Kratik-Mehta_PGP-DSBA-FEB22_Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "914e6098",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:40.093310Z",
     "start_time": "2023-01-10T13:52:37.961271Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%precision 3\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib_inline\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "# matplotlib_inline.backend_inline.set_matplotlib_formats('pdf', 'svg')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360014c2",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "# Problem 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0bb62e",
   "metadata": {},
   "source": [
    "A wholesale distributor operating in different regions of Portugal has information on annual spending of several items in their stores across different regions and channels. The data consists of 440 large retailers’ annual spending on 6 different varieties of products in 3 different regions (Lisbon, Oporto, Other) and across different sales channel (Hotel, Retail)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5bc5a79",
   "metadata": {},
   "source": [
    "## Sample of the Wholesale Customers Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3af40291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:40.140990Z",
     "start_time": "2023-01-10T13:52:40.093310Z"
    }
   },
   "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>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buyer/Spender Channel Region  Fresh  Milk  Grocery  Frozen  \\\n",
       "0              1  Retail  Other  12669  9656     7561     214   \n",
       "1              2  Retail  Other   7057  9810     9568    1762   \n",
       "2              3  Retail  Other   6353  8808     7684    2405   \n",
       "3              4   Hotel  Other  13265  1196     4221    6404   \n",
       "4              5  Retail  Other  22615  5410     7198    3915   \n",
       "\n",
       "   Detergents_Paper  Delicatessen  \n",
       "0              2674          1338  \n",
       "1              3293          1776  \n",
       "2              3516          7844  \n",
       "3               507          1788  \n",
       "4              1777          5185  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wholesale_df = pd.read_csv('datasets/Wholesale+Customers+Data.csv')\n",
    "wholesale_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57358ca",
   "metadata": {},
   "source": [
    "## Checking the types of variables in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26bb5571",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:40.156254Z",
     "start_time": "2023-01-10T13:52:40.145256Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Buyer/Spender     440 non-null    int64 \n",
      " 1   Channel           440 non-null    object\n",
      " 2   Region            440 non-null    object\n",
      " 3   Fresh             440 non-null    int64 \n",
      " 4   Milk              440 non-null    int64 \n",
      " 5   Grocery           440 non-null    int64 \n",
      " 6   Frozen            440 non-null    int64 \n",
      " 7   Detergents_Paper  440 non-null    int64 \n",
      " 8   Delicatessen      440 non-null    int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 31.1+ KB\n"
     ]
    }
   ],
   "source": [
    "wholesale_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe0682d",
   "metadata": {},
   "source": [
    "From the above output we can see that:\n",
    "- There are 440 observations of different buyers in the data.\n",
    "- There are 9 variables, out of which, 7 are of integer type and 2 are of object(categorical) type.\n",
    "- Out of the 7 integer type variables, Buyer/Spender is discrete numerical variable while the others are continuous numerical variables.\n",
    "- The dataset does not have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640f5921",
   "metadata": {},
   "source": [
    "## Checking the distributions of the continous variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bce357",
   "metadata": {},
   "source": [
    "From the below plot, it can be seen that the data for the continuous variables is not normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "669305d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.520700Z",
     "start_time": "2023-01-10T13:52:40.159254Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = wholesale_df.hist(column=['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicatessen'], \n",
    "                  figsize=(14, 7), bins=50, layout=(2, 3))\n",
    "for ax in axs.flatten():\n",
    "    ax.set_ylabel(\"Frequency\", size=14)\n",
    "plt.suptitle('Distribution of Continous Variables', size=17)\n",
    "plt.tight_layout();\n",
    "# plt.savefig('images/Figure1.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b621ee2",
   "metadata": {},
   "source": [
    "## 1.1 Use methods of descriptive statistics to summarize data. Which Region and which Channel spent the most? Which Region and which Channel spent the least?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d34ae1",
   "metadata": {},
   "source": [
    "### Descriptive Statistics of the Wholesale Customers Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9928fb",
   "metadata": {},
   "source": [
    "Descriptive Statistics tells us a brief summary of the dataset. It can include the 5-point summary for the\n",
    "numerical variables and frequency distribution of categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcfd1f90",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.568704Z",
     "start_time": "2023-01-10T13:52:41.522703Z"
    }
   },
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.5</td>\n",
       "      <td>127.161315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.75</td>\n",
       "      <td>220.5</td>\n",
       "      <td>330.25</td>\n",
       "      <td>440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <td>440</td>\n",
       "      <td>2</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>440</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3127.75</td>\n",
       "      <td>8504.0</td>\n",
       "      <td>16933.75</td>\n",
       "      <td>112151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1533.0</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>7190.25</td>\n",
       "      <td>73498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>4755.5</td>\n",
       "      <td>10655.75</td>\n",
       "      <td>92780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>25.0</td>\n",
