{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Is Fandango Still Inflating Ratings?*\n",
    "In October 2015, Walt Hickey from FiveThirtyEight published a [popular article](https://fivethirtyeight.com/features/fandango-movies-ratings/) where he presented strong evidence which suggest that Fandango's movie rating system was biased and dishonest.\n",
    "\n",
    "### Goal\n",
    "In this project, we'll analyze more recent movie ratings data to determine whether there has been any change in Fandango's rating system after Hickey's analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the Data\n",
    "We'll work with two samples of movie ratings: the [data](https://github.com/fivethirtyeight/data/tree/master/fandango) in one sample was collected previous to Hickey's analysis, while the other sample was collected [after](https://github.com/mircealex/Movie_ratings_2016_17). Let's start by reading in the two samples (which are stored as CSV files) and getting familiar with their structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>249</td>\n",
       "      <td>65709</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>88</td>\n",
       "      <td>19560</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "0  Avengers: Age of Ultron (2015)              74                   86   \n",
       "1               Cinderella (2015)              85                   80   \n",
       "2                  Ant-Man (2015)              80                   90   \n",
       "3          Do You Believe? (2015)              18                   84   \n",
       "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
       "\n",
       "   Metacritic  Metacritic_User  IMDB  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0          66              7.1   7.8             5.0                   4.5   \n",
       "1          67              7.5   7.1             5.0                   4.5   \n",
       "2          64              8.1   7.8             5.0                   4.5   \n",
       "3          22              4.7   5.4             5.0                   4.5   \n",
       "4          29              3.4   5.1             3.5                   3.0   \n",
       "\n",
       "   RT_norm  RT_user_norm         ...           IMDB_norm  RT_norm_round  \\\n",
       "0     3.70           4.3         ...                3.90            3.5   \n",
       "1     4.25           4.0         ...                3.55            4.5   \n",
       "2     4.00           4.5         ...                3.90            4.0   \n",
       "3     0.90           4.2         ...                2.70            1.0   \n",
       "4     0.70           1.4         ...                2.55            0.5   \n",
       "\n",
       "   RT_user_norm_round  Metacritic_norm_round  Metacritic_user_norm_round  \\\n",
       "0                 4.5                    3.5                         3.5   \n",
       "1                 4.0                    3.5                         4.0   \n",
       "2                 4.5                    3.0                         4.0   \n",
       "3                 4.0                    1.0                         2.5   \n",
       "4                 1.5                    1.5                         1.5   \n",
       "\n",
       "   IMDB_norm_round  Metacritic_user_vote_count  IMDB_user_vote_count  \\\n",
       "0              4.0                        1330                271107   \n",
       "1              3.5                         249                 65709   \n",
       "2              4.0                         627                103660   \n",
       "3              2.5                          31                  3136   \n",
       "4              2.5                          88                 19560   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  \n",
       "3            1793                  0.5  \n",
       "4            1021                  0.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "previous = pd.read_csv('fandango_score_comparison.csv')\n",
    "after = pd.read_csv('movie_ratings_16_17.csv')\n",
    "previous.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>metascore</th>\n",
       "      <th>imdb</th>\n",
       "      <th>tmeter</th>\n",
       "      <th>audience</th>\n",
       "      <th>fandango</th>\n",
       "      <th>n_metascore</th>\n",
       "      <th>n_imdb</th>\n",
       "      <th>n_tmeter</th>\n",
       "      <th>n_audience</th>\n",
       "      <th>nr_metascore</th>\n",
       "      <th>nr_imdb</th>\n",
       "      <th>nr_tmeter</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>76</td>\n",
       "      <td>7.2</td>\n",
       "      <td>90</td>\n",
       "      <td>79</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>48</td>\n",
       "      <td>7.3</td>\n",
       "      <td>50</td>\n",
       "      <td>83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>47</td>\n",
       "      <td>6.6</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>43</td>\n",
       "      <td>5.2</td>\n",
       "      <td>33</td>\n",
       "      <td>76</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>58</td>\n",
       "      <td>6.1</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  metascore  imdb  tmeter  audience  fandango  \\\n",
       "0      10 Cloverfield Lane  2016         76   7.2      90        79       3.5   \n",
       "1                 13 Hours  2016         48   7.3      50        83       4.5   \n",
       "2      A Cure for Wellness  2016         47   6.6      40        47       3.0   \n",
       "3          A Dog's Purpose  2017         43   5.2      33        76       4.5   \n",
       "4  A Hologram for the King  2016         58   6.1      70        57       3.0   \n",
       "\n",
       "   n_metascore  n_imdb  n_tmeter  n_audience  nr_metascore  nr_imdb  \\\n",
       "0         3.80    3.60      4.50        3.95           4.0      3.5   \n",
       "1         2.40    3.65      2.50        4.15           2.5      3.5   \n",
       "2         2.35    3.30      2.00        2.35           2.5      3.5   \n",
       "3         2.15    2.60      1.65        3.80           2.0      2.5   \n",
       "4         2.90    3.05      3.50        2.85           3.0      3.0   \n",
       "\n",
       "   nr_tmeter  nr_audience  \n",
       "0        4.5          4.0  \n",
       "1        2.5          4.0  \n",
       "2        2.0          2.5  \n",
       "3        1.5          4.0  \n",
       "4        3.5          3.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0  Avengers: Age of Ultron (2015)             5.0                   4.5   \n",
       "1               Cinderella (2015)             5.0                   4.5   \n",
       "2                  Ant-Man (2015)             5.0                   4.5   \n",
       "3          Do You Believe? (2015)             5.0                   4.5   \n",
       "4   Hot Tub Time Machine 2 (2015)             3.5                   3.0   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  \n",
       "3            1793                  0.5  \n",
       "4            1021                  0.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous = previous[['FILM', 'Fandango_Stars', \n",
    "                              'Fandango_Ratingvalue', \n",
    "                              'Fandango_votes', \n",
    "                              'Fandango_Difference']].copy()\n",
    "fandango_after = after[['movie', 'year', 'fandango']].copy()\n",
    "fandango_previous.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>movie</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  fandango\n",
       "0      10 Cloverfield Lane  2016       3.5\n",
       "1                 13 Hours  2016       4.5\n",
       "2      A Cure for Wellness  2016       3.0\n",
       "3          A Dog's Purpose  2017       4.5\n",
       "4  A Hologram for the King  2016       3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to determine whether there has been any change in Fandango's rating system after Hickey's analysis. The population of interest for our analysis is made of all the movie ratings stored on Fandango's website, regardless of the releasing year.\n",
    "\n",
    "Because we want to find out whether the parameters of this population changed after Hickey's analysis, we're interested in sampling the population at two different periods in time — previous and after Hickey's analysis — so we can compare the two states.\n",
    "\n",
    "The data we're working with was sampled at the moments we want: one sample was taken previous to the analysis, and the other after the analysis. We want to describe the population, so we need to make sure that the samples are representative, otherwise we should expect a large sampling error and, ultimately, wrong conclusions.\n",
    "\n",
    "**Sample Before**<br>\n",
    "From Hickey's article and from the [README.md](https://github.com/fivethirtyeight/data/tree/master/fandango) of the data set's repository, we can see that he used the following sampling criteria:\n",
    "\n",
    "- The movie must have had at least 30 fan ratings on Fandango's website at the time of sampling (Aug. 24, 2015).\n",
    "- The movie must have had tickets on sale in 2015.\n",
    "\n",
    "The sampling was clearly not random because not every movie had the same chance to be included in the sample — some movies didn't have a chance at all (like those having under 30 fan ratings or those without tickets on sale in 2015). It's questionable whether this sample is representative of the entire population we're interested to describe. It seems more likely that it isn't, mostly because this sample is subject to temporal trends — e.g. movies in 2015 might have been outstandingly good or bad compared to other years.\n",
