{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abstract\n",
    "Viewing the file we can explore some of the effects on the GPA (grape point average) by the other variables. Considering the short timespan for this project we will not delve deeper into the data. The file will be read, some columns and values are going to be removed, correlations are going to be measured, plots will be present, and at the end we will see some hypothesis testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from statsmodels.stats import weightstats\n",
    "from scipy.stats import mstats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author\n",
    "## Stefan Panev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "For the third dataset we will choose the food choices from here: https://www.kaggle.com/borapajo/food-choices#food_coded.csv. The file is saved in the data folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:\n",
    "* [Introduction](#first-bullet)\n",
    "* [Reading the Data and Preprocessing](#second-bullet)\n",
    "* [Correlation Analysis](#third-bullet)\n",
    "* [EDA - grouping and plotting](#fourth-bullet)\n",
    "* [Hypothesis testing via Z test](#fifth-bullet)\n",
    "* [Conclusion](#sixth-bullet)\n",
    "* [Further Development](#seventh-bullet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_choices = pd.read_csv('data\\\\food_coded.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPA</th>\n",
       "      <th>Gender</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>calories_chicken</th>\n",
       "      <th>calories_day</th>\n",
       "      <th>calories_scone</th>\n",
       "      <th>coffee</th>\n",
       "      <th>comfort_food</th>\n",
       "      <th>comfort_food_reasons</th>\n",
       "      <th>comfort_food_reasons_coded</th>\n",
       "      <th>...</th>\n",
       "      <th>soup</th>\n",
       "      <th>sports</th>\n",
       "      <th>thai_food</th>\n",
       "      <th>tortilla_calories</th>\n",
       "      <th>turkey_calories</th>\n",
       "      <th>type_sports</th>\n",
       "      <th>veggies_day</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>waffle_calories</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>we dont have comfort</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>345</td>\n",
       "      <td>car racing</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1315</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.654</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>3.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>chocolate, chips, ice cream</td>\n",
       "      <td>Stress, bored, anger</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>725.0</td>\n",
       "      <td>690</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>900</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>4.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>frozen yogurt, pizza, fast food</td>\n",
       "      <td>stress, sadness</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>500</td>\n",
       "      <td>none</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>I'm not answering this.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>3.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Pizza, Mac and cheese, ice cream</td>\n",
       "      <td>Boredom</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>725.0</td>\n",
       "      <td>690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1315</td>\n",
       "      <td>Not sure, 240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>2.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Ice cream, chocolate, chips</td>\n",
       "      <td>Stress, boredom, cravings</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>940.0</td>\n",
       "      <td>500</td>\n",
       "      <td>Softball</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>760</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GPA  Gender  breakfast  calories_chicken  calories_day  calories_scone  \\\n",
       "0    2.4       2          1               430           NaN           315.0   \n",
       "1  3.654       1          1               610           3.0           420.0   \n",
       "2    3.3       1          1               720           4.0           420.0   \n",
       "3    3.2       1          1               430           3.0           420.0   \n",
       "4    3.5       1          1               720           2.0           420.0   \n",
       "\n",
       "   coffee                      comfort_food        comfort_food_reasons  \\\n",
       "0       1                              none       we dont have comfort    \n",
       "1       2       chocolate, chips, ice cream        Stress, bored, anger   \n",
       "2       2   frozen yogurt, pizza, fast food             stress, sadness   \n",
       "3       2  Pizza, Mac and cheese, ice cream                     Boredom   \n",
       "4       2      Ice cream, chocolate, chips   Stress, boredom, cravings    \n",
       "\n",
       "   comfort_food_reasons_coded            ...             soup  sports  \\\n",
       "0                         9.0            ...              1.0     1.0   \n",
       "1                         1.0            ...              1.0     1.0   \n",
       "2                         1.0            ...              1.0     2.0   \n",
       "3                         2.0            ...              1.0     2.0   \n",
       "4                         1.0            ...              1.0     1.0   \n",
       "\n",
       "   thai_food tortilla_calories  turkey_calories  type_sports veggies_day  \\\n",
       "0          1            1165.0              345   car racing           5   \n",
       "1          2             725.0              690  Basketball            4   \n",
       "2          5            1165.0              500         none           5   \n",
       "3          5             725.0              690          NaN           3   \n",
       "4          4             940.0              500     Softball           4   \n",
       "\n",
       "   vitamins  waffle_calories                    weight  \n",
       "0         1             1315                       187  \n",
       "1         2              900                       155  \n",
       "2         1              900  I'm not answering this.   \n",
       "3         1             1315             Not sure, 240  \n",