       "      <td>742.25</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>3554.25</td>\n",
       "      <td>60869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>3.0</td>\n",
       "      <td>256.75</td>\n",
       "      <td>816.5</td>\n",
       "      <td>3922.0</td>\n",
       "      <td>40827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1524.870455</td>\n",
       "      <td>2820.105937</td>\n",
       "      <td>3.0</td>\n",
       "      <td>408.25</td>\n",
       "      <td>965.5</td>\n",
       "      <td>1820.25</td>\n",
       "      <td>47943.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count unique    top freq          mean           std   min  \\\n",
       "Buyer/Spender     440.0    NaN    NaN  NaN         220.5    127.161315   1.0   \n",
       "Channel             440      2  Hotel  298           NaN           NaN   NaN   \n",
       "Region              440      3  Other  316           NaN           NaN   NaN   \n",
       "Fresh             440.0    NaN    NaN  NaN  12000.297727  12647.328865   3.0   \n",
       "Milk              440.0    NaN    NaN  NaN   5796.265909   7380.377175  55.0   \n",
       "Grocery           440.0    NaN    NaN  NaN   7951.277273   9503.162829   3.0   \n",
       "Frozen            440.0    NaN    NaN  NaN   3071.931818   4854.673333  25.0   \n",
       "Detergents_Paper  440.0    NaN    NaN  NaN   2881.493182   4767.854448   3.0   \n",
       "Delicatessen      440.0    NaN    NaN  NaN   1524.870455   2820.105937   3.0   \n",
       "\n",
       "                      25%     50%       75%       max  \n",
       "Buyer/Spender      110.75   220.5    330.25     440.0  \n",
       "Channel               NaN     NaN       NaN       NaN  \n",
       "Region                NaN     NaN       NaN       NaN  \n",
       "Fresh             3127.75  8504.0  16933.75  112151.0  \n",
       "Milk               1533.0  3627.0   7190.25   73498.0  \n",
       "Grocery            2153.0  4755.5  10655.75   92780.0  \n",
       "Frozen             742.25  1526.0   3554.25   60869.0  \n",
       "Detergents_Paper   256.75   816.5    3922.0   40827.0  \n",
       "Delicatessen       408.25   965.5   1820.25   47943.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wholesale_df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010af6c3",
   "metadata": {},
   "source": [
    "From the above output we can see that:\n",
    "- The Channel variable has 2 unique values, with Hotel being the most frequent, occurring 298 times.\n",
    "- The Region variable has 3 unique values, with Other being the most frequent, occurring 316 times.\n",
    "- The Fresh items have the highest average annual spending in the dataset.\n",
    "- The maximum annual spending values of all the items are quite high. These values might be outliers.\n",
    "- The standard deviations of the annual spending are also large. This indicates that the data is spread over a big range.\n",
    "\n",
    "NaN shows that the values cannot be calculated for that particular variable. Like we cannot calculate mean for\n",
    "a categorical variable. And in a same way unique value cannot be calculated for a numerical variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9272c2e8",
   "metadata": {},
   "source": [
    "### Region wise Total Annual Spendings for the items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0db3108a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.600707Z",
     "start_time": "2023-01-10T13:52:41.570707Z"
    }
   },
   "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>Total_Spendings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>1555088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lisbon</th>\n",
       "      <td>2386813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>10677599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total_Spendings\n",
       "Region                 \n",
       "Oporto          1555088\n",
       "Lisbon          2386813\n",
       "Other          10677599"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wholesale_df['Total_Spendings'] = wholesale_df.eval('Fresh + Milk + Grocery + Frozen + Detergents_Paper + Delicatessen')\n",
    "region_vs_total_spendings = wholesale_df.groupby('Region')[['Total_Spendings']].sum().sort_values(by='Total_Spendings')\n",
    "region_vs_total_spendings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5af4af08",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.760721Z",
     "start_time": "2023-01-10T13:52:41.602708Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "sns.barplot(data=region_vs_total_spendings, y='Total_Spendings', x=region_vs_total_spendings.index)\n",
    "plt.title('Region wise Total Spendings')\n",
    "plt.ylabel('Total Spendings')\n",
    "plt.yticks(labels=['0', '2000000', '4000000', '6000000', '8000000', '10000000', '12000000'], \n",
    "           ticks=np.linspace(0, 12000000, 7));\n",
    "# plt.savefig('images/Figure2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6f6093",
   "metadata": {},
   "source": [
    "After calculating the Region wise Total Spending’s, we found that:\n",
    "- The buyers spent the least in the Oporto region.\n",
    "- The buyers spent the most in the Other regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3331456b",
   "metadata": {},
   "source": [
    "### Channel wise Total Annual Spendings for the items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fea497f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.776725Z",
     "start_time": "2023-01-10T13:52:41.763727Z"
    }
   },
   "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>Total_Spendings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>6619931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>7999569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total_Spendings\n",
       "Channel                 \n",
       "Retail           6619931\n",
       "Hotel            7999569"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_vs_total_spendings = wholesale_df.groupby('Channel')[['Total_Spendings']].sum().sort_values(by='Total_Spendings')\n",
    "channel_vs_total_spendings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d728aa4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.904736Z",
     "start_time": "2023-01-10T13:52:41.780722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "sns.barplot(data=channel_vs_total_spendings, y='Total_Spendings', x=channel_vs_total_spendings.index)\n",