    "\n",
    "**Sample After**<br>\n",
    "The sampling conditions for our other sample were (as it can be read in the [README.md](https://github.com/mircealex/Movie_ratings_2016_17) of the data set's repository):\n",
    "\n",
    "- The movie must have been released in 2016 or later.\n",
    "- The movie must have had a considerable number of votes and reviews (unclear how many from the README.md or from the data).\n",
    "\n",
    "This second sample is also subject to temporal trends and it's unlikely to be representative of our population of interest.\n",
    "\n",
    "---\n",
    "**Purposive Sampling**<br>\n",
    "Both these authors had certain research questions in mind when they sampled the data, and they used a set of criteria to get a sample that would fit their questions. Their sampling method is called purposive sampling (or judgmental/selective/subjective sampling). While these samples were good enough for their research, they don't seem too useful for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the Goal of our Analysis\n",
    "\n",
    "At this point, we can either collect new data or change our the goal of our analysis. We choose the latter and place some limitations on our initial goal.\n",
    "\n",
    "Instead of trying to determine whether there has been any change in Fandango's rating system after Hickey's analysis, our new goal is to determine whether there's any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. This new goal should also be a fairly good proxy for our initial goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolating the Samples we need\n",
    "\n",
    "With this new research goal, we have two populations of interest:\n",
    "\n",
    "1. All Fandango's ratings for popular movies released in 2015.\n",
    "2. All Fandango's ratings for popular movies released in 2016.\n",
    "\n",
    "---\n",
    "\n",
    "**Checking whether movies are popular in both populations**\n",
    "\n",
    "We need to be clear about what counts as popular movies. We'll use Hickey's benchmark of 30 fan ratings and count a movie as popular only if it has 30 fan ratings or more on Fandango's website.\n",
    "\n",
    "Although one of the sampling criteria in our second sample is movie popularity, the sample doesn't provide information about the number of fan ratings. We should be skeptical once more and ask whether this sample is truly representative and contains popular movies (movies with over 30 fan ratings).\n",
    "\n",
    "One quick way to check the representativity of this sample is to sample randomly 10 movies from it and then check the number of fan ratings ourselves on Fandango's website. Ideally, at least 8 out of the 10 movies have 30 fan ratings or more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Mechanic: Resurrection</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Warcraft</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Max Steel</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Me Before You</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Fantastic Beasts and Where to Find Them</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Cell</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Genius</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Sully</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       movie  year  fandango\n",
       "108                   Mechanic: Resurrection  2016       4.0\n",
       "206                                 Warcraft  2016       4.0\n",
       "106                                Max Steel  2016       3.5\n",
       "107                            Me Before You  2016       4.5\n",
       "51   Fantastic Beasts and Where to Find Them  2016       4.5\n",
       "33                                      Cell  2016       3.0\n",
       "59                                    Genius  2016       3.5\n",
       "152                                    Sully  2016       4.5\n",
       "4                    A Hologram for the King  2016       3.0\n",
       "31                Captain America: Civil War  2016       4.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.sample(10, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we used a value of 1 as the random seed. This is good practice because it suggests that we weren't trying out various random seeds just to get a favorable sample.\n",
    "\n",
    "As of January 2019, these are the fan ratings we found:\n",
    "\n",
    "\n",
    "|**Movie**|**Fan Ratings**|\n",
    "|---|:---|\n",
    "|Mechanic: Resurrection | 2250|\n",
    "|Warcraft |\t7280|\n",
    "|Max Steel\t| 494|\n",
    "|Me Before You|\t5270|\n",
    "|Fantastic Beasts and Where to Find Them | 13477|\n",
    "|Cell\t|\t18|\n",
    "|Genius\t|\t127|\n",
    "|Sully\t|\t11889|\n",
    "|A Hologram for the King |\t501|\n",
    "|Captain America: Civil War | 35143|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90% of the movies in our sample are popular. This is enough and we move forward with a bit more confidence.\n",
    "\n",
    "Let's also double-check the other data set for popular movies. The documentation states clearly that there're only movies with at least 30 fan ratings, but it should take only a couple of seconds to double-check here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fandango_previous[fandango_previous['Fandango_votes'] < 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Isolating movies released in 2015 from our *fandango_previous* data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0  Avengers: Age of Ultron (2015)             5.0                   4.5   \n",
       "1               Cinderella (2015)             5.0                   4.5   \n",
       "2                  Ant-Man (2015)             5.0                   4.5   \n",
       "3          Do You Believe? (2015)             5.0                   4.5   \n",
       "4   Hot Tub Time Machine 2 (2015)             3.5                   3.0   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  \n",
       "3            1793                  0.5  \n",
       "4            1021                  0.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0  Avengers: Age of Ultron (2015)             5.0                   4.5   \n",
       "1               Cinderella (2015)             5.0                   4.5   \n",
       "2                  Ant-Man (2015)             5.0                   4.5   \n",
       "3          Do You Believe? (2015)             5.0                   4.5   \n",
       "4   Hot Tub Time Machine 2 (2015)             3.5                   3.0   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  Year  \n",
       "0           14846                  0.5  2015  \n",
       "1           12640                  0.5  2015  \n",
       "2           12055                  0.5  2015  \n",
       "3            1793                  0.5  2015  \n",
       "4            1021                  0.5  2015  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous['Year'] = fandango_previous['FILM'].str[-5:-1]\n",
    "fandango_previous.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "2014     17\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a seperate data set\n",
    "\n",
    "fandango_2015 = fandango_previous[fandango_previous['Year'] \n",
    "                                  == '2015'].copy()\n",
    "fandango_2015['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Isolating movies released in 2016 from our *fandango_after* data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  fandango\n",
       "0      10 Cloverfield Lane  2016       3.5\n",
       "1                 13 Hours  2016       4.5\n",
       "2      A Cure for Wellness  2016       3.0\n",
       "3          A Dog's Purpose  2017       4.5\n",
       "4  A Hologram for the King  2016       3.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "2017     23\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a seperate data set\n",
    "\n",
    "fandango_2016 = fandango_after[fandango_after['year'] == 2016].copy()\n",
    "fandango_2016['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Distribution Shapes for 2015 and 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ysqrUegcPHsTbt2/x6aefom3btpxlfn5+sLS0xOnTp/Hq1SuFdWfMmAEHBwf2tZGREQYMGACGYTBw4EC4uLiwyzQ1NeHr6wsAuHfvntK2+Pn5cX6W1tHRwTfffAMA2LdvH6du8+bNFdbn8/mYNWsWAChN1QDe9UY6OzsrXVaWBQsWoFmzZuxroVCI//3vfwCAGzdusOURERFsr+WHH37I2ca8efMqNcrBy5cvERkZCWtra8ycOZOzzNvbm/NXe3n4fD40NDSU9uBVNb928eLFVVp32bJlnF8XTExMMG/ePABQy7BRERERSE9Px7Bhw9CjRw/OsokTJ+LDDz/E/fv3ERcXp7DuRx99pHAdJk+eDID7vihLVa5NRd+PAGBvbw8ej6ewjc8//xxA6Z+VHj16wNvbm1M2c+ZMWFtb4/z58+w9ISMjA7///jucnZ0xZ84cTn0tLS0sW7YMAHDgwAHOMQuFQmhqalb4mCtDdr+YN28eLC0t2XIej4eVK1dCR0cHv//+u9Iezi+++KLKebfBwcEIDAzk/Pvhhx84dUQiEZo0aaKwrr29PYYOHYr79++zucjyAgMDoa2tzb42NzfHwIEDkZmZyXnoWPY5+vrrrzm9hNra2uw9VZ62trbS7w9jY2NMmDAB6enpuHXrltJ1V6xYwbm36evrY9SoUSguLsbt27fZ8oMHD6K4uBjTpk3jPMDK4/GwdOlStne0JNm1nD9/PiwsLNhyPp+PVatWQVtbG/v371eaviZPVffADz/8EB9//LHSZcq+mwQCAXsPL+3zVhXNmzfHV199xSnz9vaGlZUVbt68ySn/448/wOfzsWzZMs79wNraGtOnT1fYtuxaKOtt5/F4St/DDR2lMdQBspwtZV9qZZHdiOS/5IF3X1Rdu3bFkSNHcOfOHYUbobKgUXYzUpZKIVumLHAGoPQnEQ8PD/B4PNy5c4dTnp6eji1btuD06dNITExEbm4uZ3lpXxadO3dWWl4e+cAVABtsiEvkScra6e7urlBfT08P7dq1w5UrVyq0T9m2unbtqvQG7enpWeHc39GjRyM4OBhdunSBr68v3N3d4ebmVuGfy5SpyrkUCoVK88ll117+OteGsj4DANCzZ0/cunULt2/fVmh7Rd8XZanKtanMfnNzcxEcHIywsDA8efIEOTk5nBzP0j4ryj6PsuuXlJTE3hOuX78OiUQCPp+vNL9aIpEAAB49esQ55sWLF8PNzQ3Dhw+Hh4cH3NzcOMFMdZR1Tc3MzODk5ITr16/j0aNHcHJy4iyv6j1Ctt+KBMpRUVEIDg7G9evX8d9//ynktr5+/ZoTpAPvAjJl6WSy+3LJ6y47/pKpPDIeHh5Kg0oA+Oeff7BlyxZER0cjOTlZIc2rtPeKshQfZe0qeU+TZ29vD0tLS4V83bKupYWFBVq3bo3bt2/jyZMnZT5kPGbMGOzcuVPhc1aVYRbLeo+kpaVh8+bNOHv2bKW+m6rC2dlZ6bW0srLi3EszMjKQlJQEa2trpe9PZdfDyckJ7du3x8GDB5GYmIiBAwfCzc0NnTp1UkiFbCwo2K0DLCws8O+//1Z6rD7ZwwpmZmZKl8ueclb2UIOyHCmBQFDuMmW9KaW1QVtbGwYGBpz9i8VieHl5ITExES4uLhg7diyMjY0hEAiQmZmJ4ODgUnNxSzvO8ijL7ZMdT8keBdmDL6amptXev+yYVbGt1atXw8HBAfv27cOWLVuwefNm8Pl89OzZEytXrqzS2KRVeQLexMSEPW8lyY5RHWNAV+czoOx9IfvDpCI9TUDVrk1F349FRUUYOnQorl+/DicnJ4wYMQJNmzZl2xgYGFjpz4r8tZI9dHrr1q1Se/4AcB4kmjVrFkxNTbF7927s2rWLzWN0dXXFsmXLKvWrhTLVuaZVvUdU1NGjR/HJJ59AR0cHvXr1gr29PfT09MDj8XDp0iVER0crvSbKrjmg/P1W1r1DQ0MDIpEImZmZnPKYmBj4+vpCKpWiZ8+e8PHxgb6+Pvh8Pm7fvo2TJ08qbZdAIFCa16ysXbL7Y1nvLfnvsPKupYWFBW7fvl3uvWPt2rVo0aIFQkJCsGnTJmzatAkCgQC9evXCypUr8cEHH5S5fkmltSUjIwO9evXCixcv0LlzZ853U0ZGBnbs2FHh50Qqoqz3REXfD6WVC4VChIWFYd26dQgNDcW3334L4F2nzYgRI7By5cpqdZbURxTs1gHu7u64dOkSLl68WKlEe9mHJSUlRelyWXpEaR8qVUpJSVHouSgoKEB2djbnQ/Xbb78hMTER/v7+WLRoEad+XFwcgoODS91HZXu+K0sW5CsbxQIo/TwrIzvnqtiWQCDAtGnT2BExoqOjERYWhgMHDmD48OGIi4ur9M9SVTmXaWlpKC4uVgh4ZcdY8n0m67EoLWiU/7KuKnV/Bmri2sjIHkgaN24c5wExAHjz5k2ZDwSWdj7kr5Xsv9OmTcO6desq3LZRo0Zh1KhRyMrKwrVr13Dy5En8+uuvGDVqFK5cuYKWLVtWeFvySl5TZT13ZV3Tmr5HrF69Gtra2rhw4QIcHR05y16+fIno6Ohq76PkvUNPT4+zrKioCGKxWOE4169fj4KCAkRERMDDw4OzbN26dWWOAFNRsvtjSkqKQtqcrL3ySl5LZUH1mzdvOPVKIxAIMGPGDMyYMQNpaWmIjo5GaGgoDh48iLt37yIuLq7CvbylvUd++eUXvHjxAkuWLIGfnx9nWVRUFHbs2FGh7aua7MHN0r5LSisXiUQICAhAQEAAEhIScPXqVfz222/47bff8PLlSxw+fLjG2lwXUc5uHTBhwgRoaGggNDQU8fHxZdYt+Zel7OcnZT+HFxYWIjY2llOvJil7IjQqKgoMw3BSJmS5aUOHDq3QNmqTrJ3KvrByc3NLzVcua1uxsbHsT8ElVfVYmzRpAh8fHwQHB2PkyJFITU1lh78BlPcQqopEImHfUyXJjqXkdZZ98SQlJSnUz8zMxJMnTxTKq9L2sj4DwLuhuwDlqQOqVt61qazqfFaULS95/WTXqnPnzuDz+VUO0gwNDdGnTx+sX78en3/+OQoKCnD27NkqbUumrGuampqK+/fvQ09PTyHYrA3Pnj1D27ZtFfZdXFys9LNRFbLjV5YyFRUVxRkCS+bp06cwNTVVCHQB1d1XZe8ZZe/pxMREpT/xl3Utk5OT8fDhQxgYGHBGBymPiYkJBg8ejB07dsDX1xcpKSlKc/Irqyqft/L+qFcFkUgEa2trvHr1SulU4xW5x9jb22PChAkICwuDpaUlIiMjKzzsW0NBwW4dYGtriyVLlqCoqAijR49WOl4j8O5NXXLIqtGjR0NTUxO7d+/Gv//+y6m7ceNGvHr1ih0vtqatX7+ek9+Vn5+P7777DsC7YF5GNoyU/M3v9u3bCg+D1LZBgwbB0NAQhw8fVvhJd+PGjRXO4wTe5WB6eXnhxYsXCr1yp06dqnC+bmFhIS5cuKDwBccwDPsXfcmHXmQPa8jG21W1VatWcf7gSktLw8aNGwFwr3OrVq1gaGiIiIgIticOeBdwLVq0SOkYsbK2KwuQS+Pj44MmTZrg2LFjCl9IISEhuHnzJtq2bVsjs2BV9tpUVmmflYSEBCxfvrzMdS9dusTOUCYTFBSEpKQkeHl5sTmZTZs2xZgxY3D37l2sWbNG6R9mL1++5Nxfzpw5ozSdSXadq3PMANiH9TZu3Mh57zAMg2XLliEvLw/jxo2DhoZGtfZTFTY2Nnj06JFCuwICAjh5zdUh+xx9//33yMjIYMtlY2MrY2try/4hUNKePXtw8eJFlbRr9OjREAgE2LFjB+czyjAMVq5cqTQIl13L9evXc35tkF3LgoICjB8/Xml6lEx+fj4uXryoMB6xVCpFamoqAMUhOauitM/bzZs3sXnzZqXrVOWeVRVjx46FVCrFypUrOechKSlJaY/zs2fP8ODBA4XynJwc5OXllfqwX0PWuI62DpszZw4kEgkCAgLQr18/dO7cGZ06dYKBgQHS0tIQFxeH+Ph4zpOntra2CAwMxLx58+Dl5QVfX1+Ym5sjNjYWV69eRbNmzSo0yLsqtG7dGu7u7hg6dCg7zm5CQgIGDRqEjz76iK03duxYbNmyBYsXL8aVK1fQokULPHnyBKdOncKQIUPU+tOKoaEhvv/+e0ybNg0DBw7kjLN7584deHp64urVq6U+ICJvw4YN6NevH5YuXYqLFy/C2dkZCQkJCA0NxYABAyr002J+fj58fX1hbW0NV1dX2NjYoKioCFeuXMHdu3fRuXNnzsMfXl5euH79Oj7++GP0798f2trasLGxwdixY6t8XmQsLCxQWFgIDw8PDBw4EIWFhQgNDUVycjKmT5/OeQBMQ0MDX3zxBVavXo0ePXpgyJAhAN59kTAMg3bt2in0lHfp0gX6+vo4fPgwNDU1YW1tDR6PhzFjxpQ61q6enh62b9+OiRMnwtfXF0OHDoW9vT3u3buH06dPw8jICEFBQTXy83Zlr01lDRgwAA4ODti+fTvu378PZ2dnJCUl4dSpU+jfv3+ZX7ADBw7EhAkT2PNx584dnD17Fk2aNMGGDRs4ddetW4enT58iMDAQBw4cYCe7SE5OxuPHj3Ht2jWsXr2afYDo008/haamJtzd3WFrawsej4fr168jOjqancigOrp06YJ58+Zh48aNcHd3h6+vLzue6+3bt+Hk5ISlS5dWax9VNWvWLPj5+aF79+4YOnQoBAIBoqOj8eTJE3h7eyv8gVEVnp6e+PTTT7F79272niobZ9fExASmpqYKE/zMmjULFy9ehLe3N3x9fWFgYIAbN24gLi4OQ4cORWhoaLXb1bJlS/j7+yMgIADdunXDiBEj2OuSmZkJJycnxMfHc+6PHh4e+PLLL7Flyxb2Wurr6yMyMhJ37txBu3btSh1hQiYvLw/Dhg2DjY0NXF1dYW1tjaKiIly+fBn37t2Dm5ub0of5Kmv8+PH48ccf4e/vj4sXL8LBwQGPHz/GqVOnMHToUKXfTb1790ZYWBi++OILDBkyBHp6ejA2NsZnn31W7faUNHfuXEREROCvv/7Co0eP0Lt3b2RlZeHIkSPw8PBAeHg457zfuXMHkyZNQocOHeDk5ARLS0ukp6fj1KlTyMzMxFdffVXtP0rrGwp265D58+fD19cXu3btwqVLl/DHH38gLy8PIpEITk5OCAwMVJgRaMqUKXBwcMDWrVsRHh6O3NxcWFpaYtq0aZg/f36NP7Ahs2fPHqxbtw4HDx5EcnIyLC0tsWjRIsydO5cTaFhaWuLEiRP49ttvERMTg/Pnz8PR0RHff/89evbsqfY8olGjRrGz7Bw9ehSamprw8PDAmTNn2C/YiuZ/tmjRAmfPnsW3336LCxcuICoqCh988AFCQkKQmppaoWBXT08PK1euxOXLl3Ht2jWcOHECOjo6sLOzw3fffYcpU6Zw/kL/+uuvkZWVhYiICGzevBkSiQSenp4qCXY1NDRw5MgRrFq1CocOHUJ6ejqaN2+Or7/+GlOnTlWoP3/+fOjo6GDPnj349ddf2Z/5ly5dyvb4lGRkZISQkBCsWbMGhw8fZn9m69q1a5kTSwwYMACnT5/Gxo0bcfHiRRw7dgympqYYN24cFixYUKEJM6qistemKtsPDQ3FihUrcOXKFTaY9PPzw+zZs8v8rAwePBiTJ0/Ghg0bcPLkSWhoaGDYsGFYvnw5Z8hB4F0u5vHjx/Hbb7/hzz//xPHjx1FQUABTU1PY2tpi2bJlnAD222+/xfnz53H37l2cO3cOQqEQ1tbW8Pf3x/Tp06v0hLy8ZcuWwdnZGTt27MCff/6JwsJC2NnZYf78+fjqq6/KnYSgpkydOhXa2toIDg5GSEgIdHR04OHhgZ9++gl//fWXSoJd4F1PaMuWLbFnzx788ssv7E/3S5cuVfr0vbe3N/bv34/vv/8ehw8fhkAggIuLC44fP45Hjx6pJNgF3g+bFxQUhJCQEBgYGKBPnz5YuXIlBg8eDEDx/rhy5Up8+OGH2LlzJztcpp2dHfz8/PDVV1+VO/GHgYEBVqxYgcuXLyMuLg7h4eHQ09ODnZ0dAgICMHny5DJ7hiuqWbNmOHHiBFasWIGoqCicO3cOrVrywqorAAAgAElEQVS1wg8//ABPT0+lnzfZpBR//fUXtm3bhqKiIjRv3lzlwa6enh4iIiKwevVqhIWFYfv27ew57Ny5M8LDwznn3cXFBfPmzcPVq1dx7tw5ZGRkoGnTpmjdujXWrl2rMNtaY8ATi8WKcxUSUkHt27fHixcvKvUTf31UXFyMDh064PXr13jx4gV0dXXV3SRCFKxZswaBgYHYtm0bJ62EkJokFovRqlUrmJmZVerZBlJ9shkN/fz8sGTJEnU3p86inF1CSsjMzEReXh6njGEYrF+/HklJSejXrx8FuoSQRik1NVUhX7uoqAiLFy/G27dvlT7cRVRD2QOAz58/Z1MV6dyXjdIYCCnh5s2bmDhxIry8vGBra4vc3Fxcu3YNd+/eRZMmTbB69Wp1N5EQQtQiNDQUa9asQa9evdCsWTOkpaUhKioKT548gYODg8KQXUR1Jk6cCIlEgg4dOsDIyAiJiYk4deoU8vPzMXv27CqNt96YULBLSAkODg4YNGgQ4uLicO7cObx9+xbm5uaYPHky5s2bV2buKCGENGQuLi7w9PRETEwMUlNTIZVKYWNjgy+++AJz585tdBMV1KaxY8fi0KFDCA8Ph1gsho6ODj788ENMnjwZY8aMUXfz6jzK2SWEEEIIIQ0W5ewSQgghhJAGi4JdQgghhBDSYFGwSwghhBBCGiwKdgkhddKXX34JOzu7Bj+GM1G9I0eOQCQS4cKFC+puCiGkDqBglxBS59y5cwf79u3DnDlzODNyvXr1Cjt27MDo0aPRoUMHmJubw87ODoMGDcL+/fs588bLi4uLw+jRo2Fvbw9LS0t4eHhg+/btKC4uVqgrFouxZcsWTJ06FW5ubjAxMYFIJMLZs2dL3X5ISAhEIlGp/37++efqnZQaxDAMzp07B39/f/To0QPNmzeHmZkZPvzwQ8ydOxcJCQmlrpufn481a9agc+fOMDc3R8uWLTF58mQ8fPhQaf3IyEgsWbKEncpYJBKhb9++FWrnxYsXMWHCBHYCgzZt2mDEiBE4ffo0p56vry+cnZ2xePFipdeXENK40NBjhJA6Z9WqVdDV1cW0adM45Tt27MCmTZtgbW2Nbt26wcrKCq9fv8bx48cxa9YsnD59Gnv27OFMUQ0A4eHhmDhxIrS1tTF8+HAYGxvj5MmTWLx4MWJjY/Hrr79y6j9//hzLli0D8G4aURMTE6SkpFSo7Z6enujWrZtCeceOHStzCmpVYWEhRo4cCQ0NDXTt2hWenp7g8/mIjY3Fnj178Oeff+LIkSNwdXVVWG/EiBGIjo5Gx44dMWPGDLx8+RJHjx7F6dOnERoais6dO3PW2blzJyIiIqCtrQ0HB4cK99wvW7YMW7ZsgampKfr37w8LCwukpqbi9u3buHLlCvr378/W5fF4mDt3LqZMmYI///xTJdNlE0LqLxp6jBBSpzx9+hQuLi4YP348tm3bxlkWGhoKkUiE7t27cwLa169fo2/fvnj58iX27t3LmU0oKysLnTp1QlZWFk6dOsUGnQUFBRg6dCji4uKwe/dujBw5kl1HLBbj9u3bcHZ2hrGxMWbOnInff/8dhw4dKrUXMiQkBLNnz4a/vz8WLVqkylNS44qKirBp0yZ8+umnaNKkCWfZunXrEBAQACcnJ0RFRXGWbdy4EStXrsSwYcOwZ88e8PnvfiwMDw/HhAkT0KZNG0RFRbHlwLsedgMDA7Rq1QpJSUno0KEDOnfuXGav+a+//oqvvvoK/fr1wy+//AI9PT2F9mtoaHDKCgsL0bp1a7Rs2bLMbRNCGj5KYyCE1Cl79+4FwzCc4FNm6NCh6NGjh0LPraWlJT755BMAwOXLlznLjh07htTUVIwcOZLTu6qtrc3OJb97927OOiKRCD179lTLIPlz586FSCTCsWPHlC7/999/IRKJMHDgQLYsOTkZS5YsQefOnWFlZQUbGxt06tQJn332Ge7evVvuPjU0NODn56cQ6ALAvHnzoKOjg/j4eKSnp7PlDMOwqRkrVqzgBLQ+Pj5wd3fHgwcPcOXKFc72unTpgrZt20IgEJTbLuBd0Lpq1SoYGhpi586dCoGurP3ytLS04OPjg7///hv379+v0L4IIQ0TBbuEkDolMjISfD5f4Sfz8mhqagIAhEJudpYs+FXWI+vp6QldXV3ExcWhsLCwii3mevbsGXbs2IGNGzciJCQEiYmJlVp//PjxAID9+/crXf77778DAMaNGwcAyMvLQ//+/bFt2zY0a9YMn3zyCSZNmoQOHTrgwoULuHnzZjWOBuDz+ew5LRnQPnv2DElJSXB0dIS9vb3Cev369QMAXLp0qVr7j4yMRGpqKnx8fKCnp4cTJ05g06ZNCAoKwrVr18pc183NDQBw/vz5arWBEFK/Uc4uIaTOyM3Nxd27d+Ho6AgDA4MKryeRSNggUD6offToEQCgRYsWCusJhULY2dnh/v37SEhIQOvWravR+ncOHjyIgwcPsq/5fD7Gjh2LDRs2QFdXt9z1XV1d0bp1a5w7dw4pKSkwMzNjl0mlUhw8eBC6uroYPnw4AODChQtITEzE9OnTERgYyNlWcXExsrOzq3U8R44cQXZ2NlxdXTkPC8rOq4ODg9L1ZOf7yZMn1dr/jRs3AADGxsbo1q2bwoNv3bp1wy+//IKmTZsqrCvryY+KisLs2bOr1Q5CSP1FPbuEkDrj9evXkEqlnACvIr755hvEx8fD29sbffr04SzLysoCABgaGipdV1aemZlZhRa/Z2dnh3Xr1uHvv//Gq1ev8ODBA/zyyy+ws7PD/v37MXPmzApva9y4cZBIJJygGXgX2L58+RKDBw9m/xiQ9bYqC6QFAgEnQK2sp0+fYsGCBRAKhVi9ejVnmey8GhkZKV1XVec1NTUVAPDTTz8BAI4fP46kpCRERUWhT58+uHLlCiZOnKh0XXNzcwDAy5cvq9UGQkj9RsEuIaTOkOWEViZA27BhA4KDg9G2bVs2IKoM2XBl8nnAldWtWzdMmzYNLVu2hK6uLiwsLODr64uwsDAYGRnh2LFjuH37doW2NWbMGAgEAoVUBlnv9YQJE9gyT09PWFtbY9OmTfD19cX27dtx/fp1SCSSah1PUlISRowYgbS0NKxfvx5dunSp1PqqOq+yocN4PB727duHbt26QV9fH05OTti3bx+aNWuGqKgoxMXFKawry7lOS0urVhsIIfUbBbuEkDpDW1sbACqcPxsYGIjvvvsOTk5O7EgN8mQ9jLKeSHmyn/lL6/mtLmtra3h7ewMAYmJiKrSOpaUlevfujfj4eNy6dQvAu/YfP34c1tbW6N69O1vXwMAAZ86cwaRJkxAfH4/FixejT58+aNGiBZYsWYK8vLxKtzkxMRE+Pj5ITEzE+vXrMWXKFIU65fXcquq8yq5pq1at4OjoyFmmo6OD3r17AwCuX7+usG5+fj5bjxDSeFGwSwipM2TpCyWf+i/NihUrsGbNGrRv3x5hYWEwNTVVWk8WID1+/FhhmUQiQWJiIoRCodKHrFRFlk9amcBT/kG1o0ePIj8/H2PHjuU8KAa8C443bdqEhw8fIjY2Fhs3boSNjQ22bduGBQsWVKqtT548gY+PD54/f45NmzZh6tSpSuvJzmtpObmycmW50pXRsmVLAKWnS8iC4YKCAoVlsveRsnxeQkjjQcEuIaTOsLCwgKmpKR4/flzmbGgLFy7EDz/8ABcXF4SFhcHExKTUurJeUGVjrV69ehV5eXno0qULtLS0qn8ApZD1OlYmoB40aBBEIhH++usvFBUVsSkMsiBYGR6Ph9atW+OTTz7BiRMnoKWlhePHj1d4nw8ePICPjw9ev36NoKAgTJo0qdS6zZs3h7W1NR4/fqx0hrUzZ84AAHr06FHh/SvTs2dP8Hg8PH78WGlqhmxYMTs7O4Vlsofo2rdvX602EELqNwp2CSF1Srdu3SAWi9lApSSGYTB37lwEBwfD3d0dR48eLTe/d9iwYTAxMcHhw4c5w3AVFBSwD119+umn1W63/IQLsvZu3rwZsbGxMDExUXh4rixaWlr46KOPkJaWhuDgYERHR8Pd3V1h9IP4+HilwWZ6ejqKiorY1JDy3Lt3D4MHD0ZaWhp2795d7qxjPB6PHdt4+fLlkEql7LLw8HBER0ejTZs2SmeTqwxbW1sMGDAA//33H7Zs2cJZdv78eZw7dw5GRkZKz61saLKSaR+EkMaHZlAjhNQpR48exeTJkxEQEIBZs2Zxlq1duxZr166FpqYmZsyYoTQX09bWlvMAF8CdLnjEiBEwNjbGiRMn8OjRIwwbNgy//PKLwoNU33zzDftgU0xMDJ49e4ZevXrB0tISAODu7s4ZBUAkEqFly5bo1KkTLC0tkZWVhdjYWMTHx0NXVxf79u1j80sr6saNG+jduzc0NDRQVFSELVu2KIw8sH37dixZsgSurq5o1aoVzMzMkJycjIiICGRkZGDt2rWYMWNGmfsRi8Xo2LEjMjIy4O7uXmpv7Pjx4zk9qIWFhRg2bBhiYmLQsWNH9OzZE0lJSTh69Cg0NTWVThccHR2NvXv3Ang31FxoaChMTEw40/1+9913nN76ly9fwtvbG0lJSejevTs6dOiA58+fIzw8HDweD7t378awYcMU2tu3b1/8+++/iI+Ph76+fpnngBDScFGwSwipUyQSCdq3bw8TExOF2bdk0/aWxdPTE+Hh4QrlcXFxWL9+PTuBhIODAyZMmIAZM2Yonc2rffv2ePHiRan7GTduHIKCgtjXS5cuxfXr1/H06VNkZGSAz+fD2toavXr1wuzZs6ucE+zu7o779+9DV1cXDx8+VBh/+OHDh/j1118RHR2NFy9eICsrC2ZmZvjggw8wY8YMeHl5lbuPxMREdOjQodx6YWFhCr2k+fn5+OGHH/DXX3/hxYsXMDAwQLdu3bBo0SK0adNGYRuyaZXLcvv2bYW0hNTUVAQGBuLEiRNITk6GgYEB3N3dMW/ePLi4uChs49GjR3B1dcXUqVOxfv36co+NENJwUbBLCKlztm7diqVLl+L06dOVHvKKEABYtGgRdu3ahdjY2FInviCENA4U7BJC6py3b9/Czc0NNjY2CA0NVXdzSD3z6tUrdOrUCZ988gkCAgLU3RxCiJrRA2qEkDpHU1OTfQhNLBaruzmknnn+/DnmzJlT6WHXCCENE/XsEkIIIYSQBot6dgkhhBBCSINFwS4hhBBCCGmwKNhtgJQNxt9Y0LE3