp0xo+fLjc3d01e/bsMlOmeXl5WrJkierWrat77rnHSWcDwMgIbgDwG3bs2KHi4mL179//hv3R0dEqLS3Ve++9pyZNmmjmzJlKTEzU8OHD9f777+vgwYPasmWLhgwZov/+979avHixfHx8qvgsANQEPIAXAH7DAw88IBcXF+3ateum69x///3KycnRP//5T3l4eOirr77SunXr9J///EdZWVkKDAxU586d9fTTTyssLKwKqwdQkxDcAAAADIKpUgAAAIMguAEAABgEwQ0AAMAgCG4AAAAGQXADAAAwCIIbAACAQRDcAAAADILgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AAAAG8f8AaNOgliFGgyMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data = data, x = \"AC\", y =\"total_consumed\", hue = \"gas_type\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b3ad03c",
+   "metadata": {},
+   "source": [
+    "**The toal consumed seems lower for SP98 using AC and not using it.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fa2ab3b8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data = data, x = \"rain\", y =\"total_consumed\", hue = \"gas_type\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83c000b0",
+   "metadata": {},
+   "source": [
+    "**When raining the total consumed of SP98 seems higher.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c59df486",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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flZVlNdomSe7u7pKk7Oxs+xQJAAAkEdzKvbCwML3xxhsF2r28vAq1vYeHh3Jycqzabge2wu4DAAAUDsGtnKtQoYJq165t8/aBgYE6efKkVVtqaqokqVq1asWqDQAAWOMcNxRLaGioEhISdO3aNUvbvn37VKFCBYWEhDiwMgAAyh5G3OysRkBFQx0zKytLly5dumOfr6+v5Xy1u+ncubPef/99jRkzRm+88YbOnj2r6OhoDRw4kFuBAABgZwQ3O8rPN9v0BAN7HdvJyVTk7T777DN99tlnd+ybN2+eunXr9rvbu7u7a9myZZo2bZpeeOEFVaxYUf369dOIESOKXAsAAPh9BDc7siU4OfLYa9asKdL6kZGRioyMLNBeu3ZtrVixosjHBwAARcM5bgAAAAZBcAMAADAIghsAAIBBENwAAAAMguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcLMjc35+uTw2AAAoGTzyyo5MTk5K2r5UmWkXSvS4nlWqK6j7EJu3//TTT/XBBx/o5MmTkqS6devq+eefV9++fSVJEREROnDggGV9k8kkLy8vBQcHa+jQoercubMkyWw264MPPtC6det04cIFNWjQQKNHj9ajjz5q2fbSpUuaOXOmvv76a0lS27ZtNWnSJAUGBtpcPwAA5YXDg1t6errmzZunf/3rX7p27ZoaNGigcePGqXXr1pKk48ePa8aMGTp69KgqVaqkiIgIDRo0yLJ9fn6+YmJitHHjRl25ckWtWrXS22+/rdq1azvk9WSmXVDmxdMOObYtNm3apOnTp2vy5MkKDQ2V2WzWvn37NGPGDP300096/fXXJUlPP/20oqKiJN0KaNevX9eyZcsUGRmpjz76SE2aNNHSpUu1YMECTZw4UY8//rj+93//V8OHD9eyZcv0yCOPSJL+9Kc/KS8vTytXrpQkTZs2TSNGjNDmzZsd8wYAAGAgDp8qHTt2rP7zn/9o3rx52rRpkx5++GENGjRIP/74oy5fvqwBAwaoTp06+vjjjxUZGan58+fr448/tmwfFxen9evXa/r06dqwYYNMJpOGDBminJwcB74q41i3bp369OmjF154QUFBQapbt65efvll9e/fX3/7298s63l4eMjf31/+/v4KCAhQUFCQ3n77bXl4eGj79u2SpGXLlumVV15ReHi46tSpo4iICD333HOKjY2VJF25ckUHDx7UkCFD1KhRIzVq1EhDhw7VsWPHdPnyZYe8fgAAjMShI27Jycn6+uuv9eGHH6ply5aSpKioKO3du1fbt2+Xh4eH3NzcNHXqVLm4uKhevXpKTk7W0qVL1bt3b+Xk5GjFihUaP368OnbsKEmKjo5W+/bttWvXLnXr1s2RL88QnJycdOjQIWVkZKhixYqW9iFDhqhXr16/u62Ly61fHzc3N/3888/KyMhQaGio1ToNGzbU1q1blZeXJ3d3d3l5eWnr1q2WEbhPPvlEderUsTo2AAC4M4cGt8qVK+uvf/2rGjdubGkzmUwym83KyMjQ0aNHFRoaagkI0q1zopYsWaK0tDSdO3dO169fV9u2bS39vr6+atSokQ4ePFis4GY2m3Xjxo0C7dnZ2crPz1deXp7y8vKs+pydnW0+nj38tp7CGDRokMaOHasOHTrokUceUatWrdSmTRs1adJEFSpUUF5ensxms8xms9X+09PTFRsbq+zsbHXq1Ene3t5yc3PT+fPnrdY7e/ascnNzlZ6erkqVKundd9/V9OnT1bp1a5lMJvn7+2v16tUF9v/r15Sfn6/MzEzlcwEGAJQqZrPZavlO/27i3sxms0wmU6HWdWhw8/X1tYyU3fbZZ5/p9OnTevzxxxUdHa369etb9QcEBEiSzp8/r5SUFElS9erVC6xz4ULxLhDIzc3V8ePH79jn4uKi7OxsqzYnJyd5enoW65jFlZOTU+Rw06FDB61evVoffvih9u3bp71790qSateurbffflvNmzdXfn6+tm/frp07d0q6dV5hdna2qlevrilTpuihhx5Sbm6uunXrpkWLFqlu3bpq3Lix4uPjLdPaV69elbu7uxISEtS0aVO9+uqrysvLU2xsrF5//XWtXLlSFSpUKFBfdna2bt68qf/3//5fMd8dAIC95ebmWi3f7d9N3Jubm1uh1nP4xQm/9u2332ry5Mnq1KmTwsLCNHPmzAIvxN3dXdKtf9AzMzMlFXyx7u7uysjIKFYtrq6uCg4OLtCenZ2t8+fPy93dXR4eHsU6hr0V9of+W61atVKrVq1kNpv13//+V3v37tWaNWsUGRmpzz//XE5OTnryySc1duxYSbdCqo+PjypVqmS1n8mTJ+udd96xXDwSHByswYMH6y9/+YuqVKmiL774Qhs3btSePXssIa1x48bq3LmzduzYoYiIiDvW5+Liolq1all+9gCA0sHV1dVquWHDhg6sxrgSExMLvW6pCW67d+/WG2+8oWbNmmnevHmSbp0Q/9uLDG6PdHl5eVmCU05OjlWIys7OLvbo1+1bXvyWk5OTnJyc5Ozs7PCp0d8qaj0pKSlaunSphg4dqmrVqkm6dU5aw4YN1blzZz3zzDM6dOiQTCaTvL29Vbdu3d/dn6+vr+bOnasZM2boypUr8vf319q1a+Xv7y8fHx8dPnxYQUFB8vX1tWzj5+enoKAgJScn37F+Z2dny2hmaQvKAFDe/Xp6727/buLeCjtNKpWCq0ol6YMPPlBkZKQ6dOigpUuXWv6BDgwMVGpqqtW6t7+vVq2aZYr0TutwX7B7c3Nz04YNG7Rt27YCfd7e3pKkqlWrFnp/UVFR+uijj+Tu7i5/f39J0ueff6727dtLujWlnZycbDXNnJmZqbNnzzrs9i0AABiJw0fc1q1bp3fffVcRERGaPHmynJx+yZKhoaFav3698vLyLKMx+/btU1BQkKpUqSIfHx95e3tr//79qlWrlqRbt5xISEhQeHi4Q16PZ5Xq916plBzTz89PgwcP1vvvv69r166pa9eu8vb2VmJiouLi4tSmTRvL/fQKo3r16po/f75q1qypGjVqaOXKlUpISND06dMlST169NDy5cs1ZswYjR49WpL0/vvvy83N7Z5XsAIAAAcHt6SkJL333nvq0qWLhg0bprS0NEufh4eHevfurWXLlikqKkqDBw/W999/r9WrV2vatGmSbo0YhYeHa+7cufLz81ONGjU0Z84cBQYGqkuXLiX+esz5+cV6gkFxj21yKvoA6pgxY1SnTh199NFHWrt2rbKyslS9enU988wzGjZsWJH2NXz4cN24cUPjx4/X9evX1bx5c61Zs8YymhYQEKB169Zpzpw5evXVV+Xk5KTWrVvrww8/tJo+BQAAd2Yy//pa3hK2ePFiRUdH37GvZ8+emjVrlr7//nvNmDFDCQkJ8vf318CBA61G0/Ly8jRv3jxt3rxZWVlZCg0N1ZQpU/Tggw/aXNeRI0ckSU2aNCnQl5WVpaSkJAUFBXHOVQng/QaA0qtfv366ePGipFunMK1bt87BFRnT7+WO33JocCutCG6lB+83AJReBDf7KEpwKxUXJwAAAODeCG4AAAAGQXADAAAwCIKbjTg1sGTwPgMA8AuCWxHdfrwHD9ItGbff518/VgUAgPLK4TfgNRpnZ2dVqlTJ8rQGLy+vIj2qAoVjNpt148YNpaamqlKlSqXu8WIAADgCwc0Gtx+n9dtHbcH+KlWqxOPLAAD4PwQ3G5hMJlWvXl0BAQHKzc11dDlllqurKyNtAAD8CsGtGJydnQkWAACgxHBxAgAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEG4FHbFSZMmFWnHM2fOLHIxAAAAuLtCB7f9+/dbfZ+amqqbN2/qgQcekL+/v9LT03XmzBm5ubkpJCTE7oUCAMq2M2fOKCYmRpL0+uuvq2bNmg6uCCh9Ch3cvvjiC8vyp59+qrlz52rhwoVq2rSppT0xMVEjR47U008/bd8qAQBlXmxsrOLj4yVJcXFxzNwAd2DTOW7R0dEaN26cVWiTpODgYI0ePVrLli2zS3EAgPLj9OnTluXk5GQHVgKUXjYFt8uXL8vHx+eOfS4uLrpx40axigIAAEBBNgW35s2bKyYmRpcvX7ZqT01N1cKFC9WmTRu7FAcAAIBfFPoct1978803FRERobCwMLVo0UKVK1dWWlqaDh8+rIoVK2rRokX2rhMAAKDcs2nELSQkRNu3b1ffvn11/fp1HT16VFlZWRo4cKC2bdumBx980N51AgAAlHs2jbhJUrVq1fTmm2/asxYAAAD8DpuDW05OjjZt2qRvvvlGly5d0nvvvacDBw7o4YcfLnC1KQAAAIrPpqnSn3/+Wb1799aMGTOUnJys77//XllZWfryyy8VERGhw4cP27tOAACAcs+m4PbnP/9Z169f144dO7RlyxaZzWZJ0vz589WkSRMtWLDArkUCAADAxuD2z3/+U6NHj1bt2rVlMpks7e7u7ho4cKCOHTtmtwIBAABwi03BLTs7W5UqVbpjn7Ozs3Jzc4tTEwAAAO7ApuDWpEkTrVu37o59n376qRo3blysogAAAFCQTVeVjh49Wv3799cf//hHdezYUSaTSdu3b9fChQv11