Analyze_AB_Test_Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze A/B Test Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![148269_5185024665e9f016d26d94_big.jpg](attachment:148269_5185024665e9f016d26d94_big.jpg)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Table of Contents\n",
    "1. [Introduction](#intro)\n",
    "2. [Part I - Probability](#probability)\n",
    "3. [Part II - A/B Test](#ab_test)\n",
    "4. [Part III - Regression](#regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "A/B tests are very commonly performed by data analysts and data scientists. It is important that we get some practice working with the difficulties of these tests.\n",
    "\n",
    "For this project, we will be working to understand the results of an A/B test run by an e-commerce website. our goal is to work through this notebook to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the needed libraries\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import timeit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='probability'></a>\n",
    "## Part I - Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the \"ab_data\" dataset\n",
    "AB_data= pd.read_csv('ab_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>936923</td>\n",
       "      <td>2017-01-10 15:20:49.083499</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>679687</td>\n",
       "      <td>2017-01-19 03:26:46.940749</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>719014</td>\n",
       "      <td>2017-01-17 01:48:29.539573</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817355</td>\n",
       "      <td>2017-01-04 17:58:08.979471</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>839785</td>\n",
       "      <td>2017-01-15 18:11:06.610965</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0\n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0\n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0\n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0\n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1\n",
       "5   936923  2017-01-10 15:20:49.083499    control     old_page          0\n",
       "6   679687  2017-01-19 03:26:46.940749  treatment     new_page          1\n",
       "7   719014  2017-01-17 01:48:29.539573    control     old_page          0\n",
       "8   817355  2017-01-04 17:58:08.979471  treatment     new_page          1\n",
       "9   839785  2017-01-15 18:11:06.610965  treatment     new_page          1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AB_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**user_id**: the id associated with each user and it's unique; hence no two users share the same id.<br>\n",
    "**timestamp**: is the date and time as when the user accessed the page. <br>\n",
    "**group**: `1- treatment`, which are the people who got experemented on using the new page. `2- control`, which are the people who kept using the old page, and they're used as a baseline measure. <br>\n",
    "**landing_page**: the `old` and the `new` page.<br>\n",
    "**converted**: `0` for the users who did not pay for the company's product, and `1` for who did. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294478"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the number of rows\n",
    "all_users=AB_data.shape[0]\n",
    "all_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 294478 entries, 0 to 294477\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   user_id       294478 non-null  int64 \n",
      " 1   timestamp     294478 non-null  object\n",
      " 2   group         294478 non-null  object\n",
      " 3   landing_page  294478 non-null  object\n",
      " 4   converted     294478 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "AB_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290584"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of unique useres in the dataset\n",
    "unique_users= AB_data['user_id'].nunique()\n",
    "unique_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35173"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the number of users converted\n",
    "converted_users= (AB_data.query('converted==1')['user_id'].nunique())\n",
    "converted_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12104245244060237"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the proportion of users converted\n",
    "prop_converted_users= converted_users/unique_users\n",
    "prop_converted_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of the converted users equals approximately  12%\n"
     ]
    }
   ],
   "source": [
    "print(\"The proportion of the converted users equals approximately {0: .0%}\".format(prop_converted_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3893"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of times the new_page and treatment don't line up\n",
    "NP_trmnt_NoLineUp=AB_data.query('(landing_page != \"new_page\" and group == \"treatment\") or (landing_page == \"new_page\" and group != \"treatment\")')['user_id'].count()\n",
    "NP_trmnt_NoLineUp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There's no missing value in ab_data dataset.\n"
     ]
    }
   ],
   "source": [
    "# missing values\n",
    "if (AB_data.isnull().values.any()) == False:\n",
    "    print(\"There's no missing value in ab_data dataset.\")\n",
    "elif (AB_data.isnull().values.any()) == True:\n",
    "    null_num= AB_data.isnull().sum()\n",
    "    if (null_num==1):\n",
    "        print(\"There's {} missing value in ab_data dataset.\".format(null_num))\n",
    "    elif (null_num>1):\n",