    "plt.title('Channel wise Total Spendings')\n",
    "plt.ylabel('Total Spendings')\n",
    "plt.yticks(labels=['0', '2000000', '4000000', '6000000', '8000000'], \n",
    "           ticks=np.linspace(0, 8000000, 5));\n",
    "# plt.savefig('images/Figure3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cfc137",
   "metadata": {},
   "source": [
    "After calculating the Channel wise Total Spending’s, we found that:\n",
    "- The buyers spent the least in the Retail channels.\n",
    "- The buyers spent the most in the Hotel channels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b104cea2",
   "metadata": {},
   "source": [
    "## 1.2 There are 6 different varieties of items that are considered. Describe and comment/explain all the varieties across Region and Channel? Provide a detailed justification for your answer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc9967b",
   "metadata": {},
   "source": [
    "### Region wise Average spendings for different variety of Items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "215b0dfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.934741Z",
     "start_time": "2023-01-10T13:52:41.906739Z"
    },
    "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lisbon</th>\n",
       "      <td>11101.727273</td>\n",
       "      <td>5486.415584</td>\n",
       "      <td>7403.077922</td>\n",
       "      <td>3000.337662</td>\n",
       "      <td>2651.116883</td>\n",
       "      <td>1354.896104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>9887.680851</td>\n",
       "      <td>5088.170213</td>\n",
       "      <td>9218.595745</td>\n",
       "      <td>4045.361702</td>\n",
       "      <td>3687.468085</td>\n",
       "      <td>1159.702128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>12533.471519</td>\n",
       "      <td>5977.085443</td>\n",
       "      <td>7896.363924</td>\n",
       "      <td>2944.594937</td>\n",
       "      <td>2817.753165</td>\n",
       "      <td>1620.601266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh         Milk      Grocery       Frozen  Detergents_Paper  \\\n",
       "Region                                                                          \n",
       "Lisbon  11101.727273  5486.415584  7403.077922  3000.337662       2651.116883   \n",
       "Oporto   9887.680851  5088.170213  9218.595745  4045.361702       3687.468085   \n",
       "Other   12533.471519  5977.085443  7896.363924  2944.594937       2817.753165   \n",
       "\n",
       "        Delicatessen  \n",
       "Region                \n",
       "Lisbon   1354.896104  \n",
       "Oporto   1159.702128  \n",
       "Other    1620.601266  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicatessen']\n",
    "region_vs_items_mean = wholesale_df.groupby('Region')[items].mean()\n",
    "region_vs_items_std = wholesale_df.groupby('Region')[items].std()\n",
    "region_vs_items_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b8d92a",
   "metadata": {},
   "source": [
    "### Region wise Coefficient of Variation of spendings for different variety of Items."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff644310",
   "metadata": {},
   "source": [
    "Coefficient of Variations is a measure of relative dispersion around the mean. It is used to compare variables\n",
    "with different ranges and units of measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "005c9bc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.949742Z",
     "start_time": "2023-01-10T13:52:41.936743Z"
    },
    "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lisbon</th>\n",
       "      <td>1.041049</td>\n",
       "      <td>1.039815</td>\n",
       "      <td>1.147670</td>\n",
       "      <td>1.030599</td>\n",
       "      <td>1.587430</td>\n",
       "      <td>0.993008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>0.848318</td>\n",
       "      <td>1.145076</td>\n",
       "      <td>1.176182</td>\n",
       "      <td>2.262291</td>\n",
       "      <td>1.766718</td>\n",
       "      <td>0.906043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>1.068277</td>\n",
       "      <td>1.327648</td>\n",
       "      <td>1.207808</td>\n",
       "      <td>1.446761</td>\n",
       "      <td>1.630040</td>\n",
       "      <td>1.994680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "Region                                                                        \n",
       "Lisbon  1.041049  1.039815  1.147670  1.030599          1.587430      0.993008\n",
       "Oporto  0.848318  1.145076  1.176182  2.262291          1.766718      0.906043\n",
       "Other   1.068277  1.327648  1.207808  1.446761          1.630040      1.994680"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_vs_items_cv = region_vs_items_std / region_vs_items_mean\n",
    "region_vs_items_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed546ed",
   "metadata": {},
   "source": [
    "From the above tables we can conclude that:\n",
    "- For all the Regions in Portugal: -\n",
    "    - Highest Averages spending’s are for Fresh items.\n",
    "    - Lowest Averages spending’s are for Delicatessen items. This means that people spend less on expensive food stores.\n",
    "    - In general, spending’s for all the items are largely spread out around the mean.\n",
    "- In Lisbon: -\n",
    "    - The spending’s for Detergents_Paper is highly spread out about its mean in comparison to other items. This could mean that the actual spending’s for Detergents_Paper are away from its mean and near the extreme values.\n",
    "- In Oporto: -\n",
    "    - The spending’s for Frozen items is highly spread out about its mean in comparison to other items. This could mean that the actual spending’s for Frozen are away from its mean and near the extreme values.\n",
    "- In Other regions: -\n",
    "    - The spending’s for Delicatessen is highly spread out about its mean in comparison to other items. This could mean that the actual spending’s for Delicatessen are away from its mean and near the extreme values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15781134",
   "metadata": {},
   "source": [
    "### Channel wise Average spendings for different variety of Items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d040391",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.965746Z",
     "start_time": "2023-01-10T13:52:41.950743Z"
    },
    "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>13475.560403</td>\n",