Vdf8axSAACA+8CmEbfWrVtr5cqV8vT01LJly2Q2m7Vq1SpdunRJS5YsUdu2be1dJwAAQLln833cQkNDtX79emVlZSkjI0Pe3t6qUKGCPWsDAADAr9gc3CTp2rVrunLliiQpIyNDGRkZlr4HHnigeJUBAADAik3B7cSJExo/frwSExPvus7x48dtLgoAAAAF2RTcpkyZosuXL2vChAl3vS0IAAAA7Mum4Hby5EnNmjVLXbt2tXc9AAAAuAubriqtWbOm8vPz7V2LJCkuLk4RERFWbZMmTVKDBg2svjp06GDpz8/P14IFC9S+fXs1a9ZMAwcOVHJy8n2pDwAAwFFsCm5jx47V/PnzdeDAAWVnZ9utmFWrVt3xOac//PCDhg8frq+++srytXXrVkt/XFyc1q9fr+nTp2vDhg0ymUwaMmSIcnJy7FYbAACAo9k0VRoUFCSz2axXX331jv0mk0kJCQmF3t/FixcVFRWlb7/9VkFBQVZ9eXl5SkxM1IgRI+Tv719g25ycHK1YsULjx49Xx44dJUnR0dFq3769du3apW7duhXhlQEAAJReNgW3SZMm6fLly3rhhRfuGKaK6tixY6pYsaK2bdum2NhYnTt3ztJ36tQpZWdnq169enfc9sSJE7p+/brVTX99fX3VqFEjHTx4kOAGAADKDJuCW0JCgmbOnKlnnnnGLkWEhYUpLCzsjn0nT56UyWTS6tWrtXfvXjk5Oaljx44aM2aMfHx8lJKSIkmqXr261XYBAQG6cOGCzTWZzWbduHHD5u0BAEVjNputlvkMLv34mdmH2WyWyWQq1Lo2BbeAgAB5enrasmmR/fe//5WTk5Nq1KihxYsXKzk5WbNnz9bJkye1evVqZWZmSpLc3NystnN3d7e6IXBR5ebmci86AChBubm5Vst8Bpd+/Mzs57c55m5sCm5Dhw7V+++/r6CgINWpU8eWXRRaZGSk+vfvL19fX0lS/fr15e/vrxdffFFHjhyRh4eHpFvnut1elqTs7OxihUtXV1cFBwcXr3gAQKG5urpaLTds2NCB1aAw+JnZx+890OC3bApuO3fu1Llz5/T000/L19dX3t7eVv0mk0m7d++2ZdcFmEwmS2i7rX79+pKklJQUyxRpamqqatWqZVknNTVVISEhxTqul5eXzdsDAIrm11NFfAYbAz8z+yjsNKlkY3Dz9/dXly5dbNm0yMaNG6f09HQtX77c0nbkyBFJUnBwsGrWrClvb2/t37/fEtyuXLmihIQEhYeHl0iNAAAAJcGm4DZz5kx713FX3bt312uvvaZFixapW7duSkpK0jvvvKPu3btbrjQNDw/X3Llz5efnpxo1amjOnDkKDAwssXAJAABQEmwKbiXpySef1Pz587V48WItXrxYPj4+evbZZzVmzBjLOqNGjdLNmzf11ltvKSsrS6GhoVq+fHmhT/QDAAAwApuCW0hIyD3nY229smTWrFkF2p566ik99dRTd93G2dlZ48eP1/jx4206JgAAgBHYFNxGjhxZILhdv35dhw4d0unTp/XGG2/YpTgAAAD8wqbgFhkZede+N998U0ePHlXv3r1tLgoAAAAF2fSQ+d/To0cP7dixw967BQAAKPfsHtxOnTqlmzdv2nu3AAAA5Z5NU6UxMTEF2vLz83XhwgXt2LHjrs8dBQAAgO3sFtwkydvbW126dNGkSZOKVRQAAAAKsim4nThxwt51AAAA4B7sdo7bpUuXdOzYMeXl5dlrlwAAAPgVm4Lb9evXNWnSJK1Zs0aStGPHDj355JPq06ePunfvrgsXLti1SAAAANgY3ObOnaudO3eqcuXKkqS//OUvCgkJUUxMjFxcXDR37ly7FgkAAAAbz3Hbs2ePJk6cqO7du+v48eM6d+6cJkyYoE6dOunmzZt6++237V0nAABAuWfTiFt6errq1q0rSfrXv/4lFxcXPfbYY5KkihUrKjs7234VAgAAQJKNwa1GjRr64YcfJEn/+Mc/1Lx5c3l7e0uSvvzySz344IP2qxAAAACSbAxu/fr106xZs/T000/r+PHj6tevn6RbzzBdtWqV+vbta9ciAQAAYOM5bhEREfLz89OBAwcUGRmpZ5555tbOXFw0depUvfjii3YtEgAAADYGN0nq1q2bunXrZtUWHR1d7IIAAABwZzYHt6SkJH355Ze6ceOG8vPzrfpMJpNGjhxZ7OIAAADwC5uC29atWzVp0iSZzeY79hPcAAAA7M+m4LZo0SI9+uijmj59ugIDA2UymexdFwAAAH7DpqtKz58/r8GDB6t69eqENgAAgBJiU3ALCgrieaQAAAAlzKbgNm7cOMXFxWn//v08JQEAAKCE2HSO24wZM5SWlqb+/fvfsd9kMikhIaE4dQEAAOA3bApuzz33nL3rAACgTDPn58vkZNNEFxykNP7MbApur7/+ur3rAACgTDM5OSlp+1JlppWdc8Rzr6VbLSesfsdxxdiZZ5XqCuo+xNFlFGDzDXhzcnK0efNm7d+/X1euXFHlypXVunVr9ezZU+7u7vasEQCAMiEz7YIyL552dBl2Y87Ls1ouS6+ttLIpuF25ckWvvPKKTpw4oQceeED+/v5KSkrS9u3btXbtWq1bt04+Pj72rhUAAKBcs2ni9i9/+YtSUlL0wQcf6IsvvtCGDRv0xRdf6IMPPlBaWprmz59v7zoBAADKPZuC2549ezRmzBi1bt3aqr1169YaNWqU/vGPf9ilOAAAAPzCpuB2/fp11axZ8459NWvWVHp6enFqAgAAwB3YFNzq1q2rf/7zn3fs27Nnj2rXrl2sogAAAFCQTRcnDBo0SGPHjlVOTo6effZZVa1aVT/99JM+/fRTbdy4UVOnTrVzmTCSM2fOKCYmRtKtW8fcbXQWAAAUjU3B7ZlnntGpU6e0ePFibdy4UZJkNpvl5uamkSNH6sUXX7RrkTCW2NhYxcfHS5Li4uI0c+ZMB1cEAEDZYPN93EaMGKHw8HB99913ysjIUMWKFdW8eXP5+vrasz4Y0OnTv9zHJzk52YGVAABQttj8HIdt27Zp9uzZ6tChg5599ll5eXnp1Vdf1a5du+xZHwAAAP6PTcFt8+bNmjBhgjIzMy1tVapU0YMPPqjRo0cT3gAAAO4Dm4LbihUrNHjwYM2bN8/SFhQUpIULF2rAgAGKi4uzW4EAAAC4xabgdubMGT3++ON37Hv88ceVlJRUrKIAAABQkE3BLSAgQN9///0d+xISElS5cuViFQUAAICCbLqqtEePHlq0aJEqVKigzp07y8/PTz///LN2796tmJgYvfLKK/auEwAAoNyzKbgNGzZMP/74o959911Nnz7d0m42m9W1a1dFRkbarUAAAADcYlNwc3Fx0bx58zRixAjFx8crPT1dPj4+atWqlUJCQuxdIwAAAFSMG/BKUnBwsIKDg+/an5eXp8aNG2vTpk16+OGHi3MoAACAcs/mG/AWltlsvt+HAAAAKBfue3ADAACAfRDcAAAADILgBgAAYBAENwAAAIMguAEAABhEqQtucXFxioiIsGo7fvy4wsPD1bx5cz3xxBNavny5VX9+fr4WLFig9u3bq1mzZho4cKCSk5NLsmwAKHH5+Vy1D5Q3xbqPm72tWrVKCxYsUGhoqKXt8uXLGjBggDp37qxp06bpu+++07Rp01SpUiX17t1b0q2wt379es2cOVPVqlXTnDlzNGTIEG3fvl1ubm6OejkAcF85OZkU++HXOpea4ehS7OLylUyr5cnzdziwGvtq1uABvdi1uaPLQBlwX4ObyWRSaGioKlSo8LvrXbx4UVFRUfr2228VFBRk1ffRRx/Jzc1NU6dOlYuLi+rVq6fk5GQtXbpUvXv3Vk5OjlasWKHx48erY8eOkqTo6Gi1b99eu3btUrdu3e7b6wPKgjNnzigmJkaS9Prrr6tmzZoOrghFcS41Q6fOXXZ0GXZxMy/farmsvC5JesDf19EloIwodHDbunVrkXbco0cPOTk5ac2aNfdc99ixY6pYsaK2bdum2NhYnTt3ztIXHx+v0NBQubj8Umrbtm21ZMkSpaWl6dy5c7p+/bratm1r6ff19VWjRo108OBBghtwD7GxsYqPj5d0a/R65syZDq4IAHA3hQ5uEydOLPROTSaTevToUej1w8LCFBYWdse+lJQU1a9f36otICBAknT+/HmlpKRIkqpXr15gnQsXLhS6ht8ym826ceOGzdsXlslkkpu7u5ydSt3phriLvPx85WRnl5mngvz6fNBTp06VyO89is9kMsnT09PRZQBlXmZm5n3/vDebzTKZTIVat9DBbc+ePTYXVBxZWVkFzlNzd3eXJGVnZysz89Y5EXdaJyPD9vM+cnNzdfz4cZu3LyxPT081atSI81QMokZARY186TElJSVZfveMLjc312q5JH7vUXy3PzsA3F8l9Xlf2HPyCx3catSoUeiD2zOZenh4KCcnx6otOztbkuTl5SUPDw9JUk5OjmX59jrF+WvU1dVVwcHBNm9fWLcTNuepGEtQUFCZGXFzdXW1Wm7YsKEDq0FhFfavcwDFUxKf94mJiYVe1+aLE/7+97/rwIEDys3Ntbyg29OL3333nfbu3Wvrrq0EBgYqNTXVqu3299WqVdPNmzctbbVq1bJaJyQkxObjmkwmeXl52bw9yrayNEX16wDA7z0AWCuJz/ui/CFmU3CLiYlRTEyMfHx8dPPmTbm6usrFxUU///yznJyc9Pzzz9uy2zsKDQ3V+vXrlZeXJ2dnZ0nSvn37FBQUpCpVqsjHx0fe3t7av3+/JbhduXJFCQkJCg8Pt1sdAAAAjmbTGfFbtmzRc889pwMHDqh///568skn9c0332jTpk2qVKmSHnroIbsV2Lt3b127dk1RUVFKTEzU5s2btXr1ag0bNkzSrTnh8PBwzZ07V3v27NGJEyf0pz/9SYGBgerSpYvd6gAAAHA0m4LbxYsX9cc//lEmk0kPP/ywDh8+LElq3Lixhg8fro0bN9qtwCpVqmjZsmVKSkpSz549FRMTowkTJqhnz56WdUaNGqU+ffrorbfe0ksvvSRnZ2ctX76cm+8CAIAyxaapUi8vL8t8bJ06dXT27FllZWXJw8NDDRs21NmzZ20uaNasWQXamjZtqg0bNtx1G2dnZ40fP17jx4+3+bgAAAClnU0jbk2aNNGWLVskSbVq1ZKzs7O++eYbSdKPP/7ISBcAAMB9YNOI2/DhwzVgwABdvXpVixcv1nPPPaeJEyeqTZs2+uqrr9S5c2d71wkAAFDu2RTcQkNDtWnTJv3www+SpClTpsjJyUmHDh1S165dNWnSJLsWCQAAABuD2/nz51WvXj3LfdLc3d317rvvSrp149tjx46pZcuW9qsSAAAAtp3j1qlTp7s+Fuf777/XgAEDilUUAAAACir0iNvs2bOVnp4u6dYTEuLi4lS5cuUC6x0/flw+Pj52KxAAAAC3FDq41atXT3FxcZJuPZrh6NGjBa4edXZ2lo+PD+e4AQAA3AeFDm59+vRRnz59JElhYWGKi4sr1rNAAQAAUDQ2XZzwxRdfWJZ//PFHXb16VZUrV1bt2rXtVhgAAACs2RTcJGn79u2aPXu2fvrpJ0tb1apVNW7cOPXo0cMetcGgXDwqKifn2v8tV3JsMQAAlCE2j7iNHz9ebdu21dixY1W1alWlpqZq27ZtmjRpkipVqqQnnnjCzqXCKHxqttXVM//+v+U2Dq4GAICyw6bgtmjRInXt2lXR0dFW7b1799af/vQnLVmyhOBWjrl4VFTlh55ydBn3RUUfD5nz82VysulOOnAQfmYAygqbgtvJkycVGRl5x76ePXtq9OjRxSoKKK0qeLjJ5OSkpO1LlZl2wdHl2EXutXSr5YTV7ziumPvAs0p1BXUf4ugyAMAubApulStXttzT7bcuX77MQ+ZR5mWmXVDmxdOOLsMuzHl5Vstl5XUBQFlk09xBu3bttHDhQp0/f96q/dy5c4qNjdVjjz1ml+IAAADwC5tG3MaOHavevXura9euat68ufz9/XXp0iV999138vX11bhx4+xdJwAAQLln04jbqVOntH79ekVERCgrK0tHjx5VVlaWIiIitGXLFn333Xd2LhMAAAA2jbi98sor2rBhg8aPH1+g79///rcmTZqkbt26Fbs4AAAA/KLQwe3NN9/UhQu3rqIzm82aOnWqvL29C6x36tQpVa1a1X4VAgAAQFIRpkqfeuopmc1mmc1mS9vt729/OTk5qXnz5po5c+Z9KRYAAKA8K/SIW1hYmMLCwiRJERERmjp1qurVq3ffCgMAAIA1m85xW7Nmjb3rAAAAwD3wDBgAAACDILgBAAAYBMENAADAIAhuAAAABkFwA8q5AG/XOy4DAEofm64qBVB2PBviJ/Pxny3LAIDSi+AGlHMBFdw0uHWgo8sAABQCU6UAAAAGQXADAAAwCIIbAACAQRDcAAAADILgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AAAAGQXADAAAwCIIbAACAQRDcAACATQK8Xe+4jPuHh8wDAACbPBviJ/Pxny3LuP8IbgAAwCYBFdw0uHWgo8soV5gqBQCUCi4eFX+1XMlxhQClGMENAFAq+NRsKzffGnLzrSGfmm0cXQ5QKjFVCgAoFVw8KqryQ085ugygVGPEDQAAwCAIbgAAAAZBcAMAADAIQwS3c+fOqUGDBgW+Nm7cKEk6fvy4wsPD1bx5cz3xxBNavny5gysGAACwP0NcnPDDDz/I3d1du3fvlslksrT7+Pjo8uXLGjBggDp37qxp06bpu+++07Rp01SpUiX17t3bgVUDAADYlyGC28mTJxUUFKSAgIACfatXr5abm5umTp0qFxcX1atXT8nJyVq6dCnBDQAAlCmGmCr94YcfFBwcfMe++Ph4hYaGysXllwzatm1bJSUlKS0traRKBAAAuO8MM+Lm7++vfv366dSpU6pdu7ZGjBih9u3bKyUlRfXr17da//bI3Pnz51WlShWbjmk2m3Xjxo1i134vJpNJnp6e9/04QHmXmZkps9ns6DLshs8OoGSUxGeH2Wy2OhXs95T64JaTk6NTp07J09NTEyZMkJeXl7Zt26YhQ4Zo5cqVysrKkpubm9U27u7ukqTs7Gybj5ubm6vjx48Xq/bC8PT0VKNGje77cYDyLikpSZmZmY4uw2747ABKRkl9dvw2y9xNqQ9ubm5uOnjwoFxcXCwvqnHjxvrxxx+1fPlyeXh4KCcnx2qb24HNy8vL5uO6urredXrWngqbsAEUT1BQUJkbcQNw/5XEZ0diYmKh1y31wU26cwCrX7++vvrqKwUGBio1NdWq7/b31apVs/mYJpOpWMEPQOnCtCIAW5TEZ0dR/hAr9RcnnDhxQi1atFB8fLxV+9GjRxUcHKzQ0FB9++23ysvLs/Tt27dPQUFBNp/fBgAAUBqV+uBWv359PfTQQ5o2bZri4+P1448/aubMmfruu+80fPhw9e7dW9euXVNUVJQSExO1efNmrV69WsOGDXN06QAAAHZV6qdKnZyctHjxYs2dO1djxozRlStX1KhRI61cuVINGjSQJC1btkwzZsxQz5495e/vrwkTJqhnz54OrhwAAMC+Sn1wkyQ/Pz+99957d+1v2rSpNmzYUIIVAQAAlLxSP1UKAACAWwhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIAhuAAAABkFwAwAAMAiCGwAAgEEQ3AAAAAyC4AYAAGAQBDcAAACDILgBAAAYBMENAADAIMpEcMvPz9eCBQvUvn17NWvWTAMHDlRycrKjywIAALCrMhHc4uLitH79ek2fPl0bNmyQyWTSkCFDlJOT4+jSAAAA7MbwwS0nJ0crVqxQZGSkOnbsqJCQEEVHR+vixYvatWuXo8sDAACwG5PZbDY7uoji+P777/X888/r888/V1BQkKX9pZdeUoMGDTR16tQi7/PQoUMym81ydXW1Y6V3ZzKZdOValvLy80vkeLCdm6uLKni66eaNqzLn5zm6HBSCyclZLl4+MvhH3R3x2WEcfHYYT0l+duTm5spkMqlly5b3XNflvldzn6WkpEiSqlevbtUeEBCgCxcu2LRPk8lk9d+S4OvtUWLHQvG5ePk4ugQUUUn+/1yS+OwwFj47jKckPjtMJlOhj2P44JaZmSlJcnNzs2p3d3dXRkaGTfts0aJFsesCAACwN8Of4+bhceuvzd9eiJCdnS1PT09HlAQAAHBfGD643Z4iTU1NtWpPTU1VYGCgI0oCAAC4Lwwf3EJCQuTt7a39+/db2q5cuaKEhAS1bt3agZUBAADYl+HPcXNzc1N4eLjmzp0rPz8/1ahRQ3PmzFFgYKC6dOni6PIAAADsxvDBTZJGjRqlmzdv6q233lJWVpZCQ0O1fPnyAhcsAAAAGJnh7+MGAABQXhj+HDcAAIDyguAGAABgEAQ3AAAAgyC4AQAAGATBDQAAwCAIbgAAAAZBcAMKKT8/XwsWLFD79u3VrFkzDRw4UMnJyY4uC4CBxMXFKSIiwtFlwMAIbkAhxcXFaf369Zo+fbo2bNggk8mkIUOGKCcnx9GlATCAVatWacGCBY4uAwZHcAMKIScnRytWrFBkZKQ6duyokJAQRUdH6+LFi9q1a5ejywNQil28eFGDBw/W/PnzFRQU5OhyYHAEN6AQTpw4oevXr6tt27aWNl9fXzVq1EgHDx50YGUASrtjx46pYsWK2rZtm5