       "4         2              760                       190  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_choices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 61)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_choices.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is a lot smaller compared to the previous 2. We have only 125 rows and 61 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GPA', 'Gender', 'breakfast', 'calories_chicken', 'calories_day',\n",
       "       'calories_scone', 'coffee', 'comfort_food', 'comfort_food_reasons',\n",
       "       'comfort_food_reasons_coded', 'cook', 'comfort_food_reasons_coded.1',\n",
       "       'cuisine', 'diet_current', 'diet_current_coded', 'drink',\n",
       "       'eating_changes', 'eating_changes_coded', 'eating_changes_coded1',\n",
       "       'eating_out', 'employment', 'ethnic_food', 'exercise',\n",
       "       'father_education', 'father_profession', 'fav_cuisine',\n",
       "       'fav_cuisine_coded', 'fav_food', 'food_childhood', 'fries', 'fruit_day',\n",
       "       'grade_level', 'greek_food', 'healthy_feeling', 'healthy_meal',\n",
       "       'ideal_diet', 'ideal_diet_coded', 'income', 'indian_food',\n",
       "       'italian_food', 'life_rewarding', 'marital_status',\n",
       "       'meals_dinner_friend', 'mother_education', 'mother_profession',\n",
       "       'nutritional_check', 'on_off_campus', 'parents_cook', 'pay_meal_out',\n",
       "       'persian_food', 'self_perception_weight', 'soup', 'sports', 'thai_food',\n",
       "       'tortilla_calories', 'turkey_calories', 'type_sports', 'veggies_day',\n",
       "       'vitamins', 'waffle_calories', 'weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_choices.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the columns contain not numerical information from quizes, such columns will be removed, others are categorical values, they will stay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_choices = food_choices.drop(['comfort_food', 'comfort_food_reasons', 'diet_current', 'eating_changes', 'father_profession',\n",
    "                                  'fav_cuisine','food_childhood', 'healthy_meal','ideal_diet','meals_dinner_friend', \n",
    "                                  'mother_profession', 'type_sports'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 49)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_choices.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have only 49 columns, we will decrease the number of columns further for the exploration part and we can return here if we want to include all the information in a regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GPA', 'Gender', 'breakfast', 'calories_chicken', 'calories_day',\n",
       "       'calories_scone', 'coffee', 'comfort_food_reasons_coded', 'cook',\n",
       "       'comfort_food_reasons_coded.1', 'cuisine', 'diet_current_coded',\n",
       "       'drink', 'eating_changes_coded', 'eating_changes_coded1', 'eating_out',\n",
       "       'employment', 'ethnic_food', 'exercise', 'father_education',\n",
       "       'fav_cuisine_coded', 'fav_food', 'fries', 'fruit_day', 'grade_level',\n",
       "       'greek_food', 'healthy_feeling', 'ideal_diet_coded', 'income',\n",
       "       'indian_food', 'italian_food', 'life_rewarding', 'marital_status',\n",
       "       'mother_education', 'nutritional_check', 'on_off_campus',\n",
       "       'parents_cook', 'pay_meal_out', 'persian_food',\n",
       "       'self_perception_weight', 'soup', 'sports', 'thai_food',\n",
       "       'tortilla_calories', 'turkey_calories', 'veggies_day', 'vitamins',\n",
       "       'waffle_calories', 'weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_choices.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpa_data = food_choices.drop(['calories_day', 'comfort_food_reasons_coded', 'cook','comfort_food_reasons_coded.1',\n",
    "                              'cuisine', 'diet_current_coded','eating_changes_coded', 'eating_changes_coded1', 'eating_out',\n",
    "                               'employment','ethnic_food', 'exercise', 'father_education',\n",
    "                               'fav_cuisine_coded', 'fav_food', 'fries', 'fruit_day', 'grade_level',\n",
    "                               'greek_food', 'healthy_feeling', 'ideal_diet_coded', 'income',\n",
    "                               'indian_food', 'italian_food', 'life_rewarding', 'marital_status',\n",
    "                               'mother_education', 'nutritional_check', 'on_off_campus',\n",
    "                               'parents_cook', 'pay_meal_out', 'persian_food',\n",
    "                               'self_perception_weight', 'soup', 'sports'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 14)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we are left with only 14 columns. This can be seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPA</th>\n",
       "      <th>Gender</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>calories_chicken</th>\n",
       "      <th>calories_scone</th>\n",
       "      <th>coffee</th>\n",
       "      <th>drink</th>\n",
       "      <th>thai_food</th>\n",
       "      <th>tortilla_calories</th>\n",
       "      <th>turkey_calories</th>\n",
       "      <th>veggies_day</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>waffle_calories</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>3.654</td>\n",
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       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>725.0</td>\n",
       "      <td>690</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>900</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>I'm not answering this.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
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       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>725.0</td>\n",
       "      <td>690</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1315</td>\n",
       "      <td>Not sure, 240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>940.0</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>760</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     GPA  Gender  breakfast  calories_chicken  calories_scone  coffee  drink  \\\n",
       "0    2.4       2          1               430           315.0       1    1.0   \n",