       "      <td>3451.724832</td>\n",
       "      <td>3962.137584</td>\n",
       "      <td>3748.251678</td>\n",
       "      <td>790.560403</td>\n",
       "      <td>1415.956376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>8904.323944</td>\n",
       "      <td>10716.500000</td>\n",
       "      <td>16322.852113</td>\n",
       "      <td>1652.612676</td>\n",
       "      <td>7269.507042</td>\n",
       "      <td>1753.436620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fresh          Milk       Grocery       Frozen  \\\n",
       "Channel                                                          \n",
       "Hotel    13475.560403   3451.724832   3962.137584  3748.251678   \n",
       "Retail    8904.323944  10716.500000  16322.852113  1652.612676   \n",
       "\n",
       "         Detergents_Paper  Delicatessen  \n",
       "Channel                                  \n",
       "Hotel          790.560403   1415.956376  \n",
       "Retail        7269.507042   1753.436620  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_vs_items_mean = wholesale_df.groupby('Channel')[items].mean()\n",
    "channel_vs_items_std = wholesale_df.groupby('Channel')[items].std()\n",
    "channel_vs_items_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8fc792",
   "metadata": {},
   "source": [
    "### Channel wise Coefficient of Variation of spendings for different variety of Items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e62ddfdd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.980746Z",
     "start_time": "2023-01-10T13:52:41.967746Z"
    },
    "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>1.026428</td>\n",
       "      <td>1.260867</td>\n",
       "      <td>0.894849</td>\n",
       "      <td>1.505745</td>\n",
       "      <td>1.396596</td>\n",
       "      <td>2.222828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1.009365</td>\n",
       "      <td>0.903246</td>\n",
       "      <td>0.751543</td>\n",
       "      <td>1.096932</td>\n",
       "      <td>0.865408</td>\n",
       "      <td>1.114267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
       "Channel                                                             \n",
       "Hotel    1.026428  1.260867  0.894849  1.505745          1.396596   \n",
       "Retail   1.009365  0.903246  0.751543  1.096932          0.865408   \n",
       "\n",
       "         Delicatessen  \n",
       "Channel                \n",
       "Hotel        2.222828  \n",
       "Retail       1.114267  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_vs_items_cv = channel_vs_items_std / channel_vs_items_mean\n",
    "channel_vs_items_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad4f896",
   "metadata": {},
   "source": [
    "From the above tables we can conclude that:\n",
    "- For all the Channels in Portugal: -\n",
    "    - In general, spending’s for all the items are largely spread out around the mean.\n",
    "- For Hotels in Portugal: -\n",
    "    - Highest Averages spending’s are for Fresh items.\n",
    "    - Lowest Averages spending’s are for Detergents_Paper.\n",
    "    - The spending’s for Delicatessen is highly spread out about its mean in comparison to other\n",
    "      items. This could mean that most of the values are less than the mean while only few are\n",
    "      more than the mean.\n",
    "- For Retail outlets in Portugal: -\n",
    "    - Highest Averages spending’s are for Grocery items.\n",
    "    - Lowest Averages spending’s are for Frozen items.\n",
    "    - The spending’s for Delicatessen items is highly spread out about its mean in comparison to\n",
    "      other items. This could mean that most of the values are less than the mean while only few\n",
    "      are more than the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf72646a",
   "metadata": {},
   "source": [
    "## 1.3 On the basis of a descriptive measure of variability, which item shows the most inconsistent behaviour? Which items show the least inconsistent behaviour?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d53a4c5",
   "metadata": {},
   "source": [
    "Using the Coefficient of Variation (CV) as a measure of variability, as the means of all the items are highly\n",
    "different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b936945",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:41.996745Z",
     "start_time": "2023-01-10T13:52:41.982747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fresh               1.053918\n",
       "Milk                1.273299\n",
       "Grocery             1.195174\n",
       "Frozen              1.580332\n",
       "Detergents_Paper    1.654647\n",
       "Delicatessen        1.849407\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df = wholesale_df[items]\n",
    "items_cv = items_df.std() / items_df.mean()\n",
    "items_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eae2837",
   "metadata": {},
   "source": [
    "From the above table we can see that:\n",
    "- Delicatessen items show the most inconsistent behaviour as its CV is very high.\n",
    "- Fresh items show the least inconsistent behaviour as its CV is very low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e792e03",
   "metadata": {},
   "source": [
    "## 1.4 Are there any outliers in the data? Back up your answer with a suitable plot/technique with the help of detailed comments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e906eeeb",
   "metadata": {},
   "source": [
    "Box Plot is a very effective visualization to detect outliers. It also shows the five point summary of the\n",
    "variables, which helps us to identify the distribution of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b93ac24f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.220763Z",
     "start_time": "2023-01-10T13:52:41.998748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=wholesale_df[items], fliersize=3)\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Spendings')\n",
    "plt.title('Itemwise Box Plots');\n",
    "# plt.savefig('images/Figure4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fcd301",
   "metadata": {},
   "source": [
    "From the above box plot we can see that the spending’s for all the items in the dataset have outliers. The plot\n",
    "also confirms that the spending’s are Positively skewed as the spread of data above the median is more than\n",
    "spread of data below the median. These outliers can be buyers who buy in large quantities at once or buyers\n",