o1a+bocmBwZeJZpcD9lpKSIkmqXr26VXtAQIAuXLjgiJIAGERYWJjCwsIcXQbKCEbcgELIzMyUJLm5uVm1u7u7Kzs72xElAQDKIYIbUAgeHh6SVOBChOzsbHl6ejqiJABAOURwAwrh9hRpamqqVXtqaqoCAwMdURIAoBwiuAGFEBISIm9vb+3fv9/SduXKFSUkJKh169YOrAwAUJ5wcQJQCG5ubgoPD9fcuXPl5+enGjVqaM6cOQoMDFSXLl0cXR4AoJwguAGFNGrUKN28eVNvvfWWsrKyFBoaquXLlxe4YAEAgPvFZDabzY4uAgAAAPfGOW4AAAAGQXADAAAwCIIbAACAQRDcAAAADILgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAgCG4AAAAGQXADAAAwCIIbAACAQRDcAOB3HDt2TK+++qpatWqlFi1aqH///vrPf/4jSZo4caLCwsKs1j979qwaNGigzZs3S5L279+vBg0aaN++fRo4cKCaNWumRx99VLNnz9bNmzdL/PUAMDaCGwDcxbVr1zR48GBVrlxZCxYsUHR0tDIzMzVo0CBdvXq1SPt644031KpVKy1evFjPPvusVqxYoU2bNt2nygGUVS6OLgAASqvExET9/PPPioiIUKtWrSRJdevW1fr163Xt2rUi7ev555/XyJEjJUnt2rXT7t279a9//Ut9+/a1e90Ayi6CGwDcxUMPPSQ/Pz+99tprevrpp9WxY0e1a9dOEyZMKPK+WrRoYfV9YGCgbty4Ya9SAZQTTJUCwF1UqFBBa9euVceOHbVjxw699tprateunaZMmaLs7Owi7cvDw8PqeycnJ5nNZnuWC6AcYMQNAH5H3bp1NWfOHOXl5en777/XJ598og8//FAPPvigTCaT8vLyrNZnFA3A/cSIGwDcxeeff662bdvq0qVLcnZ2VosWLTR16lT5+voqJSVFFSpU0OXLl61G3w4dOuTAigGUdYy4AcBdtGzZUvn5+Ro5cqSGDh2qChUq6LPPPtPVq1f1hz/8QXl5eVqzZo0mT56s559/Xv/973+1YsUKOTs7O7p0AGUUI24AcBcBAQFatmyZfHx8FBUVpWHDhunYsWNauHCh2rZtq8cee0xvvvmmDh06pCFDhujvf/+7YmJiCG4A7huTmbNjAQAADIERNwAAAIMguAEAABgEwQ0AAMAgCG4AAAAGQXADAAAwCIIbAACAQRDcAAAADILgBgAAYBAENwAAAIMguAEAABgEwQ0AAMAg/j9vBz8Uv1E5wQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(data = data, x = \"sun\", y =\"total_consumed\", hue = \"gas_type\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d9c422",
+   "metadata": {},
+   "source": [
+    "**When it's too sunny, the total consumed of SP98 seems higher.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f90fbc28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data = data, x = \"diff_temp\", y =\"total_consumed\", hue = \"gas_type\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67dfbe69",
+   "metadata": {},
+   "source": [
+    "**Except for four extreme values, the toal consumed seems lower for SP98 for every value of difference in temperature between inside and outside.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5270d4e1",
+   "metadata": {},
+   "source": [
+    "# Hypothesis contrast\n",
+    "- H0: both gas types consume the same ammount in the same conditions.\n",
+    "- H1: they have different behaviours in the same conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48db09a5",
+   "metadata": {},
+   "source": [
+    "## Visualization: correlation and collinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "acd53224",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gdcma\\AppData\\Local\\Temp\\ipykernel_13832\\1371328609.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  sns.heatmap(data.corr(),vmin=-1, vmax=1, annot=True);\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(data.corr(),vmin=-1, vmax=1, annot=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6ba9559",
+   "metadata": {},
+   "source": [