    "        print(\"There're {} missing values in ab_data dataset.\".format(null_num))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "AB_data_new= AB_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I made a copy to keep my original data intact, in case anything went wrong during the analysis process "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "AB_data_new.drop(AB_data_new.query('(landing_page != \"new_page\" and group == \"treatment\") or (landing_page == \"new_page\" and group != \"treatment\") or (landing_page != \"old_page\" and group == \"control\") or (landing_page == \"old_page\" and group != \"control\")').index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I deleted the rows where treatment is not aligned with new_page or control is not aligned with old_page, because we cannot be sure if these rows truly received the new or old page. Hence it's useless to me. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3893"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making sure that the dropping process went well by subtracting the number of all users from our new updated dataset; the result should equal NP_trmnt_NoLineUp which I already calculated \n",
    "check= all_users - AB_data_new.shape[0]\n",
    "check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making sure that the droped rows were the intended ones, the resault of the code should be 0\n",
    "AB_data_new[((AB_data_new.landing_page == 'new_page')==(AB_data_new.group == 'treatment')) == False].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dropping process went very well. The rows that has mismatching were eliminated successfully. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290584"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of unique user_ids in AB_data_new\n",
    "AB_data_new['user_id'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290585"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AB_data_new.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it shows from the previous two cells that there's one repeated user id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>773192</td>\n",
       "      <td>2017-01-09 05:37:58.781806</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2893</th>\n",
       "      <td>773192</td>\n",
       "      <td>2017-01-14 02:55:59.590927</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id                   timestamp      group landing_page  converted\n",
       "1899   773192  2017-01-09 05:37:58.781806  treatment     new_page          0\n",
       "2893   773192  2017-01-14 02:55:59.590927  treatment     new_page          0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the duplicated user_id with the row information\n",
    "AB_data_new[AB_data_new.duplicated(subset=['user_id'], keep= False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting rid of one of the duplicates\n",
    "AB_data_new.drop(AB_data_new[(AB_data_new['user_id'] == 773192) & (AB_data_new['timestamp'] == '2017-01-09 05:37:58.781806')].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I got rid of one of the duplicates since the only difference between them is the timestame, and this difference does not affect my work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2893</th>\n",
       "      <td>773192</td>\n",
       "      <td>2017-01-14 02:55:59.590927</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id                   timestamp      group landing_page  converted\n",
       "2893   773192  2017-01-14 02:55:59.590927  treatment     new_page          0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the succsess of the dropping process \n",
    "AB_data_new[(AB_data_new['user_id'])==773192]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there's only one user with the id **773192**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34753"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of converted users in AB_data_new\n",
    "new_converted_users= AB_data_new.query('converted == 1')['user_id'].nunique()\n",
    "new_converted_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the probability of an individual converting regardless of the page they receive\n",
    "new_prob_converted_users= new_converted_users/AB_data_new.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of an individual converting regardless of the page they receive equals 11.96%\n"
     ]
    }
   ],
   "source": [
    "print(\"The probability of an individual converting regardless of the page they receive equals {:.2%}\".format(new_prob_converted_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17489"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the users who converted while they're in the control group\n",
    "control_converted_users= AB_data_new.query('group == \"control\" and converted == 1')['user_id'].nunique()\n",
    "control_converted_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145274"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the users in the control group\n",
    "control_users= AB_data_new.query('group == \"control\"')['user_id'].nunique()\n",
    "control_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the probability that users converted while they were in the control group\n",
    "prob_control_converted_users= control_converted_users/control_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that users converted while they were in the control group equals 12.04%\n"
     ]
    }
   ],
   "source": [
    "print(\"The probability that users converted while they were in the control group equals {:.2%}\".format(prob_control_converted_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17264"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the users who converted while they're in the treatment group\n",
    "treatment_converted_users= AB_data_new.query('group == \"treatment\" and converted == 1')['user_id'].nunique()\n",
    "treatment_converted_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145310"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the users in the treatment group\n",
    "treatment_users= AB_data_new.query('group == \"treatment\"')['user_id'].nunique()\n",
    "treatment_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the probability that users converted while they were in the treatment group\n",
    "prob_treatment_converted_users= treatment_converted_users/treatment_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that users converted while they were in the treatment group equals 11.88%\n"