    "who buy expensive items."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ab361f",
   "metadata": {},
   "source": [
    "## 1.5 On the basis of your analysis, what are your recommendations for the business? How can your analysis help the business to solve its problem? Answer from the business perspective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e80708",
   "metadata": {
    "tags": []
   },
   "source": [
    "- From the above histograms we can see that, most of the buyers spend less for all items. So the wholesaler can keep more stock of reasonable items as compared to expensive items to meet demand expectations.\n",
    "- __Fresh__ items are the most popular items bought by the customers. Keeping higher stock of the items might help increase sales and meet demands.\n",
    "- __Delicatessen__ items are the least bought items. Reducing the stock of these items will help decrease inventory costs.\n",
    "- __Oporto__ region has the lowest annual spendings. Increasing the stock of __Fresh__ and __Grocery__ items in this region might help increase sales.\n",
    "- In __Lisbon__ region, __Detegent Paper__ and __Delicatessen__ items have the lowest sales. Reducing their stocks will help decrease inventory.\n",
    "- The __Other__ regions combined have the highest sales. Increasing the customer base and keeping the prices reasonable can help increase sales and profit in this region.\n",
    "- For __Hotel__ channels, providing offers on __Detegent Paper__ and __Delicatessen__ can help increase sales of these items. Also keeping high stocks of __Fresh__ items will help meet demand expectations.\n",
    "- For __Retail__ channels, providing offers on __Frozen__ and __Delicatessen__ can help increase sales of these items. Also keeping high stocks of __Milk__ and __Grocery__ items will help meet demand expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9fb78a",
   "metadata": {},
   "source": [
    "# Problem 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a227d6f7-18e4-4af9-91e7-25af64d1e9ed",
   "metadata": {},
   "source": [
    "The Student News Service at Clear Mountain State University (CMSU) has decided to gather data about the undergraduate students that attend CMSU. CMSU creates and distributes a survey of 14 questions and receives responses from 62 undergraduates (stored in the __Survey__ data set)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0d0b7d-d591-4295-8d55-d7cf46adc910",
   "metadata": {},
   "source": [
    "## Sample of the Survey Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffa97443-042c-431f-aec2-375e5270725c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.252766Z",
     "start_time": "2023-01-10T13:52:42.222768Z"
    },
    "tags": []
   },
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>360</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Gender  Age   Class       Major Grad Intention  GPA  Employment  \\\n",
       "0   1  Female   20  Junior       Other            Yes  2.9   Full-Time   \n",
       "1   2    Male   23  Senior  Management            Yes  3.6   Part-Time   \n",
       "2   3    Male   21  Junior       Other            Yes  2.5   Part-Time   \n",
       "3   4    Male   21  Junior         CIS            Yes  2.5   Full-Time   \n",
       "4   5    Male   23  Senior       Other      Undecided  2.8  Unemployed   \n",
       "\n",
       "   Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0    50.0                  1             3       350   Laptop            200  \n",
       "1    25.0                  1             4       360   Laptop             50  \n",
       "2    45.0                  2             4       600   Laptop            200  \n",
       "3    40.0                  4             6       600   Laptop            250  \n",
       "4    40.0                  2             4       500   Laptop            100  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df = pd.read_csv('datasets/Survey-1.csv')\n",
    "survey_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8602b3-63f3-4d14-a65b-88f918aa49d2",
   "metadata": {},
   "source": [
    "## Checking the types of variables in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a45e401-f4e8-41f1-809d-df10b20b8f7c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.268767Z",
     "start_time": "2023-01-10T13:52:42.254768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ID                 62 non-null     int64  \n",
      " 1   Gender             62 non-null     object \n",
      " 2   Age                62 non-null     int64  \n",
      " 3   Class              62 non-null     object \n",
      " 4   Major              62 non-null     object \n",
      " 5   Grad Intention     62 non-null     object \n",
      " 6   GPA                62 non-null     float64\n",
      " 7   Employment         62 non-null     object \n",
      " 8   Salary             62 non-null     float64\n",
      " 9   Social Networking  62 non-null     int64  \n",
      " 10  Satisfaction       62 non-null     int64  \n",
      " 11  Spending           62 non-null     int64  \n",
      " 12  Computer           62 non-null     object \n",
      " 13  Text Messages      62 non-null     int64  \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "survey_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc38e875",
   "metadata": {},
   "source": [
    "From the above output we can see that:\n",
    "- There are 62 observations from different students in the data.\n",
    "- There are 14 variables, out of which, 6 are of integer type, 2 are of float type and 6 are of object(categorical) type.\n",
    "- The dataset does not have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2dae5ef-0aa6-4ac3-a25c-cbcc2249c40e",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.1. For this data, construct the following contingency tables (Keep Gender as row variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1a6828-89e6-4f87-b161-f404631e7a4e",
   "metadata": {},
   "source": [
    "### 2.1.1. Gender and Major"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac78fd0d-fa4c-45eb-856d-a8d810f21a62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.332772Z",
     "start_time": "2023-01-10T13:52:42.270769Z"
    }
   },
   "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>Major</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>CIS</th>\n",
       "      <th>Economics/Finance</th>\n",
       "      <th>International Business</th>\n",
       "      <th>Management</th>\n",