+    "**The total consumed is highly correlated with speed. It shows very low correlation with the gas type, and the rest of the variables.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74663d0d",
+   "metadata": {},
+   "source": [
+    "## Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "14326f1f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>     <td>total_consumed</td>  <th>  R-squared:         </th> <td>   0.306</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.295</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   27.94</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Fri, 22 Dec 2023</td> <th>  Prob (F-statistic):</th> <td>1.21e-27</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>02:00:20</td>     <th>  Log-Likelihood:    </th> <td> -2308.7</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   388</td>      <th>  AIC:               </th> <td>   4631.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   381</td>      <th>  BIC:               </th> <td>   4659.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>        <td>  -90.9174</td> <td>   19.552</td> <td>   -4.650</td> <td> 0.000</td> <td> -129.361</td> <td>  -52.473</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>gas_type[T.SP98]</th> <td>   -1.4108</td> <td>    9.895</td> <td>   -0.143</td> <td> 0.887</td> <td>  -20.867</td> <td>   18.045</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>speed</th>            <td>    4.5108</td> <td>    0.354</td> <td>   12.753</td> <td> 0.000</td> <td>    3.815</td> <td>    5.206</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>AC</th>               <td>   -2.5420</td> <td>   18.788</td> <td>   -0.135</td> <td> 0.892</td> <td>  -39.483</td> <td>   34.399</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>rain</th>             <td>    4.1141</td> <td>   15.392</td> <td>    0.267</td> <td> 0.789</td> <td>  -26.150</td> <td>   34.378</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sun</th>              <td>    5.2353</td> <td>   18.441</td> <td>    0.284</td> <td> 0.777</td> <td>  -31.025</td> <td>   41.495</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>diff_temp</th>        <td>   -0.4349</td> <td>    0.782</td> <td>   -0.556</td> <td> 0.578</td> <td>   -1.972</td> <td>    1.102</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>406.253</td> <th>  Durbin-Watson:     </th> <td>   1.373</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>17930.537</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 4.584</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td>35.016</td>  <th>  Cond. No.          </th> <td>    201.</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:         total_consumed   R-squared:                       0.306\n",
+       "Model:                            OLS   Adj. R-squared:                  0.295\n",
+       "Method:                 Least Squares   F-statistic:                     27.94\n",