     ]
    }
   ],
   "source": [
    "print(\"The probability that users converted while they were in the treatment group equals {:.2%}\".format(prob_treatment_converted_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145310"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the numbers of users who received the new page\n",
    "NewPage_users= AB_data_new.query('landing_page == \"new_page\"')['user_id'].nunique()\n",
    "NewPage_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5000619442226688"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the probability that an individual received the new page\n",
    "prob_NewPage= NewPage_users/AB_data_new.shape[0]\n",
    "prob_NewPage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that an individual received the new page equals 50.01%\n"
     ]
    }
   ],
   "source": [
    "print(\"The probability that an individual received the new page equals {:.2%}\".format(prob_NewPage))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notwithstanding the page that the users received, the probability of them converting is 11.96%. And if we took into consideration that the users were in the contol group (hence received the old page), the probability of them converting is 12.04%. Moreover, if we took into account that the users were in the treatment group (hence received the new page), the probability of them converting is 11.88%. <br>\n",
    "If we look at this point at the numbers, we would probably say that the old page did a better job making the users convert, though it's a negligible convert. **Yet**, we can hardly consider these numbers a sufficient evidence; because the resaults are pretty close to each other and couldn't help us decide whether any page would lead to more conversions or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ab_test'></a>\n",
    "## Part II - A/B Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now, let's consider for the sake of argument that I need to make the decision just based on all the data provided. If I want to assume that the old page is better unless the new page proves to be definitely better at a Type I error rate of 5%, the null hypotheses would be **H0: $p_{new}$ - $p_{old}$<=0**, and the alternative hypotheses would be **H1: $p_{new}$ - $p_{old}$ >0.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assume under the null hypothesis, $p_{new}$ and $p_{old}$ both have \"true\" success rates equal to the **converted** success rate regardless of page - that is $p_{new}$ and $p_{old}$ are equal. Furthermore, let's assume they are equal to the **converted** rate in **ab_data.csv** regardless of the page.<br>\n",
    "we're going to perform the sampling distribution for the difference in converted between the two pages over 10,000 iterations of calculating an estimate from the null:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11959708724499628"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the convert rate for 𝑝𝑛𝑒𝑤 under the null\n",
    "p_new= AB_data_new.query('converted == 1')['user_id'].nunique()/AB_data_new.user_id.nunique()\n",
    "p_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11959708724499628"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the convert rate for 𝑝𝑜𝑙𝑑 under the null\n",
    "p_old= AB_data_new.query('converted == 1')['user_id'].nunique()/AB_data_new.user_id.nunique()\n",
    "p_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145310"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the number of unique users who has new page using AB_data_new dataframe\n",
    "n_new= AB_data_new.query('landing_page == \"new_page\"')['user_id'].nunique()\n",
    "n_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145274"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the number of unique users who has old page using AB_data_new dataframe\n",
    "n_old= AB_data_new.query('landing_page == \"old_page\"')['user_id'].nunique()\n",
    "n_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simulate n𝑛𝑒𝑤 transactions with a convert rate of p𝑛𝑒𝑤 under the null\n",
    "new_page_converted= np.random.choice([0,1], n_new, p=(p_new, 1-p_new))\n",
    "new_page_converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Simulate n𝑜𝑙𝑑 transactions with a convert rate of p𝑜𝑙𝑑 under the null\n",
    "old_page_converted= np.random.choice([0,1], n_old, p=(p_old, 1-p_old))\n",
    "old_page_converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0008170246352350308"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 𝑝𝑛𝑒𝑤 - 𝑝𝑜𝑙𝑑 for the simulated values in the previous two cells\n",
    "(new_page_converted.mean())-(old_page_converted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simulate 10,000 𝑝𝑛𝑒𝑤 - 𝑝𝑜𝑙𝑑 values like what we just did \n",
    "new_converted_simulation = np.random.binomial(n_new, p_new, 10000)/n_new\n",
    "old_converted_simulation = np.random.binomial(n_old, p_old, 10000)/n_old\n",
    "p_diffs = new_converted_simulation - old_converted_simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  23.,  181.,  744., 1931., 2881., 2482., 1272.,  404.,   76.,\n",
       "           6.]),\n",
       " array([-0.00429   , -0.00339045, -0.0024909 , -0.00159135, -0.0006918 ,\n",
       "         0.00020775,  0.0011073 ,  0.00200685,  0.0029064 ,  0.00380595,\n",
       "         0.0047055 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a histogram of the p_diffs\n",
    "plt.hist(p_diffs, color='crimson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The proportion of the p_diffs that are greater than the actual difference observed in ab_data.csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribution under the null hypothesis\n",
    "null_v= np.random.normal(0, p_diffs.std(), p_diffs.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of users who converted in the new page and the old page\n",
    "new_page_converted_users= AB_data_new.query('converted == 1 and landing_page == \"new_page\"')['user_id'].nunique()\n",
