ENews_Express_Full_Code_Manju_Govindarajan.ipynb

{
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   "cell_type": "markdown",
   "id": "6yDxo9u0d9Na",
   "metadata": {
    "id": "6yDxo9u0d9Na"
   },
   "source": [
    "# Project Business Statistics: E-news Express\n",
    "\n",
    "**Marks: 60**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9Vt1PLLqmXNn",
   "metadata": {
    "id": "9Vt1PLLqmXNn"
   },
   "source": [
    "\n",
    "## Business Context\n",
    "\n",
    "The advent of e-news, or electronic news, portals has offered us a great opportunity to quickly get updates on day-to-day events occurring globally. Information on these portals is retrieved electronically from online databases, processed using a variety of software, and then transmitted to users. There are multiple advantages of transmitting new electronically, like faster access to content and ability to utilize different technologies such as audio, graphics, video, and other interactive elements that are either not being used or aren’t common yet in traditional newspapers.\n",
    "\n",
    "E-news Express, an online news portal, aims to expand its business by acquiring new subscribers. With every visitor to the website taking certain actions based on their interest, the company plans to analyze these actions to understand user interests and determine how to drive better engagement.  The executives at E-news Express are of the opinion that there has been a decline in new monthly subscribers compared to the past year because the current webpage is not designed well enough in terms of outline & recommended content to keep customers engaged long enough to make a decision to subscribe.\n",
    "\n",
    "\n",
    "## Problem statement and objective\n",
    "\n",
    "The design team of the company has researched and created a new landing page that has a new outline & more relevant content shown compared to the old page. In order to test the effectiveness of the new landing page in gathering new subscribers, the Data Science team conducted an experiment by randomly selecting 100 users and dividing them equally into two groups. The existing landing page was served to the first group (control group) and the new landing page to the second group (treatment group). Data regarding the interaction of users in both groups with the two versions of the landing page was collected. Being a data scientist in E-news Express, you have been asked to explore the data and perform a statistical analysis (at a significance level of 5%) to determine the effectiveness of the new landing page in gathering new subscribers for the news portal by answering the following questions:\n",
    "\n",
    "1. Do users spend more time on new landing page than on existing landing page?\n",
    "\n",
    "2. Is conversion rate (the proportion of users who visit the landing page and get converted) for new page greater than conversion rate for old page?\n",
    "\n",
    "3. Does converted status depend on preferred language?\n",
    "\n",
    "4. Is time spent on new page the same for different language users?\n",
    "\n",
    "\n",
    "## Data Dictionary\n",
    "\n",
    "The data contains information regarding interaction of users in both groups with two versions of landing page.\n",
    "\n",
    "1. user_id - Unique user ID of person visiting website\n",
    "\n",
    "2. group - Whether user belongs to first group (control) or second group (treatment)\n",
    "\n",
    "3. landing_page - Whether landing page is new or old\n",
    "\n",
    "4. time_spent_on_the_page - Time (in minutes) spent by user on landing page\n",
    "\n",
    "5. converted - Whether user gets converted to a subscriber of news portal or not\n",
    "\n",
    "6. language_preferred - Language chosen by user to view landing page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a3d218",
   "metadata": {
    "id": "a6a3d218"
   },
   "source": [
    "## Import all necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "658c5dec",
   "metadata": {
    "id": "658c5dec"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6970dd99",
   "metadata": {
    "id": "6970dd99"
   },
   "source": [
    "## Reading Data into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3b95640",
   "metadata": {
    "id": "d3b95640"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>time_spent_on_the_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_preferred</th>\n",
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       "      <th>0</th>\n",
       "      <td>546592</td>\n",
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       "      <td>3.48</td>\n",
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       "      <td>Spanish</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>546468</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>7.13</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
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       "      <th>3</th>\n",
       "      <td>546567</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>3.02</td>\n",
       "      <td>no</td>\n",
       "      <td>French</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Spanish</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>546446</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>5.15</td>\n",
       "      <td>no</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>546544</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>6.52</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>546472</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>7.07</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>546481</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>6.20</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>546483</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>5.86</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id      group landing_page  time_spent_on_the_page converted  \\\n",
       "0    546592    control          old                    3.48        no   \n",
       "1    546468  treatment          new                    7.13       yes   \n",
       "2    546462  treatment          new                    4.40        no   \n",
       "3    546567    control          old                    3.02        no   \n",
       "4    546459  treatment          new                    4.75       yes   \n",
       "..      ...        ...          ...                     ...       ...   \n",
       "95   546446  treatment          new                    5.15        no   \n",
       "96   546544    control          old                    6.52       yes   \n",
       "97   546472  treatment          new                    7.07       yes   \n",
       "98   546481  treatment          new                    6.20       yes   \n",
       "99   546483  treatment          new                    5.86       yes   \n",
       "\n",
       "   language_preferred  \n",
       "0             Spanish  \n",
       "1             English  \n",
       "2             Spanish  \n",
       "3              French  \n",
       "4             Spanish  \n",
       "..                ...  \n",
       "95            Spanish  \n",
       "96            English  \n",
       "97            Spanish  \n",
       "98            Spanish  \n",
       "99            English  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('abtest.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29feace5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
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       "      <th>time_spent_on_the_page</th>\n",
       "      <th>converted</th>\n",
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       "      <td>no</td>\n",
       "      <td>Spanish</td>\n",
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       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>546481</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>6.20</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>546483</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>5.86</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id      group landing_page  time_spent_on_the_page converted  \\\n",