       "1  3.654       1          1               610           420.0       2    2.0   \n",
       "2    3.3       1          1               720           420.0       2    1.0   \n",
       "3    3.2       1          1               430           420.0       2    2.0   \n",
       "4    3.5       1          1               720           420.0       2    2.0   \n",
       "\n",
       "   thai_food  tortilla_calories  turkey_calories  veggies_day  vitamins  \\\n",
       "0          1             1165.0              345            5         1   \n",
       "1          2              725.0              690            4         2   \n",
       "2          5             1165.0              500            5         1   \n",
       "3          5              725.0              690            3         1   \n",
       "4          4              940.0              500            4         2   \n",
       "\n",
       "   waffle_calories                    weight  \n",
       "0             1315                       187  \n",
       "1              900                       155  \n",
       "2              900  I'm not answering this.   \n",
       "3             1315             Not sure, 240  \n",
       "4              760                       190  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf14b691d0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tds8+hm8+Kx0m9GDn6i9Z9OhUTv5wjGb92xNYOIR7erViwUNTeLPHizz0Qj8v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1mAR8M4OITMNNKHdWwD6fwV2QonGN/VRyJkm7Gtdr0ymPss32y64SkT5ATXXzrJSa8+Z7oMbi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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf14cc5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(gpa_data.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by creating a correlation matrix from the library seaborn with a heat map inside. The closer to one the higher the correlation, closer to zero mark the lack of correlation, while negative values mark negative correlation. For more information about correlation you can check https://www.kdnuggets.com/2017/02/datascience-introduction-correlation.html. Correlation does not mean a causal relationship between the variables. There is also the false correlation effect meaning we are witnessing just a coincidence. There is also the issue that the Person correlation coefficient is only for a linear relationships. The diagonal values are correlated with themselves so they have a coefficient of 1. Looking at the matrix there does not seem to be a stong correlation. We will plot the highest values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scater Plot Correlation Coefficient = 0.48')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sJQgREYmlBCEiIrGUIEREJJYShIiIxFKCEBGRWEoQIiISSwlCRERiKUGIiEgs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\nEZFYShAiIhLr/wE3Fna3fWExXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf14bfda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gpa_data['turkey_calories'], gpa_data['tortilla_calories'])\n",
    "plt.xlabel('turkey_calories')\n",
    "plt.ylabel('tortilla_calories')\n",
    "plt.title('Scater Plot Correlation Coefficient = 0.48')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The turkey calories and the tortilla calories have a 0.48 correlation coefficient. It seems that turkey may be a major part of the tortilla, although there are always other ingredients. Other scatterplots will likely be random in nature. The likely reason for the lack of any correlation is likely that this are mostly dummy variables created for specific values of categorical ones. For more information on the subject here: https://www.kdnuggets.com/2015/12/beyond-one-hot-exploration-categorical-variables.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scater Plot Correlation Coefficient = 0.11')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pN5vNNjqWyTOWlahfSCeWN7rdatKU962apam/aeortO3+zlv8fsn6cvc/qXMAZwF1ZnYE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\nKaUEICKSUkoAIiIppQQgIpJS/x9mzCWfxoWD5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf14643e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gpa_data['coffee'], gpa_data['veggies_day'])\n",
    "plt.xlabel('coffee')\n",
    "plt.ylabel('veggies_day')\n",
    "plt.title('Scater Plot Correlation Coefficient = 0.11')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values don't seem to be correlated, this is due to the reason that some of them are categorical values. If we want to more exploration we should consider the chi-square test for more information see here :https://en.wikipedia.org/wiki/Chi-squared_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA - grouping and plotting\n",
    "Now lets explore the GPA with a histogram. We use the unique function, 3 values are not numeric and will be removed. We now have only 122 rows. The format of the data is string, this can be seen in the describe function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2.4', '3.654', '3.3', '3.2', '3.5', '2.25', '3.8', '3.904', '3.4',\n",
       "       '3.6', '3.1', nan, '4', '2.2', '3.87', '3.7', '3.9', '2.8', '3',\n",
       "       '3.65', '3.89', '2.9', '3.605', '3.83', '3.292', '3.35',\n",
       "       'Personal ', '2.6', '3.67', '3.73', '3.79 bitch', '2.71', '3.68',\n",
       "       '3.75', '3.92', 'Unknown', '3.77', '3.63', '3.882'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data['GPA'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpa_data = gpa_data[gpa_data['GPA'] != 'Personal ']\n",
    "gpa_data = gpa_data[gpa_data['GPA'] != 'Unknown']\n",
    "gpa_data = gpa_data[gpa_data['GPA'] != '3.79 bitch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(122, 14)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     120\n",
       "unique     35\n",
       "top       3.5\n",
       "freq       13\n",
       "Name: GPA, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data['GPA'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   7.,  27.,  19.,  21.,   4.,  12.,  10.,   3.,  14.]),\n",
       " array([  0. ,   3.4,   6.8,  10.2,  13.6,  17. ,  20.4,  23.8,  27.2,\n",
       "         30.6,  34. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r151+v863L0ZEvPXWWzFs2LAYPHhw9O3bN6ZMmVJz2caNG6N9+/ZNWgB7poqKiqLtE8Wc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\n0f8HmVTje2lSGrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1510de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"GPA histogram\")\n",