    "old_page_converted_users= AB_data_new.query('converted == 1 and landing_page == \"old_page\"')['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# actual convert rate\n",
    "new_actual_convert= new_page_converted_users/n_new\n",
    "old_actual_convert= old_page_converted_users/n_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0015782389853555567"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#observed difference in converted rate \n",
    "converte_observed_difference= new_actual_convert - old_actual_convert\n",
    "converte_observed_difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f8afec25460>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the proportion of the p_diffs that are greater than the actual difference observed in ab_data.csv\n",
    "plt.hist(p_diffs, color='crimson')\n",
    "plt.axvline(x=converte_observed_difference,color ='greenyellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9076"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the proportion of the p_diffs that are greater than the actual difference observed in ab_data.csv\n",
    "p_value1=(null_v >converte_observed_difference).mean()\n",
    "p_value1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In scientific studies, there's a value calld the p-value. If we assumed that the null hypothesis was true and the p-value was less than 0.05, that would indicate there's a low probability in finding values remarkably greater-than, less-than or equal to `converte_observed_difference`. Since `p_old` (or `p_new`) does not fall within the critical region, we fail to reject the null hypothesis (p_old > Alpha --> 0.11959708724499628>0.05). Therefore, with a Type I error rate of 5% there's a higher probability of conversion in the old page than the new page. Also, the p_value is greater than Alpha, hence we accept the null hypothesis.<br>\n",
    "P-values are the probability of obtaining an effect at least as extreme as the one in our sample data, assuming the truth of the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "source: https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-significance-levels-alpha-and-p-values-in-statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# useing a built-in to achieve similar results to what we did above.\n",
    "import statsmodels.api as sm\n",
    "\n",
    "convert_old = old_page_converted_users\n",
    "convert_new = new_page_converted_users\n",
    "n_old=n_old\n",
    "n_new=n_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using stats.proportions_ztest to compute my test statistic and p-value\n",
    "Ztest_score, p_value2 = sm.stats.proportions_ztest(np.array([convert_new,convert_old]),np.array([n_new,n_old]), alternative = 'larger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.3109241984234394, 0.9050583127590245)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ztest_score, p_value2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The minus sign before the `Ztest_score` value indicats that the value is below the mean.<br>\n",
    "Since the `Ztest_score` (which is 1.3109241984234394) is not greater than 1.96 (which is the the critical value with significance of 5%), we fail to reject the null hypothesis; which means that the old page has a better converted rate than the new page or equal to it. Moreover, the `p-value` (which is 0.9050583127590245) is not less than 0.05, and it is nearly similar to the p-vale that we calculated previously (which is 0.9026). And these conculsions proves that the z-test is consistent with what we achieved so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjt7f_rkvPsAhWVQhUIHe4mCpYQFjABegQIBhAC&url=http%3A%2F%2Fcourses.washington.edu%2Furbdp520%2FUDP520%2FABHypTest.pdf&usg=AOvVaw1wbETcMrBlWGHcFeDhTm5O"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='regression'></a>\n",
    "## Part III - A regression approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since each row is either a conversion or no conversion, Logistic Regression would be the ideal type of regression in this case. And also because we are intrested in knowing the odds of conversion, and Logistic Regression works with the categorical outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a column for the intercept\n",
    "AB_data_new['intercept'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we need an intercept term so that our regression output does not automatically predict probability 1/2 when our variables are all zero.<br><br>\n",
    "source: https://www.quora.com/Why-do-we-add-a-column-of-1s-intercept-in-multiple-logistic-regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>intercept</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted  \\\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0   \n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0   \n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0   \n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0   \n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1   \n",
       "\n",
       "   intercept  new_page  old_page  \n",
       "0          1         0         1  \n",
       "1          1         0         1  \n",
       "2          1         1         0  \n",
       "3          1         1         0  \n",
       "4          1         0         1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a dummy variable column for which page each user received\n",
    "AB_data_new= AB_data_new.join(pd.get_dummies(AB_data_new['landing_page']))\n",
    "AB_data_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>intercept</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "      <th>ab_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted  \\\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0   \n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0   \n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0   \n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0   \n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1   \n",