Po31uAE69saosR43QMdOqo6CXUIIIYQQ0mBRsEsIIYQQQhosCnYJIYQQQkiDRcEuIYQQQghpsITqboA6SSQS5ObmqrsZKqetrY3MzMxa36+enh6Ewkb9liKEEEJIHdNoIxOJRILs7GyIRCLweDx1N0eltLS0oK2tXav7ZBgGYrEYBgYGFPASQgghpM5otGkMubm5DTLQVRcejweRSNQge8oJIYQQUn812mAXAAW6KkbnkxBCCCF1TaMOdgkhhBBCSMNGwS4hhBBCCGmwKNglhBBCCCENFgW7hBBCCCGkwaJgt57ZuHEjvLy8YGNjgxYtWmDMmDGIj4/n1GEYBmvWrEGbNm1gYWEBHx8f3L9/n1Nnw4YN8Pb2hpWVFUQikdJ9iUQihX8///xzjR0bIYQQQoiqUbBbz1y5cgWffvopTp06hdDQUAiFQvj6+iIjI4Ot8+OPP2Lbtm0IDAzE+fPnYWpqiuHDhyM7O5utU1hYiMGDB2PmzJll7m/Lli14+PAh+2/cuHE1dmyEEEIIIapGo/+XYBQdWqv7y3QfWul1Dh8+zHn9008/wdbWFjExMRg4cCAYhsHOnTsxZ84cDBs2DAAQFBQER0dHHDp0CFOmTAEALFmyBABw7NixMvdnZGQEc3PzSreTEEIIIaQuoJ7dei4nJwdSqZRNRUhMTERKSgp69+7N1tHR0YGHhwdiY2Mrvf2FCxfCwcEBXl5e+PnnnyGVSlXWdkIIIYSQmkY9u/XcwoUL0b59e3Tp0gUAkJycDAAwNTXl1DM1NcXr168rte3Fixeje/fu0NPTw8WLF/HNN98gLS0Nfn5+qmk8IYQQUob8IgZ3XxbhdWYxJBJzZD1/i/ZWGtAU0iRGpOIo2K3HFi9ejJiYGJw8eRICgYCzTH42M4ZhKj3D2YIFC9j/d3Z2hlQqxffff0/BLiGEkBqXUyjF2fuFyCti/r9EiIfJErzKLEbfNtrQ0aCAl1QMBbslVCWHVl0WLVqEw4cPIywsDPb29my5LL82JSUF1tbWbHlqaqpCb29lubi4ICsrCykpKTAzM6vWtgghhJDSFEsZXH5cMtB9L7uAwdUnhejTWoumqScVQjm79ZC/vz8OHTqE0NBQtGrVirPMzs4OZmZmiIyMZMsKCgoQHR0NNze3au337t270NbWhpGRUbW2QwghhJTl3xQJMvIUA12ZlGwpnqUV12KLSH1GPbv1zPz583HgwAHs27cPIpGIzdHV09ODvr4+eDwepk6dik2bNsHR0REtW7bEhg0boKenh48++ojdzosXL5CRkYHnz58DAO7cuQMAcHBwgL6+Pk6cOIGUlBS4urpCR0cHly9fxpo1azBp0iRoaWnV/oETQghpFCTFDOJfF3HKLAz5yMnNR07x+++fe6+K0NxEQL27pFwU7NYzu3btAgB2WDEZf39/LFq0CADw+eefQyKRwM/PD2KxGC4uLjh8+DAMDAzY+gEBAfj999/Z1z169AAAhIWFoXv37tDQ0MCuXbuwZMkSSKVS2NvbY9GiRZg6dWpNHyIhhJBG7FmaBIWS9681BICHgxYeP3mFezkWkP5/h29OIYNXmVI0EwmUb4iQ/0fBbj0jFovLrcPj8bBo0SI2+FUmKCgIQUFBpS7v27cv+vbtW6U2EkIIIVX1NJWbnuBoJoS2Bg9agmLYNhEgoUT6wsPkIgp2SbkoZ5cQQgghdUJmvhRpudzx3B1N3/fLtTHX4Cx7kyVF3lsa/52UjYJdQgghhNQJz9IknNdmBnzoab0PVZro8WGixw1dXmTQg2qkbBTsEkIIIaROSJILXB2aKmZb2hhz0xYo2CXloWCXEEIIIWqXUyhFVsH74cZ4AKyV5OPKB7v/ZUtRoGQ8XkJkKNglhBBCiNq9zuT20Joa8JVOC2ygzYdI5305o2RdQkqiYJcQQgghavdSzA1YrYxKH2VBfgSG5GwKdknpKNglhBBCiFpJpQxSsrmjKpQV7FoYcpe9yZKCYSiVgShHwS4hhBBC1Co9TwpJiVhXWwMw0il9ZrSm+nzwSyzOe8sgp5CCXaIcBbuEEEIIUav/cri9umb6ZU8DLODzYKrPDWGSs2i8XaIcBbuEEEIIUav/5FIYTA3KD0/kUxlScihvlyhHwW49s3HjRnh5ecHGxgYtWrTAmDFjEB8fz6nDMAzWrFmDNm3awMLCAj4+Prh//z6nzoYNG+Dt7Q0rKyuIRKJS93fgwAF069YN5ubmcHBwwPTp02vkuAghhDRODMPgP7lA1cyg/CmAm8r17KblUM8uUY6C3XrmypUr+PTTT3Hq1CmEhoZCKBTC19cXGRkZbJ0ff/wR27ZtQ2BgIM6fPw9TU1MMHz4c2dnZbJ3CwkIMHjwYM2fOLHVfwcHBWLZsGb744gtER0cjLCwMgwYNqtHjI4QQ0rhkFTAoLDFxmoag7HxdGRM9PkrWyi5kUCihvF2iSHFqkkZs/7W8Wt3feFfdSq9z+PBhzuuffvoJtra2iImJwcCBA8EwDHbu3Ik5c+Zg2LBhAICgoCA4Ojri0KFDmDJlCgBgyZIlAIBjx44p3Y9YLMbKlSsREhICLy8vtvyDDz6odJsJIYSQ0qTncntkm+rxwS8jX1dGKOBBpMtDRt77ADc1R6owLBkh1LNbz+Xk5EAqlbKpCImJiUhJSUHv3r3ZOjo6OvDw8EBsbGyFtxsZGYni4mKkpKTAzc0Nbdu2xYQJE5CQkKDqQyCEENKIpedxg90mehUPTZrqcQPbtFzK2yWKKNit5xYuXIj27dujS5cuAIDk5GQAgKmpKaeeqakpUlJSKrzdhIQESKVSbNiwAatXr8a+ffsgkUgwePBg5OXVbg84IYSQhku+Z7cywa6JXN5uKuXtEiUo2K3HFi9ejJiYGPz2228QCLh/3coP2cIwTJnDuMiTSqUoKipCYGAg+vbtCxcXF+zYsQOpqak4efKkStpPCCGkcZMyDDLke3Z1K9GzKxfsZuTR5BJEEeXsllCVHFp1WbRoEQ4fPoywsDDY29uz5ebm5gCAlJQUWFtbs+WpqakKvb1lkW2ndevWbJmRkREsLCyQlJRUzdYTQgghQHYBw5lMQksI6GpWvGPGQIsHIR/sNgolQH4RU6ltkIaPenbrIX9/fxw6dAihoaFo1aoVZ5mdnR3MzMwQGRnJlhUUFCA6Ohpubm4V3kfXrl0BAI8fP2bLcnJykJycDBsbm2oeASGEEKKYr2usy6/Ur5A8Hg8iXfneXerZJVzUs1vPzJ8/HwcOHMC+ffsgEonYHF09PT3o6+uDx+Nh6tSp2LRpExwdHdGyZUts2LABenp6+Oijj9jtvHjxAhkZGXj+/DkA4M6dOwAABwcH6Ovro2XLlhg0aBAWLlyIH374ASKRCGvWrEHTpk3h7e1d+wdOCCGkwalOvq6MsQ6fk6ubkUcjMhAuCnbrmV27dgEAO6yYjL+/PxYtWgQA+PzzzyGRSODn5wexWAwXFxccPnwYBgYGbP2AgAD8/vvv7OsePXoAAMLCwtC9e3cA74Y1W7x4McaOHQuGYdC1a1eEhoZCV7f+pHsQQgipu6qTrytjrNCzSw+pES4KdusZsVhcbh0ej4dFixaxwa8yQUFBCAoKKnM7BgYG2Lp1K7Zu3VrpdhJCCCFlYRhGJT27Il1u2gMFu0Qe5ewSQgghpNblFHIfTtMUAHpVeLBMpMOdSS2nkEFRMeXtkvco2CWEEEJIrcvM5/bAiir5cJqMUMCDgTZ3PTH17pISKNglhBBCSK3LzOf2vhrpVD0kobxdUhYKdgkhhBBS68RyPbtGOlUfG5eCXVIWCnYJIYQQUusU0hiq0bMrP9aufK8xadwadbBLUwqqFp1PQgghFSFlGGQVqC6NwUguZzezgKYNJu812mBXT08PYrGYPgwqwjAMxGIx9PT01N0UQgghdVxOAQNpia9fbSGgJax6GoOu5rtpg2WKit9NG0wI0IjH2RUKhTAwMEBWVpa6m6JyWVlZMDQ0rPX9GhgYQChstG8pQgghFaSYr1u9vjcejwdDHT5n3N6sAga6mtXaLGkgGnVkIhQKYWRkpO5mqFxKSgpsbGzU3QxCCCFEKVWOxMBuQ5uH9NyS+5DCwpCmDSaNOI2BEEIIIeoh/3BadUZieL8NbkiTRQ+pkf9HwS4hhBBCapUqR2KQMVTykBohAAW7hBBCCKlFUimDrELVpzEY6sgPP0bBLnmHgl1CCCGE1JqctwxKDoSkrQFoVmMkBhl9LR74JTZTKAEKaEQGAgp2CSGEEFKLsuXG1zXUVk0owufxFFIZsiiVgYCCXUIIIYTUomy5ANRAq/q9ujLyqQz0kBoBKNglhBBCSC2SnznNQEU9uwBgJLctekiNABTsEkIIIaQWyffsyqceVIf8tuT3RRonCnYJIYQQUmvkc3ZV2bMrvy35fZHGiYJdQgghhNQKSTGDvBIjJPDwbhQFVTGQ69nNectAKqWAt7GjYJcQQgghtSJbbnxdPS0eBHzVBbsaAh60he9fM8y7gJc0bhTsEkIIIaRWKIzEoMJ83ffbpFQGwkXBLiGEEEJqhcJIDFqqD0PkA2h6SI1QsEsIIYSQWlGTIzHIKPTsFlLPbmNHwS4hhBBCakVNjrHLblOLenYJFwW7hBBCCKkV1LNL1IGCXUIIIYTUuEIJg7fF71/zeYCuZg0Eu3I9u3mFDIpp+LFGTe3B7q5du+Ds7Axzc3P07NkTUVFRZdb/888/0a1bN1haWqJVq1aYNm0akpOTa6m1hBBCCKmKLCUjMfB4qg92hQIedDXeb5cBkEO9u42aWoPdw4cPY+HChfj6669x6dIldOnSBaNGjcKLFy+U1o+JicH06dMxbtw4REdHIyQkBA8ePMDUqVNrueWEEEIIqQz5IcAMayBfV4ZGZCAlqTXY3bZtG8aPH49JkyahdevWWL9+PczNzfHzzz8rrX/t2jVYWVlh9uzZsLe3h6urK6ZNm4br16/XcssJIYQQUhkKY+yqcOY0eZS3S0riicVitbwD3r59C0tLS+zevRu+vr5s+fz58xEfH4+IiAiFdeLi4uDj44O9e/diwIABSE9Px9SpU2FoaIhffvml1H09evSoJg6BEEIIIRX0JNcY6UW67Gt7nQyYauXVyL5eF+gjqcCIfW2qmQt7XXGN7IvUDEdHR5VtS1h+lZqRlpaG4uJimJqacspNTU2RkpKidJ0uXbpg165dmDZtGvLz8yGRSODl5YWgoKAy96XKE1YfPHr0qNEdswwde+M79sZ63AAde2M89vp83E/jC4Ci9727LWwtYG4oqPD6lTl2nQwJkh6/ZV/ztQzg6Ghaxhp1W32+7nWB2h9Qk09OZxim1IT1Bw8eYOHChfDz88OFCxfw119/ITk5GXPmzKmNphJCCCGkinIKuWkM+pTGQGqJ2np2TUxMIBAIFHpxU1NTFXp7ZTZu3IhOnTrhyy+/BAC0a9cOurq6GDhwIJYuXQpra+sabzchhBBCKqeomEGh5P1rHg/QqYFhx2T0tXjg4d1IDACQ95aBRMpAyK+5fZK6S209u5qamvjwww8RGRnJKY+MjISbm5vSdfLz8yEQcH/ykL1mGPqrjRBCCKmL5If+0tPkgV8Dw47JCPg8hTF8c6l3t9FSaxrD7NmzsX//fuzduxcPHz6Ev78/3rx5gylTpgAApk+fjunTp7P1BwwYgIiICOzevRsJCQmIiYmBv78/OnToABsbG3UdBryQMrcAACAASURBVCGEEELKUJspDKXtg8babbzUlsYAACNGjEB6ejrWr1+P5ORktG3bFgcPHoStrS0AICkpiVN/woQJyMnJwc6dO/HNN9/A0NAQ3bt3x4oVK9TRfEIIIYRUgHygqa9V831t+lo8JGeXbIMUQMUfiCMNh1qDXQD47LPP8NlnnyldFh4erlAm39tLCCGEkLpNMditjZ5dPoD38xPLT2pRF/HzsqH18l8IM/8DT1IEqa4h3praApSqWS1qD3YJIYQQ0rCpJY1BWz6NoW7PoqaR8hw6T2+DVyKwFeRmQif3LtoIdVAssQeEmuprYD2m9qHHCCGEENKwyT8cVltpDGW1oS7RSE6E7pNbnEC3JH1JPvTuxwDFEqXLSdko2CWEEEJIjWEYRo1pDO/lFDJ1cuQmQXY6dJ7dKbeeMEcM7ef3a6FFDQ8Fu4QQQgipMflFDKQlYkxNAaAprPlgV1MAaJR4Hq2YedeWOqW4GDqPb3J6dBkeH3ktOyK7Yx9IDJpwqmu9eQZBVlptt7Leo2CXEEIIITVGHSMxAO9maK3rw49pvXoMQUEupyzPsROKTG0g1dZDbhs3FGvpcpZrJ/xDD6xVEgW7hBBCCKkx6khheL8vxVSGuoJXVAitV485ZYVmdpCYWL0vEGogv2VHTh1hrhjCjDe10cQGg4JdQgghhNQYdYzEUNq+6tKIDFovH4EnfT80mlSoiQI7J4V6xYYmyNA04K776kmNt68hoWCXEEIIITUmR258W33t2gs96moaA6+oEJpvEjhlhc1aAkINpfVf6TblvBZmp0OQnVFTzWtwKNglhBBCSI1RZxqDQR1NY9BMTgCPed/LLNXUxlvz5qXWzxdqo8jIlFOmkfK8xtrX0FCwSwghhJAaQ2kMcqRSxV5dyxaAoOypjN9acINhzbSXQIk0CFI6CnYJIYQQUiMkxQwKSsyDwAOgq1l7wa6uJg8l91ZQ9K5N6qSR9gr8okL2NcMX4K2ZbbnrSURmkJaYQY1XLIFGOj2oVhEU7BJCCCGkRsinDehp8cDn1V6wy+fzoFvH8nY1UxI5r9+a2pSaq8vB56OoqTWnSOO/F6psWoNFwS4hhBBCaoQ6UxhK26c6Uxl4BbkQyk0KIZ+eUJa3Zjac18LM/wBJkUra1pBRsEsIIYSQGqGuCSVKqksPqWnK9cRK9I0h1TUopbYiqa4hirX12Nc8hnkX8JIyUbBLCCGEkBqhzpEYStun2oJdhoHmf0mcoiK5ntpy8XiQGFtwiihvt3wU7BJCCCGkRlAaw3uCnAzwC/PY1wyPj7cmzSq9naIm3GBXmJEMMHVglIk6jIJdQgghhNSIupDGUFemDNZIe8V5XdTEomIPpskpNjDmjMrALy6CICu92u1ryCjYJYQQQojKMQxTZ9MYGKaWA16GUQx2Tayqti0eHxJjM04R5e2WjYJdQgghhKhcfhEDaYmYUlMAaAprP9jVFPKgWWK+Binzrm21SZCTAf7bAvY1wxdAIjIrY42ySYwo2K0MCnYJIYQQonJ1IYWhtH3XdiqDQq+usTkgEFZ5exKjppzXghwxDUFWBgp2CSGEEKJydSGFobR912qwq8oUBtkmNbVRrPN+yDIeAGFWarW22ZBRsEsIIYQQlasLIzGUtu/aHJFBkCtWaQqDjHzvrjCTgt3SULBLCCGEEJWrU2kM2nJpDAW117MrzEjmvJaIzKqVwsBuh4LdCqNglxBCCCEqV5fTGLJrMY1BmJHCeV1kbK6S7UoMm6LkUQjys8ErequSbTc0FOwSQgghROXqchpDbi2lMfDeFkCYK+aUqSKFAQAg1IBU15BTJMih8XaVoWCXEEIIISolKWZQUGJwAB4AXU31Bbu6mjzwSuy+QAIUFdd8765QzO3VleiJwGhqq2z7EoMmnNeC7AyVbbshoWCXEEIIISqV85YbSOpq8cDnqy/Y5fN40NOs/REZNOTzdY1V1Kv7/4rlgl0hzaSmFAW7hBBCCFGpupTCUFobanxEBqlUYbIHiUg1+brs9gzlenZzMgBp7Y00UV9QsEsIIYQQlZIf7aBuBLu1O7GEIDsdvGIJ+1qqoYlifZFK98Fo6kBaIi2Cx0ghyM1U6T4aAgp2CSGEEKJSdWnYsfdtqN00BoUUBpE5OInDqsDjKebt0kNqCtT/7iOEEEJIg1IX0xgMajmNQSjmBruqGnJMXrG+Mee1IEdcSs3Gi4JdQgghhKhUbh0aY5dtQy1OLMEryIUgP4d9zYAHiZFpjexLPjVCkENpDPIo2CWEEEKIyjAMUy/SGHLfMpAyNRPwashNJFFs2AQQatTIvor1jLiTSxTkAJKiUuv/H3t3HiRXed0P//vcpdeZnn2TRqMR2pDYZGSDjQMExXlJvMaUsWNClYuYilylOIlDQqDKdkg5v3JRSlXK5VIKx5iKSfCCK4DNS+wffo1ijFlsYwRGEmLQPtLse09vt+993j9GM9333p6ZXm5PL/P9VLmSe3t77miGPn36POesR5X/7SMiIqK6kTCA7Ba2ugr41MqtJ7MOAX/WlF5LAvFUeYJdVwmDx10YbFQNVrDRfoqb1GwY7BIREZFnctXrCq83ZhVpTToymGloM+O2U+ky1esuvaSrlIF1u9kY7BIREZFnqrGEYdFadGTQZschZCbgt/whWMEGz18nmxl2BLvzDHazVc9vIBEREdU8d7BbHVldwL2WuTJ0ZNCmnCUMnd63HHNgZndlDHaJiIjIM9XYdmxR2csYpHRtTit3CQMAmKEIZFZArSZjEEaq7K9bKxjsEhERkWdqqozB4/ZjSmwOSiq+dCwVFelIu6evkZOq5tikxuzuour5DSQiIqKaV0tlDF4PlnB2YUhH2gF1bVpRuEoZ2JFhCYNdIiIi8kTalIgbmWBXAAj7qifYDfkElKzlpEwglfYuu+saEdzS6dlzr8YMN9mOlfnZNXvtasdgl4iIiDzhzOqGfAKKUj3BrhCifB0Z0imoc1O2U+UaEZyLGYrYjtUYM7uLGOwSERGRJ1yb0wLVE+guKlcpgz49BpE1y8wMNkL6Q548dz6cwa4SjwKmuWavX80Y7BIREZEnnFnSxiranLaoXB0ZnC3H1qILg30BOqys4FoAUONza7uGKlV9v4VERERUk+aqeHPaorKUMUgJbdrecmwtSxgWubK73KQGgMEuEREReaSae+wuKsdgCTU6BSWd6WsrVQ1mQ0vJz1soM+ys2+UmNYDBLhEREXnE2be2mnrsLipHGUPOqWnK2l+7GbJ3ZFDZkQEAg10iIiLygCUl5lOOmt0a2KAWS0pYVmkBrz41bDte83rdS9yZ3RlAejwlrgYx2CUiIqKSxVMS2TGjXwN0tfqCXU0VCOiZYwm4gvRCiGQMaiyzEUwCSDdXJtiV/hCkkhliIcw0RNZEt/WKwS4RERGVzL05rXpDDGeXiFJKGZyDJMzGVkjdV/TzlUQId3aXpQwMdomIiKh01Twm2CnsYa9dbdJewlCJLgzZXHW73KTGYJeIiIhKF01UfyeGRZ61HzPT0GYnbKfSLd3FLssTVqjRdqyw1y6DXSIiIiqda6BEoHpDDK/KGLTpMQiZCfJNfwhWsKGktZXKDNqD3ex64vWqen8TiYiIqGY4+9XWVma3uDIGZ71uuqUbEJW9bndmN7ruOzIw2CUiIqKSSClrqma3IeDO7MpCA0IpoU07+utWuF4XAKTuh6VlNsgJaUFJzFdwRZXHYJeIiIhKkjIBw8wcqwII6tUb7AY0QM2KgAwTSKYLew41OgXFSC4dS1WDGWnzaIWlYd2uHYNdIiIiKkmuzWmiwl/nr0QIUXIpgz5x0XZcqalpubBu1646/lWIiIioZrl67Fbx5rRFzk1qc4kCyhikhD45ZDuVbu3xYlmecGV2GewSERERFa+W6nUXOUcZOzfYrUSdn4GSzEwmk0KpinrdRa7MLssYiIiIiIrnLAGojWC3+Myu5ihhSLd0Aqrmybq8wI4Mdgx2iYiIqCTuzG71hxcRR2Z3NpFnZjdHCYPRusGrZXlC6n5YOjsyLKr+30YiIiKqalFHVrSxRjO7+bQfU+ZnoGYFjtVWwrDICkZsx+u5IwODXSIiIipa2pSIGZkgUQAI10