    "plt.hist(gpa_data['GPA'].dropna(), bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WzZdrAwAAJExoAwAASJjQBgAAkDChDQAAIGFCGwAAQMKENgAAgIQJbQAAAAkT2gAAABIm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\nFggp9kxIZHpjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1501dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"GPA histogram\")\n",
    "plt.hist(gpa_data['GPA'].dropna(), bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 histograms have been plotted, the first with 10 bins, the second with 20, the second is a better considering the xticks are visible. We can see that data is fairly close to normal distribution a little elongated at the tails, there is also some rounding in the marks. Now we will convert the gpa data to float and will drop the NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpa = gpa_data['GPA'].astype(float).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iH6RYIqGGdsKBAwewe/du0XGCCgsPUQDIyMjAnDlzWlZP/i3UkATRkYjoJLTY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf16d3b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('GPA data violin plot')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.violinplot(x=gpa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a violin plot of the gpa data we see it has an elongated left tail. Lets explore more. The skew function gives us a value below 0 confirming our previous observations for a left asymmetry. Also the mean is smaller then the median, if the distribution was symmetric they will be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.74915003409886494"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    120.000000\n",
       "mean       3.415558\n",
       "std        0.390139\n",
       "min        2.200000\n",
       "25%        3.200000\n",
       "50%        3.500000\n",
       "75%        3.700000\n",
       "max        4.000000\n",
       "Name: GPA, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    122.000000\n",
       "mean       1.393443\n",
       "std        0.490528\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        2.000000\n",
       "max        2.000000\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data['Gender'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we move to the gender variable, if we use the describe function the mean is less then 1.5 meaning there are more 1s, then 2s. 1 stands for females so they are dominating the sample. Lets use a pie chart to visualize their proportions. There are 74 females and 48 males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data[gpa_data['Gender'] == 1]['Gender'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data[gpa_data['Gender'] == 2]['Gender'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender = gpa_data.groupby('Gender').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender.index = ['Female', 'Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratio = (gpa_data[gpa_data['Gender'] == 1]['Gender'].count())/(gpa_data[gpa_data['Gender'] == 2]['Gender'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5416666666666667"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the index as male and female. The ration between the two is 1.54."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf1705e438>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QSIgfZUCLz1nUcNQhUZ/dHjMVD46+126u9tIfLGsLOJFThf/3r5MwdP7yDI64\nxE6YAznqkOh63TdyDuYlzBQdw2awrC0kv6wRr649gbqmdgBAZIwJLuEXUaIvFhuMSGZcnVzw+3FL\nMCFstOgoNoVlbUFaXTve25GB5kGnUajLEx2HSHZCPYPxxOTlGOIVIjqKzWFZW5jRbML605uwN/+I\n6ChEsjJ+SAp+P24J3Bz4jI+esKyt5GjxCXz4/afo4BVbiHqkVCiRljQXc4bfLjqKTWNZW1FJYzn+\ndnwNypqrREchskneLp54dOIyjAgaJjqKzWNZW1mnqRMbs3dgZ95B8E9N9JOUkEQ8PPZ++Lr5iI4i\nCyzrAXK+9iL+fmI9alrqRUchEspN7Yr/k7wA06NuFh1FVljWA6i9sx3rf9iCg4XHREchEmJUcDwe\nHns//DW+oqPIDstagFOVOfgg81/QtjeJjkI0INycXJGePA+3RU8RHUW2WNaC6Dta8MmZ/+BQ0Tfc\nl012bWTQMKwYm44Adz/RUWSNZS1YYUMJ1pz6HHn1NzaDH5Gt8tMMwv2j7sak8LGio9gFlrUNkCQJ\nR4tP4JMz29DY3iw6DlG/uKicMTf+dswZNgPODnY1F2tiWduQts52bD67C7svHobJzMt6kbwooMCk\niLFYnPRb+GkGiY5jd1jWNqiy+RLW/7AZp6vOio5C1CcxvkOxNGUB4vyjREexWyxrG5ZbW4BNZ3ci\nuzpXdBSiawrxDMT8hNmYHDHWoa40LgLLWgZya/Pxec5O5NRcEB2FCAAwxCsE8xLuxM3hY3hhgAHC\nspaRczUXsensTpyt4fSrJEa4dyjuSZyJ8UNSWNIDjGUtQ2dr8rD57C6WNg2YSJ8w3JM4C2NDR3F3\nhyAsaxkr0pZhT95hfFP2PTpNnaLjkJ1RQIERQcMwK246bho8UnQch8eytgPNHXocLDiGfflHUd/G\nq6ZT/2jUbpg2dALuiJmKwV7BouPQj1jWdsRkNiGzIgt7Lh7G+dp80XFIZsK9Q3FHzDRMGToOrk4u\nouPQVVjWdqpYW4Z9BV/jeNlJtBhaRcchG6VSqjA+NBl3xE5DfECs6DjUA5a1nTOajDhVlYOjJSdw\nujIHnWaj6EgkmAIKxPlHYXL4WEwMGw0vV0/RkagPWNYORG9oQUbZKXxd8h1yawsggW+9I4nwDsWk\niLGYHD4W/u6cT1puWNYOqralHl+XfIfjpSdR0lQhOg5ZSZC7PyZFjMGk8LEI8x4sOg71A8uaUNfS\ngJOV2ThVlY2cmjyeBihjCigQ5RuOlJARGB0yAjF+Q0VHIgthWdMVOowGZFefx8nKHJyqyoa2jVez\nsXUezu5ICo7H6JARSA5O4D47cjRgAAAD/UlEQVRoO8Wypl8lSRKKtGU4XZWDc7V5yKsrQofJIDqW\nw1NAgaE+Q5AyOBEpISMQ6xsJpZJDv+0dy5r6zGQ2oVBbivO1+citzUduXQH0hhbRseyeSqlC1KBw\nxAfEID4gFsP8o+Dh7C46Fg0wljXdMEmSUN5chfO1+ThfexF5dYWobW0QHUv2fFy9EOcXhVi/SMT6\nRSLaNwIuvOKKw2NZk0XpO1pQ3FiGIm05ihrLUKItQ6WuGibJLDqazVEpVRjsEYgwn1CEew9GuHco\nhvoM4Wl1dE0sa7I6o8mICt0llDZWoqy5EuVNVahuqUNNSz06jB2i41mdUqGEn5sPwrwHI/xnxTzY\nMwhOKifR8UgmWNYkVHO7DjUt9ahuqUWNvh41LfWoaalDjb4OdW1aWVyL0knpBD/NIAS6+8Jf44cA\nd1/4a3wR6O4Hf3c/+Ln5QKVUiY5JMseyJpslSRJaOluh62iBrkOP5g49dB166Ax6NHffp4Pe0AqD\n0YBOs7Hry9SJTrMRRpMRBnPnrxa+AgqolCo4KVU/fneCk0IFlVIJFycXeDi7w8NZAw9nd3i6uF9x\n+/KXl6sHfFy9OBE/WR3LmuyeJEnd5Q2gu5zlvrV74sQJLFmyBG+88QZmzZrVfX9qaioSExOxevXq\nXzxn69atKCwsxJ/+9KeBjEoWwM0BsnsKhQLOKjU0zm7QOLvB2clZ9kV9WVRUFHbu3Nl9+8KFC2hr\naxOYiKyFRzeIZGz48OEoLi5Gc3MzvLy8sGPHDqSmpqKqqgr/+te/sG/fPhiNRnh6euLtt9++4rkb\nNmzAzp07oVAoMGvWLCxZskTQWlBfcMuaSOZmzJiB/fv3Q5IknDlzBikpKTCbzWhsbMS6devw6aef\nwmg0Ijs7u/s5+fn52L17Nz799FN8+umnOHDgAAoLCwWuBfWGW9ZEMpeamoqXXnoJYWFhGDNmDABA\nqVRCrVbj8ccfh0ajwaVLl2A0/jSXeV5eHiorK7F06VIAQFNTE0pLSxEVFSViFagPWNZEMhcWFobW\n1lZs2LABjz/+OMrKyqDX63HgwAFs2rQJbW1tmDdvHn5+LkFUVBRiYmLwz3/+EwqFAuvWrUNcXJzA\ntaDesKyJ7MCsWbOwfft2REZGoqysDCqVCm5ubpg3bx6cnZ0REBCAmpqa7t8fPnw4Jk6ciEWLFsFg\nMCApKQlBQUEC14B6w1P3iIhkgAcYiYhkgGVNRCQDLGsiIhlgWRMRyQDLmohIBljWREQywLImIpIB\nljURkQywrImIZIBlTUQkAyxrIiIZYFkTEckAy5qISAZY1kREMsCyJiKSAZY1EZEMsKyJiGSAZU1E\nJAMsayIiGWBZExHJAMuaiEgG/j/1EnGVByrulQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1705ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Gender pie plot')\n",