       "\n",
       "   intercept  new_page  old_page  ab_page  \n",
       "0          1         0         1        0  \n",
       "1          1         0         1        0  \n",
       "2          1         1         0        1  \n",
       "3          1         1         0        1  \n",
       "4          1         0         1        0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding an ab_page column, which is 1 when an individual receives the treatment and 0 if control\n",
    "AB_data_new['ab_page'] = pd.get_dummies(AB_data['group']) ['treatment']\n",
    "AB_data_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to use statsmodels to import my regression model. Instantiate the model, and fit the model using the two columns I created above to predict whether or not an individual converts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Logistic regression model for ab_page and the converted variable\n",
    "Logistic_model1 = sm.Logit(AB_data_new['converted'], AB_data_new[['intercept','ab_page']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.366118\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "Results1 = Logistic_model1.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:              converted   No. Observations:               290584\n",
      "Model:                          Logit   Df Residuals:                   290582\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Wed, 11 Nov 2020   Pseudo R-squ.:               8.077e-06\n",
      "Time:                        21:51:19   Log-Likelihood:            -1.0639e+05\n",
      "converged:                       True   LL-Null:                   -1.0639e+05\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.1899\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept     -1.9888      0.008   -246.669      0.000      -2.005      -1.973\n",
      "ab_page       -0.0150      0.011     -1.311      0.190      -0.037       0.007\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(Results1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, The p-value associated with ab_page is 0.190. This Logit Regression model is attempting to predict whether a user will convert based on whether their page is new or old. The null hypotheses is that there's no difference between the two groups (treatment and control). While the Alternative hypotheses is that there's difference.<br>\n",
    "In Part II the assumption is that the old page is better except if the new page proves to be better at a `Type I` error rate of 5%. This part has different explanatory varibale or factor for the result in comparison to Part II."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "Meanwhile, let's consider other things that might influence whether or not an individual converts; such as the time (timestamp variable). We can look and see if the converted rate is determined by a particular time or day when the users are using the website. And we can do so by turning the `timestamp` variable into a categorical variable, where it segments the time into `morning`, `afternoon`, `evening` and `past midnight`, or into `weekends` and `weekdays`. As helping and productive as this might seem, by adding additional terms into our regression model we're adding up to the complexity of interpretation. Also, the added terms might not affect the outcome, or it might affect the other participating variables; which might result in erroneous outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>Now along with testing if the conversion rate changes for different pages, we'll also add an effect based on which country a user lives. But first, we'll need to read in the countries.csv dataset and merge together our datasets on the approporiate rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>user_id</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>834778</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>928468</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>822059</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>711597</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>710616</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>909908</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>811617</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>938122</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>887018</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>820683</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id country\n",
       "0   834778      UK\n",
       "1   928468      US\n",
       "2   822059      UK\n",
       "3   711597      UK\n",
       "4   710616      UK\n",
       "5   909908      UK\n",
       "6   811617      US\n",
       "7   938122      US\n",
       "8   887018      US\n",
       "9   820683      US"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the \"countries\" dataset\n",
    "countries= pd.read_csv('countries.csv')\n",
    "countries.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>intercept</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "      <th>ab_page</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>936923</td>\n",
       "      <td>2017-01-10 15:20:49.083499</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>679687</td>\n",
       "      <td>2017-01-19 03:26:46.940749</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>719014</td>\n",
       "      <td>2017-01-17 01:48:29.539573</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817355</td>\n",
       "      <td>2017-01-04 17:58:08.979471</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>839785</td>\n",
       "      <td>2017-01-15 18:11:06.610965</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted  \\\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0   \n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0   \n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0   \n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0   \n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1   \n",