       "0    546592    control          old                    3.48        no   \n",
       "1    546468  treatment          new                    7.13       yes   \n",
       "2    546462  treatment          new                    4.40        no   \n",
       "3    546567    control          old                    3.02        no   \n",
       "4    546459  treatment          new                    4.75       yes   \n",
       "..      ...        ...          ...                     ...       ...   \n",
       "95   546446  treatment          new                    5.15        no   \n",
       "96   546544    control          old                    6.52       yes   \n",
       "97   546472  treatment          new                    7.07       yes   \n",
       "98   546481  treatment          new                    6.20       yes   \n",
       "99   546483  treatment          new                    5.86       yes   \n",
       "\n",
       "   language_preferred  \n",
       "0             Spanish  \n",
       "1             English  \n",
       "2             Spanish  \n",
       "3              French  \n",
       "4             Spanish  \n",
       "..                ...  \n",
       "95            Spanish  \n",
       "96            English  \n",
       "97            Spanish  \n",
       "98            Spanish  \n",
       "99            English  \n",
       "\n",
       "[100 rows x 6 columns]"
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df = data.copy()  # create a copy of original dataset for further use\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12753856",
   "metadata": {
    "id": "e1ad11d4"
   },
   "source": [
    "### Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2320550",
   "metadata": {},
   "outputs": [
    {
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       "      <th>time_spent_on_the_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_preferred</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>546592</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
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       "      <td>no</td>\n",
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       "      <td>English</td>\n",
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       "      <th>2</th>\n",
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       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>4.40</td>\n",
       "      <td>no</td>\n",
       "      <td>Spanish</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>546567</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>3.02</td>\n",
       "      <td>no</td>\n",
       "      <td>French</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>4.75</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
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      "text/plain": [
       "   user_id      group landing_page  time_spent_on_the_page converted  \\\n",
       "0   546592    control          old                    3.48        no   \n",
       "1   546468  treatment          new                    7.13       yes   \n",
       "2   546462  treatment          new                    4.40        no   \n",
       "3   546567    control          old                    3.02        no   \n",
       "4   546459  treatment          new                    4.75       yes   \n",
       "\n",
       "  language_preferred  \n",
       "0            Spanish  \n",
       "1            English  \n",
       "2            Spanish  \n",
       "3             French  \n",
       "4            Spanish  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing first few rows of dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82191a13",
   "metadata": {},
   "outputs": [
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       "      <td>546544</td>\n",
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       "      <td>new</td>\n",
       "      <td>7.07</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>546481</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>6.20</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>546483</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>5.86</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id      group landing_page  time_spent_on_the_page converted  \\\n",
       "95   546446  treatment          new                    5.15        no   \n",
       "96   546544    control          old                    6.52       yes   \n",
       "97   546472  treatment          new                    7.07       yes   \n",
       "98   546481  treatment          new                    6.20       yes   \n",
       "99   546483  treatment          new                    5.86       yes   \n",
       "\n",
       "   language_preferred  \n",
       "95            Spanish  \n",
       "96            English  \n",
       "97            Spanish  \n",
       "98            Spanish  \n",
       "99            English  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing last few rows of dataset\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0360f737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view shape of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2236eadb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 6 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   user_id                 100 non-null    int64  \n",
      " 1   group                   100 non-null    object \n",
      " 2   landing_page            100 non-null    object \n",
      " 3   time_spent_on_the_page  100 non-null    float64\n",
      " 4   converted               100 non-null    object \n",
      " 5   language_preferred      100 non-null    object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# check data types of columns in dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4daa82ba",
   "metadata": {},
   "source": [
    "####Observations:\n",
    "\n",
    "* There are two numerical variables: user_id, time_spent_on_the_page\n",
    "* There are 4 categorical variables: group,landing_page,converted,language_preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f130f3f5",
   "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>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>time_spent_on_the_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_preferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>546517.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.377800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52.295779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.378166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>546443.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>546467.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>546492.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.415000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>546567.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.022500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>546592.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.710000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id    group landing_page  time_spent_on_the_page converted  \\\n",
       "count      100.000000      100          100              100.000000       100   \n",
       "unique            NaN        2            2                     NaN         2   \n",
       "top               NaN  control          old                     NaN       yes   \n",
       "freq              NaN       50           50                     NaN        54   \n",
       "mean    546517.000000      NaN          NaN                5.377800       NaN   \n",
       "std         52.295779      NaN          NaN                2.378166       NaN   \n",
       "min     546443.000000      NaN          NaN                0.190000       NaN   \n",
       "25%     546467.750000      NaN          NaN                3.880000       NaN   \n",
       "50%     546492.500000      NaN          NaN                5.415000       NaN   \n",