CwG9AALSsKSltAwlj9cb7xQdtxurkD0HSPV1c60zHJbT13ZGCwS0REREVzdmII+wVUpfqDXSFE4ZvUpAV9/ILtlNHe6/XSPMGODBkMdomIiKhoc45aV2cAWc0ijlKG2VU2qWnTY65BEkZLd1nWViozZC9jWM8dGRjsEhERUdGcAaKzf201c2V2V9mkpo/ZSxiMtg2Aqnq+Li9YjjKG9dyRoXZ+I4mIiKjqOAPESLB2MrsFtR9LG64uDKkqLWEALnVk0OwdGUQyVsEVVQ6DXSIiIiqaM7MbqaHMbsSff2bXNzYIITO3W74gzEhb2dbmBWd2V41HK7SSyqqd30giIiKqKlJKV3/aWqrZdWV2kxJWrq/6pYRv5LTtVKpzEyCq+1pzljKsQwx2iYiIqCjJNGCYmWNVACFfdQeA2XyagD9r8JklgVjKHeyqsxO2rKiEQKqrfw1WWBpn+zEGu0REREQFyNWJQVR5ttMpn44M/qFTtuN0azekL1DWdXnBVcaQWJ/BbvUMciYiIqKa4uyx6ywL8JSUUGfGoE8MQY3NQJhpSM0HM9wEo6V7oX62iEC7MSAwlhUDziUsoCnTYUGJzUGfGrY9JtndX+xVrCkrwMwuwGCXiIiIirRW9bpqdBq7Zs4gPPGW6zZtdgL+oVMwgw1I9u5caAdWQNC7EKBnajGcHRn8Fwdsx2a4CWakvbALqBArEIIUAuJSHbJiJIG0UZUT38qJZQxERERUFGdg6CwJ8II+eg7hN3+BcDqx4v3UeBShgVcROv5yQS22Gh0dGbIDeCUedU1MS2zcXvUb05YIxZXdXY8dGRjsEhERUVHKndn1XXwHoZNHljKT+dBnxtB45LArSF1OJLh8zW7g7DHba5uBBqRbe/JeSzWwgmHbsbIO63ZZxkBEREQFk1KWNbOrj19A8Owx13mjtRupjj5Y/iCUxDz0iSHoExchkFmLsEyEBl5FMjqNxOZdgFh+XY1+AQEsPTqWkjBMiUB0wl2ru2ln7WR1LzGDjdCRuY71WLfLYJeIiIgKFktJWFmxrk8F/Jo3gaASm0Xwndds5ywIJLZfC6N9Y+ZcuAnptg1IxncieOoNaLPjtsf4h05CnZ9BbMdeSN2f87VURaAxIGwZ3Zl5Ay2nXrfdL93QslAPXGOsgD2zq8bnKrSSymEZAxERERXM2aLLs04MloXQwG9t08qkEBiIbLIFuraHBBswv/t9iG+5CtKRxdVmx9Hwxs+hzk0t+5JNjlKG+OAFqIl527nE5t01l9UFACvYaDtW4vPL3LN+VTzYffjhh3H11Vejq6sLN998M1588cUV759KpfB//s//wdVXX43Ozk5ceeWVeOihh9ZotURERASUr17XP/g21Nis7Vz8smsw5wsv84hLhECqewvmr7gBlqMHrpJKIHz0BfiGTwM56n8jjrXPzcVtx8muzVU/Gng5rsESiSgglx+LXI8qGuw+8cQTuO+++3DPPffg+eefx3XXXYfbb78d58+fX/Yxn/3sZ/Gzn/0MX/va1/DrX/8a//Ef/4ErrrhiDVdNREREM3F70OjMjhZDJGLwX3zHdi7VthFGZ1/ez2E2tiJ61U1IN9qDUyElgqd/t9CtwZG1da59CpnA2gyEkdhcw3GGpsPKKuEQUkJJ5N+toh5UtGb30KFDuOOOO/CZz3wGAHDw4EH87Gc/wyOPPIJ//Md/dN3/ueeew89//nO89tpraGtb+CXevHnzmq6ZiIiIgJm4PTvY5EFmN3DumK18wdL9SGy5quDnkb4A5ne/D4Fzx1zTz/SZMWhHDsNo74XR1gMrEEarkQAQWrrPpFgIdqWiIr59L6DW9hYnK9iw0GP3EiURdU1Xq2cVy+ymUikcOXIE+/bts53ft28fXnnllZyPeeaZZ/Cud70Lhw4dwu7du3Httdfi3nvvRTS6/nYWEhERVYqU0h3slpjZVeem4Ju4aDuX6NsFqfuKe0JFQaL/SszveDekotpuEtKCb+wcwm+9gsYjz6Hn9Mu2FmNzCCIFFbFt18JsaC7u9auIq5RhnXVkqNhHlYmJCZimiY6ODtv5jo4OjI6O5nzMmTNn8PLLL8Pv9+PRRx/FzMwM7r33XgwPD+PRRx9d9rUGBgaWva1ercdrXsRrX3/W63UDvPb1qBqu27AUpMxMv1kFFobOncRwCcndbTP2EsZ5LYDj0wlgJnO9xV67v2kzNkeHETFyf32vwUIEccwsZneFwJvhfliTUWCy8j9voLR/9654HJuyjqPDF3F2Pv/exZWwfft2z56r4nl54djZKKV0nVtkWRaEEPjmN7+JpqYmAAulD7fddhtGR0fR2dmZ83Fe/sBqwcDAwLq75kW89vV37ev1ugFe+3q89mq57uFZE5jNfC3eFFKxY0fx61Ki02gcP247J3e8C9ubMgmxUq9dyisQGzuPwODbUHJMWGuR85gRmVIGZcNObG2veJgEoPRr16YiwFuZRGKLT4GvCn6P1krF/hXb2tqgqqorizs+Pu7K9i7q6upCT0/PUqALADt27AAADA4OLhvsEhERkXe8LmEIXLBnLdMNLTAj7SU9p4sQMDr7YHRsgjY1DG16DFp0CkgbgKYjIlQgayKx8xpr2XovY6hYza7P58OePXtw+PBh2/nDhw/j+uuvz/mY9773vRgeHrbV6J48eRIAsGnTppyPISIiIm85OzE0lxDsimQM2uSQ7Vyyd0f5etoKgXRrDxKXXY3o1Tcjeu0HEL36ZoR7um13m66jYFf6Q7b+w0o6BaRTFVzR2qpo67EDBw7gO9/5Dh599FGcOHEC//AP/4Dh4WHcddddAID9+/dj//79S/f/xCc+gdbWVhw4cADHjx/Hyy+/jPvuuw8f+9jHls0GExERkbe8zOz6Rs4iO6w1Q41IN6/9N7XNIUf7sVh117QWRIgck9TWz3CJihaj3HbbbZicnMTBgwcxMjKCXbt24fHHH0df30I/vcHBQdv9Gxoa8NRTT+Hee+/Fvn370NzcjA996EM525QRERGR93J2Yii27Zhlwjdy1nYq2b2lIpPKIgEBRWBpBHLckEgYEgG99qam5WIFwrZRwUpiHmZjSwVXtHYqXnl999134+6778552zPPPOM6t337djz55JPlXhYRERHlkEgDKTNzrCpA2F9cQKhPXFz4Sv0Sqeow2ntLXWJRVEWgKShsGd3puIVuXV3hUbXDCoaBrInJSmL91O1WfFwwERER1Y6ZmDOrqyzbRWk1vuEztuNU56aKDnBocZUy1E/drhlwblJbP2UMDHaJiIgob84AsClYXKCrxKML3RCypLr6i12WJ+o52HVOTGNml4iIiCiHSUcA6AwQ86WP2YdIpCNtFR9h6+wqUVfBrnODWmIekHW0CW8FDHaJiIgob84AsDVcRCghJXyOYDfVUfkWos7AfTYhYVprGxCm0hKjcybGoibSpnevLXW/bWyyMNMQRnKFR9SPim9QIyIiotqQNiXmEvYArJjMrjYzBiWVmeAgFRVG24aS11cqnyYQ9gnMpxauUcqFnsKt4fJ3ZIilLLxxwcCZCXOpI4SqADs6NQSlB68vBKxgA9T5maVTSmIepi9Q+nNXOWZ2iYiIKC/TcQvZoW6DX0BXCw/EnCUMRltPRTemZatE3e7YnIkfH03g1Hgm0AUA0wKOD6fxVrQdqXTpWV7TUcqwXiapMdglIiKivDgDv6LqdU0T+uSw7VQ1lDAscl7TxHx5g92JqInn3k4imV7+PjHTh1+8kyy5pMJydGRQE+ujIwODXSIiohpjSQlZgc1FzqlircWUMEyPQFiZRr2WHoAZaS95bV5pCzuDXXOZe5YulrLw84EkTEc8HfIJBB3DLEbmLLx50Sjp9aygI7O7TjoyVMd3BkRERLQiw5QYjEdw7PU45lMSugp0R1RcuUEvuiNCobzoxKBPXLQdG209FZmYtpy2Bvs1Tcck0qaEVkS5xkqklHjpVAoJR0b3ih4NV23UYVrA4beTGI9mfubHh9Pob9OKHs/szOyul167zOwSERFVuamYhR8fTWAo2bi0ecowgfNTC7Werw+myp7ptaR0DZRoKbQTg5mGPjViO1UNG9Oy+TWBxqzxxxLlKWV4ezSNkTn78+7u1nBNrw+KWKiFvmmb35bhtSTw2/Mp51Plzdl+TFkn7ccY7BIREVWxuYSF504kEE0uH5QcHUrj5dPlDXhn4xLZnbACOlxfta9Gmx61lzD4AjAbW71aomfaHUH8uMfBbsKQeOOCvSShq1HBNb267VxAF7i2z35uaMbCZJHrkboPluZbOhbSgkjFi3quWsJgl4iIqEqlTYn/HVh589Ki0xOmK4DykjPgK6Ze11XC0LqhqkoYFrU7Shkmot4Gu29cMGBklQJrCvDeLb6cY5f7WlQ0qPZ+uMeGiv93dg2XWAcdGRjsEhERVanfXTRcfW23dqi4/dogbt7uh9+x8+boUBqDU3lExkUYj9o3arU3qMvccxm5Shjaq6uEYZHz2sajpmdZ86mYhXfG7P9GV23UEfbnDsmEEOgJzNnOnZsyMZ8sLgB3b1Kr/7pdBrtERERVaDpm4a1he1DUpsdw3WYfdFVgY7OK/2dXAAFHwPvKmRQShvflDOOO7KYz+7manCUMDS2erM1rTUEBNevyEmks1UqX6sigvea2MSCwo3PlfgFNWhLNQXvW9/REcV0i1uMmNQa7REREVeh3Fw3bAIeQT2BzaNr2VXdjQMGN2/22SoBkGnj1XPGbmHJJpSVmHRlmZ4uu1ehT9t661VrCAACKEK7rG5srvZRhLGpiaMb+PNdu8kFVVv45CAFs7bAHxKfG00Vlm82gI9hdB+3HGOwSERFVmZm4hfNT9szd3k06VOEObjoaVFy1wb6J6eykidE57/rDOrsRNAULnJwmLWhTo7ZTRmu3F0srmw5H5nrEg5/n7xw11R0NCjY05ReK9bdpyI6Jo0mJsSJqiV0dGVizS0RERGvNuQGpJSTQ27J8jezuHg0tIXvw+ZuzKVge1Zk6A2dnt4LVqHNTUNKZbLOl6lXZhSFbV8T+8x6etUqq2x2dMzE8aw9Or96o59yUlotfE+httq/p3GThAbgr2E3GAav8I5EricEuERFRFUmmJc46gpgrelYOihQh8O4+n+3cdFzi5Jg3m9VGHV/hdzYWtjnNOR443dIJKNUdgrQ3KLZMaiwlV2z/thpnVrezUXEF1KvZ3Ga///mpIjbOqRosX2DpUEBCScYKe44aU92/aUREROvMmYk0rKz4JewX2LRCVndRR6OK/lb7/d68mEbaKi27mzalq4yhs7GA8EFKaFPOYLe6SxgAQFOEq5RheLa4UobROdM1QMJZepKPnohq2zgXN9z/NvlwD5eo71IGBrtERERVQkrpaku1rV3L+6vuPZt0VzA0MFJadnc8atmC7wa/WLZNVi5KIgo1q72VFAJGc2dJa1or3Y7Mq3NzWb5yDZAoNKsLAJoqsKHJ/rjBqcIDcHOddWRgsEtERFQlpuMSM/FMZCkAXNa+cluqbCGf4mpjdXTIgGEWn911bszqKiSrC0BzljBE2gGt8KxmJfQ4AsuhWbPgTPnIrOkqA7lqY/HX78zyX5wpom7X1WuXmV0iIiJaA+cm7VnYniYFQV9h7bl29+jQs+KhlAkcHy5+4pYzm1lwva6zhKHKuzBkawkJhLJGIptWYaUMUrrHAndHlIJ/htmcAfh0XCJeYA9gZ69dlZldIiIiKjcp3RvTNrfmn9Vd5NcELu+yZw7fGk4XNWginpKYjNmD3e6m/AM1YSShzk3ZzhktXQWvo1KEENjoyKReKKBsYGTWcrUHKyWrCyz8+zp7AA8VWEvMzC4RERGtuamYfbe/IrBiu7GVXN6t2UYJpy13O7N8OL8ibw0rCOr5Z5q1qRFk39sMRSD9oYLXUUnOdl/np0yYeZQy5Mrq9jQp6Ch0zHIOPY7evEMFljJY/rBtYImSSgBmecZMVwMGu0RERFXggiNg6WlSCxvckEVXBXb32DOIb4+mMZ8sbIPVhWn7mjYWkNUF3C3Hqn2QRC6djQp8jrIQ588llwvTJsYdnRKuLqIDQy7OUobh2QJbkCkKLMeHDiVRv6UMDHaJiIiqwEVHAOXMKBZqe6dmqze15MII4nwZpnR9Pb6xkDWZJrSZMftz1kDLMSdVEehvs5eTnBxfOQtqWhK/PW//WW9oUtDmQVYXWBjVrGVFcMk0XOOcV2O5xgYz2CUiIqIySeTol+r8qrpQmiJc9aGnx03MxPPL7g5OmTCz7hryCdeUthVff3YMwsoEy5YvACvclPfjq8nWDnuwOzxjYS6x/M/xxEjaVpIiAOzp9S17/0Ipwt0DeGyusKy9s9euWsdjgwv+S3r22Wdh1flYOSIiorXkzKC2hARCvtLzUVvaVUQCmQBVAnh9ML/s7ukJe/ayv1XNu98vsFCvm81o6QYKeHw1aQkptkBfYqGlWy7RpIU3HRn0bZ0amkPe5hedHR1GowXW7Tp77TKzm/GpT30Kl19+Oe6//34cOXKkHGsiIiJaV5wlDM6azGIpQuCaXnt2d3DaXLV91nzSwsisPbHVX0C/X0gJ3RHspmuoC0Muu7rdWfJZR3bXkhIvn04hnXXapwJXl9iBIZcOR79jZy/f1aynjgwFB7vf+973cOONN+LRRx/Fvn37cP311+Nf//VfMTg4WI71ERER1TVLSlfw6ZySVYreZtXVqurXZ1MrdhQ4MZK27dZvDgo0BwuYmhabXdjhf4lUVKSb2vN+fDXqa1XR6MiSv3QqBevSxjApJX57znAFndf06vBr3me028IKlKynjaVkQRsQTefI4Dru8bibewAAIABJREFUtVtwsHvrrbfiW9/6Fk6cOIGvf/3r6OnpwT//8z/jmmuuwUc+8hE89thjmJubK8daiYiI6s7UvIVkVsWArgLtDd595S2EwN4+e2ZxLiFxbCj3JqtU2j2yeEdXYZlJV1a3qQNQvAvgK0ERAlc5uilMzFv41ZkUYikLvzqbwtujjqEgEQXbOgrvlZwPVRGu35NCsrvSH4IUmccr6RSQTnm2vmpS9F9TQ0MD/uzP/gxPPfUUjh49igceeABTU1P4/Oc/j507d+Luu+/Gz372My/XSkREVHecAUp3RIXicW1re4PqCrqODhkYz1Hn+eZFw/Y1fEADtrQVFqhqjqlptTRIYiWbW1VXR4pT4yaeej2Bk2P2n2VQF7h+i6+gOudCOTepjc4VULcrBKyAvf1YvU5S8+Sjo2EYSKVSSKVSkFKisbERL730Ej7xiU/ghhtuwJtvvunFyxAREdWdEUeA0tlYnkZJe3p1BLLiXUsCL7yTsn31PTlv4cSIO6urKvkHbCKVgBadtp2r9XrdRUIIXN/vW3Wwhq4Cv7/D78kmw5U4N6k5+/quxr1JrT7rdov+V5iZmcG3v/1tfPCDH8SePXtw8OBB7N69G9/73vdw7NgxvPnmm/jud7+L+fl5fP7zn/dyzURERHXBktI1TrarsTxf9/s0gff029tfxQyJ/++tJMbmFlqS/eKdpK1WN+QT2NlV2Nfw2vSo7TgdboL0BYpddtUJ6AL7dvoR9uUOeBv8Ah+4PIAWj7sv5OKsxZ6JSxhm/v12XZvU6jSzW3AhyTPPPIPvf//7ePbZZ5FMJvHud78bBw8exG233Ybm5mbbff/oj/4Io6OjuOeeezxbMBERUb2YjkkYWYldvwY0Bcv3tfemFg2Xd1l4Kyt7O5+S+OlbyZz3f3efXvAUt3rrwpBLU1DBH18RwPFhA+emTMRTEmGfwKZWFbu6C/+ZFcunCUQCwjZQYnLeQlckvw9M5jrJ7BYc7N55553YuHEjDhw4gE9/+tPYtm3bive/4oorcPvttxe9QCIionrlLGHoaCisl20x9mzSEUtJnJtaub5zW4eG3pYCwwTLdGd2a3BqWj58msA1vT5c01vZdbSFFcwmMv+WEwUEu84pamqd9totONh98skncfPNN+f9x7h3717s3bu34IURERHVO+fmtK5I+b/6VoTADZf5oJ9LuTZVLdrSpuLdfYX3htVmJ+xT03Q/zBqdmlYrWsMKTk9kfuaTBdTtOqeoKfEoIGXNDv9YTsF/VT/4wQ/w6quvLnv7q6++igMHDpS0KCIiononpXTtnnduOCoXRRG4vt+PfTv96IooUAWgiIUs4fu3+vDeLT4oBWxKW+ScmpZu6aq7wKnaOOt2nWOnVyJ1P6SayXsKy4TI6o9cLwoOdr/zne/g9OnTy95+9uxZfPe73y1pUURERPVuYTNR5tinLgxvWEvdERV/sDOAT+4N4lN7g7h1dwCbW7XiSilyTE2rl5Zj1awlZB8uMZ+SSBh5blITIkfdbv2VMnj+fcnk5CT8fr/XT0tERFRXnG2i2huUstfrLkcIUfJrK/EolGRs6VgKZWGYBJWVqrin2xWS3XV2ZFDrcJNaXjW7v/zlL/HCCy8sHT/99NM4deqU637T09N44okncOWVV3q3QiIiojrkHOjQ1lDbE8acgyTSTe2AWp7pYWTX1qBgMpYJcCfmTdfwi+W4eu3G12mw+4tf/AIPPvgggIVPf08//TSefvrpnPfdvn07vvrVr3q3QiIiojo04eiv2xEu/+a0cloPLceqVVtYwUDWcUGb1ILOYLf+yhjyCnY///nP48///M8hpcTll1+Of/mXf8FHPvIR232EEAiFQgiHw8s8CxEREQFAKi0xk7DXVbY11G6wK4wU1LlJ2znW664d5wCLqVj+gyVMZ0eG9VrGEA6Hl4LY119/He3t7QiFQqs8ioiIiHJx1lQ2BcWaDSIoB216BNmrN0ONkH7GCWslEhBQxMIIaACIGwub1AKrjDUGcmR2EzHAsgCldj98ORV8JX19fQx0iYiISjAedW9Oq2X6pL1e16jTQRLVSlWEa/LedCzPUgZVg6VnxjkLSNtGw3qwamb3wx/+MBRFwRNPPAFN01zlC7kIIfCjH/3IkwUSERHVm/F5++a09nANb04zc0xNa2Wwu9ZaQgqmYpnfq6mYhe6mfCephaEYmf66SjzqyvjWslU/SkopYVmZTweWZUFKueL/su9PREREGVJK1+a0Ws7sajOj9qlpvgDMcHMFV7Q+uet284/FTFcpQ33V7a6a2X3mmWdWPCYiIqL8RZMSqazErq4u1FzWKlcJQ2sPp6ZVgCvYjRcyNtge7Kp11pGhdj9KEhER1aBJR8atJVS5YRIlk5arv67BEoaKcA6WmI1LpK38ujI4B0vUW2a34GD3+PHjrnrc559/Hrfddhv27duHQ4cOebY4IiKieuPsgdpWw/111dkJKGlj6djSdJiRtgquaP3yaQIN/syHJglgJs/sbr0Plih4tMkDDzwAAPjoRz8KABgcHMQdd9wBv9+Pjo4OfOlLX0JLSwvuuOMOTxdKRERUD5zBrvPr51qiTwzZjtMt3YCo3eupdS0hBdFk9iY1ibY8xh9Y/hCkEBByIROsGEkgbQCaXq6lrqmCfyPfeOMN3HDDDUvHjz/+OCzLwi9+8Qu8/PLLuPXWW/Hwww97ukgiIqJ6IKV0lTG01mpmV0roLGGoKs2OD055tx9TFFiOvshKon7qdgv+C5ucnERbW+Yrip/+9Ke48cYbsWHDBgDArbfeinfeece7FRIREdWJaFLCcGxOa/TXZr2uGp2Gksq0q5KKinRTZwVXRM2OXrv5ljEA7uESah3V7RYc7HZ0dODcuXMAgOnpafzmN7/BLbfcsnR7Mpn0bnVERER1xFnC0FrDm9P0iQu243RzJ6DWcL/gOuDcpDZdQkeGeqrbLbhm95ZbbsG///u/IxKJ4IUXXgAAfPCDH1y6