       "5   936923  2017-01-10 15:20:49.083499    control     old_page          0   \n",
       "6   679687  2017-01-19 03:26:46.940749  treatment     new_page          1   \n",
       "7   719014  2017-01-17 01:48:29.539573    control     old_page          0   \n",
       "8   817355  2017-01-04 17:58:08.979471  treatment     new_page          1   \n",
       "9   839785  2017-01-15 18:11:06.610965  treatment     new_page          1   \n",
       "\n",
       "   intercept  new_page  old_page  ab_page country  \n",
       "0          1         0         1        0      US  \n",
       "1          1         0         1        0      US  \n",
       "2          1         1         0        1      US  \n",
       "3          1         1         0        1      US  \n",
       "4          1         0         1        0      US  \n",
       "5          1         0         1        0      US  \n",
       "6          1         1         0        1      CA  \n",
       "7          1         0         1        0      US  \n",
       "8          1         1         0        1      UK  \n",
       "9          1         1         0        1      CA  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging the two datasets on user_id\n",
    "ABData_and_countries= AB_data_new.merge(countries, on ='user_id', how='left')\n",
    "ABData_and_countries.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['UK', 'US', 'CA'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the countries in country column to create the dummy variables\n",
    "countries.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the dummy variables\n",
    "ABData_and_countries[['CA','UK','US']] = pd.get_dummies(ABData_and_countries['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>intercept</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "      <th>ab_page</th>\n",
       "      <th>country</th>\n",
       "      <th>CA</th>\n",
       "      <th>UK</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>936923</td>\n",
       "      <td>2017-01-10 15:20:49.083499</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>679687</td>\n",
       "      <td>2017-01-19 03:26:46.940749</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>CA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>719014</td>\n",
       "      <td>2017-01-17 01:48:29.539573</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817355</td>\n",
       "      <td>2017-01-04 17:58:08.979471</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>UK</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>839785</td>\n",
       "      <td>2017-01-15 18:11:06.610965</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>CA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted  \\\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0   \n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0   \n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0   \n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0   \n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1   \n",
       "5   936923  2017-01-10 15:20:49.083499    control     old_page          0   \n",
       "6   679687  2017-01-19 03:26:46.940749  treatment     new_page          1   \n",
       "7   719014  2017-01-17 01:48:29.539573    control     old_page          0   \n",
       "8   817355  2017-01-04 17:58:08.979471  treatment     new_page          1   \n",
       "9   839785  2017-01-15 18:11:06.610965  treatment     new_page          1   \n",
       "\n",
       "   intercept  new_page  old_page  ab_page country  CA  UK  US  \n",
       "0          1         0         1        0      US   0   0   1  \n",
       "1          1         0         1        0      US   0   0   1  \n",
       "2          1         1         0        1      US   0   0   1  \n",
       "3          1         1         0        1      US   0   0   1  \n",
       "4          1         0         1        0      US   0   0   1  \n",
       "5          1         0         1        0      US   0   0   1  \n",
       "6          1         1         0        1      CA   1   0   0  \n",
       "7          1         0         1        0      US   0   0   1  \n",
       "8          1         1         0        1      UK   0   1   0  \n",
       "9          1         1         0        1      CA   1   0   0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABData_and_countries.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Logistic_model2 = sm.Logit(ABData_and_countries['converted'], ABData_and_countries[['intercept','new_page', 'UK', 'US']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.366113\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "Results2 = Logistic_model2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:              converted   No. Observations:               290584\n",
      "Model:                          Logit   Df Residuals:                   290580\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Wed, 11 Nov 2020   Pseudo R-squ.:               2.323e-05\n",
      "Time:                        21:51:20   Log-Likelihood:            -1.0639e+05\n",
      "converged:                       True   LL-Null:                   -1.0639e+05\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.1760\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept     -2.0300      0.027    -76.249      0.000      -2.082      -1.978\n",
      "new_page      -0.0149      0.011     -1.307      0.191      -0.037       0.007\n",
      "UK             0.0506      0.028      1.784      0.074      -0.005       0.106\n",
      "US             0.0408      0.027      1.516      0.130      -0.012       0.093\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(Results2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, based on the p-value shown it does not appear that the country had an impact on the conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>Though we have now looked at the individual factors of country and page on conversion, we would now like to look at an interaction between page and country to see if there's significant effects on conversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a new intereacton variable between new_page and country US, UK and CA\n",