       "75%     546567.250000      NaN          NaN                7.022500       NaN   \n",
       "max     546592.000000      NaN          NaN               10.710000       NaN   \n",
       "\n",
       "       language_preferred  \n",
       "count                 100  \n",
       "unique                  3  \n",
       "top               Spanish  \n",
       "freq                   34  \n",
       "mean                  NaN  \n",
       "std                   NaN  \n",
       "min                   NaN  \n",
       "25%                   NaN  \n",
       "50%                   NaN  \n",
       "75%                   NaN  \n",
       "max                   NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary for all variables\n",
    "df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e653f98",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Mean time spent on the page is 5.37 minutes. Minimum time spent is 0.19, max time is 10.71 minutes respectively. Standard deviation being 2.71 minutes.\n",
    "* There are 3 unique language users, Spanish being the most common, 34(out of 100) are Spanish speakers.\n",
    "* There are 2 unique values in \"converted\" column, yes and no. 54(out of 100) users got converted to subscribers.\n",
    "* There are 2 unique groups, control and treatment, each with 50 users as described in business context.\n",
    "* There are two unique landing pages, old and new, each with 50 users as described in business context.\n",
    "* user_id is the unique id of person visiting website, no meaningful insights from analyzing this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e64a242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96ee2ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                   0\n",
       "group                     0\n",
       "landing_page              0\n",
       "time_spent_on_the_page    0\n",
       "converted                 0\n",
       "language_preferred        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26badd22",
   "metadata": {},
   "source": [
    "There are no missing values in any column of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b7221dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Check for duplicate rows\n",
    "df[df.duplicated()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcb3aa7",
   "metadata": {},
   "source": [
    "There are no duplicate rows in dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f3b2c9",
   "metadata": {
    "id": "68f3b2c9"
   },
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a82500",
   "metadata": {
    "id": "1932d0f3"
   },
   "source": [
    "#### Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08120832",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df,x='time_spent_on_the_page', kde = True) #histogram to plot time spent on page distribution\n",
    "plt.show()\n",
    "sns.boxplot(data=df,x='time_spent_on_the_page')  #boxplot to show 5 point summary of time spent on page column\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07b4847",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Time_spent_on_the_page histogram suggests normal distribution.\n",
    "* Values range between 0.2 - 11 minutes.\n",
    "* Mean time spent on page is 5.37 minutes.\n",
    "* Minimum time spent on page is 0.19 minutes.\n",
    "* Maximum time spent on page is 10.71 minutes.\n",
    "* Standard deviation is 2.37 minutes.\n",
    "* 50% of subscribers spend about 5.41 minutes on the page.\n",
    "* Q1(1st quartile) is about 4 minutes, Q3(3rd quartile) is about 7 minutes.\n",
    "* There are no outliers in data, no unusually small or large data points in terms of time spent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee6dcab",
   "metadata": {},
   "source": [
    "#### Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11a34810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "control      50\n",
       "treatment    50\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['group'].value_counts() # returns unique values in \"group\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81c776c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='group') #plot to show counts of control and treatment groups \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb9c76c",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Per problem statement, there are two unique values in \"group\" column - Control and Treatment.\n",
    "* Per problem statement, Control and Treatment groups have equal users(50)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4266f7c1",
   "metadata": {},
   "source": [
    "#### Landing page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fee69ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "old    50\n",
       "new    50\n",
       "Name: landing_page, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['landing_page'].value_counts() # returns unique values in \"landing_page\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81c2ac18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='landing_page') #plot to show counts of landing page\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453180ec",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Per problem statement, there are two unique landing pages, old and new, viewed by equal number of users(50)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e97e0d8",
   "metadata": {},
   "source": [
    "#### Converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58e9a1d0",
   "metadata": {
    "id": "58e9a1d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    54\n",
       "no     46\n",
       "Name: converted, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['converted'].value_counts() # returns unique values in \"converted\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a95839bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='converted') # plot to show counts of converted status\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6fcf69b",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* 54 out of 100 users got converted to subscribers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b35103",
   "metadata": {},
   "source": [
    "#### Language preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b378b0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spanish    34\n",
       "French     34\n",
       "English    32\n",
       "Name: language_preferred, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['language_preferred'].value_counts() # returns unique values in \"language_preferred\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1629c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='language_preferred') #plot to show counts of language preferred\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1887935",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Out of 100 users, there are 34 Spanish, 34 French and 32 English users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad2de2f",
   "metadata": {
    "id": "fad2de2f"
   },
   "source": [
    "### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0f7419",
   "metadata": {},
   "source": [