    "gender['GPA'].plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us explore the weights column, we will store it as the variable weight. Taking the unique function we will see that some of the data is not valid like \"I'm not answering this. \", 'Not sure, 240' or '144 lbs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight = gpa_data['weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['187', '155', \"I'm not answering this. \", 'Not sure, 240', '190',\n",
       "       '180', '137', '125', '116', '110', '264', '123', '185', '145',\n",
       "       '170', '135', '165', '175', '195', '105', '160', '167', '115',\n",
       "       '205', nan, '128', '150', '140', '120', '100', '113', '168', '169',\n",
       "       '200', '265', '192', '210', '112', '144 lbs', '130', '127', '129',\n",
       "       '260', '184', '138', '156'], dtype=object)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight = weight[weight.values != \"I'm not answering this. \"]\n",
    "weight = weight[weight.values != \"Not sure, 240\"]\n",
    "weight = weight[weight.values != \"144 lbs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight = weight.astype(float).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87711484152763297"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    117.000000\n",
       "mean     157.880342\n",
       "std       31.003660\n",
       "min      100.000000\n",
       "25%      135.000000\n",
       "50%      155.000000\n",
       "75%      180.000000\n",
       "max      265.000000\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are again converted to float, they as rightly asymmetric considering the the skewness coefficient is above 0. We can see its descriptive statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf172c5668>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uLITDaPDKYvbs2ezatQuAXr16Xd5Jq2XUqFG2TSZaLetIKJnmo0HhQT7sPpRP\nibESXy9Xe8cRbVyDxeLDDz8EYOnSpbz00kstEki0frm191hIM1SDaovFybOl9OshV/DCvhosFps3\nb2bkyJHcdtttfPbZZ9esnzJlis2CidYrt8CIWq2ic4AUi4Z0DfIFkGIhHEKDxeLAgQOMHDnS2hR1\nNSkW4kYpikJOvoGg9h64aGW2mYZYJxSU4bPCATRYLObOnQvA8uXLrcuMRiNnz56lR48eDR7YYrHw\n6quvcvjwYXQ6HUuXLiUsLMy6PjU1leTkZLRaLXPmzGHkyJEUFxezYMECKioqCAwMZPny5bi7u7Nl\nyxbeffddAPr06cMrr7wik885qQvGSozlVdzWrb29ozi8zgGeuGjVnDwrI6KE/TV5NNSiRYsoLi5m\n/PjxzJ07l/fee6/BfTZu3IjZbCYlJYX58+ezYsUK67rCwkLWrVtHcnIySUlJrFq1CrPZzJo1a5gw\nYQLr16+nT58+pKSkYDQaefPNN3nvvfdITU0lODiY8+fP39qnFnYjI6GaTqNR06WjN6fyDNTUWOwd\nR7RxTbrP4p///Cfvvfce//3vfxk9ejQvvvgiCQkJPPnkk9fdR6/XM2zYMAD69+9PRkaGdV16ejrR\n0dHodDp0Oh2hoaFkZmai1+t54oknAIiLi2PVqlVEREQQGRnJH/7wB3JycnjggQfw9/dvNLNer2/K\nR3Mozpi5VlOz7zpycSRUTfk5h/i8jpChId66KszVFr7bsosOvi7W5Y6e+3okd8tqztxNKhYAgYGB\nbNmyhdmzZ6PVaqmsrGxwe6PRiJfX5Q5MjUZDdXU1Wq0Wo9GIt/flvyw9PT0xGo11lnt6emIwGDh/\n/jw7d+7ks88+w8PDg1mzZtG/f3+6du3a4PvHxsY29aM5BL1e73SZa91I9t3Z6cAF4u6MonuXdrYN\n1ghnOOc5xmPsP3EQr/ZdiO0XDDhH7vpI7pZ1M7kbKi5Naobq3r07TzzxBLm5uQwePJhnnnmGvn37\nNriPl5cXJpPJ+tpisaDVautdZzKZ8Pb2rrPcZDLh4+NDu3bt6Nu3Lx06dMDT05MBAwbIBIZOLEcm\nELwh1mk/pJNb2FmTisWyZct47LHHSElJQafTMWnSJF5//fUG94mJiSEtLQ2Affv2ERkZaV0XFRWF\nXq+nsrISg8FAVlYWkZGRxMTEsGXLFgDS0tKIjY3l9ttv58iRIxQXF1NdXc3+/fvp3r37zX5eYWc5\n+QYC/T1wc23yRW2bFn7F8Fkh7KlJP7FlZWUcOXKEXbt2oSgKAIcOHeKpp5667j5jxoxh27ZtzJgx\nA0VRWLZsGWvXriU0NJTRo0cvSvSiAAAgAElEQVSTmJjIzJkzURSFefPm4erqypw5c1i4cCGpqan4\n+fmxcuVKPDw8mD9/Po899hgA48aNq1N4hPMwlJk5b6gktlegvaM4jXberrTzcuWEFAthZ00qFk8/\n/TTe3t706NGjyUNW1Wo1S5YsqbMsIiLC+nVCQgIJCQl11gcEBJCUlHTNse677z7uu+++Jr2vcFwy\nEurmhAf5sO9oIabyKjzdXRrfQQgbaFKxOHfuHGvXrrV1FtHK1RaLUCkWNyS888VikZ1XSp+ucn+K\nsI8m9Vn07t2bzMxMW2cRrZx1AsFOUixuhDzbQjiCJl1ZHD16lKlTp9K+fXtcXV1RFAWVSsWmTZts\nnU+0ItZmKJlt9oZIsRCOoEnF4s9//rOtc4g24FS+AX8fN2l3v0FdOl58oqAMnxX21KRmqODgYH76\n6SdSU1Px9/dn9+7dBAcH2zqbaEXKKqo4d6Fc+itugs5FQ3AHT06eLbWORhSipTWpWLz11lts2bKF\nb7/9lpqaGjZs2FBnrichGpNbIP0VtyI8yJfyymoKzpfbO4poo5pULLZu3cqbb76Jq6srXl5erF27\n1nrDnRBNcbm/Qu7cvhnh8kxuYWdNKhZqdd3NzGbzNcuEaIjcY3FrrM+2kE5uYSdN+o0/btw4nnnm\nGUpLS/nggw+YNWsWEyZMsHU20YpYh81KsbgptVcWcie3sJcmjYYaMWIEgYGB5OTkoNfrefrppxkx\nYoSNo4nWJCffgK+XDl8vV3tHcUod2rnj6aYl+2wp3Gbf2XpF29RgsSgqKmLu3LkcO3aMsLAwtFot\nO3bsoKKigtjY2DrTjAtxPZVVNeQVm+Tu41ugUqkIC/Ih82QxVdW+9o4j2qAGm6FWrlxJbGwsW7du\nJTU1ldTUVLZv306vXr0anXVWiFqnC4woijRB3arwIB8sChSUVNk7imiDGryy2Lt3L1999VWdZS4u\nLjz77LNMnjzZpsFE61HbKRsuw2ZvSbfgi81PeefNdk4i2qIGryxcXetvX1apVDIaSjRZ9qViEXap\nk1bcnIjgi81PZ4vlykK0vAZ/4zc0HXlTpyoX4mTepSsLKRa3JCzIG41axdnzUixEy2uwGero0aOM\nHj36muWKolBYWGizUKJ1yT5bSntfN7w8dPaO4tRctBrCOvlwKr+EmhoLGo1c3YuW02Cx+Oabb1oq\nh2iljGVmikoqiJGn4zWLbsG+HD9TQm6hkbBOcqUmWk6DxUImCxS36nLntvxiaw4RIb5s3A1ZuSVS\nLESLkutYYVPSud28Ii6NiMo6fcHOSURbI8VC2NTJvItzQknndvOonSPq+GmZUFC0LCkWwqayz5ai\nVqvo0lFmm20O7q5aAny0HD9dgsUiz7YQLUeKhbAZRVHIzisluIMnLlqNveO0GkF+LpRVVJNfXGbv\nKKINkWIhbKbwfDllFdXSEdvMOvlfHIIs/RaiJUmxEDYjN+PZRpDfxWeYZ+VKv4VoOVIshM2cPCMj\noWwh6NKVhXRyi5Zks2JhsVhYvHgx06dPJzExkezs7DrrU1NTiY+PJyEhgc2bNwNQXFzMI488wsyZ\nM3nmmWcoLy+vc7zHHnuMf/7zn7aKLJpZ7bBZubJoXu46NYH+HhzLvYCiSCe3aBk2KxYbN27EbDaT\nkpLC/PnzWbFihXVdYWEh69atIzk5maSkJFatWoXZbGbNmjVMmDCB9evX06dPH1JSUqz7vP3225SU\nyF9SziTrdAmeblo6+nvYO0qr0yOkHaUmMwXnyxvfWIhmYLNiodfrGTZsGAD9+/cnIyPDui49PZ3o\n6Gh0Oh3e3t6EhoaSmZlZZ5+4uDi2b98OwNdff41KpSIuLs5WcUUzK6+s5sw5I12DfWXSSRuIDPUD\n4Mip83ZOItqKJj1W9WYYjUa8vC6PrddoNFRXV6PVajEajXWesufp6YnRaKyz3NPTE4PBwJEjR/jv\nf//L6tWreffdd5v8/nq9vvk+TAtxxsy1rs5+qrASRQEvbaVDfy5HztYQpaIAgB92Z+JRk2fnNE3n\nrOdbctuwWHh5eWEymayvLRYLWq223nUmkwlvb2/rcjc3N0wmEz4+Pnz22Wfk5+fz8MMPc/r0aVxc\nXAgODm70KiM2NtY2H8xG9Hq902WuVV/2s1uPA4UMjulBbGyofYI1wlnPuV6vZ/yogXy46QtKza5O\n8xmc+Xy3ldwNFRebFYuYmBg2b97M+PHj2bdvH5GRkdZ1UVFRvP3221RWVmI2m8nKyiIyMpKYmBi2\nbNlCfHw8aWlpxMbG8vjjj1v3e+eddwgICJDmKCdQO1Kndi4j0bzcXbWEdvLhWO4Fma5ctAibFYsx\nY8awbds2ZsyYgaIoLFu2jLVr1xIaGsro0aNJTExk5syZKIrCvHnzcHV1Zc6cOSxcuJDU1FT8/PxY\nuXKlreIJG8s6XYJOqyYkUKb5sJXIUD9Oni3lVL6Brp197R1HtHI2KxZqtZolS5bUWRYREWH9OiEh\ngYSEhDrrAwICSEpKuu4xf/vb3zZvSGETVdUWTuWV0rWzr/zFa0ORoe34dmc2R06dl2IhbE5+kkWz\ny8k3UF2j0C1YfoHZUu2IqMPZMiJK2J4UC9Hsjl+asyhCioVNhXb0xlWn4WiOzBElbE+KhWh2WZc6\nt+XKwrY0GjXdQ9pxKq+U8spqe8cRrZwUC9HssnJLUKtkTqiW0KNLOywKZOXK1YWwLSkWolnV1FjI\nOl1CaCcf3HQ2Gz8hLukZdrHf4ueTxXZOIlo7KRaiWWXnGTBX1Vh/iQnb6h3uD0ixELYnxUI0q9q5\ninp0kWLREtr7utPR34PMk8XymFVhU1IsRLOqLRaRoXLndkvp09UfQ1kVuQUGe0cRrZgUC9GsjuZc\nwFWnIbSjd+Mbi2bRp2t7AA6dkKYoYTtSLESzKa+s5lReKd1D2smd2y2oT9eL/RaHThTZOYlozeQn\nWjSbY7kXsCiX7ywWLSMk0BtvDxe5shA2JcVCNJuj0l9hF2q1il7h/uQXl1FUIk/OE7YhxUI0myOn\nLt4YFikjoVpcbb+FDKEVtiLFQjSbw6fO087LlQ5+7vaO0ubU9ltkZEm/hbANKRaiWRScL+PchXJ6\nhfvJM7ftoEcXP1x1GtKPnbN3FNFKSbEQzaK2c/W2bu3tnKRtctGqua1be3LyDRSXVtg7jmiFpFiI\nZlE7bLO27Vy0vH7dAwDk6kLYhBQL0SwOHS9C56KRacntKKp7BwDSjxbaOYlojaRYiFtWbraQnWeg\nV5gfWrkZz266Bvvi6e4iVxbCJuQnW9yynMJKQJqg7E2jVhHVPYD84jLyikz2jiNaGSkW4pZlF5qB\ny8M3hf1ESb+FsBEpFuKWZedXolar5BkWDqC2WOw/Iv0WonlJsRC3pKyiitPFZiK7tMPDzcXecdq8\nLh29CWjnzk+HC6ipsdg7jmhFpFiIW5KRVYSiQL8eHewdRQAqlYoBvTtiLK/i8KW5uoRoDlIsxC3Z\nf2mYphQLx3FH744A7Pk5385JRGsixULckn1HC9FqVPQKl/4KRxHVPQAXrVqKhWhWWlsd2GKx8Oqr\nr3L48GF0Oh1Lly4lLCzMuj41NZXk5GS0Wi1z5sxh5MiRFBcXs2DBAioqKggMDGT58uW4u7vzwQcf\n8MUXXwAwfPhwnnrqKVvFFjfgfGkFp/IMRHRyxUWrsXcccYmbq5a+EQH8dLiAcxfKCWgnEzuKW2ez\nK4uNGzdiNptJSUlh/vz5rFixwrqusLCQdevWkZycTFJSEqtWrcJsNrNmzRomTJjA+vXr6dOnDykp\nKeTk5PCf//yH5ORkUlJS2Lp1K5mZmbaKLW5AbRNU105udk4irhbbOxAAfaZcXYjmYbNiodfrGTZs\nGAD9+/cnIyPDui49PZ3o6Gh0Oh3e3t6EhoaSmZlZZ5+4uDi2b99Op06d+Nvf/oZGo0GtVlNdXY2r\nq6utYosbsPfS8MxuneT/w9EMuNRvsfuQFAvRPGzWDGU0GvHy8rK+1mg0VFdXo9VqMRqNeHt7W9d5\nenpiNBrrLPf09MRgMODi4oK/vz+KovDGG2/Qp08funbt2uj76/X65v9QNuZMmS0WhR0HzuLlrqaT\nn4tTZb9Sa84d4KNFn5nH9h27cXVxjO7J1ny+HVFz5rZZsfDy8sJkujzlgMViQavV1rvOZDLh7e1t\nXe7m5obJZMLHxweAyspKXnjhBTw9PXnllVea9P6xsbHN+GlsT6/XO1Xmn08UU1Z5mrGDwlCrapwq\ney1nO+e1mpp7dOHPpHx3BIt7Z2L7BbdAsoa19vPtaG4md0PFxWZ/bsTExJCWlgbAvn37iIyMtK6L\niopCr9dTWVmJwWAgKyuLyMhIYmJi2LJlCwBpaWnExsaiKAq//vWv6dmzJ0uWLEGjkY5UR7DrUB5w\neZimcDx3RXUGYNv+M3ZOIloDm11ZjBkzhm3btjFjxgwURWHZsmWsXbuW0NBQRo8eTWJiIjNnzkRR\nFObNm4erqytz5sxh4cKFpKam4ufnx8qVK9m4cSO7du3CbDbzww8/APDss88SHR1tq+iiCXYfykOn\nVdMvsgMHD8gvI0cUHuRDUIAne37Op8JcjZvOZj/uog2w2XePWq1myZIldZZFRERYv05ISCAhIaHO\n+oCAAJKSkuosGzNmDAcOHLBVTHET8ovLyM4zMKB3R/kF5MBUKhV3RXXmk/8dZe/hAgb37WzvSMKJ\nOUavl3AqOzPOAjCwjzRBObrapqgf9snVn7g1UizEDfth32nUKhjUN8jeUUQjIkJ8Ce7gxc6Ms5jK\nq+wdRzgxKRbihhQUl5GZfZ6+3QPw85ab8RydSqVi1IAumKstbJWObnELpFiIG7J1/2kAhvUPsXMS\n0VQjYkNQqeB/e07ZO4pwYlIsxA1J23cajVrFkChpgnIWgX4e9I0I4NCJYs6ek8etipsjxUI02elC\nI1m5JUT3DMTbQ2fvOOIGjL6jCwAbd8vVhbg5UixEk323MxuAETHSBOVshkR1xsvdhW93ZFNVXWPv\nOMIJSbEQTVJVbWHT7hy8PVwYLKOgnI6bTsvdA0O5YKyUO7rFTZFiIZpk16E8LhgrGTmgCzoXmXLF\nGY0f0hWVCr7YdsLeUYQTkmIhmuTr7ScBuOfOsIY3FA4rKMCT2F4dycw+zxF5Pre4QVIsRKNOnClh\n39FC+kYEENbJx95xxC2YHNcNgE/+d9TOSYSzkWIhGvXZliwApoyIaGRL4ej69ehAz1A/fjxwlpNn\nS+0dRzgRKRaiQUUl5aTtzSW4gxcDeslcUM5OpVIxfczFxwV8vPGIndMIZyLFQjQodeMRqmsUpo7o\njlqtsncc0QwG9O5It2Bffth/mhNnSuwdp47qGgu5BQb2Hy3k4PEi8opMKIpi71gCG05RLpxfXpGJ\nb3ZkE9Te03pTl3B+KpWK2eN78+pfd5D0nwxee2IIKpX9/hBQFIWfDhfw7c5s9h4upLyyus56H08d\ng/sGcc+dYUSG+tkppZBiIa5r/TeZ1FgUZo7rhVYjF6GtSWyvjsT2CkSfWcCug3ncebt97p3Zf7SQ\npP9kcOLMxf6ToPaeDIkKokM7D2osFvKKyjh4/Bzf7Mjmmx3ZDOjdkYfv60N4kAy0aGlSLES9Dh4v\nYrM+l26dfYnrb//nN4vm9+ik29l7ZDNJ/zlIv8gOLfogK1N5FWs27Cdt72lUKhgeHcKUERF0D2l3\nzbY1FoX9Rwv5eNMR9vycz97DBdw/ugfT747ERSv3/LQUKRbiGtU1Fv6yYT8Ac6ZFSV9FK9WlozdT\n4iL49PtjfPjlzzw+pW+LvG9mdjFv/kNPQXEZkaHteDI+ih5drt+8pFGriOkZSHRkB/SZBazZsJ+U\n746wMyOPhbMHEBLo3SK52zppWxDXSP7uMNl5Bu65M4xe4f72jiNsaOa4XoQEevH5D8fZf6TQpu9l\nURQ+3nSEhX/eSuH5MqbfHckbTw1rsFBcSaVSMaB3R/68YCRjB4Vx8mwp8/64he9/yrVpbnGRFAtR\nx4Gsc3y88QiBfu78cuJt9o4jbMzVRcO8B2PQqFW88Y89FJwvs8n7FJdWsO5/5/jwy59p5+XK0ieH\n8NC9vdHcRF+Yh5sLTz3Qn+ceGoBKpWLlR3re/WQ/lVUyQaItSbEQVgXny3hz3R5QqVgwawBe7i72\njiRaQGSoH49P7Uupyczra3c1++NXdx3KY+7KzZzIr+SOPh1ZPX8EUd073PJxh0UH88d5wwkP8uHr\nH0+y4E9p5OQbbj2wqJcUCwGAsczM7/+2g/OGSh6ZeBu9u0rzU1ty7+Bwxg4K4/jpEn7/tx3XDF+9\nGZVVNbz3aTqvJe3EVF7NuFhfXn7kTny9XJsh8UXBHbx46+k47h0cfrFZ6u0tbJJndtiEFAvBBUMl\nL/5lO6fyDEwY2pXJcTKtR1ujUqmYM60fcf2D+flkMS+s2UpRSflNH+9A1jmeWfU9X2w7QWgnb1Y9\nE8egnt42uZ/D1UXDr+/vx8LZA9CoVbydvJdV6/UYm/kKqa2T0VBt3IkzJSz/f7s5e87EuMHh/Gpy\ny4yIEY5Ho1Yxb2YMrjoN3+06xTN/3MKvp/W7oeeXnD1nYv23mXyvz0WlgonDuvHwfX1wddFQfNaG\n4YGh/YLpHtKON/+xh836XPYeKeTxyX0Z2r+zXW86bC2kWLRR1TUWPv/hOP/4OhNzVQ0Jd0fy0Lhe\n8kPVxmk1an6b0J+wIB/+3xeHWPbBLvr36ED8yO5E9eiApp5h1DUWhQPHCtm4K4cf9p/GYlGICPHl\n19P6tfgd153ae/KHp4bxr++PkfztYd74xx6++jGAh+7tRZ+u7Vs0S2sjxaKNqa6xsHX/GVI3HiEn\n34C3h47fPRTLIDvdwSscj0qlYnJcBDE9A3n/swPsO1LIvqOF+Hrp6BXmT1CAJ64uGkzlVZw5ZyIz\nu5iyiot9HKGdvJlxd0+G9Otcb2FpCVqNmgdGRzK0XzDvf3aAPT/ns/DPW4nqHsD4IV258/ZOMiPB\nTbBZsbBYLLz66qscPnwYnU7H0qVLCQu7/OCc1NRUkpOT0Wq1zJkzh5EjR1JcXMyCBQuoqKggMDCQ\n5cuX4+7uXu+2ounKK6s5nF3Mjow8fjxwhuLSStQqGDsojNnj++DjqbN3ROGAunT05rUnhnA4u5iN\nu3PYdfAsOw/mXbNd5wBP4qJDGBkbQu9wf4e5Og0K8OSVxwZx6EQR67/JZP/Rc6QfO4e3h44BvQMZ\neFsneof7097X3d5RnYLNisXGjRsxm82kpKSwb98+VqxYwV/+8hcACgsLWbduHRs2bKCyspKZM2dy\n1113sWbNGiZMmEB8fDzvv/8+KSkp3HffffVuq9PZ5hecoigoCigXX1xcdvlLQLF+rVyxD1csu3KW\nzKu3RVGu2O/ytqaKGkqMlZfe+/JOdd7j6mNe2r/CXE1ZZTVlFdWUV1RTYqqkoLiMgvPl5OQbOJVX\niuXSPt4eLtZO7E7tPW/yLIm2pGeYPz3D/PnN/f24YKik4HwZ5qoaPNxcCGjn7vB/bPTp2p6lT95F\nTr6Br388ydb9Z9isz2Wz/uLNfO193Qjr5ENHfw8C/T3w93HDy90Fz0v/ig3VFBSXodGo0GrUaDRq\ntGoVarWKunXx8osrl9fdRFXv8uasr7Yq1jYrFnq9nmHDhgHQv39/MjIyrOvS09OJjo5Gp9Oh0+kI\nDQ0lMzMTvV7PE088AUBcXByrVq2iS5cu9W4bFRXV7JkPnSji5f/7EbO9bu75tPl7AHUuGnqF+xMZ\n6kdsr0BujwiQS3Bx09p5u9LOu/mGvrakLh29+dWUvjw2+XayckvQH87n6KkLHM05z0+HCxre+fNr\nr6gc1axxvZgxpmezH9dmxcJoNOLl5WV9rdFoqK6uRqvVYjQa8fa+PJ+Lp6cnRqOxznJPT08MBsN1\nt22MXq+/4czFxmrCOrhQVXPxtFyv8quuWHnl3wn1/jWhqvv1tcet5y8NVSPvfcVCFaDTqnB1UeHq\nosbVRY27Tk07Lw3tPLV4uakvze1USY0hh/37cq7z6W/NzZxvRyC5W5Yj5e7uB939tNzbrwOVVRYu\nmGo4b6zGVGGhospChfniv6oaBYvlYke+RQGLRaHGctXV/nXe43qP4lCu++IWqaDaVIhef/F3ZHOe\nb5sVCy8vL0wmk/W1xWJBq9XWu85kMuHt7W1d7ubmhslkwsfH57rbNiY2Nvamco8ZflO73TK9Xn/T\nme3NWbNL7pYluVvWzeRuqLjYrD0iJiaGtLQ0APbt20dkZKR1XVRUFHq9nsrKSgwGA1lZWURGRhIT\nE8OWLVsASEtLIzY29rrbCiGEaDk2u7IYM2YM27ZtY8aMGSiKwrJly1i7di2hoaGMHj2axMREZs6c\niaIozJs3D1dXV+bMmcPChQtJTU3Fz8+PlStX4uHhUe+2QgghWo7NioVarWbJkiV1lkVEXJ5GIiEh\ngYSEhDrrAwICSEpKuuZY9W0rhBCi5ciwGCGEEI2SYiGEEKJRUiyEEEI0SoqFEEKIRkmxEEII0SiV\nolzvHkPn5Uh3iQohhDO53o18rbJYCCGEaF7SDCWEEKJRUiyEEEI0SoqFEEKIRkmxEEII0SgpFkII\nIRolxUIIIUSjbDbrrGjYlClTrA9xCgkJYfr06bz++utoNBqGDh3KU089ZeeEde3fv5+33nqLdevW\nkZ2dzaJFi1CpVPTo0YNXXnkFtVrNn//8Z77//nu0Wi0vvPCCTR59eyu5Dx48yJNPPkl4eDgADz74\nIOPHj3e43FVVVbzwwgucPn0as9nMnDlz6N69u8Of8/pyd+rUyeHPeU1NDS+99BInTpxAo9GwfPly\nFEVx+PNdX26DwWC7862IFldRUaFMnjy5zrJJkyYp2dnZisViUR577DElIyPDTumu9f777ysTJkxQ\nHnjgAUVRFOWJJ55QduzYoSiKorz88svKt99+q2RkZCiJiYmKxWJRTp8+rcTHx9szsqIo1+ZOTU1V\nkpKS6mzjiLk/+eQTZenSpYqiKEpxcbEyfPhwpzjn9eV2hnP+3XffKYsWLVIURVF27NihPPnkk05x\nvuvLbcvzLc1QdpCZmUl5eTmPPPIIs2fPZvfu3ZjNZkJDQ1GpVAwdOpQff/zR3jGtQkNDeeedd6yv\nDx48yMCBAwGIi4tj+/bt6PV6hg4dikqlonPnztTU1FBcXGyvyMC1uTMyMvj++++ZNWsWL7zwAkaj\n0SFzjxs3jqefftr6WqPROMU5ry+3M5zzu+++m9deew2AM2fOEBAQ4BTnu77ctjzfUizswM3NjUcf\nfZSkpCR+//vf8/zzz+Pu7m5d7+npicFgsGPCusaOHWt9fjpcfFC9SqUCLmc1Go14eXlZt3GEz3B1\n7qioKJ577jk++ugjunTpwrvvvuuQuT09PfHy8sJoNDJ37lyeeeYZpzjn9eV2lnOu1WpZuHAhr732\nGmPHjnWK8w3X5rbl+ZZiYQddu3Zl0qRJqFQqunbtire3NxcuXLCuN5lM+Pj42DFhw9Tqy982tVm9\nvLwwmUx1ltf2yTiKMWPGcPvtt1u/PnTokMPmPnv2LLNnz2by5MlMnDjRac751bmd6Zz/4Q9/4Jtv\nvuHll1+msrLSutyRzzfUzT106FCbnW8pFnbwySefsGLFCgDy8/MpLy/Hw8ODU6dOoSgKW7duZcCA\nAXZOeX19+vRh586dAKSlpTFgwABiYmLYunUrFouFM2fOYLFY8Pf3t3PSuh599FHS09MB+PHHH7nt\nttscMve5c+d45JFH+N3vfsf9998POMc5ry+3M5zzzz77jP/7v/8DwN3dHZVKxe233+7w57u+3E89\n9ZTNzreMhrKD+++/n+eff54HH3wQlUrFsmXLUKvVLFiwgJqaGoYOHUq/fv3sHfO6Fi5cyMsvv8yq\nVavo1q0bY8eORaPRMGDAAKZPn47FYmHx4sX2jnmNV199lddeew0XFxcCAgJ47bXX8PLycrjc7733\nHqWlpaxZs4Y1a9YA8OKLL7J06VKHPuf15V60aBHLli1z6HN+zz338PzzzzNr1iyqq6t54YUXiIiI\ncPjv8fpyBwUF2ex7XGadFUII0ShphhJCCNEoKRZCCCEaJcVCCCFEo6RYCCGEaJQUCyGEEI2SYiFE\nC8jPz+dXv/pVg9u88847daYnqZWTk8MLL7xgq2hCNIkUCyFaQMeOHfnrX/96U/ueOXOGnJycZk4k\nxI2RYiHEDZo4cSJZWVkAzJ8/n1deeQWAvXv38vjjj/P+++8zdepUJk2axBtvvIGiKOTm5jJq1CgA\n8vLyeOihh5g4cSLz588nLi7Oeuz09HRmzJjByJEjrVcZS5cuJSMjg9///vct/EmFuEyKhRA3aPjw\n4dZZgY8cOcJPP/0EwA8//MCIESPIyMjgk08+4bPPPiM/P5///Oc/dfZ//fXXuffee/n8888ZN24c\n+fn51nVFRUV8+OGHbNiwgaSkJIxGIy+99BK33367tSgJYQ9SLIS4QbXF4tixY3Tv3h21Wk1RURFp\naWkcOHCA9PR04uPjmTp1KhkZGRw7dqzO/tu2bWPy5MnAxcnerpw0ctiwYeh0Ovz9/fHz86OkpKRF\nP5sQ1yNzQwlxg6Kjo1m0aBHbt29n4MCBtG/fnq+//prq6mq8vb15+OGH+eUvfwlAaWkpGo2G8+fP\nW/fXaDRcb5adK6dUV6lU191OiJYmVxZC3CCtVktUVBTr1q1j4MCBDBo0iPfee4/hw4czaNAg/v3v\nf2MymaiuruY3v/kN33zzTZ39Bw8ezOeffw7Ali1bKC0tbfD9NBoN1dXVNvs8QjSFFAshbsLw4cMp\nLy8nIiKCgQMHUlRUxIgRIxg1ahT33HMPCQkJTJgwgV69ejF16tQ6+7744ot8++23TJkyha+++qrR\nZ5dERERgMBj43e9+Z8uPJESDZNZZIVrYhx9+yJAhQ+jevTsHDx7k5Zdf5tNPP7V3LCEaJH0WQrSw\nsLAwnn32WdRqNa6urkQfJNQAAAA1SURBVNbnKAvhyOTKQgghRKOkz0IIIUSjpFgIIYRolBQLIYQQ\njZJiIYQQolFSLIQQQjTq/wMuOHolpjBPVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1734a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Density Plot of the Weight')\n",
    "plt.xlabel('weight')\n",
    "weight.plot.density()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis testing via Z test\n",
    "Now lets use a different approach this time we will plot a density plot. Looking at the plot confirms that is is slightly asymmetrical to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average = gpa_data['GPA'].astype(float).dropna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4155583333333315"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpa_data = gpa_data[gpa_data['weight'] != \"I'm not answering this. \"]\n",
    "gpa_data = gpa_data[gpa_data['weight'] != \"Not sure, 240\"]\n",
    "gpa_data = gpa_data[gpa_data['weight'] != \"144 lbs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "high_gpa = gpa_data[gpa_data['GPA'].astype(float) > average]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "low_gpa = gpa_data[gpa_data['GPA'].astype(float) <= average]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPA</th>\n",
       "      <th>Gender</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>calories_chicken</th>\n",
       "      <th>calories_scone</th>\n",
       "      <th>coffee</th>\n",
       "      <th>drink</th>\n",
       "      <th>thai_food</th>\n",
       "      <th>tortilla_calories</th>\n",
       "      <th>turkey_calories</th>\n",
       "      <th>veggies_day</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>waffle_calories</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.654</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>725.0</td>\n",
       "      <td>690</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>900</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>940.0</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>760</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>940.0</td>\n",
       "      <td>690</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1315</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>980.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>940.0</td>\n",
       "      <td>345</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>900</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>940.0</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GPA  Gender  breakfast  calories_chicken  calories_scone  coffee  drink  \\\n",
       "1   3.654       1          1               610           420.0       2    2.0   \n",
       "4     3.5       1          1               720           420.0       2    2.0   \n",
       "6     3.8       2          1               610           420.0       2    1.0   \n",
       "10    3.5       1          1               610           980.0       2    2.0   \n",
       "11  3.904       1          1               720           420.0       2    1.0   \n",
       "\n",
       "    thai_food  tortilla_calories  turkey_calories  veggies_day  vitamins  \\\n",
       "1           2              725.0              690            4         2   \n",
       "4           4              940.0              500            4         2   \n",
       "6           5              940.0              690            4         1   \n",
       "10          2              940.0              345            5         2   \n",
       "11          5              940.0              500            5         1   \n",
       "\n",
       "    waffle_calories weight  \n",
       "1               900    155  \n",
       "4               760    190  \n",
       "6              1315    180  \n",
       "10              900    116  \n",
       "11              900    110  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_gpa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 14)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_gpa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPA</th>\n",
       "      <th>Gender</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>calories_chicken</th>\n",
       "      <th>calories_scone</th>\n",
       "      <th>coffee</th>\n",
       "      <th>drink</th>\n",
       "      <th>thai_food</th>\n",
       "      <th>tortilla_calories</th>\n",
       "      <th>turkey_calories</th>\n",
       "      <th>veggies_day</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>waffle_calories</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>345</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1315</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>610</td>\n",