       "      <th>Other</th>\n",
       "      <th>Retailing/Marketing</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Major   Accounting  CIS  Economics/Finance  International Business  \\\n",
       "Gender                                                               \n",
       "Female           3    3                  7                       4   \n",
       "Male             4    1                  4                       2   \n",
       "Total            7    4                 11                       6   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  Total  \n",
       "Gender                                                            \n",
       "Female           4      3                    9          0     33  \n",
       "Male             6      4                    5          3     29  \n",
       "Total           10      7                   14          3     62  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_vs_major = pd.crosstab(index=survey_df['Gender'], columns=survey_df['Major'], margins=True, margins_name='Total')\n",
    "gender_vs_major"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada6299c-7fac-4ca6-adcb-c8ffd1f229f0",
   "metadata": {},
   "source": [
    "### 2.1.2. Gender and Grad Intention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa541c4d-9be9-414c-a64a-e7c9d23a4487",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.380776Z",
     "start_time": "2023-01-10T13:52:42.334776Z"
    }
   },
   "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>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes  Total\n",
       "Gender                                   \n",
       "Female           9         13   11     33\n",
       "Male             3          9   17     29\n",
       "Total           12         22   28     62"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_vs_grad_intent = pd.crosstab(index=survey_df['Gender'], columns=survey_df['Grad Intention'], margins=True, margins_name='Total')\n",
    "gender_vs_grad_intent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6142fe-1144-4239-ba56-7a1aa0f14db7",
   "metadata": {},
   "source": [
    "### 2.1.3. Gender and Employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb800878-0f8b-4af4-bf25-ee4e9ef13d74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.428780Z",
     "start_time": "2023-01-10T13:52:42.382778Z"
    }
   },
   "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",
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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>Employment</th>\n",
       "      <th>Full-Time</th>\n",
       "      <th>Part-Time</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Employment  Full-Time  Part-Time  Unemployed  Total\n",
       "Gender                                             \n",
       "Female              3         24           6     33\n",
       "Male                7         19           3     29\n",
       "Total              10         43           9     62"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_vs_employment = pd.crosstab(index=survey_df['Gender'], columns=survey_df['Employment'], margins=True, margins_name='Total')\n",
    "gender_vs_employment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd57edc-45bf-4c81-845d-298126dfa889",
   "metadata": {},
   "source": [
    "### 2.1.4. Gender and Computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40405a7e-a5a4-45aa-adb9-5e5d71f53404",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.476784Z",
     "start_time": "2023-01-10T13:52:42.430783Z"
    }
   },
   "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>Computer</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Tablet</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Computer  Desktop  Laptop  Tablet  Total\n",
       "Gender                                  \n",
       "Female          2      29       2     33\n",
       "Male            3      26       0     29\n",
       "Total           5      55       2     62"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_vs_computer = pd.crosstab(index=survey_df['Gender'], columns=survey_df['Computer'], margins=True, margins_name='Total')\n",
    "gender_vs_computer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e16e0a-5fa3-421b-8563-5ee99c7a565b",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.2. Assume that the sample is representative of the population of CMSU. Based on the data, answer the following question:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3835613-accf-4910-bca3-b96275ce8cd9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.2.1. What is the probability that a randomly selected CMSU student will be male?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac8c426",
   "metadata": {},
   "source": [
    "Out of the total 62 students, 29 students are male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b20a6337-3bac-41ce-8a7f-9eb3a52f3db5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.492785Z",
     "start_time": "2023-01-10T13:52:42.478785Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence the probability that a randomly selected CMSU student will be male is 0.468.\n"
     ]
    }
   ],
   "source": [
    "male_prob = 29 / 62\n",
    "print(f\"Hence the probability that a randomly selected CMSU student will be male is {round(male_prob, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614f494a-2569-4cd3-8fa8-dd6d3895a273",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.2.2. What is the probability that a randomly selected CMSU student will be female?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "867cc042",
   "metadata": {},
   "source": [
    "Out of the total 62 students, 33 students are female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb9a5fd5-fd37-4806-a392-7fb445b963d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.508786Z",
     "start_time": "2023-01-10T13:52:42.494790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence the probability that a randomly selected CMSU student will be female is 0.532.\n"
     ]
    }
   ],
   "source": [
    "female_prob = 33 / 62\n",
    "print(f\"Hence the probability that a randomly selected CMSU student will be female is {round(female_prob, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2fa1fa-09d6-4bc9-9542-8524e5040a85",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.3. Assume that the sample is representative of the population of CMSU. Based on the data, answer the following question:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2a50ae-cfca-45e0-913e-e4338c6c7dad",