    "#### Landing page vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f070dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AUHVlXcxhzZo1OeOMM9KrV6/ssMMO2X777dOrV69MmzYt7777bqUzAgAAtKqyZpS+853vZM6cObnwwgszcuTIJO9dMvyss87KX//61/z85z+vaEgAAIDWVFZRuv766/PLX/4yY8eObR7bc889M2jQoHz5y19WlAAAgHatrKV3tbW12WGHHdYZ32GHHdK1a9cNzQQAAFBVZRWlk08+Oeeee24aGhqaxxoaGnL++efnO9/5TsXCAQAAVENZS+8WLlyYP/7xjxkwYED22muvJMmjjz6ad955J5/5zGdy1FFHNR974403ViYpAABAKymrKG2xxRY5+uijW4wNHDiwIoEAAACqrayidNVVV32k4+6+++40NDSkpqamnLcBAACoirK+o/RRjR07Ni+//PLGfAsAAICK26hFqVgsbszTAwAAbBQbtSgBAAC0R4oSAABACUUJAACgxEYtSoVCYWOeHgAAYKNwMQcAAIASZd1HKUnWrFmTefPm5bnnnsv48eNTV1eXV155JT179kyPHj2SJKtWrapY0I6mWCymvr6+2jHYBLz/98zvHK2ltrbWqgIA2rWyitKLL76Yz372s1m8eHEaGhpyyCGHpK6uLhdeeGHq6+vz85//vNI5O5z6+vqMHTu22jHYxIwbN67aEdhEzJ07N926dat2DAAoW1lL7yZNmpThw4fnjTfeaPEP4bhx4/LHP/6xYuEAAACqoawZpbvuuit33313unbt2mJ8++23z8svv1yRYJuS1Xt/JcVOZa+ChL+tWEya1rz3vFOXxHIoNpJC05r0eOT6ascAgIoo63+dNzU1pbGxcZ3xl156KXV1dRscalNT7NQl6bxZtWPQoXX9+4fABnL5HgA6krKW3h1yyCG55JJLmrcLhUJWr16dH/3oRzn88MMrlQ0AAKAqyppR+ulPf5qDDjoow4YNS319fcaPH5//+q//ytZbb53rr7fsAgAAaN/KKkrbbrttHnnkkVx//fV5+OGH09TUlBNPPDHHHXecqxwBAADtXtlXEOjWrVtOOOGEnHDCCZXMAwAAUHVlF6Vnnnkm8+bNy7Jly9LU1NRi35lnnrnBwQAAAKqlrKJ05ZVX5tvf/na23nrr9OvXr8Xd1wuFgqIEAAC0a2UVpfPOOy/nn39+pk6dWuk8AAAAVVfW5cHfeOONHHPMMZXOAgAA0CaUNaN0zDHH5Pbbb8+ECRMqnQcANhnF4n/fprdh3fu4A7Q77/9b9v6/ce3RRy5K//zP/9z8fMiQITnjjDNy3333ZY899shmm23W4tiJEydWLiEAdFANDQ3Nz79z11ZVTAJQeQ0NDenevXu1Y5TtIxeln/70py22e/Tokfnz52f+/PktxguFgqIEAAC0ax+5KC1atGhj5vibXn755UydOjVz587N22+/nY997GP513/91+yzzz5VywQAG6qmpqb5+eWjl6emcxXDAFRAQ+N/z5C//29ce1TWd5TOOeecfO9731tnKu3tt9/ORRddVNHLg7/xxhvZf//9c9BBB2Xu3Lnp06dPnnvuuWyxxRYVew8AqIb3316jpnMUJaBDef/fuPaorKvenX322Vm9evU642+99VbOPvvsDQ71fhdccEEGDhyYq666Kvvuu2922GGHfOYzn8lOO+1U0fcBAABYq6yiVCwWP7AhPvroo+ndu/cGh3q/3/3udxk+fHiOOeaY9OnTJx//+Mdz5ZVXfujxDQ0NWblyZYsHAADA+livorTlllumd+/eKRQK+djHPpbevXs3P3r16pVDDjkkX/rSlyoa8Pnnn88VV1yRnXfeObfddlsmTJiQiRMn5he/+MUHHj9jxoz06tWr+TFw4MCK5gEAADq+9fqO0iWXXJJisZgTTjghZ599dnr16tW8r2vXrtlhhx0ycuTIigZsamrK8OHDM3369CTJxz/+8TzxxBO54oor8vWvf32d408//fRMmTKleXvlypXKEgAAsF7Wqygdf/zxSZLBgwdn//33T5cuf/vlM2fOzIQJEzbowgv9+/fPsGHDWowNHTo0v/nNbz7w+JqamnZ/hQ0AAKC6yvqO0oEHHvh3S1KSTJ8+Pa+//no5b9Fs//33z9NPP91i7Jlnnsn222+/QecFAAD4MGUVpY+qWCxu8DkmT56c++67L9OnT8+zzz6b2bNnZ9asWTn55JMrkBAAAGBdG7UoVcKIESMyZ86cXH/99dl9991z7rnn5pJLLslxxx1X7WgAAEAHVdYNZ1vb5z//+Xz+85+vdgwAAGAT0eZnlAAAAFqbogQAAFBioxalT33qU+nWrdvGfAsAAICKK/s7Sk1NTXn22WezbNmyNDU1tdh3wAEHJEluueWWDUsHAABQBWUVpfvuuy/jx4/Piy++uM4lwAuFQhobGysSDgAAoBrKKkoTJkzI8OHDc/PNN6d///4pFAqVzgUAAFA1ZRWl//qv/8qvf/3rDBkypNJ5AAAAqq6siznst99+efbZZyudBQAAoE0oa0bpu9/9bk499dQsXbo0e+yxRzbbbLMW+/fcc8+KhAMAAKiGsorS0UcfnSQ54YQTmscKhUKKxaKLOQAAAO1eWUVp0aJFlc4BAADQZpRVlLbffvtK5wAAAGgzyr7h7HPPPZdLLrkkTz31VAqFQoYOHZpJkyZlp512qmQ+AACAVlfWVe9uu+22DBs2LA888ED23HPP7L777rn//vuz22675Y477qh0RgAAgFZV1ozSD37wg0yePDkzZ85cZ3zq1Kk55JBDKhIOAACgGsqaUXrqqady4oknrjN+wgkn5Mknn9zgUAAAANVUVlHaZptt8sgjj6wz/sgjj6RPnz4bmgkAAKCqylp694//+I/51re+leeffz6jRo1KoVDIXXfdlQsuuCCnnnpqpTMCAAC0qrKK0hlnnJG6urr85Cc/yemnn54k2XbbbXPWWWdl4sSJFQ0IAADQ2soqSoVCIZMnT87kyZOzatWqJEldXd06x919990ZPnx4ampqNiwlAABAKyrrO0rvV1dX94ElKUnGjh2bl19+eUPfAgAAoFVtcFH6W4rF4sY8PQAAwEaxUYsSAABAe6QoAQAAlCjrYg4AQGU1NBaSWLLOxlEsJu80vfe8a6ekUKhuHjqu9/6WdQwbtSgV/LcQAD6S79zVu9oRAHgfF3MAAAAoUdaM0qc//enceOON2WKLLVqMr1y5MkceeWT+9Kc/JUnzPZYAgHXV1tZm7ty51Y7BJqC+vj7jxo1LksyZMye1tbVVTsSmoL3/npVVlObNm5d33nlnnfH6+vrceeedGxwKADYFhUIh3bp1q3YMNjG1tbV+7+AjWK+i9NhjjzU/f/LJJ7N06dLm7cbGxtx6663ZbrvtKpcOAACgCtarKO29994pFAopFAr59Kc/vc7+bt265bLLLqtYOAAAgGpYr6K0aNGiFIvF7LjjjnnggQeyzTbbNO/r2rVr+vTpk86dO1c8JAAAQGtar6K0/fbbJ0mampo2ShgAAIC2oOz7KD3zzDOZN29eli1btk5xOvPMMzc4GAAAQLWUVZSuvPLKfPvb387WW2+dfv36tbixbKFQUJQAAIB2rayidN555+X888/P1KlTK50HAACg6jqV86I33ngjxxxzTKWzAAAAtAllFaVjjjkmt99+e6WzAAAAtAllLb0bMmRIzjjjjNx3333ZY489stlmm7XYP3HixIqEAwAAqIayitKsWbPSo0ePzJ8/P/Pnz2+xr1AoKEoAAEC7VlZRWrRoUaVzAAAAtBllfUdprXfeeSdPP/101qxZU6k8AAAAVVdWUXrrrbdy4oknpnv37tltt92yePHiJO99N2nmzJkVDQgAANDayipKp59+eh599NHMmzcvtbW1zeMHH3xwbrjhhoqFAwAAqIayvqN000035YYbbsgnP/nJFAqF5vFhw4blueeeq1g4AACAaihrRun//t//mz59+qwz/uabb7YoTgAAAO1RWUVpxIgRufnmm5u315ajK6+8MiNHjqxMMgAAgCopa+ndjBkz8tnPfjZPPvlk1qxZk0svvTRPPPFE7r333nXuqwQAANDelDWjNGrUqNx999156623stNOO+X2229P3759c++992afffapdEYAAIBWVdaMUpLsscceueaaayqZBQAAoE0ouyg1NjZmzpw5eeqpp1IoFDJ06NAcccQR6dKl7FMCAAC0CWW1mv/4j//IEUcckaVLl2aXXXZJkjzzzDPZZptt8rvf/S577LFHRUMCAAC0prK+o/TNb34zu+22W1566aU8/PDDefjhh7NkyZLsueee+da3vlXpjAAAAK2qrBmlRx99NAsWLMiWW27ZPLblllvm/PPPz4gRIyoWDgAAoBrKmlHaZZdd8tprr60zvmzZsgwZMmSDQwEAAFRTWTNK06dPz8SJE3PWWWflk5/8ZJLkvvvuyznnnJMLLrggK1eubD62Z8+elUnawRSLxf/eaHy3ekEAKuV9f8ta/I0DgHaorKL0+c9/PknypS99KYVCIcl//6P4hS98oXm7UCiksbGxEjk7nIaGhubndY/+sopJACqvoaEh3bt3r3YMAChbWUXpz3/+c6VzAAAAtBllFaUDDzyw0jk2OTU1Nc3PV+315aTzZlVMA1ABje82z5C//28cALRHZRWlW2+9NT169Mjo0aOTJD/72c9y5ZVXZtiwYfnZz37W4mp4fLC1SxaTvFeSFCWgA2nxNw4A2qGyrnr3/e9/v/mCDY8//nimTJmSww8/PM8//3ymTJlS0YAAAACtrawZpUWLFmXYsGFJkt/85jf5whe+kOnTp+fhhx/O4YcfXtGAAAAAra2sGaWuXbvmrbfeSpL84Q9/yKGHHpok6d27d4tLgwMAALRHZc0ojR49OlOmTMn++++fBx54IDfccEOS5JlnnsmAAQMqGhAAAKC1lTWjdPnll6dLly759a9/nSuuuCLbbbddkmTu3Ln57Gc/W9GAAAAAra2sGaVBgwbl97///TrjP/3pT1tsz5w5MxMmTMgWW2xRVjgAAIBqKGtG6aOaPn16Xn/99Y35FgAAABW3UYtSsVjcmKcHAADYKDZqUaq0GTNmpFAo5JRTTql2FAAAoANrN0XpwQcfzKxZs7LnnntWOwoAANDBtYuitHr16hx33HG58sors+WWW1Y7DgAA0MG1i6J08skn53Of+1wOPvjgv3tsQ0NDVq5c2eIBAACwPsq6PPhH9alPfSrdunXboHP88pe/zEMPPZQFCxZ8pONnzJiRs88+e4PeEwAA2LSVPaP03HPPZdq0afnKV76SZcuWJUluvfXWPPHEE83H3HLLLenfv3/Z4ZYsWZJJkybluuuuS21t7Ud6zemnn54VK1Y0P5YsWVL2+wMAAJumsorS/Pnzs8cee+T+++/PjTfemNWrVydJHnvssfzoRz+qWLiHHnooy5Ytyz777JMuXbqkS5cumT9/fv75n/85Xbp0SWNj4zqvqampSc+ePVs8AAAA1kdZRekHP/hBzjvvvNxxxx3p2rVr8/hBBx2Ue++9t2LhPvOZz+Txxx/PI4880vwYPnx4jjvuuDzyyCPp3Llzxd4LAABgrbK+o/T4449n9uzZ64xvs802Wb58+QaHWquuri677757i7HNN988W2211TrjAAAAlVLWjNIWW2yRV199dZ3xhQsXZrvtttvgUAAAANVU1ozS+PHjM3Xq1Pzbv/1bCoVCmpqacvfdd+d73/tevv71r1c6Ywvz5s3bqOcHAAAoa0bp/PPPz6BBg7Lddttl9erVGTZsWA444ICMGjUq06ZNq3RGAACAVlXWjNJmm22W6667Luecc04WLlyYpqamfPzjH8/OO+9c6XwAAACtboNuOLvTTjtlp512qlQWAACANqGsolQsFvPrX/86f/7zn7Ns2bI0NTW12H/jjTdWJBwAAEA1lFWUJk2alFmzZuWggw5K3759UygUKp0LAACgasoqStdee21uvPHGHH744ZXOAwAAUHVlXfWuV69e2XHHHSudBQAAoE0oqyidddZZOfvss/P2229XOg8AAEDVlbX07phjjsn111+fPn36ZIcddshmm23WYv/DDz9ckXAAAADVUFZR+sY3vpGHHnooX/3qV13MAQAA6HDKKko333xzbrvttowePbrSeQAAAKqurO8oDRw4MD179qx0FgAAgDahrKL0k5/8JKeddlpeeOGFCscBAACovrKW3n31q1/NW2+9lZ122indu3df52IOr7/+ekXCAQAAVENZRemSSy6pcAwAAIC2o6yidPzxx1c6BwAAQJvxkYvSypUrmy/gsHLlyr95rAs9AAAA7dlHLkpbbrllXn311fTp0ydbbLHFB947qVgsplAopLGxsaIhAQAAWtNHLkp/+tOf0rt37yTJVVddlYEDB6Zz584tjmlqasrixYsrmxAAAKCVfeSidOCBBzY/P+GEE5pnl95v+fLlOfjgg32HCQDYaIrFYurr66sdo115/8/Lz648tbW1H7iiio6rrIs5rF1iV2r16tWpra3d4FAAAB+mvr4+Y8eOrXaMdmvcuHHVjtAuzZ07N926dat2DFrRehWlKVOmJEkKhULOOOOMdO/evXlfY2Nj7r///uy9994VDQgAANDa1qsoLVy4MMl7M0qPP/54unbt2ryva9eu2WuvvfK9732vsgkBAN6ntrY2c+fOrXaMdqVYLKahoSFJUlNTYwlZGaya2vSsV1H685//nCT5h3/4h1x66aUuAw4AtLpCoWAJVBnevxII+PvK+o7SVVddVekcAAAAbUanagcAAABoaxQlAACAEooSAABACUUJAACghKIEAABQQlECAAAooSgBAACUUJQAAABKKEoAAAAlFCUAAIASihIAAEAJRQkAAKCEogQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBJdqh2ApNC0JsVqh6DjKhaTpjXvPe/UJSkUqpuHDquw9vcMADoARakN6PHI9dWOAAAAvI+ldwAAACXMKFVJbW1t5s6dW+0YbALq6+szbty4JMmcOXNSW1tb5URsCvyeAdDeKUpVUigU0q1bt2rHYBNTW1vr9w4A4COw9A4AAKCEogQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBKKEgAAQAlFCQAAoISiBAAAUEJRAgAAKKEoAQAAlFCUAAAASihKAAAAJRQlAACAEooSAABACUUJAACghKIEAABQQlECAAAooSgBAACUUJQAAABKtPmiNGPGjIwYMSJ1dXXp06dPjjzyyDz99NPVjgUAAHRgbb4ozZ8/PyeffHLuu+++3HHHHVmzZk0OPfTQvPnmm9WOBgAAdFBdqh3g77n11ltbbF911VXp06dPHnrooRxwwAFVSgUAAHRkbb4olVqxYkWSpHfv3h+4v6GhIQ0NDc3bK1eubJVcAABAx9Hml969X7FYzJQpUzJ69OjsvvvuH3jMjBkz0qtXr+bHwIEDWzklAADQ3rWrovSd73wnjz32WK6//voPPeb000/PihUrmh9LlixpxYQAAEBH0G6W3n33u9/N7373u/zlL3/JgAEDPvS4mpqa1NTUtGIyAACgo2nzRalYLOa73/1u5syZk3nz5mXw4MHVjgQAAHRwbb4onXzyyZk9e3Z++9vfpq6uLkuXLk2S9OrVK926datyOgAAoCNq899RuuKKK7JixYqMGTMm/fv3b37ccMMN1Y4GAAB0UG1+RqlYLFY7AgAAsIlp8zNKAAAArU1RAgAAKKEoAQBsAu65554ce+yxueeee6odBdoFRQkAoIOrr6/PxRdfnNdeey0XX3xx6uvrqx0J2jxFCQCgg7vuuuuyfPnyJMny5csze/bsKieCtk9RAgDowF566aXMnj27+UrCxWIxs2fPzksvvVTlZNC2KUoAAB1UsVjMpZde+qHjbsMCH05RAgDooBYvXpwHH3wwjY2NLcYbGxvz4IMPZvHixVVKBm2fogQA0EENGjQoI0aMSOfOnVuMd+7cOfvuu28GDRpUpWTQ9ilKAAAdVKFQyKRJkz50vFAoVCEVtA+KEgBABzZgwICMHz++uRQVCoWMHz8+2223XZWTQdumKAEAdHDHHXdcttpqqyTJ1ltvnfHjx1c5EbR9ihIAQAdXW1ubKVOmpG/fvpk8eXJqa2urHQnavC7VDgAAwMY3atSojBo1qtoxoN0wowQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBKKEgAAQAlFCQAAoISiBAAAUEJRAgAAKKEoAQAAlFCUAAAASihKAAAAJRQlAACAEooSAABACUUJAACghKIEAABQQlECAAAooSgBAACUUJQAAABKKEoAAAAlFCUAAIASihIAAEAJRQkAAKCEogQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBKKEgAAQAlFCQAAoISiBAAAUEJRAgAAKKEoAQAAlFCUAAAASihKAAAAJRQlAACAEooSAABACUUJAACghKIEAABQQlECAAAooSgBAACUUJQAAABKKEoAAAAlFCUAAIASihIAAEAJRQkAAKCEogQAAFBCUQIAACjRpdoB4KMqFoupr6+vdox25/0/Mz+/9VdbW5tCoVDtGABAK1OUaDfq6+szduzYasdo18aNG1ftCO3O3Llz061bt2rHAABamaV3AAAAJcwo0W7U1tZm7ty51Y7R7hSLxTQ0NCRJampqLCNbT7W1tdWOAABUgaJEu1EoFCyBKlP37t2rHQEAoF1pF0vv/uVf/iWDBw9ObW1t9tlnn9x5553VjgQAAHRgbb4o3XDDDTnllFPywx/+MAsXLsynPvWpjB07NosXL652NAAAoIMqFIvFYrVD/C377bdfPvGJT+SKK65oHhs6dGiOPPLIzJgx4+++fuXKlenVq1dWrFiRnj17bsyoAABAG7Y+3aBNzyi98847eeihh3LooYe2GD/00ENzzz33fOBrGhoasnLlyhYPAACA9dGmi9Jf//rXNDY2pm/fvi3G+/btm6VLl37ga2bMmJFevXo1PwYOHNgaUQEAgA6kTReltUovZ1wsFj/0Esenn356VqxY0fxYsmRJa0QEAAA6kDZ9efCtt946nTt3Xmf2aNmyZevMMq1VU1OTmpqa1ogHAAB0UG16Rqlr167ZZ599cscdd7QYv+OOOzJq1KgqpQIAADq6Nj2jlCRTpkzJ1772tQwfPjwjR47MrFmzsnjx4kyYMKHa0QAAgA6qzRelY489NsuXL88555yTV199NbvvvntuueWWbL/99tWOBgAAdFBt/j5KG8p9lAAAgKQD3UcJAACgGhQlAACAEooSAABACUUJAACghKIEAABQQlECAAAo0ebvo7Sh1l79fOXKlVVOAgAAVNPaTvBR7pDU4YvSqlWrkiQDBw6schIAAKAtWLVqVXr16vU3j+nwN5xtamrKK6+8krq6uhQKhWrHgapYuXJlBg4cmCVLlrjxMsAmyr8F8N5M0qpVq7LtttumU6e//S2kDj+j1KlTpwwYMKDaMaBN6Nmzp38cATZx/i1gU/f3ZpLWcjEHAACAEooSAABACUUJNgE1NTX50Y9+lJqammpHAaBK/FsA66fDX8wBAABgfZlRAgAAKKEoAQAAlFCUAAAASihK0MHNmzcvhUIh/+///b8PPebqq6/OFlts0WqZAADaOkUJAACghKIEAABQQlGCDqChoSETJ05Mnz59Ultbm9GjR+fBBx/80OOvvvrqDBo0KN27d8+4ceOyfPnyVkwLQCWMGTMmEydOzGmnnZbevXunX79+Oeuss5r3r1ixIt/61rfSp0+f9OzZM5/+9Kfz6KOPNu/r3LlzHnrooSRJsVhM7969M2LEiObXX3/99enfv3+rfiZoSxQl6ABOO+20/OY3v8k111yThx9+OEOGDMlhhx2W119/fZ1j77///pxwwgk56aST8sgjj+Sggw7KeeedV4XUAGyoa665Jptvvnnuv//+XHjhhTnnnHNyxx13pFgs5nOf+1yWLl2aW265JQ899FA+8YlP5DOf+Uxef/319OrVK3vvvXfmzZuXJHnsscea/3PlypVJ3vuO64EHHlitjwZV54az0M69+eab2XLLLXP11Vdn/PjxSZJ33303O+ywQ0455ZSMGDEiBx10UN54441sscUWGT9+fN54443MnTu3+Rxf/vKXc+utt/7NCz4A0LaMGTMmjY2NufPOO5vH9t1333z605/OoYcemnHjxmXZsmWpqalp3j9kyJCcdtpp+da3vpVTTz01zzzzTP793/89l156ae666648//zzOffcc3P44Ydnl112yeTJkzNhwoRqfDyoOjNK0M4999xzeffdd7P//vs3j2222WbZd99989RTT61z/FNPPZWRI0e2GCvdBqB92HPPPVts9+/fP8uWLctDDz2U1atXZ6uttkqPHj2aH4sWLcpzzz2X5L2ideedd6apqSnz58/PmDFjMmbMmMyfPz9Lly7NM888Y0aJTVqXagcANszaSeFCobDOeOnY+48HoP3bbLPNWmwXCoU0NTWlqakp/fv3b15a935rbwdxwAEHZNWqVXn44Ydz55135txzz83AgQMzffr07L333unTp0+GDh3aCp8C2iZFCdq5IUOGpGvXrrnrrrtaLL1bsGBBTjnllHWOHzZsWO67774WY6XbALRvn/jEJ7J06dJ06dIlO+ywwwces/Z7SpdffnkKhUKGDRuWbbfdNgsXLszvf/97s0ls8iy9g3Zu8803z7e//e18//vfz6233ponn3wy//iP/5i33norJ5544jrHT5w4MbfeemsuvPDCPPPMM7n88stz6623ViE5ABvLwQcfnJEjR+bII4/MbbfdlhdeeCH33HNPpk2blgULFjQfN2bMmFx77bU58MADUygUsuWWW2bYsGG54YYbMmbMmOp9AGgDFCXoAGbOnJmjjz46X/va1/KJT3wizz77bG677bZsueWW6xz7yU9+Mv/7f//vXHbZZdl7771z++23Z9q0aVVIDcDGUigUcsstt+SAAw7ICSeckI997GP58pe/nBdeeCF9+/ZtPu6ggw5KY2Nji1J04IEHprGx0YwSmzxXvQMAAChhRgkAAKCEogQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBKKEgDrbcyYMTnllFM26nucddZZ2XvvvZu3v/GNb+TII4/cqO8JAGt1qXYAAPgoLr300rhHOgCtRVECoF3o1atXtSMAsAmx9A6ADXLttddm+PDhqaurS79+/TJ+/PgsW7asef+8efNSKBTyxz/+McOHD0/37t0zatSoPP300y3OM3PmzPTt2zd1dXU58cQTU19f32J/6dK7MWPGZOLEiTnttNPSu3fv9OvXL2eddVaL1/znf/5nRo8endra2gwbNix/+MMfUigUctNNN/3dz/XCCy+kUCjkl7/8ZUaNGpXa2trstttumTdvXvMxjY2NOfHEEzN48OB069Ytu+yySy699NIW51mzZk0mTpyYLbbYIltttVWmTp2a448/vsVnKRaLufDCC7PjjjumW7du2WuvvfLrX//672YEYONRlADYIO+8807OPffcPProo7npppuyaNGifOMb31jnuB/+8If5yU9+kgULFqRLly454YQTmvf96le/yo9+9KOcf/75WbBgQfr3759/+Zd/+bvvfc0112TzzTfP/fffnwsvvDDnnHNO7rjjjiRJU1NTjjzyyHTv3j33339/Zs2alR/+8Ifr/fm+//3v59RTT83ChQszatSofPGLX8zy5cub32PAgAH51a9+lSeffDJnnnlm/uf//J/51a9+1fz6Cy64INddd12uuuqq3H333Vm5cuU6RW3atGm56qqrcsUVV+SJJ57I5MmT89WvfjXz589f77wAVEgRANbTgQceWJw0adIH7nvggQeKSYqrVq0qFovF4p///OdikuIf/vCH5mNuvvnmYpLi22+/XSwWi8WRI0cWJ0yY0OI8++23X3GvvfZq3j7++OOLRxxxRIsMo0ePbvGaESNGFKdOnVosFovFuXPnFrt06VJ89dVXm/ffcccdxSTFOXPm/N3PuGjRomKS4syZM5vH3n333eKAAQOKF1xwwYe+7qSTTioeffTRzdt9+/YtXnTRRc3ba9asKQ4aNKj5s6xevbpYW1tbvOeee1qc58QTTyx+5Stf+bs5Adg4zCgBsEEWLlyYI444Ittvv33q6uoyZsyYJMnixYtbHLfnnns2P+/fv3+SNC/Re+qppzJy5MgWx5duf5D3n3Ptedee8+mnn87AgQPTr1+/5v377rvvR/xUH5yjS5cuGT58eJ566qnmsZ///OcZPnx4ttlmm/To0SNXXnll82dfsWJFXnvttRbv27lz5+yzzz7N208++WTq6+tzyCGHpEePHs2PX/ziF3nuuefWOy8AleFiDgCU7c0338yhhx6aQw89NNdee2222WabLF68OIcddljeeeedFsduttlmzc8LhUKS95aubYj3n3Ptedees1gsNr9Ppa09769+9atMnjw5P/nJTzJy5MjU1dXloosuyv333/+Bx69VfN/V+9bmvfnmm7Pddtu1OK6mpmZjxAfgIzCjBEDZ/vM//zN//etfM3PmzHzqU5/Krrvu2uJCDh/V0KFDc99997UYK91eX7vuumsWL16c1157rXnswQcfXO/zvD/HmjVr8tBDD2XXXXdNktx5550ZNWpUTjrppHz84x/PkCFDWswC9erVK3379s0DDzzQPNbY2JiFCxc2bw8bNiw1NTVZvHhxhgwZ0uIxcODA9c4LQGWYUQKgbIMGDUrXrl1z2WWXZcKECfmP//iPnHvuuet9nkmTJuX444/P8OHDM3r06Fx33XV54oknsuOOO5ad7ZBDDslOO+2U448/PhdeeGFWrVrVfDGH9Zlp+tnPfpadd945Q4cOzU9/+tO88cYbzReiGDJkSH7xi1/ktttuy+DBg/N//s//yYMPPpjBgwc3v/673/1uZsyYkSFDhmTXXXfNZZddljfeeKM5Q11dXb73ve9l8uTJaWpqyujRo7Ny5crcc8896dGjR44//viyfwYAlM+MEgBl22abbXL11Vfn3/7t3zJs2LDMnDkzP/7xj9f7PMcee2zOPPPMTJ06Nfvss09efPHFfPvb396gbJ07d85NN92U1atXZ8SIEfnmN7+ZadOmJUlqa2s/8nlmzpyZCy64IHvttVfuvPPO/Pa3v83WW2+dJJkwYUKOOuqoHHvssdlvv/2yfPnynHTSSS1eP3Xq1HzlK1/J17/+9YwcOTI9evTIYYcd1iLDueeemzPPPDMzZszI0KFDc9hhh+Xf//3fWxQuAFpXoVh0m3MANg133313Ro8enWeffTY77bTT3zz2hRdeyODBg7Nw4cLsvffeFcvQ1NSUoUOH5ktf+lJZs28AtA5L7wDosObMmZMePXpk5513zrPPPptJkyZl//33/7slqZJefPHF3H777TnwwAPT0NCQyy+/PIsWLcr48eNbLQMA68/SOwA6rFWrVuWkk07Krrvumm984xsZMWJEfvvb3yZJpk+f3uJy3O9/jB07tmIZOnXqlKuvvjojRozI/vvvn8cffzx/+MMfMnTo0Iq9BwCVZ+kdAJuk119/Pa+//voH7uvWrds6l+oGYNOiKAEAAJSw9A4AAKCEogQAAFBCUQIAACihKAEAAJRQlAAAAEooSgAAACUUJQAAgBKKEgAAQIn/DyEm/Rg4TAyBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot to show distribution of landing page vs time spent on page\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(data=df,x='landing_page',y='time_spent_on_the_page')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aaadc52",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Mean time spent on new landing page is higher than old landing page.\n",
    "* Minimum time spent on new page is higher than minimum time spent on old page.\n",
    "* There are some outliers in time spent on new landing page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7880407a",
   "metadata": {},
   "source": [
    "#### Conversion status vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5476c23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot to show distribution of conversion status vs time spent on page\n",
    "plt.figure(figsize=(9, 9))\n",
    "sns.boxplot(data = df, x = 'converted', y = 'time_spent_on_the_page')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dea3063",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Users who got converted to subscribers spent more mean time on page than users who did not convert."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deed88f8",
   "metadata": {},
   "source": [
    "#### Language Preferred vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9992240c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot to show distribution of language preferred vs time spent on page\n",
    "plt.figure(figsize=(9, 9))\n",
    "sns.boxplot(data = df, x = 'language_preferred', y ='time_spent_on_the_page')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca64e37f",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Mean time spent on page by all three language users is between 5 - 6 minutes.\n",
    "* English users spent the highest mean time on page, followed by Spanish and French."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27906576",
   "metadata": {
    "id": "27906576"
   },
   "source": [
    "## 1. Do users spend more time on new landing page than existing landing page?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fdf0c8",
   "metadata": {
    "id": "e5fdf0c8"
   },
   "source": [
    "### Step 1: Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bf805b",
   "metadata": {
    "id": "6eZJa41eg21n"
   },
   "source": [
    "#### Landing page vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eefcc888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot to show distribution of landing page vs time spent on page\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(data=df,x='landing_page',y='time_spent_on_the_page')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bd10d1",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Median time spent on landing page(old) is about 4.2 minutes.\n",
    "* Median time spent on landing page(new) is about 6 minutes.\n",
    "* Time spent on old landing page ranges between 0 and 10 minutes(approximately).\n",
    "* Time spent on new landing page ranges between 3 and 7 minutes (approximately).\n",
    "* New landing page shows some outliers in data (users outside the range of time spent on page), some users spending less than the 2 minutes, some users spending more than 10 minutes.\n",