/a233sLGjRu9WyEREVGdqKsShomLtlNG24YKLYYWhf0CqgKYl37NkmnkPTbYdHZkqKP2YwUHu1/+8pfxzjvv4Etf+hJ0XccDDzyAvr4+AEAikcBTTz2FT37yk54vlIiIqNblyuzWInV2wlXCYLR0VXBFBACKEIgEBKZimZZj03EL3frqGXdXZreOyhgKDnY7Ojrw4x//GLOzswgEAvD5fEu3SSnxox/9CL29vZ4ukoiIqNbV0+Y0ZwmD0doNqAWHFFQGzUH72OCZuIXuSB7BrrNmNx4FpKyLASFF/2ZGIhHXuWAwiKuuuqqkBREREdWjuRyb0xpqcXOaZblLGNpZvlgtmoIKgOxgN7/BElL3Qyrq0uhnYZkQRhLSFyjHMtdUUcGuaZp47rnncObMGUxNTUFK+w9SCIF7773XkwUSERHVg3rZnKbNjNkHSag6uzBUkaZiN6kJATPYAG1+ZumUEo/CXI/B7htvvIE777wTg4ODriB3EYNdIiIiu6l6KWEYH7QdG20bAKU2r6Ue5Wo/JqXM64OVFWgAsoPdRBRmU7vna1xrBQe7f/d3f4doNIr//M//xPvf/340NzeXY11ERER1xdngvyYnp6UN6JOOQRIsYagqIZ+ApgDpS79uhgnEUhLhPEpmLEdHBjU+D2OZ+9aSoiao/fVf/zU+9KEPMdAlIiLK05Tj62TntKtaoE9cWKrpBADLF4AZaVvhEbTWhBCuUoZ863adm9TqpSNDwX9pnZ2d0DTuuCQiIspXwpBIZKXIFAFEArVXr+sbOWc7TnX21cVu/XrjLGXIt27XrNPBEgUHu3/xF3+B733vezCMekhsExERlZ+zXrcpKKDUWJCozM9Cm59eOpYAUh19lVsQLcud2c0v2LUCjsESyRhg5T+FrVoVnKLdsGEDNE3D+973Ptx5553o7e2FmmM84Mc//nFPFkhERFTr6qFe1zdqz+qmmzogA6EKrYZW4iyRybeMAZoOSw9AMRYGhggpoSTmYYUavV7imio42P3sZz+79P//0z/9U877CCEY7BIREV3iqtcN1liwa6bhG7MHu0Yns7rVypXZTViwpMzr2wQr1ABlJjMdT4nPrb9g9+mnny7HOoiIiOpWrWd2fWPnIcz00rGl+RamplFVCmiATwVSl/YSmtZCR4Z8hpiYwUZoM+NLx2o8ivQK968FBQe7v/d7v1eOdRAREdUl05KYSdi/Rq6pYFdK+IZO2U6luvoBZfURtFQZix0ZxqKZD1kzcYkG/+qPdXVkqINNakX/tcXjcbz44ov44Q9/iPHx8dUfQEREtA7NJiSyZzCFfAI+rXY2p2lTI1AT80vHUgikuvsrtyDKi7Pbx2wiz44MrmB3zrM1VUpRwe5DDz2EnTt34sMf/jDuuusuHD16FAAwMTGBvr4+PProo54ukoiIqFY5OzE420JVO//QSdux0bYRsg5GyNa7iKNuN99g1wra63PVeBRYZmJurSg42H3sscdw//334wMf+AC+/vWv20YGt7W14ZZbbsGTTz7p6SKJiIhqlTPYraUSBnVmHNrshO1csueyCq2GChEJOILdPDsySN0PqWaqXIVlQqTinq5trRX8F3fo0CHceuuteOSRR/DHf/zHrtv37NmDEydOeLI4IiKiWlfLm9MCg/b383RTO6wGTk+tBU1FljFACJi5srs1rOC/uJMnT+LWW29d9va2tjZMTEwsezsREdF6IaV0Ta+qlTHB6syYK6ub6N1ZodVQoUJ+ASUr3k2mFyb55cO9Sa2263YL/otrbGzEzMzMsrefPHkS7e3tJS2KiIioHsQNiWRW3yZVQV7tnypOWgieedN2ymhqhxlpq9CCqFCKECVsUrNndpXYOsvs3nTTTXjssceQTCZdt124cAHf/va38YEPfMCTxREREdWyqZg9k9YcVGpiTLBv5BzUmD2bl9y0q0KroWK56nYTeWZ2Q/bMrrreMrtf/OIXMT4+jt///d/HN7/5TQgh8NOf/hQPPPAA3v/+90PXddx7773lWCsREVFNqcV6XZFKIHDuuO1cqn0jzMaWCq2IihVxdP6YjRfXkaHWe+0W/Fd32WWX4Sc/+Qm6u7vx4IMPQkqJQ4cO4Wtf+xquueYa/OQnP8HGjRvLsVYiIqKa4hoTHKryrK6UCJ58HcI0MqcUFYm+3RVcFBXLndnNM9j1hyBF5rFKOgVhuL/RrxUFT1ADgJ07d+LJJ5/E9PQ0Tp06Bcuy0N/fz1pdIiKiLK7MbrC6M7u+4dPQp0ds5xKbLof0Byu0IipFk+P3zTnJb1lCwAo2QI3NLp1S4lGYeh4j2KpQQcFuMpnE97//fRw+fBinT59GNBpFQ0MDLrvsMuzbtw+f/OQn4fP5yrVWIiKimpG2JOYcwUU1d2JQZ8YROHPUdi7d2IoU++rWrEbHZsj5pETaktCU1b9hMF3B7lzNblDM+6/u6NGjuO666/A3f/M3eOqpp3D69GnE43GcPn0aTz75JP7qr/4K733vewvusfvwww/j6quvRldXF26++Wa8+OKLeT3upZdeQltbG973vvcV9HpERERrYSZmITvUbfAL6Gp1ljGoc1MIn/gVRNaKpaohvnUPUAMb6ig3TRUI++z/fs4PYMvJOUmtRuUV7EajUXz605/G2NgYvvSlL+Ho0aM4e/as7f9+8YtfxPDwMP70T/8U8/Pzqz8pgCeeeAL33Xcf7rnnHjz//PO47rrrcPvtt+P8+fMrPm56ehqf+9zncPPNN+f1OkRERGttKu7uxFCNtOlRhI+/BGGmbedj26519Vul2lP8JjVnr906D3Yfe+wxDA4O4vvf/z6+8IUvYMOGDbbbN2zYgL/927/Fd7/7XZw9exbf+c538nrxQ4cO4Y477sBnPvMZ7Ny5EwcPHkRXVxceeeSRFR/3l3/5l/j0pz+N97znPXm9DhER0Vqr+k4MZhr+s8cQOv6yK9BN9O1CurW7QgsjLxXbfsw9Ra1224/l9Zf37LPPYt++fbjxxhtXvN/NN9+MW265BT/5yU9Wfc5UKoUjR45g3759tvP79u3DK6+8suzjHn74YYyOjuLv//7v81k6ERFRRUzFqrATg5RQ4lH4B0+g8bWfIXDxHThXldi4A8mN2yuyPPJeU7EdGYJhWxmOkowDjg9FtSKvDWrHjh3D/v3783rCm266CQ899NCq95uYmIBpmujo6LCd7+jowOjoaM7HHD16FA8++CB++tOfQlXVvNYDAAMDA3nft16sx2texGtff9brdQO89molJTAZ7UF2Tmlm5BwS42bJz53ruoW04DdTCJgGNJmGallQpQVFWlClCVVa0C0TATMJXeZegwQwGOrESEIBqvRnW83/5uVW7LXPpX0AMrHW6HQcAwPn8nrslaoPATO1dHzxxDHM62vTmWP7du8+cOUV7E5NTaGzszOvJ+zo6MDU1FTeCxCOwncppescsNAJ4rOf/Sy+8pWvoL+/P+/nB7z9gdWCgYGBdXfNi3jt6+/a1+t1A7z2ar72aNLCb95ILB3rKnDFzi05398KkX3dIpWAPnYe+vQo1LlJCJlnW6kcLF8A8a17EGnuRKSkFZZPtf+bl1Mp154wJN46El86Tkkftm3bltfvonpiGpgcWjre3NYEo2tzUeuopLyC3WQyCV3X83tCTUMqlVr1fm1tbVBV1ZXFHR8fd2V7AWB4eBhvvfUWDhw4gAMHDgAALMuClBJtbW34wQ9+4CqJICIiqgRnvW5zUCk50F0kkjEEzr8FffxCSQEusDAwItXVj8SmnYBaVOt9qnJ+DfCpQOpSQt+UwHxKosGfR/uxUCP0rGBXjc/BWOH+1Srv3+wzZ87g1VdfXfV+p0+fzuv5fD4f9uzZg8OHD+NP/uRPls4fPnwYH/3oR13337Bhg6st2be+9S0cPnwY//Vf/4W+vr68XpeIiKjcpmJl6K8rJbriE2h87QSEzK/uMufTKCrSjS1It/Yg1d4LaPkls6g2CSEQCSoYj2Z+Z2YTEg15zIcwQ/Y8f3bf3VqSd7D71a9+FV/96ldXvd9yZQi5HDhwAPv378fevXtx/fXX45FHHsHw8DDuuusuAFiqE/7GN74BXdexe7d9XGF7ezv8fr/rPBERUSVNxz3uxJA2EBp4FU3zufe0AIDlC8IMNkD6ApCqDqlqC//TNEDVIVUdViAEKxBm79x1JhIQGM/qHDYbt7ChafW9T5Yj2FVitdmRIa9g99ChQ2V58dtuuw2Tk5M4ePAgRkZGsGvXLjz++ONLWdrBwcGyvC4REVE5uToxBIsPLkUqgfDxl3Nm1cxAA1Ld/TBauiAD4aJfg+rbQvuxzMbEfNuPWYEQpFCWvklQjCSEkYSssbHBeQW7d9xxR9kWcPfdd+Puu+/Oedszzzyz4mPvv/9+3H///eVYFhERUVEMUyKazAQTAsUPlBBGEuFjL7qmV0lVR3zzbhidfczS0qoiAecUtTzLYIQCyzk2ODYHs6kOg10iIiLKj3NzWmNAQCtmTLCZRuj4K65AN93QjNiOd0P6Q6Usk9aRYgdLAAt1u9nBrhqbhdnU7tna1gKDXSIiIg8563WLyupKieCp16HNT9ufWw9D7L6BnROoIA1+ASEW+j8DQNyQMEwJPY8PYWbIMUmtBut2q2x2IRERUW1zdmIoZnOab+QMfOMXbOfSTe04GelloEsFUxSBRkersbwnqbk2qdVeRwYGu0RERB5yjwku7K1WiU4jcOZN2zkz2Ij5nddBCr5tU3EanaUM8fxKGVztx+JzmRRxjeBfDRERkUcsKXO0HSugXteyEDp5xDYsQqoaYjvfw4wulcS5SS3fzO5CK7vM754w0xCp+AqPqD4MdomIiDwSTUqYWTGEXwOCev7Brv/iO64WY7Gte2AFG7xaIq1TRW9SE6Lm63YZ7BIREXkkVwlDvoOWlPgc/INv286l2jci3bbBs/XR+lV0+zG4SxlqrW6XwS4REZFHnG3HWgroxBA4c9Q2BtjSfEj0X+nZ2mh9izh+F+cSElaetbdWsLbHBjPYJSIi8ogzs5tvJwZtehT6tH0UcGLLlTU3qYqql18T8GeVfZsSiKXy3aTGMgYiIiJCkW3HpIXAmaO2U+nGNhhtG71cGpG7I0O+Y4Mdwa4SjwJW/mUQlcZgl4iIyAMJQyJuZIIHRbjrJHPRR88vtHPKEu+/gmOAyXOujgzxPDsy6H5YWd8yCGlBiddOdpfBLhERkQec9bpNQQFFWSVgtSwELgzYTqU6emE1NHu9PKLSxgaHm2zH6vyMJ2taCwx2iYiIPDDl6q+7+lusPnYeSjK2dCyFgsSmXZ6vjQgosSODM9itoU1qDHaJiIg84Go7tlonhlxZ3c4+SH/Q66URAWBml4iIiErgaju2SmY3V1Y3uXF7WdZGBAANfmErBY8bEoaZ5ya1XMFujYwNZrBLRERUItOSmEkU0IlBMqtLa09RBBr9xY0Ntvwh99jgrA9r1YzBLhERUYlm4tKW5Ar7BHza8pvT9IkhZnWpIlztx+KFjA2uzVIGBrtEREQlyjUmeFlSwjd00nbK6OhlVpfWhKv9WCmb1OZrY5Mag10iIqISuSenLZ/VVecmoUWnbeeSPVvLsi4iJ+cmtbmCNqk5xgYzs0tERLQ+TBfQdszvzOo2d7omVBGVi7eZXQa7REREdU9K6c7sLtN2TEnMQ5sctp1LbmBWl9ZOJOjO7Mo8uypYwUZIkXm8YiQgjKSn6ysHBrtEREQliKUkDDNzrKtA2J+7jME3dArZt5ihCMxIe3kXSJTFrwn4M00VYEpgPpVnKYOiuL6FqIXsLoNdIiKiEuQaJiFEjmA3bcA3es52KrlhK5DrvkRl5OrIUOfDJRjsEhERlWAqll9/Xd/YIISVSQFbuh9G28ayro0oF1fdbryEul3HZstqxGCXiIioBBPzpu04Z7ArJXwjp22nUl39gMK3YVp7JY0NbmixHavRKU/WVE78KyMiIiqBM7PbGna/taqzE1Dj0aVjCYFU1+ayr40oF2dmd66QjgyhiH2TWioBkUp4trZyYLBLRERUpHhKIm5kgl1FAE0Bdw2ub+SM7Tjd2g3pC5R7eUQ5lZLZhaLkKGWo7uwug10iIqIiTbqGSShQFHuwK1IJ6JNDtnPJ7v5yL41oWQ1+YdsXGTckDLOQUoZm23G11+0y2CUiIiqSs163NUe9rm/0HERWH1Mz0MB2Y1RRiiLQ6C9huIQr2GVml4iIqC5NztsDBFe9rpTwjZy1nUp197PdGFVcSe3HHJvUtOg0kOdgikpgsEtERFSk1TanaVMjUFLxpWOpqEh1bFqTtRGtpJT2Y1YgDKnqS8fCTENJzHu2Nq8x2CUiIipCLGWtujnNN2xvN2a0bwQ0HUSV5tykNlfIJjUhkK6hUgYGu0REREVYbXOakpiHPjNmu0+yq38tlka0Kldmt4CaXSBXv93q3aTGYJeIiKgIrnpdx+Y0V7uxhmZYjmwYUaVEgu7Mriyg7ta1SW2OmV0iIqK6suLmNMuEPnrednuKWV2qIn5NwK9ljk0JzKdKCHZjM4BlLnPvymKwS0REVISVNqfpE0NQ0qmlY6nqMNo2rtnaiPJRSkcG6QvA8geXjoWUVVvKwGCXiIioQPPJlTenudqNdWwCVHXN1keUj1I6MgBAurHVdqzNTZa8pnJgsEtERFSgCUcJQ1s4szlNic1Bm5uw3Z7q2rxmayPKV0kdGQCYjW22Y3WWwS4REVFdGI+6g91Fro1pkTZYoca1WBZRQUrtyJCO5MjsVuFwCQa7REREBRp3ZHbbGy69nZpp+MYGbbcxq0vVypnZLaRmFwCsYKNjuIQBJT7nydq8xGCXiIioAKYlXZ0YFoNdfeIihGksnbc0H4zWnjVdH1G+GvzCNrk6bkgYZoHDJZx1u1VYysBgl4iIqABTMQtWVjwQ0gVCvoW3U+fGNKOzD1C4MY2qk6IINPpLHC7hKGVQHfXq1YDBLhERUQFcm9MuZXWV+RlojpGpqc6+NVsXUTFKaT8G5OjIwMwuERFRbXNuTlssYXBuTDOaOmAFG9ZqWURFKbX9mNnQDCky4aSSikMkYp6szSsMdomIiArg2pwWVrgxjWpWqe3HoKgwG1tsp7TZ8VKX5SkGu0RERHmKGxLzSfswidawAt/4IETWqFRL9yPd0l2JJRIVpNT2YwCQbmq3HWszYyWtyWsMdomIiPI0ETVtxy0hBaoAfEOnbedTnZsBhW+xVP0iQXdmVxbYKzcdcQa741XVb5d/iURERHkayzFMQp2dgJrVW1RCINXFjWlUG/yagF/LHJsSmE8VOEmtoQUyq+uIYiShJKJeLbFkDHaJiIjyNDpnD3Y7GhT4h07ZzqVbuyH9obVcFlFJSu3IAEVBOmIfHazNVE/dLoNdIiKiPBime5hEly8JbWrYdi7Zc9laLouoZKV2ZABy1e0y2CUiIqop41EL2fmuxoBA88QZZIcJZigC09F3lKjaldyRAe66XbWK6nYZ7BIREeVhdM6+Oa0zLOAbPWc7l+y5DLb5q0Q1wIuODFa4CZamLx0rpgHVMWSlUhjsEhER5cFZr7tBTkGYxtKxpflgtG1c62URlcyZ2S24ZhcAhEC6qcN2SpsaLWVZnmGwS0REtIq0JV1jgjfNvGM7TnVtBlQVRLWmwS9sX0jEDQnDLKKUoaXLdqxNM9glIiKqCRNRC1bWe3+DZqIpmfmKVgqBVFf/2i+MyAOKItDo92C4RHOn7Vibn4ZIJUpamxcY7BIREa1ieNZer7vBmrQdG+29kP7gWi6JyFMltx8DIHU/0uFm27lqyO4y2CUiIlrF0IyjhMEYsR0nN2xby+UQec65SW2uiMwuAKRbHNldBrtERETVLWFITMbsb/y9MpPZNVq6YYUa13pZRJ5ybVKLF9c2zFnKoE+PAVZxgbNXGOwSERGtYMRRwtAu5xBCauk4uZFZXapgbRsyAAAgAElEQVR9XrQfAxZGB1uab+lYmAbU2YmS1lYqBrtEREQrGHIEu70y88adbmzlEAmqC5Gge7CELGYohBBIt3TbTumTF0tZWskY7BIRES1DSumu180KdhObLl/rJRGVhV8T8GuZY1MC86niShmMth7bsT45XNFpagx2iYiIljGTkIgbmTdpTZrokdMAFsajmk3tyz2UqOZ40ZEBANJN7ZBqJnJWjCTUuckVHlFeDHaJiIiWcWHK0XJMTkHFQgCQ2LSzEksiKhtX3W68yI1ligqj2T5gQp8cKnZZJWOwS0REtIzBaXuw2y/HAABGcyfMSFsllkRUNs6ODHNFZnaBXKUMQxUrZWCwS0RElEMsZblGBC8Gu0nW6lId8qojA7DQgkyKTJipJONQo1MrPKJ8GOwSERHl4MzqdsoZhJFCqr0XZkPzMo8iql3OzO5MCcEuVA3pFkcpw9hg8c9XAga7REREOQw66nW3WKOQiopE364KrYiovBoCAkpWcjdhLAxVKVaqo9d2rE9crMiACQa7REREDsm0dA2T2CLHkNy4HdIfrNCqiMpLEQJNQXspw0yxm9QApJu7YGl65vnTqYqMD2awS0RE5HBuwoBE5k2/Wc4jEhBI9myt4KqIyq/JWcpQQrALRYHRtsF2Sh9f+1IGBrtEREQOZy9GbcfbrWHEL7sGUNUKrYhobTSF7KHhdLy0DgpGu6OUYXIYwkgtc+/yYLBLRESUJTYygtF0wHZuc5vCARK0LjQHPczsAjAbW2H6Q0vHQlprnt1lsEtERHSJSMZw7tyE7Vw3ZuDbsqNCKyJaW86a3em4BVlKf1whYHT22U75Rs6sac9dBrtEREQAkDYQOv4KBqS9XVJ/dwjI2mRDVM/CPgEtKzo0TNhGZhcj1dkHKTJBtBqPrun4YAa7RERE0kLo7d/gQsKHOZHptqBAorcnUsGFEa0tIQSagt7W7UpfAOmWbts538iZkp6zEAx2iYhofbMsBAd+C31mDL8Tm2w39bVq8GlimQcS1admD9uPLUp1bbYd6xNDEKlEyc+bDwa7RES0flkWQgOvwjdxEVMI4bxi34S2o0ur0MKIKseZ2fUi2E03dbg2qq1VdpfBLhERrUvCSCJ87EXok0MAgNeUftvtbWEF7Q1sNUbrj7Mjw3TMg81kQiDVc5ntlG/4DGCaue/vIQa7RES07qhzk2j43S+gXdokM4sA3hb2msLLmdWldcrZa3cmUWJHhktSnX2QaubvSkmnoI+fL/l5V1PxYPfhhx/G1Vdfja6uLtx888148cUXl73vj370I3z84x/H1q1b0dvbiz/4gz/A//zP/6zhaomIqKaZJvznjiP85gtQkrGl079WtkKKzFtiJCCwqZVZXVqfAhrgz/qsZ1pANOlBdlfVXLW7/ounyt6GrKLB7hNPPIH77rsP99xzD55//nlcd911uP3223H+fO4o/5e//CVuuukmPP7443j++efxh3/4h7jzzjtXDJCJiIggJfTxC2g88hwCFwaQvf1mDI14W+mx3X13jw5FcGMarU+5OzKUXrcLAMnuy+xtyBJR6BMXPXnu5VQ02D106BDuuOMOfOYzn8HOnTtx8OBBdHV14ZFHHsl5/wcffBBf+MIXsHfvXlx22WW47777sGfPHjzzzDNrvHIiIqoJl6Y1Nbzxc4QGXoWSittutgD8PHCN7VxTUKC/jVldWt/KUrcLQPqDMNo32s75B0+UNbtbsWA3lUrhyJEj2Ldvn+38vn378Morr+T9PNFoFM3NzV4vj4iIapmZhm/oNBpfew6hgd9Cjc267mLpfrzafSPGHKOB37XJx6wurXstjrrdqZg3mV0ASG7cgezQVo2XN7tbser7iYkJmKaJjo4O2/mOjg6Mjo7m9Rzf/OY3cfHiRXzqU59a8X4DAwNFr7NWrcdrXsRrX3/W63UDvHYn1TLRmZhEZ3wKusy9y9sCMBpoxYB/A46O+2y3NWtxzI9ewEB+b0MVwX/z9Wmtr30+rQPoXDoem01iYOCcZ8/f729Ce3Jm6Vic+h0GJueBSx80t2/f7tlrVXyrqXB8epZSus7l8sMf/hBf/vKX8a1vfQt9fX0r3tfLH1gtGBgYWHfXvIjXvv6ufb1eN8Brz752kYzDP3QSvpGzEFbuIFcCSLf2ING3C1IN4dTxJGRWfklXgd+/ogUhX1u5l180/pvz2tdK2pI4/mp86S8kaWnYvGWbZ0NWlHgP5JHnlurng2YKu1tCMDp6PXn+bBULdtva2qCqqiuLOz4+7sr2Ov3whz/E5z73OTz00EP44Ac/WM5lEhFRFRNGEv7zJ+AbPQuxTM2fFAqMjk1IbtgKK9iAuCFx+EQCsZT9/u/Z7EPIV/EmRURVQVMEIkGBmaxRwdNxC52N3tSzW8EGGB298I0NLp0LnD8Oo60HULytma/YX7XP58OePXtw+PBh2/nDhw/j+uuvX/ZxTz75JPbv349/+7d/w8c+9rFyL5OIiKqQkBK+CwNofO1n8I+cyRnoSlVDYsM2zF37AcS3XgMr2IDJeQvPHkvY3sABYEenhv62in/ZSVRVWoLlq9sFLtXuZn2bryTj8A2d9vQ1gAqXMRw4cAD79+/H3r17cf311+ORRx7B8PAw7rrrLgDA/v37AQDf+MY3AAD//d//jf379+MrX/kKbrjhBoyMjABYCJxbWloqcxFERLSmlPkZ7Jo+jeBEMuftlu5HsmfrQj9PTQcAJNMSx4YMnBhJw3LExRubVVy7SS/3solqTktIwZnJTFmQ18GuFWxAqqsf/uFMgBu48DaMzk2Qut+z16losHvbbbdhcnISBw8exMjICHbt2oXHH398qQZ3cHDQdv9HHnkE6XQa999/P+6///6l8+9///vZfoyIqN5JCf+FAfgHT+TM5Fr+EBIbt8Ho2AQoKlJpifEZE+cn0zg7aSKd4326p0nB+7f6oCjsvkDkVM6ODIuSvTvgGzsPYaYBAMJMwz/4NhJbrvLsNSr+nc3dd9+Nu+++O+dtzgCWAS0R0TqVNhB65zXoU8OumyxVR7x3B0YjfZiIAeNn0piYT2E2sXLfzm0dGt69mcMjiJbT7BwbHJewLOnph0Op+5Ho3YHg2WNL53zDZ+or2CUiIlqJSMYRPv4S1HjUdn4OfrzdsAODvm6MDkmkLxh5PV9AB97T58OmVr4FEq0koAsEdYG4sfDB0ZLAbEKiOeTtB8RU9xb4h88sjfAW8HbABP/SiYioaimxOYSPvwQllQCw0D7snGjDa0o/hkQLkACwSgZ3UUADLu/WsaNTg6Yym0uUj5aQQHwm8zc2FbNcGd+SKSri/VcgfOLX3j7vJQx2iYioKinxOYSP/hJKOgUAGEITXlB3YlxE8n6OpqBAe1jBplYN3RGFJQtEBWoJKbg4k6nVnYpZ2FKG10m3dMNo6YI+NeL5czPYJSKiqiMS8wgfewlKOgUDCl5UduCYsnKzeb8GdDSoaGtQ0B5W0BpWoDODS1SStdikBgAQAvH+K6HNjC87GKZYDHaJiKiqiFRiIdBNJTCFEP6vejWmREPO+7aFFWxuVdHdpKIpIPKawElE+XOWLEzGrLyn3RZKBsJIbtyOwPm3PH1eBrtERFQ9TBOht34FNRnDRTTjx+oepIT7rapFj+P67S1oDXPiGVE5NfoFdBUwLiVbDROYS0pEAuX5YJncsBX62ODqdywA/ytBRETVQUoETx2BNj+NM6Id/6/6Lleg2xgQ+IOdfmwLTzLQJVoDQgjX39rkfJlKGYCFzWpbr/H2KT19NiIioiL5Lr4D3/gFnBVt+L/K1TCFaru9v1XFH+8OoCuiLvMMRFQObY5SholyBrsAzEibp8/HMgYiIqo4dXYCgXPHMYQmPKtcDUvY31z39OrY1a2xJpeoAtrWMrNbBgx2iYioooSRQmjgVcwiiB+re5B2ZHSv6/dhWwffrogqxVXGELNgSVkzrfxYxkBERJVzqU7XTBn4sXoNkkK33fyezToDXaIKC/kEAll/hqa1MDq4VjDYJSKiitFHz0KbHMZzym5Xe7GrNujY3qkv80giWitrvknNYwx2iYioIkQyjuDZYzgiNuOU0mW7bXOriis3MKNLVC3awvbyonJvUvMSg10iIlp7UiJ46nWMm0H8Stlqu6k5KHB9v4+b0YiqiHOT2sS8t1POyonBLhERrTl9fBCYHsdP1SttnRd8KnDTdj80jvklqirOMobpuIRp1UbdLoNdIiJaUyKVQOD0m/ilsgMzImy77fotPjT4+dZEVG0CukDYl/kQKiUwFauNUgb+F4WIiNZU4MxRDFoRHFd6bee3dqjY1MI6XaJq5SxlGI8y2CUiIrJRZ8YhJobxvHK57XxjQGDvJl+FVkVE+WhvYLBLRES0PMtC8PTv8CtlK+ZEcOm0APC+LT7W6RJVOWewOxa1IGX11+0y2CUiojXhGzmN8bjAG2KT7fyOLg3tDeoyjyKiatEaUqBmRY5xQ2I+xWCXiIgIIpWAfv5t/K+6C8hqKRb2CVyzkYMjiGqBoghX3e5YDZQyMNglIqKyC5w7htetXkyKRtv56/pZvkBUSzqcdbtzDHaJiGidU+emMD82id8oW2znt7Sp6Gli+QJRLXGWHI1Fq3+4BINdIiIqHynhP3MU/6vusg2P8GvAtX3svkBUa5yZ3em4RCpd3XW7DHaJiKhstKlhvD0fxLBosZ3f2+eDX2P5AlGt8WkCTUH732611+0y2CUiovKwLJhn3sFLynbb6Q1NCja3snyBqFZ1OkoZRuequ5SBwS4REZWFPnIWvzT6YIjMVDRNkXjPZh+EYFaXqFZ1Rezh48gsM7tERLTepA1cGJzAGaXDdvqaXh/Cfr71ENWyzkZ7ZncyZlV13S7/i0NERJ6Tg6fwgtxmO9ceAnZ0ass8gohqRUB31+1WcykDg10iIvKUSMbx6oiGhMh0W1Agcf1lAZYvENWJLkd2d6SK++0y2CUiIk+NnTqHt5Ue27krezQ0BfmWQ1Qv3HW7zOwSEdE6kJ6dwYtz9jrdFj2NXRvYU5eonjgzu9NxiYRRnXW7DHaJiMgbUuJ3J2cQFYGlUwIS120LQ1VYvkBUT3yaQGvIHkYOzVRndpfBLhEReWL4wgSOp9tt53Y3G2hrYE9donrU02QPIy8y2CUionqVSFl4aUi3nYuIJK64LFKhFRFRufU02T/IDs+asGT1lTIw2CUiopJIKfGrt2cQRybYFdLCDZtVaCrfZojqVXuDAj0r3k2mgcn56uvKwP8KERFRSU4MpTAY99vOvSs4gdYOZnWJ6pkiBHoi9uzu0AyDXSIiqiMjsyZeu5C2neuSM9i5vWOZRxBRPXGWMlRj3S6DXSIiKkosZeGXJ5OQyHRa8EkDN7ZHIQKhCq6MiNbKBkewOzFvIV5lLcgY7BIRUcEMU+L5gRQS9qQu9okT8PX1V2RNRLT2gj53C7LBqfQy964MBrtERFQQy5J44WQSkzF7bd5e6xS6N3UAmr7MI4moHm1qsWd3z09VVykDg10iIsqbJSVeOp1ybULps8bxLn0Uqc7NFVoZEVWKM9gdmbOQTFdPKQODXSIiyotlSbx4MoWzk/asTbucxR9av0Oqfzeg8G2FaL2JBBU0BTK1+1ICF6arJ7vL/yoREdGqkmmJw28ncc7x9WSjjOFD/397dx4dVXn+Afx7586SFSaZLBMgIRACSVijSACXIKlo1ELVogXbo1iVKmjrwQpiPRY9lSrF0yriqY2xosYjWy1CKT8riywNiAVRlhAJAQJZyEqW2e/9/RESmMxkJZk7c/P9nJMzzDvvnXmfPGTmyc37vtd1GJpwI5wRsQqNjoiUNsSPpzKw2CUiog5VNriw7ZgV5fXuUxfC5SbMdP0PIbDDmpgGCEI7z0BEahcfoXW7X1rn8pupDCx2iYjIK4dLxuESO744bkODzf1Da4DchFmubzAAVthNg+EKi1BolETkDyJCBIQbrvzCK8nA2Wr/2JVB23kXIiLqT6wOGUWVThwvc8Dm5bMqTq7B7a4jCIYDskaEdWia7wdJRH5FEAQkRmnx3XlHa9vpSheSY5TfnYXFLhFRgHFJMmqaJFy0hcBy3o4muwynBEgSIAPQagCtCOg0AnRaAXoR0IsC9C3/1grQiwK0IuCUAIdTRr1NRm2ThLJLLlxskCC189fHNKkEN0kFENHcwTY4GbIh2HfBE5HfGmYS3YrdykYJ9VYJ4UHKTiRgsUtEFAAabBLOVrtwvtaFqsaWYjQCsPjmz4RBOiBTU4ThjUWtbZIhBLZBST55fSLyf2EGDaLDNLjYcGV+/+kqJ8YN1is4Kha7RER+S5JllNS4UFDudPvw8CWtBkgxazFGX4mIH4rcHrMkjgY0YjtHElF/NCxKi4sN9tb7RRddGDNIhkbBBawsdomI/Iwsyzhd5cL3FxweC8N8ZUCQgKRoLYaZtAgSnAj/9nu3xx0Do+GMMCsyNiLyXwkRIr45C7gu/37e5JBxvsaF+EjlSk4Wu0REfqSi3oVvztpR09RxkRuiF2CQmxBnCkeoQYBOFCBenhbnlACnS4bD1byjgt0J2F0y7E4Zdhcu38pwui7P7RUFBOsEDAhuvsa9eYDoNscu6PQJaOzW1vuyIMCaOIZbjRGRB71WwDCTFj9cvDLF6mSFk8UuEVF/Z3fK+N85O4oq29+IPSpMg6GRIoYYRYQaNCgsLEFyvKlPxyXW10BfdtqtzRaXBCkkvE9fl4gCV3KMe7FbXi+hziJhYLAyC9VY7BIRKay0zoX9p+1ocniezdUIQKJJRIpZB6OvPygkCcFF3+Lq87cuQwhsQ0b6dhxEFFAiQjwXqhWUOzEpUZmFaix2iYgUIskyjpx34Fip9x0VEiJFTBiiQ5hBmbMhhtJTEJsuubVZh48DRH50EFHHRsa0WahW6cSYQVqE6H3/fsZ3LCIiBTTZJewrsqOi3nOXhQFBAiYl6hETrtxOB5rGOhjOFbi12aOGwGmMUWhERBRI4iNFhJ4X0Hh5ka0kA8fLnLg+wfdnd3m5YCIiH7tY78K/j1o9Cl0BQJpZi+zRQYoWupBcCPnhEAT5yvgkrR7WxNHKjYmIAopGEDDa7H71tB8qnLB4ma7V52Px+SsSEfVjRZVOfFlgg7XNzIVgnYCsFAMmxOshapTd5cBwrsBj+oJl+DjIOoNCIyKiQDQsSkSw7sr7mUsGjl5wdHBE32CxS0TkA7Is4/A5O/JP2z0uxWseoFH+bO5l4qUqGC784NZmjxoMp2mQQiMiokAlagSkxbnPmC2scKLO4tuL5LDYJSLqYw6XjN0/2HGszHMh2ug4LaaNNCBIp/yetYLDjpDC/7ntviDpg2AZNlaxMRFRYBsRrUWo4cq7igzg0Dl7+wf0ARa7RER9qNEm4YvjVpTUuu+fqxGAqcP1GD9Er+hlNFvJMoJ/+B80dotbsyVpAqBV9rr2RBS4RI2A9CHuc3cv1Ek4X9v+nuK9jcUuEVEfqW6U8H/Hbai1uM9bCNICP0oxINHkPxviGM4XQldb4dZmixvO3ReI6JrFR4iIDnMvOQ8U22F3+maxGotdIqI+UFLjxBcnrB4rjyNCBNyeFoSoMOXn57bQ1pTDcO6EW5szLALWhDSFRkREaiIIAq5P0LtNkbI4ZHxz1jfTGVjsEhH1IlmWcbzMga9+sMPVZg3GEKOI21KCEKrQRSK80TRdQkjhN+7zdLV6NI2cCGj8Z5xEFNgiQzUei9VOV7lwutL7RXV6E9/JiIh6iSTL+PqMA4fOeW6tkxKrxU0j9NCKfjA/9zLBbkXoif0QXFc+bGQAluTrIBuClRsYEanSmEE6DAx2fw88cMaOmqa+3Z2BxS4RUS+wO2XsOmnDDxfdz1IIAG4YqsN1CX6yEK2F04HQE/uhsbkvSLMmjuE8XSLqE6JGwJRhBly9lbhLAr4qtKHJ3ncFL4tdIqJrVGeRsO24FaWX3N+sdSIwbaQByTG6do5UiMuJ0BP5EBvr3JptsYmwm4cpNCgi6g8iQzW4Yaj7Di+Ndhk7Ttpg66MFayx2iYiuwblqJ7Yds6Le6v4mHaoXcFtKEOIG+s9CNADNhW7BAWjra9yaHQOjYU0cA/jT2WciUqWkaC1GRLvP362zyNhe4Lmotzew2CUi6gFJlnG4xI7dp+xwtvnrmylUgxlpQTCG+NlbrNOO0OP/hbau0r05PBJNo27ggjQi8pmJCToMNrqfDKhpkvF/x629foU1vrMREXVTg03Cf07YcKzUcxXx8CgRP0oxuF0P3h8IdivCju7zOKPrCh2IxpQMQPSfPX+JSP00GgE3JekRE+5eijbaZGw7Zu3V1+K7GxFRN5ypcuLAGTscbS7+oxGA6xN0GBGtheBnUwHE+mqEFHwNjcPm1u4KGYDG1MmA1s/mFBNRvyBqBExLNmDPKRsu1F05m9v2r2XXisUuEVEXWOwyDp6141yN5yUug3TAzUkGRIf72fxcWYau4iyCT38HQXb/9HCGRzaf0WWhS0QK0ooCbhlhwIEzdhRV9s0lhFnsEhF1QJZlnKp04dA5z7O5ADBooAaThxkQ5G/TFhw2BBd9C111mcdjjohYNCVPBEQ/K86JqF/SaARkJOphCnXim7MOSL28Ro3FLhFRO8ouNRe5NU2e77waAZgwRIdRsX42bUGWoas8j6Az30Pj8LwUp3XQCNgSUrnrAhH5FUEQkByjQ1SYiPzTts4P6AYWu0REbVQ1uPDdBYfbHLKrRYZqkJGoR4Sf7bagaahFcPH30NZXezwma0RYkibAETVYgZEREXVNRIgGt6cF9epzstglIkLzdIXSSxKOlzpQXu+9yBU1wPjBOoyM1frV1dDE+hoYzp+Erqbc6+POUCMsyddBCg7z8ciIiLqvt99fWewSUb/WaJNwusqFokonGmztTxRLiBQxYYgOYQY/OZvrciLKWovQI19B21jrtYssaGAbnAzb4GTuoUtE/RaLXSLqV2RZxiWrjPO1LpyvdeFiQ8d73ESHaZAe3zyPTHFOB3Q15dDWlEFXU46BUvsrlx0RsbAOHc2zuUTU77HYJSJVc7pk1FklVDZIuFgv4WKD1KXLUcaGa5AWp4N5gEaZBWiyDI2tCZrGOmgvVUFbXw1NYx06G4kzLAK2+FFwGmN8MkwiIn+neLGbk5ODN998E+Xl5UhJScHy5csxderUdvvv2bMHL7zwAk6cOAGz2Yxf//rXeOSRR3w4YiLyJ5IMNNklWOwymuwyGi/f1ttk1FmkDqcmtKURgPgIESmxWpj6+kyuJEFwOSA4bNDYrBDsFmhsFmhsTRAt9dBYGiB0cOa2LcfAaNgGjYBrYBR3WiAiuoqixe7GjRuxZMkSrFy5EpMnT0ZOTg5mz56N/Px8xMfHe/QvLi7G/fffjwcffBDvvvsu8vPzsWjRIphMJsyaNUuBCIiuuNjgwiWLZ2HVbqklt9x0XozJ7dy5uv2iLRRSucPz2E6evrPxddinvedp5wBvzW2PkyHDJQEuqbmQdUkyXDIgSYBLluF0AXanDPvlW5c8GN98e22XljTp7BgRYsHwEAsMGhm4JAOXWgZ3ZYStJWTbb6osA7IEQZKuunU1F7QtbS4nBKejucB1OrpVyLbHrtFCihsGe0wC5KDQa34+IiI1UrTYffvttzF37lw89NBDAIAVK1bgyy+/RG5uLl566SWP/u+//z7MZjNWrFgBABg1ahQOHjyIVatWsdglxRVXuVBY4VRwBEacPetZ7JInrexCnFyLRPkiEuWLCHPaAAuAKqVH1jlXcDgcEbFwRsbhROlFJCeMVHpIRER+TbFi12634/Dhw3jqqafc2qdPn479+/d7PebAgQOYPn26W1tWVhY++eQTOBwO6HS87CURtSHLGAALTHID4uRamOVaRKEeYpfOVytL0uohBYfDFWaEc0AkXOGRkHWGKx3KKpUbHBFRgFCs2K2qqoLL5UJ0dLRbe3R0NCoqKrweU1FRgWnTpnn0dzqdqKqqgtls9npcYWFhr4w5kPTHmFsoFXtd00AAXPnuawIkGGQnQmBDmGxDGKwIk60IhxURciOMaIQOHe+4oAQZgEvQwCmIsIs62DU6ODRa2DU6WEU9LFoDnILYPP/WAaCqofmrDf6s9z/9NW6AsfcnycnJvfZcii9Qa7vKWZblDlc+e+vvrf1qvfkNCwSFhYX9LuYWSsaurXQirN77PMx2/3cKLTedLygS2r3TrK62FkajsfPn6eSl2nu4K2ueOouzwz6XaQQBoqZ5sVjzbfP9ln9rNYBeK0AvNt9eOnoQQ0K0zc/s9uQGyDBAEiJx5cKTVw+k7UjaHn9Vf+HK/dbzwW2P12ggCxpAIwKCBrJG49Yma0TIWh1kUQdZqwNErdtz6C5/hXTy/bkaf9b7X+z9NW6AsffX2HuDYsWuyWSCKIoeZ3ErKys9zva2iImJ8dpfq9UiMjKyz8ZK1BXDorQYFqXc74+F9jokD+1/201VBxthGsEPASIi8k6xS+ro9XpMmDABO3bscGvfsWMHMjIyvB4zadIk7Ny506N/eno65+sSERERkQdFrx+5YMEC5OXlYc2aNSgoKMDixYtRVlaGefPmAQDmz5+P+fPnt/afN28eLly4gCVLlqCgoABr1qxBXl4eFi5cqFQIREREROTHFJ2ze++996K6uhorVqxAeXk5UlNTsXbtWiQkJAAASkpK3PonJiZi7dq1WLp0KXJzc2E2m/Haa69x2zEiIiIi8krxBWqPPvooHn30Ua+PbdmyxaPtpptuwldffdXXwyIiIiIiFVB0GgMRERERUV9isUtEREREqsVil4iIiIhUi8UuEREREakWi10iIiIiUi0Wu0RERESkWix2iYiIiEi1WOwSERERkWqx2CUiIiIi1WKxS0RERESqxWKXiIiIiFSLxS4RERERqZZQW1srKz0IIiIiIqK+wDO7RERERKRaLHaJiIiISO0WLJYAAA5eSURBVLVY7BIRERGRarHYJSIiIiLVYrFLRERERKrFYpeIiIiIVCugi92cnByMGzcOsbGxyMzMxL59+zrsv2fPHmRmZiI2Nhbjx49Hbm6uj0bau7oT9+7du2E0Gj2+Tp486cMR9469e/fiZz/7GVJTU2E0GvHxxx93eszRo0dx5513wmw2IzU1Fa+99hpkObB22+tu3GfOnPGa8//85z8+GnHveOONN3DrrbciPj4eSUlJeOCBB3Ds2LFOj1NDznsSu1ry/re//Q1Tp05FfHw84uPjcdttt2Hbtm0dHqOGnHc3brXk25uVK1fCaDTit7/9bYf91JD3q3UlbrXkffny5R4xjBw5ssNjriXf2t4YtBI2btyIJUuWYOXKlZg8eTJycnIwe/Zs5OfnIz4+3qN/cXEx7r//fjz44IN49913kZ+fj0WLFsFkMmHWrFkKRNAz3Y27RX5+PiIiIlrvR0VF+WK4vaqxsRFpaWmYM2cOfvWrX3Xa/9KlS7jnnnswdepUbN++HYWFhViwYAFCQkLw1FNP+WDEvaO7cbfYsGEDxowZ03r/6vwHgj179uCXv/wlrrvuOsiyjFdffRU/+clPsH///nZjUUvOexJ7i0DP+6BBg7Bs2TIkJSVBkiR88sknePDBB7Fz5063uFqoJefdjbtFoOe7ra+//hoffPABRo8e3WE/teS9RVfjbqGGvCcnJ2Pz5s2t90VRbLfvteY7YIvdt99+G3PnzsVDDz0EAFixYgW+/PJL5Obm4qWXXvLo//7778NsNmPFihUAgFGjRuHgwYNYtWpVQBW73Y27RXR0NEwmk6+G2SdmzJiBGTNmAACefPLJTvuvW7cOFosF77zzDoKDg5GWloaTJ09i9erVWLhwIQRB6Osh94ruxt0iMjISsbGxfTWsPrdx40a3+3/961+RkJCA/Px8ZGdnez1GLTnvSewtAj3vd911l9v9F198Ee+99x6+/vprr0WfWnLe3bhbBHq+r1ZXV4fHHnsMb731Fl5//fUO+6ol70D34m6hhrxrtdoux3Ct+Q7IaQx2ux2HDx/G9OnT3dqnT5+O/fv3ez3mwIEDHv2zsrJw6NAhOByOPhtrb+pJ3C2mTZuGUaNGYebMmfjqq6/6cph+48CBA5gyZQqCg4Nb27KyslBaWoozZ84oODLf+MUvfoERI0bg9ttvxz//+U+lh3PNGhoaIEkSjEZju33UmvOuxN5CTXl3uVzYsGEDGhsbMWnSJK991JjzrsTdQk35/s1vfoNZs2YhMzOz075qynt34m6hhrwXFxcjNTUV48aNwyOPPILi4uJ2+15rvgOy2K2qqoLL5UJ0dLRbe3R0NCoqKrweU1FR4bW/0+lEVVVVn421N/UkbrPZjDfeeAMffvghPvzwQyQnJ2PWrFnYu3evL4asqPZy3vKYWoWFheGVV17B+++/j3Xr1uGWW27BvHnz8Omnnyo9tGuyZMkSjB07tsMPf7XmvCuxqynvR48exeDBgxETE4NnnnkGH330Ubt/3lVTzrsTt5ryDQAffPABioqK8MILL3Spv1ry3t241ZL3iRMnYvXq1Vi3bh3efPNNlJeXY8aMGaiurvba/1rzHbDTGAB4nLaWZbnDU9ne+ntr93fdiTs5ORnJycmt9ydNmoSzZ8/irbfewo033tin4/QHasl5d5hMJrc5TOnp6aiursZf/vIXPPDAAwqOrOeWLl2K/Px8/Pvf/+5wXhegvpx3NXY15T05ORm7d+9GXV0dNm3ahCeeeAKbN29GWlqa1/5qyXl34lZTvgsLC/Hyyy9j69at0Ov1XT4u0PPek7jVkvfbbrvN7f7EiRMxYcIE5OXlYeHChV6PuZZ8B+SZXZPJBFEUPar5yspKj8q/RUxMjNf+Wq0WkZGRfTbW3tSTuL25/vrrUVRU1NvD8zvt5RxAt75fahDIOX/++eexYcMGbNq0CYmJiR32VVvOuxO7N4Gad71ej+HDhyM9PR0vvfQSxo4di9WrV3vtq6acdydubwI13wcOHEBVVRWmTJkCk8kEk8mEvXv3IicnByaTCTabzeMYNeS9J3F7E6h5v1pYWBhSUlLajeNa8x2Qxa5er8eECROwY8cOt/YdO3YgIyPD6zGTJk3Czp07Pfqnp6dDp9P11VB7VU/i9ua7774L+IntXTFp0iT897//hdVqbW3bsWMH4uLiMHToUAVH5nuBmvPFixdj/fr12LRpU6fb0gDqynl3Y/cmUPPeliRJsNvtXh9TU87b6ihubwI133fddRf27duH3bt3t36lp6fjvvvuw+7du72e9VRD3nsStzeBmverWa1WFBYWthvHteY7IItdAFiwYAHy8vKwZs0aFBQUYPHixSgrK8O8efMAAPPnz8f8+fNb+8+bNw8XLlzAkiVLUFBQgDVr1nR4utxfdTfu1atXY/PmzTh16hSOHz+OZcuWYcuWLXjssceUCqHHGhoacOTIERw5cgSSJKGkpARHjhzBuXPnAADLli3DzJkzW/v/9Kc/RXBwMJ588kkcO3YMmzZtwp///Gc8+eSTAfNnLqD7cefl5WHdunUoKChAYWEh3nrrLeTk5ODxxx9XKoQeefbZZ5GXl4ecnBwYjUaUl5ejvLwcDQ0NrX3UmvOexK6WvP/+97/Hvn37cObMGRw9ehTLli3Dnj17MHv2bADqzXl341ZLvgHAaDQiLS3N7SskJAQRERFIS0uDIAiqzHtP4lZL3n/3u99hz549KC4uxsGDB/HQQw+hqakJc+bMAdD7P+cBO2f33nvvRXV1NVasWIHy8nKkpqZi7dq1SEhIAACUlJS49U9MTMTatWuxdOlS5Obmwmw247XXXguobceA7sftcDjw4osvorS0FEFBQa39W7ayCiSHDh3Cj3/849b7y5cvx/LlyzFnzhy88847KCsrw+nTp1sfHzhwIP7xj3/g2Wefxa233gqj0YgFCxYE3C843Y0bAP70pz/h3LlzEEURSUlJWLVqVUDN5wKaL54CwONndPHixXj++ecBQLU570nsgDryXl5ejscffxwVFRUYMGAARo8ejfXr1yMrKwuAenPe3bgBdeS7q9Sa986oNe8XLlzAo48+iqqqKkRFRWHixIn44osvWmuZ3s63UFtbG7iXGyEiIiIi6kDATmMgIiIiIuoMi10iIiIiUi0Wu0RERESkWix2iYiIiEi1WOwSERERkWqx2CUiIiIi1WKxS0RERESqxWKXiMiHjh49iocffhhjx45FbGwsUlJScOedd2L58uWtfd599118/PHHCo6SiEg9eFEJIiIfyc/Px8yZMxEbG4u5c+di8ODBKC0txcGDB7F9+3ZUVVUBAG644QbExMRgy5YtCo+YiCjwBezlgomIAs0bb7yBkJAQ7Ny5EyaTye2x0tLSPn1tWZZhs9kQFBTUp69DRORvOI2BiMhHTp8+jdTUVI9CFwDi4uIAAGPHjkVhYSH27t0Lo9EIo9GIsWPHAgDsdjv+8Ic/YNq0aRg6dCjMZjOysrLwr3/9y+P5jEYjnnnmGXz22WeYOnUqYmJisGHDBgDArl27kJ2djaFDh2Lw4MGYOHEiFi1a1IeRExEph9MYiIh85L777sP+/fuxdevW1gK2rc2bN+PZZ5/FgAEDWgvQ0NBQ3H333aiqqkJGRgbuuecejBo1ChaLBevWrcN3332H9evXIysrq/V5jEYjUlJSUFFRgcceewyxsbEYO3YswsPDkZmZibS0NNx///0ICQlBcXExtm3bhn379vnk+0BE5EssdomIfGTXrl245557AADp6emYMmUKbr75ZmRmZrpNL2hvzq7L5YLT6YTBYGhts9vtuPnmmxEXF4fPPvustd1oNEIQBOzatQvjxo1rbX/nnXfw/PPP49SpU17PMBMRqQ2nMRAR+UhmZia2bt2KO+64AwUFBVi1ahUeeOABjBw5Eh999FGnx4ui2Fro2u121NTUoL6+HjfeeCMOHz7s0T8jI8Ot0AWA8PBwAMCWLVsgSVIvREVE5N9Y7BIR+VBGRgby8vJw5swZ7Nq1C0uXLgUALFy4ELt27er0+DVr1mDy5MmIjY3FsGHDkJSUhNzcXNTV1Xn0TUxM9Gi77777kJGRgaeffhojRozAww8/jLVr18LhcFxzbERE/ojFLhGRAkRRxPjx4/Hcc8/hww8/BACsXbu2w2PWr1+Pp59+GsOGDcPq1auxfv16fPbZZ5g9ezZk2XNGWnBwsNe2rVu3YtOmTfj5z3+OwsJCPP7448jKyoLFYumd4IiI/AiLXSIihV1//fUAgLKyMgCAIAhe+23cuBGJiYnIy8vDnDlz8KMf/QjTpk3r9utpNBrccsstePnll7F3716sXLkSR44cweeff97jGIiI/BWLXSIiH9m1a5fXebJffPEFACA5ORkAEBISgtraWo9+oigCgNtZ3OLiYmzevLnLY6iurvZoGz9+PAB4fU0iokDHi0oQEfnIkiVL0NDQgLvvvhujRo2CJEn49ttv8emnnyIyMhJPPPEEgOadGv7+97/jj3/8I0aMGIHQ0FBkZ2cjOzsbn3/+OebMmYPs7GxcuHAB7733HpKSkvD99993aQyvv/469uzZg9tvvx0JCQmora1Fbm4uQkNDcccdd/Rl+EREimCxS0TkI6+88go2bdqE7du346OPPoLNZoPZbMbs2bOxaNEiDB06FEBzUVxaWorVq1fj0qVLiI+PR3Z2NubOnYvKykq899572LlzJ4YPH45XX30VRUVFXS5277zzTpSUlOCTTz5BZWUlIiMjccMNN+C5555DQkJCX4ZPRKQI7rNLRERERKrFObtEREREpFosdomIiIhItVjsEhEREZFqsdglIiIiItVisUtEREREqsVil4iIiIhUi8UuEREREakWi10iIiIiUi0Wu0RERESkWv8PWde3yIAKb3kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x561.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.style as style\n",
    "style.use('fivethirtyeight')\n",
    "from numpy import arange\n",
    "\n",
    "font = {'family': 'serif',\n",
    "        'color':  'black',\n",
    "        'weight': 'bold',\n",
    "        'size': 14,\n",
    "        }\n",
    "\n",
    "fandango_2015['Fandango_Stars'].plot.kde(label='2015', figsize=(10, 7.8), color = '#FFB7A2')\n",
    "fandango_2016['fandango'].plot.kde(label='2016', color='#96BBE6')\n",
    "plt.title(\"Comparing distribution shapes for Fandango's ratings\\n(2015 vs 2016)\", y = 1.0)\n",
    "plt.xlim(0, 5)\n",
    "plt.xlabel('Stars')\n",
    "plt.xticks(arange(0, 5.1, 0.5))\n",
    "plt.legend(loc='upper left', fontsize=14)\n",
    "plt.savefig('density.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The striking aspect of the figure above is:\n",
    "\n",
    "- The 2016 distribution is slightly shifted to the left relative to the 2015 distribution.\n",
    "\n",
    "The slight left shift of the 2016 distribution is very interesting for our analysis. It shows that ratings were slightly lower in 2016 compared to 2015. This suggests that there was a difference indeed between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We can also see the direction of the difference: the ratings in 2016 were slightly lower compared to 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Relative Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     8.527132\n",
       "3.5    17.829457\n",
       "4.0    28.682171\n",
       "4.5    37.984496\n",
       "5.0     6.976744\n",
       "Name: Fandango_Stars, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2015['Fandango_Stars'].value_counts(normalize=True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5     3.141361\n",
       "3.0     7.329843\n",
       "3.5    24.083770\n",
       "4.0    40.314136\n",
       "4.5    24.607330\n",
       "5.0     0.523560\n",
       "Name: fandango, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2016['fandango'].value_counts(normalize=True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2016, very high ratings (4.5 and 5 stars) had significantly lower percentages compared to 2015. In 2016, under 1% of the movies had a perfect rating of 5 stars, compared to 2015 when the percentage was close to 7%. Ratings of 4.5 were also more popular in 2015 — there were approximately 13% more movies rated with a 4.5 in 2015 compared to 2016.\n",
    "\n",
    "The minimum rating is also lower in 2016 — 2.5 instead of 3 stars, the minimum of 2015. There clearly is a difference between the two frequency distributions.\n",
    "\n",
    "For some other ratings, the percentage went up in 2016. There was a greater percentage of movies in 2016 that received 3.5 and 4 stars, compared to 2015. 3.5 and 4.0 are high ratings and this challenges the direction of the change we saw on the kernel density plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determining the Direction of the Change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fandango 2015\n",
      "[4.0852713178294575, 4.0, 4.5] \n",
      "\n",
      "Fandango 2016\n",
      "[3.887434554973822, 4.0, 4.0]\n"
     ]
    },
    {
     "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>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4.085271</td>\n",
       "      <td>3.887435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2015      2016\n",
       "Mean    4.085271  3.887435\n",
       "Median  4.000000  4.000000\n",
       "Mode    4.500000  4.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_stats(data, col):\n",
    "    return list([data[col].mean(), data[col].median(), \n",
    "                data[col].mode()[0]])\n",
    "    \n",
    "print('Fandango 2015')\n",
    "print(get_stats(fandango_2015, 'Fandango_Stars'), '\\n')\n",
    "\n",
    "print('Fandango 2016')\n",
    "print(get_stats(fandango_2016, 'fandango'))\n",
    "\n",
    "summary = pd.DataFrame(index=['Mean', 'Median', 'Mode'])\n",
    "summary['2015'] = get_stats(fandango_2015, 'Fandango_Stars')\n",
    "summary['2016'] = get_stats(fandango_2016, 'fandango')\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary['2015'].plot.bar(color = '#FFB7A2', align = 'center', label = '2015', width = .25)\n",
    "summary['2016'].plot.bar(color = '#96BBE6', align = 'edge', label = '2016', width = .25,\n",
    "                         rot = 0, figsize = (8,5))\n",
    "\n",
    "plt.title('Comparing summary statistics: 2015 vs 2016', y=1.07)\n",
    "plt.ylim(0,5.5)\n",
    "plt.yticks(arange(0,5.1,.5))\n",
    "plt.ylabel('Stars')\n",
    "plt.legend(loc = 'upper center')\n",
    "plt.savefig('summary.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04842683568951993"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(summary.loc['Mean'][0] - summary.loc['Mean'][1]) / summary.loc['Mean'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The mean rating was lower in 2016 with approximately 0.2. This means a drop of almost 5% relative to the mean rating in 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Our analysis showed that there's indeed a slight difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We also determined that, on average, popular movies released in 2016 were rated lower on Fandango than popular movies released in 2015.\n",
    "\n",
    "We cannot be completely sure what caused the change, but the chances are very high that it was caused by Fandango fixing the biased rating system after Hickey's analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}