    "ABData_and_countries['UK_new_page'] = ABData_and_countries['new_page'] * ABData_and_countries['UK']\n",
    "ABData_and_countries['US_new_page'] = ABData_and_countries['new_page'] * ABData_and_countries['US']\n",
    "ABData_and_countries['CA_new_page'] = ABData_and_countries['new_page'] * ABData_and_countries['CA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>intercept</th>\n",
       "      <th>new_page</th>\n",
       "      <th>old_page</th>\n",
       "      <th>ab_page</th>\n",
       "      <th>country</th>\n",
       "      <th>CA</th>\n",
       "      <th>UK</th>\n",
       "      <th>US</th>\n",
       "      <th>UK_new_page</th>\n",
       "      <th>US_new_page</th>\n",
       "      <th>CA_new_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:48.556739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45.159739</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06.154213</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03.143765</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26.210827</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   timestamp      group landing_page  converted  \\\n",
       "0   851104  2017-01-21 22:11:48.556739    control     old_page          0   \n",
       "1   804228  2017-01-12 08:01:45.159739    control     old_page          0   \n",
       "2   661590  2017-01-11 16:55:06.154213  treatment     new_page          0   \n",
       "3   853541  2017-01-08 18:28:03.143765  treatment     new_page          0   \n",
       "4   864975  2017-01-21 01:52:26.210827    control     old_page          1   \n",
       "\n",
       "   intercept  new_page  old_page  ab_page country  CA  UK  US  UK_new_page  \\\n",
       "0          1         0         1        0      US   0   0   1            0   \n",
       "1          1         0         1        0      US   0   0   1            0   \n",
       "2          1         1         0        1      US   0   0   1            0   \n",
       "3          1         1         0        1      US   0   0   1            0   \n",
       "4          1         0         1        0      US   0   0   1            0   \n",
       "\n",
       "   US_new_page  CA_new_page  \n",
       "0            0            0  \n",
       "1            0            0  \n",
       "2            1            0  \n",
       "3            1            0  \n",
       "4            0            0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABData_and_countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create logistic regression\n",
    "Logistic_model3 = sm.Logit(ABData_and_countries['converted'], ABData_and_countries[['intercept','new_page', 'US_new_page', 'UK_new_page','UK','US']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.366109\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "Results3 = Logistic_model3.fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:              converted   No. Observations:               290584\n",
      "Model:                          Logit   Df Residuals:                   290578\n",
      "Method:                           MLE   Df Model:                            5\n",
      "Date:                Wed, 11 Nov 2020   Pseudo R-squ.:               3.482e-05\n",
      "Time:                        21:51:21   Log-Likelihood:            -1.0639e+05\n",
      "converged:                       True   LL-Null:                   -1.0639e+05\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.1920\n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "intercept      -2.0040      0.036    -55.008      0.000      -2.075      -1.933\n",
      "new_page       -0.0674      0.052     -1.297      0.195      -0.169       0.034\n",
      "US_new_page     0.0469      0.054      0.872      0.383      -0.059       0.152\n",
      "UK_new_page     0.0783      0.057      1.378      0.168      -0.033       0.190\n",
      "UK              0.0118      0.040      0.296      0.767      -0.066       0.090\n",
      "US              0.0175      0.038      0.465      0.642      -0.056       0.091\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(Results3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "intercept      0.134794\n",
       "new_page       0.934776\n",
       "US_new_page    1.048001\n",
       "UK_new_page    1.081428\n",
       "UK             1.011854\n",
       "US             1.017682\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(Results3.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>The new_page's p-value is 0.195 which quite similar to the previous ones.<br>\n",
    "As we can see from the `Logit Regression Results`, the new_page's coefficient differe from US_new_page's and UK_new_page's. Also, z-score for the variables weren't large enough for predicting conversion rate, and the p-values are grearter than 0.05 except for the intercept. For that reason, the location of the user who's using the page is irrelevant in terms of conversion rate.<br>\n",
    "Moreover, the probability of conversion is 7.0% for every unit new_page decreases, holding all other varibles constant. Also, the likelihood of conversion for UK and new_page's users is 1.08 higher than CA and new_page's users, and 1.18% for the users in UK than CA, holding all other varibles constant. Additionally, the likelihood of conversion for US and new_page's users is 1.04 higher than CA and new_page's users, and 1.75% for the users in US than CA, holding all other varibles constant.<br><br>\n",
    "In the end, we don't have enough evidence to reject the null hypothesis based on our A/B testing. So under these circumstances, there's no point in switching to the new page, when the old page is working just as well."
   ]
  }
 ],
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