    "* Visual analysis suggests users spend more time on new versus old landing page. Lets continue to test statistically."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b3dafd",
   "metadata": {
    "id": "56b3dafd"
   },
   "source": [
    "### Step 2: Define null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2389167c",
   "metadata": {
    "id": "d2ac77ef"
   },
   "source": [
    "Null $H_0$: Mean time spent on new landing page is less than or equal to, mean time spent on old landing page.\n",
    "\n",
    "Alternate $H_a$: Mean time spent on new landing page is more than, mean time spent on old landing page.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ee4907",
   "metadata": {
    "id": "c7ee4907"
   },
   "source": [
    "### Step 3: Select Appropriate test and Decide significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c183cd7",
   "metadata": {
    "id": "2c183cd7"
   },
   "source": [
    "* This is a one-tailed test concerning two population means from two independent populations, population standard deviations are unknown. \n",
    "* Two sample independent t-test will be used.\n",
    "* As given in problem statement, select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30951b2",
   "metadata": {},
   "source": [
    "Check for two independent sample t-test assumptions:\n",
    "\n",
    "* Data is continuous - yes, time spent on landing pages are measured on a continuous scale.\n",
    "* Normally distributed populations - population distributions are assumed to be normal.\n",
    "* Populations are independent - new and old landing page users are two independent populations.\n",
    "* Unequal population standard deviations - as sample std deviations are different,population std deviations are assumed to be different.\n",
    "* Random sampling from populations - yes, users were selected randomly.\n",
    "* Yes, we can use two independent sample t-test as assumptions are satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fed2eac",
   "metadata": {
    "id": "5fed2eac"
   },
   "source": [
    "### Step 4: Collect and prepare data to calculate p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f5a59495",
   "metadata": {
    "id": "f5a59495"
   },
   "outputs": [],
   "source": [
    "# data frame subset for new landing page users \n",
    "time_spent_new = df[df['landing_page'] == 'new']['time_spent_on_the_page']\n",
    "\n",
    "# data frame subset for old landing page users\n",
    "time_spent_old = df[df['landing_page'] == 'old']['time_spent_on_the_page']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49bd324e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample standard deviation of time spent(in minutes) on new page is: 1.82\n",
      "Sample standard deviation of time spent(in minutes) on old page is: 2.58\n"
     ]
    }
   ],
   "source": [
    "print('Sample standard deviation of time spent(in minutes) on new page is:', round(time_spent_new.std(),2))\n",
    "print('Sample standard deviation of time spent(in minutes) on old page is:', round(time_spent_old.std(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fac8594a",
   "metadata": {
    "id": "fac8594a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.0001392381225166549\n"
     ]
    }
   ],
   "source": [
    "# import required function\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# calculate test_statistic and p_value\n",
    "test_stat, p_value = ttest_ind(time_spent_new, time_spent_old, equal_var = False, alternative = 'greater')\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359b12f8",
   "metadata": {
    "id": "359b12f8"
   },
   "source": [
    "### Step 5: Draw inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10b2bcad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As p-value 0.0001392381225166549 is less than level of significance, we reject null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# print the conclusion based on p-value\n",
    "if p_value < 0.05:\n",
    "    print(f'As p-value {p_value} is less than level of significance, we reject null hypothesis.')\n",
    "else:\n",
    "    print(f'As p-value {p_value} is greater than level of significance, we fail to reject null hypothesis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b16dd4b",
   "metadata": {
    "id": "6b16dd4b"
   },
   "source": [
    "* Since p_value is less than 0.05, null hypothesis is rejected.\n",
    "* Thus, there is enough statistical evidence to say users spend more time on new landing page than existing landing page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "353e9d24",
   "metadata": {
    "id": "353e9d24"
   },
   "source": [
    "## 2. Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840be390",
   "metadata": {},
   "source": [
    "### Step 1: Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b7f52898",
   "metadata": {
    "id": "b7f52898"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked barplot to analyze new page conversion rate and old page conversion rate\n",
    "pd.crosstab(df[\"landing_page\"],df[\"converted\"],normalize='index').plot(kind=\"bar\", figsize=(6,8),stacked=True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b43d7ad",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "* Visual analysis suggests new landing page has higher conversion rate than old landing page. Lets continue to test statistically."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d239831f",
   "metadata": {},
   "source": [
    "### Step 2: Define null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca4cd0c",
   "metadata": {},
   "source": [
    "Null $H_0$: Conversion rate of new landing page is less than or equal to, conversion rate of old landing page.\n",
    "\n",
    "Alternate $H_a$: Conversion rate of new landing page is greater than conversion rate of old landing page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2899e1ea",
   "metadata": {},
   "source": [
    "### Step 3: Select Appropriate test and Decide significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f08a390",
   "metadata": {},
   "source": [
    "* This is a one-tailed test concerning two population proportions from two independent populations. \n",
    "* Two proportions z-test will be used.\n",
    "* As given in problem statement, select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d79e6a0",
   "metadata": {},
   "source": [
    "Check for Two proportions z-test assumptions:\n",
    "\n",
    "* Binomally distributed population - Yes, user is either converted(yes) or not(no).\n",
    "* Random sampling from population - Yes, users were selected randomly.\n",
    "* Can the binomial distribution approximated to normal distribution - Yes.\n",
    "* Yes, assumptions satisfied, Two proportions z-test can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f2f3e7",
   "metadata": {},
   "source": [
    "### Step 4: Collect and prepare data to calculate p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c06c3dde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numbers of users served new and old pages are 50 and 50 respectively\n",
      "The numbers of users converted in control and treatment groups are 21 and 33 respectively\n",
      "Conversion rate for new page: 0.66\n",
      "Conversion rate for old page: 0.42\n"
     ]
    }
   ],
   "source": [
    "# import required fuction\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "# number of converted users in treatment group\n",
    "new_converted = df[df['group'] == 'treatment']['converted'].value_counts()['yes']\n",
    "\n",
    "# number of converted users in control group\n",
    "old_converted = df[df['group'] == 'control']['converted'].value_counts()['yes']\n",
    "\n",
    "n_control = df.group.value_counts()['control'] # total number of users in control group(old page)\n",
    "n_treatment = df.group.value_counts()['treatment'] # total number of users in treatment group(new page)\n",
    "\n",
    "print('The numbers of users served new and old pages are {0} and {1} respectively'.format(n_control, n_treatment ))\n",