       "      <td>980.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>940.0</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1315</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>725.0</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1315</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>725.0</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>760</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>430</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>580.0</td>\n",
       "      <td>345</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GPA  Gender  breakfast  calories_chicken  calories_scone  coffee  drink  \\\n",
       "0   2.4       2          1               430           315.0       1    1.0   \n",
       "5  2.25       1          1               610           980.0       2    2.0   \n",
       "7   3.3       1          1               720           420.0       1    2.0   \n",
       "8   3.3       1          1               430           420.0       1    1.0   \n",
       "9   3.3       1          1               430           315.0       2    1.0   \n",
       "\n",
       "   thai_food  tortilla_calories  turkey_calories  veggies_day  vitamins  \\\n",
       "0          1             1165.0              345            5         1   \n",
       "5          4              940.0              345            1         2   \n",
       "7          1              725.0              500            4         2   \n",
       "8          5              725.0              345            3         2   \n",
       "9          4              580.0              345            5         1   \n",
       "\n",
       "   waffle_calories weight  \n",
       "0             1315    187  \n",
       "5             1315    190  \n",
       "7             1315    137  \n",
       "8              760    180  \n",
       "9              900    125  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_gpa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 14)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_gpa.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally lets split the table into 2 parts of low and high gpa with the dividing part being the mean or 3.41.We will test if there is  difference in the weight of the 2 groups. This will tell us if the weight is having a statistically significant effect. Lets use a boxplot first. We again need to remove the invalid rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XqVR4BC+Q/MjhjICISOEYBERECscgICJSOAYBEZHCMQiIiBSOQUBEpHAMAiIihWMQEBEp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\nkcIxCIiIFO7/AVh1v9dq3ekEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf17548588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([low_gpa['weight'].astype(float).dropna(), high_gpa['weight'].astype(float)], labels = [\"low gpa weights\", \"high gpa weights\"])\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.title('Boxplot of the weights')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance it seems that the low gpa have an average higher weight and a more right asymmetrical. Lets use the skew function to see more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.043960686080514"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_gpa['weight'].astype(float).dropna().skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69642826590945905"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_gpa['weight'].astype(float).dropna().skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both have a right asymmetricity more evident in the low_gpa weights. According this study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5222954/ there is a negative link between the weigh of the student and their academic performance. Let see if our results will agree with the study. \n",
    "<br>\n",
    "This time we will use a z test, considering we have more then 30 observations in both population (according to other sources its 50) otherwise we will do Sudent's t test.\n",
    "The null hypothesis H0 is that we don't have a difference, the alternative H1 is that we have. We assume a normal distribution of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'low gpa weights histogram')"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf1764ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(low_gpa['weight'].astype(float).dropna(), bins = 20)\n",
    "plt.title('low gpa weights histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'high gpa weights histogram')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf18704518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(high_gpa['weight'].astype(float).dropna(), bins = 20)\n",
    "plt.title('high gpa weights histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The higher one is closer to the normal, although both have 3 total outliers. We have enough population so that the outliers don't have a major effect so we decide to leave them here this time. If should be tried to remove them to see if there is a major difference in the results.\n",
    "<br>\n",
    "Now lets start the z test you can read more here about it http://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.ztest.html. The samples are assumed to be independent, i don't see how the weight of someone will affect the weight of other people, so lets keep this assumption present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z, p = weightstats.ztest(low_gpa['weight'].astype(float).dropna(),high_gpa['weight'].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1980326329585269, 0.23090431307306936)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is grater then our default value of 0.05 so we conclude that the H0 hypothesis is the correct one. Our study contradicts the previous study. The likely have a larger sample size, our samples the distributions are not entirely normal but lets assume they are. More evaluation is needed in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "We had some short introduction to this file, we have gained some understanding of the makeups of some columns and performed a hypothesis testing to if the gpa has an effect on the weight. This turned out negative contradicting the mentioned study above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Development\n",
    "Provided we have more time, more of the variables need to be explored, some regression model can be tried out, more in depth exploration of the categorical variables is needed."
   ]
  }
 ],
 "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": "3.6.3"
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 },
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}