+       "Date:                Fri, 22 Dec 2023   Prob (F-statistic):           1.21e-27\n",
+       "Time:                        02:00:20   Log-Likelihood:                -2308.7\n",
+       "No. Observations:                 388   AIC:                             4631.\n",
+       "Df Residuals:                     381   BIC:                             4659.\n",
+       "Df Model:                           6                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "====================================================================================\n",
+       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------------\n",
+       "Intercept          -90.9174     19.552     -4.650      0.000    -129.361     -52.473\n",
+       "gas_type[T.SP98]    -1.4108      9.895     -0.143      0.887     -20.867      18.045\n",
+       "speed                4.5108      0.354     12.753      0.000       3.815       5.206\n",
+       "AC                  -2.5420     18.788     -0.135      0.892     -39.483      34.399\n",
+       "rain                 4.1141     15.392      0.267      0.789     -26.150      34.378\n",
+       "sun                  5.2353     18.441      0.284      0.777     -31.025      41.495\n",
+       "diff_temp           -0.4349      0.782     -0.556      0.578      -1.972       1.102\n",
+       "==============================================================================\n",
+       "Omnibus:                      406.253   Durbin-Watson:                   1.373\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            17930.537\n",
+       "Skew:                           4.584   Prob(JB):                         0.00\n",
+       "Kurtosis:                      35.016   Cond. No.                         201.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = smf.ols(\"total_consumed ~ gas_type + speed + AC + rain + sun + diff_temp\", data = data).fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26954c06",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "786d542c",
+   "metadata": {},
+   "source": [
+    "**The difference of total consumed between both gas types is not statistically significant.**\n",
+    "\n",
+    "**The decision of choosing one or the other would be made according to price, availability or preference.**\n",
+    "\n",
+    "**It is likely that there exists a statistically significant difference between prices for trips made with one gas type or the other. In that case, it would be interesting to develop a model to predict how much a certain trip would cost considering gas type and the rest of explanatory factors.**"
+   ]
+  }
+ ],
+ "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/df_clean.csv b/data/df_clean.csv
new file mode 100644
index 0000000..788fca9
--- /dev/null
+++ b/data/df_clean.csv
@@ -0,0 +1,389 @@
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