    "print('The numbers of users converted in control and treatment groups are {0} and {1} respectively'.format(old_converted, new_converted))\n",
    "\n",
    "conversion_rate_new = new_converted/n_treatment  # old page conversion rate\n",
    "print(\"Conversion rate for new page:\", conversion_rate_new)\n",
    "conversion_rate_old = old_converted/n_control # new page conversion rate\n",
    "print (\"Conversion rate for old page:\", conversion_rate_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e791967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.016052616408112556\n"
     ]
    }
   ],
   "source": [
    "# set counts of converted users\n",
    "converted_count = np.array([old_converted,new_converted])\n",
    "\n",
    "# set sample sizes\n",
    "nobs = np.array([n_control, n_treatment])\n",
    "\n",
    "test_stat, p_value = proportions_ztest(converted_count, nobs)\n",
    "print('The p-value is ' + str(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c52d7c",
   "metadata": {},
   "source": [
    "### Step 5: Draw Inference\n",
    "\n",
    "* As p-value is less than significance level 0.05, null hypothesis is rejected. \n",
    "* Thus, there is enough statistical evidence to say conversion rate of new landing page is greater than conversion rate of old landing page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49bfa2d",
   "metadata": {
    "id": "d49bfa2d"
   },
   "source": [
    "## 3. Are conversion and preferred language independent or related?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be3f11d",
   "metadata": {},
   "source": [
    "### Step 1: Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fd2e7729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked barplot to analyze dependence between conversion status and preferred langauge\n",
    "pd.crosstab(df[\"converted\"],df[\"language_preferred\"],normalize='index').plot(kind=\"bar\", figsize=(6,8), stacked=True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62094c46",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "* Visual analysis suggests English language users showing most conversions, followed by Spanish and French."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1152ee1a",
   "metadata": {
    "id": "b0e63f17"
   },
   "source": [
    "### Step 2: Define null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2c28da",
   "metadata": {},
   "source": [
    "Null hypothesis: $H_0:$ Conversion is independent of preferred language.\n",
    "\n",
    "Alternate hypothesis:  $H_a$: Conversion is not independent on preferred language."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f559445",
   "metadata": {},
   "source": [
    "### Step 3: Select Appropriate test and Decide significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bcea88",
   "metadata": {},
   "source": [
    "* This is a test of independence, concerning two categorical variables - converted status and preferred language.\n",
    "* Chi-square test of independence will be used.\n",
    "* As given in problem statement, select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afafdc2f",
   "metadata": {},
   "source": [
    "Check for Chi-square test of independence assumptions:\n",
    "* Categorical variables - Yes\n",
    "* Expected value of number of sample observations in each level of the variable is at least 5 - Yes, the number of observations in each level is greater than 5.\n",
    "* Random sampling from the population - Yes, we are informed that the collected sample is a simple random sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4a1845",
   "metadata": {},
   "source": [
    "### Step 4: Collect and prepare data to calculate p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1785dab",
   "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>language_preferred</th>\n",
       "      <th>English</th>\n",
       "      <th>French</th>\n",
       "      <th>Spanish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>converted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "language_preferred  English  French  Spanish\n",
       "converted                                   \n",
       "no                       11      19       16\n",
       "yes                      21      15       18"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import required function\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# contingency table with converted users, preferred language variables\n",
    "contingency_table = pd.crosstab(df[\"converted\"],df[\"language_preferred\"])\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "71610020",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.21298887487543447\n",
      "As p-value 0.21298887487543447 is greater than level of significance, we fail to reject null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# print p_value using chi2_contingency test\n",
    "chi, p_value, dof, expected = chi2_contingency(contingency_table)\n",
    "print('The p-value is', p_value)\n",
    "\n",
    "# print conclusion based on p-value\n",
    "if p_value < 0.05:\n",
    "    print(f'As p-value {p_value} is less than level of significance, we reject null hypothesis.')\n",
    "else:\n",
    "    print(f'As p-value {p_value} is greater than level of significance, we fail to reject null hypothesis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c49a7b4",
   "metadata": {},
   "source": [
    "### Step 5: Draw Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e474a4c4",
   "metadata": {},
   "source": [
    "* As p-value is greater than significance level 0.05, we fail to reject null hypothesis.\n",
    "* Thus, there is enough statistical evidence to say conversion rate is independent of language."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d585a90",
   "metadata": {
    "id": "8d585a90"
   },
   "source": [
    "## 4. Is the time spent on new page same for different language users?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b23f03",
   "metadata": {},
   "source": [
    "### Step 1: Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b422f395",
   "metadata": {
    "id": "b422f395"
   },
   "outputs": [],
   "source": [
    "# DataFrame for users who got served the new page\n",
    "df_new = df[df['landing_page'] == 'new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6bb0c1c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spanish    17\n",
      "French     17\n",
      "English    16\n",
      "Name: language_preferred, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# number of users for each language\n",
    "print(df_new[\"language_preferred\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de832825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language_preferred\n",
      "English    6.663750\n",
      "French     6.196471\n",
      "Spanish    5.835294\n",
      "Name: time_spent_on_the_page, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# mean time spent on new landing page by different language users\n",
    "print(df_new.groupby(\"language_preferred\")[\"time_spent_on_the_page\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae9e4529",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot to visually plot time spent on new page for different language users\n",
    "plt.figure(figsize=(8,8))\n",
    "a = sns.boxplot(x = \"language_preferred\", y = \"time_spent_on_the_page\", showmeans = True, data = df_new, hue = 'language_preferred')\n",
    "a.set_title(\"Time spent on new landing page for language users(3 levels)\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e48a3b2",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "* English users spend highest average time on new page (6.66 minutes), followed by French(6.19 minutes) and Spanish(5.83 minutes)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a65120",
   "metadata": {},
   "source": [
    "### Step 2: Define null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e466cb",
   "metadata": {},
   "source": [
    "Null: $H_0:$  Mean time spent on new landing page is same for three language users.\n",
    "\n",
    "Alternate: $H_a:$ At least one, mean time spent on new landing page, is different from other two languages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df3c644",
   "metadata": {},
   "source": [
    "### Step 3: Select Appropriate test and Decide significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc6da48",
   "metadata": {},
   "source": [
    "This is a problem, concerning three population means. Analysis of Variance Test(ANOVA) will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca63a02",
   "metadata": {},
   "source": [
    "Check normality and equality of variance assumptions: \n",
    "\n",
    "* For testing of normality, Shapiro-Wilk’s test is applied to the response variable.\n",
    "* For equality of variance, Levene test is applied to the response variable.\n",
    "* As given in problem statement, select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e9de40",
   "metadata": {},
   "source": [
    "### Shapiro-Wilk’s test\n",
    "\n",
    "Null hypothesis:\n",
    "\n",
    ">$H_0:$ Mean time spent on the page follows a normal distribution\n",
    "\n",
    "Alternate hypothesis:\n",
    "\n",
    ">$H_a:$ Mean time spent on the page does not follow a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a280698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.8040016293525696\n"
     ]
    }
   ],
   "source": [
    "# Assumption 1: Normality\n",
    "# import the required function\n",
    "from scipy import stats\n",
    "\n",
    "# find the p-value\n",
    "w, p_value = stats.shapiro(df_new['time_spent_on_the_page']) \n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc6986a7",
   "metadata": {},
   "source": [
    "* Since p_value is greater than 0.05, we fail to reject null hypothesis.\n",
    "* Hence, there is enough evidence to say time_spent_on_the_page follows a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b34641",
   "metadata": {},
   "source": [
    "### Levene’s test\n",
    "\n",
    "Null hypothesis:\n",
    "\n",
    ">$H_0$: All the population variances are equal\n",
    "\n",
    "Alternate hypothesis:\n",
    "\n",
    ">$H_a$: At least one variance is different from the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7024b4d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.46711357711340173\n"
     ]
    }
   ],
   "source": [
    "#Assumption 2: Homogeneity of Variance\n",
    "#import required function\n",
    "\n",
    "from scipy.stats import levene\n",
    "statistic, p_value = levene( df_new['time_spent_on_the_page'][df_new['language_preferred']==\"English\"], \n",
    "                             df_new['time_spent_on_the_page'][df_new['language_preferred']==\"French\"], \n",
    "                             df_new['time_spent_on_the_page'][df_new['language_preferred']==\"Spanish\"])\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9939229",
   "metadata": {},
   "source": [
    "* Since p_value is greater than 0.05, we fail to reject null hypothesis.\n",
    "* Hence, there is enough evidence to say all population variances are equal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "859d6ea8",
   "metadata": {},
   "source": [
    "### ANOVA test assumptions\n",
    "\n",
    "* The populations are normally distributed - Yes, the normality assumption is verified using Shapiro-Wilk’s test.\n",
    "* Samples are independent simple random samples - Yes, we are informed that the collected sample is a simple random sample.\n",
    "* Population variances are equal - Yes, the homogeneity of variance assumption is verified using Levene's test.\n",
    "* One way Anova test assumptions are satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816d3c78",
   "metadata": {},
   "source": [
    "### Step 4: Collect and prepare data to calculate p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "108fdb63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.43204138694325955\n"
     ]
    }
   ],
   "source": [
    "#import required function\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "# perform one-way anova test\n",
    "test_stat, p_value = f_oneway(df_new.loc[df_new['language_preferred'] == 'English', 'time_spent_on_the_page'],\n",
    "                              df_new.loc[df_new['language_preferred'] == 'French', 'time_spent_on_the_page'],\n",
    "                              df_new.loc[df_new['language_preferred'] == 'Spanish', 'time_spent_on_the_page'])\n",
    "print('The p-value is ' + str(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dc35de",
   "metadata": {},
   "source": [
    "### Step 5: Draw Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a9d8e7",
   "metadata": {},
   "source": [
    "* As p-value is greater than 0.05, we fail to reject null hypothesis.\n",
    "* Hence, there is enough statistical evidence to say mean time spent on new landing page is same for the three language users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3acbc947",
   "metadata": {
    "id": "3acbc947"
   },
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b4c9aa",
   "metadata": {},
   "source": [
    "* Mean time spent on old landing page is 4.2 minutes, new landing page is 6 minutes.\n",
    "* There is enough statistical evidence to say users spend more time on new landing page than existing landing page.\n",
    "* Treatment group(50 users) was served new landing page, control group(50 users) was served old landing page. \n",
    "* 54 out of 100 users got converted to subscribers. 42% conversion rate for old page(21/50), 66% conversion rate for new page(33/50).\n",
    "* There is enough statistical evidence to say conversion rate of new landing page is greater than conversion rate of old landing page.\n",
    "* Out of 100 users, there are 34 Spanish, 34 French and 32 English users.\n",
    "* There is enough statistical evidence to say conversion status and language are independent.\n",
    "* Mean time spent on new page by English users is 6.66 minutes, French users 6.19 minutes, Spanish users 5.83 minutes. \n",
    "* There is enough statistical evidence to say mean time spent on new landing page is same for three language users."
   ]
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    "## Business Recommendations"
   ]
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    "\n",
    "* Enews Express needs to look at more data, month over month trend for current versus past year to further understand user patterns.\n",
    "* User demographics like age, gender,occupation, geographic factors like city, country of residence needs to be involved in analysis.\n",
    "* Enews Express can continue efforts to boost content, incorporate multimedia elements on landing page, adding demographic specific ads, optimize mobile device usage, localize content per age/location demographic, add live chat feature for interaction to increase user engagement.\n",
    "* In addition to number of users, KPI metrics like number of user sessions, average session duration, number of pages per session, scroll depth, subscriber acquisition cost can be used to analyze user engagement.\n",
    "* Understanding ranking position of Enews Express portal among search engines is to be considered.\n",
    "* Enews Express can optimize website loading speed for ease of user interaction.\n",
    "* Enews Express can meet target audience expectations by running creative mobile marketing offers, keep subscription process steps quick and easy.\n",
    "* Enews Express to test all marketing offers.\n",
    "* Enews Express can use social media sentiment analysis, to gauge social media interaction with website content.\n",
    "* Social media analytics involving average view duration can show what content is popular, which can drive decisions about how to boost content and in turn drive up subsribtion rate.\n"
   ]
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    "id": "U0dAd4i0iCHo"
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   "source": [
    "___"
   ]
  }
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