   "metadata": {},
   "source": [
    "### 2.3.1. Find the conditional probability of different majors among the male students in CMSU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f39de073-1f9d-4fcb-868c-93801d1ec117",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.524790Z",
     "start_time": "2023-01-10T13:52:42.510788Z"
    }
   },
   "outputs": [],
   "source": [
    "account_male_prob = gender_vs_major.loc['Male', 'Accounting'] / gender_vs_major.loc['Male', 'Total']\n",
    "cis_male_prob = gender_vs_major.loc['Male', 'CIS'] / gender_vs_major.loc['Male', 'Total']\n",
    "economics_male_prob = gender_vs_major.loc['Male', 'Economics/Finance'] / gender_vs_major.loc['Male', 'Total']\n",
    "business_male_prob = gender_vs_major.loc['Male', 'International Business'] / gender_vs_major.loc['Male', 'Total']\n",
    "manage_male_prob = gender_vs_major.loc['Male', 'Management'] / gender_vs_major.loc['Male', 'Total']\n",
    "other_male_prob = gender_vs_major.loc['Male', 'Other'] / gender_vs_major.loc['Male', 'Total']\n",
    "market_male_prob = gender_vs_major.loc['Male', 'Retailing/Marketing'] / gender_vs_major.loc['Male', 'Total']\n",
    "undecided_male_prob = gender_vs_major.loc['Male', 'Undecided'] / gender_vs_major.loc['Male', 'Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b5ea7f94-632e-4915-91a7-fc80511c0b1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.540788Z",
     "start_time": "2023-01-10T13:52:42.526790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional probability that a Male student has an Accounting major is 0.138.\n",
      "Conditional probability that a Male student has a CIS major is 0.034.\n",
      "Conditional probability that a Male student has an Economics/Finance major is 0.138.\n",
      "Conditional probability that a Male student has an International Business major is 0.069.\n",
      "Conditional probability that a Male student has a Management major is 0.207.\n",
      "Conditional probability that a Male student has Other major is 0.138.\n",
      "Conditional probability that a Male student has a Retailing/Marketing major is 0.172.\n",
      "Conditional probability that a Male student has an Undecided major is 0.103.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Conditional probability that a Male student has an Accounting major is {round(account_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has a CIS major is {round(cis_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has an Economics/Finance major is {round(economics_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has an International Business major is {round(business_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has a Management major is {round(manage_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has Other major is {round(other_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has a Retailing/Marketing major is {round(market_male_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Male student has an Undecided major is {round(undecided_male_prob, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6862993-2448-4f94-ab38-d1f42dcedc5b",
   "metadata": {},
   "source": [
    "### 2.3.2. Find the conditional probability of different majors among the female students in CMSU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "18eb7891-020b-4d76-9256-5b5573640f12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.556792Z",
     "start_time": "2023-01-10T13:52:42.542792Z"
    }
   },
   "outputs": [],
   "source": [
    "account_female_prob = gender_vs_major.loc['Female', 'Accounting'] / gender_vs_major.loc['Female', 'Total']\n",
    "cis_female_prob = gender_vs_major.loc['Female', 'CIS'] / gender_vs_major.loc['Female', 'Total']\n",
    "economics_female_prob = gender_vs_major.loc['Female', 'Economics/Finance'] / gender_vs_major.loc['Female', 'Total']\n",
    "business_female_prob = gender_vs_major.loc['Female', 'International Business'] / gender_vs_major.loc['Female', 'Total']\n",
    "manage_female_prob = gender_vs_major.loc['Female', 'Management'] / gender_vs_major.loc['Female', 'Total']\n",
    "other_female_prob = gender_vs_major.loc['Female', 'Other'] / gender_vs_major.loc['Female', 'Total']\n",
    "market_female_prob = gender_vs_major.loc['Female', 'Retailing/Marketing'] / gender_vs_major.loc['Female', 'Total']\n",
    "undecided_female_prob = gender_vs_major.loc['Female', 'Undecided'] / gender_vs_major.loc['Female', 'Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d961614-6d02-4d93-9835-61e5bc5d28c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.573792Z",
     "start_time": "2023-01-10T13:52:42.564793Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional probability that a Female student has an Accounting major is 0.091.\n",
      "Conditional probability that a Female student has a CIS major is 0.091.\n",
      "Conditional probability that a Female student has an Economics/Finance major is 0.212.\n",
      "Conditional probability that a Female student has an International Business major is 0.121.\n",
      "Conditional probability that a Female student has a Management major is 0.121.\n",
      "Conditional probability that a Female student has Other major is 0.091.\n",
      "Conditional probability that a Female student has a Retailing/Marketing major is 0.273.\n",
      "Conditional probability that a Female student has an Undecided major is 0.0.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Conditional probability that a Female student has an Accounting major is {round(account_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has a CIS major is {round(cis_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has an Economics/Finance major is {round(economics_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has an International Business major is {round(business_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has a Management major is {round(manage_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has Other major is {round(other_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has a Retailing/Marketing major is {round(market_female_prob, 3)}.\")\n",
    "print(f\"Conditional probability that a Female student has an Undecided major is {round(undecided_female_prob, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc02273-3cb2-44bf-97b3-59807c05558b",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.4. Assume that the sample is a representative of the population of CMSU. Based on the data, answer the following question:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f265612-ca06-4a51-b12a-8411ba83ed7c",
   "metadata": {},
   "source": [
    "### 2.4.1. Find the probability that a randomly chosen student is a male and intends to graduate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d0b52a",
   "metadata": {},
   "source": [
    "Out of the 62 students, 17 students are male who intends to graduate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32e17437-5652-417c-ab92-9f624f496854",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.604796Z",
     "start_time": "2023-01-10T13:52:42.592796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence the probability that a randomly chosen student is a male and intends to graduate is 0.274.\n"
     ]
    }
   ],
   "source": [
    "grad_yes_male = gender_vs_grad_intent.loc['Male', 'Yes'] / 62\n",
    "print(f\"Hence the probability that a randomly chosen student is a male and intends to graduate is {round(grad_yes_male, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c051e39c-7395-4d04-88c2-29235b79e397",
   "metadata": {},
   "source": [
    "### 2.4.2 Find the probability that a randomly selected student is a female and does NOT have a laptop. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdac7e81",
   "metadata": {},
   "source": [
    "Out of the 62 students, 4 students are female who do not have a laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a48fea20-c7f9-4b76-b028-7c4fe90f34e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.620795Z",
     "start_time": "2023-01-10T13:52:42.606796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence the probability that a randomly chosen student is a female and does NOT have a laptop is 0.065.\n"
     ]
    }
   ],
   "source": [
    "no_laptop_female = gender_vs_computer.loc['Female', ['Desktop', 'Tablet']].sum() / 62\n",
    "print(f\"Hence the probability that a randomly chosen student is a female and does NOT have a laptop is {round(no_laptop_female, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18a0c56-06cf-48c3-9614-3767d9ebe61c",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.5. Assume that the sample is representative of the population of CMSU. Based on the data, answer the following question:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef42d68-d73c-4fc5-b474-d7bc2cb2f152",
   "metadata": {},
   "source": [
    "### 2.5.1. Find the probability that a randomly chosen student is a male or has full-time employment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6dbacd4b-9652-44cf-b2f9-933a5eb181e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.636796Z",
     "start_time": "2023-01-10T13:52:42.622797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 62 students, 29 are male, 10 have full time jobs, and 7 are both male and have full time jobs.\n",
      "Hence the probability that a randomly chosen student is a male or has full-time employment is 0.516.\n"
     ]
    }
   ],
   "source": [
    "full_time_male = (gender_vs_employment.loc['Male', 'Total'] + \n",
    "                  gender_vs_employment.loc['Total', 'Full-Time'] - \n",
    "                  gender_vs_employment.loc['Male', 'Full-Time']) / 62\n",
    "print(f\"Out of the 62 students, 29 are male, 10 have full time jobs, and 7 are both male and have full time jobs.\\n\"\n",
    "      f\"Hence the probability that a randomly chosen student is a male or has full-time employment is {round(full_time_male, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2512a2e-036c-4849-a232-267e0a049c95",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2.5.2. Find the conditional probability that given a female student is randomly chosen, she is majoring in international business or management."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6457f1c",
   "metadata": {},
   "source": [
    "Out of the 33 female students, 4 are majoring in International Business and 4 are majoring in Management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8cbf9e08-6909-461d-b71a-10c8ea30030a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.652797Z",
     "start_time": "2023-01-10T13:52:42.639798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence the conditional probability that given a female student is randomly chosen, she is majoring in international business or management is 0.242.\n"
     ]
    }
   ],
   "source": [
    "business_manage_female = gender_vs_major.loc['Female', ['International Business', 'Management']].sum() / gender_vs_major.loc['Female', 'Total']\n",
    "print(f\"Hence the conditional probability that given a female student is randomly chosen, she is majoring in \"\n",
    "      f\"international business or management is {round(business_manage_female, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3c694e-7da7-40a4-889a-1d715268fc4d",
   "metadata": {},
   "source": [
    "## 2.6.  Construct a contingency table of Gender and Intent to Graduate at 2 levels (Yes/No). The Undecided students are not considered now and the table is a 2x2 table. Do you think the graduate intention and being female are independent events?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0a6e874-33a5-422f-a11c-3aa58466e405",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.700804Z",
     "start_time": "2023-01-10T13:52:42.654799Z"
    }
   },
   "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>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Yes  Total\n",
       "Gender                        \n",
       "Female           9   11     20\n",
       "Male             3   17     20\n",
       "Total           12   28     40"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_vs_intent_2_levels = survey_df.loc[survey_df['Grad Intention'] != 'Undecided', ['Gender', 'Grad Intention']]\n",
    "gender_vs_intent_2_levels = pd.crosstab(index=gender_vs_intent_2_levels['Gender'], \n",
    "                                        columns=gender_vs_intent_2_levels['Grad Intention'], \n",
    "                                        margins=True, margins_name='Total')\n",
    "gender_vs_intent_2_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0bfb6535-2b41-4e34-813d-5793c4b37843",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.716803Z",
     "start_time": "2023-01-10T13:52:42.704802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hence using the above mentioned formula we find that the graduate intention and being female are not independent events.\n"
     ]
    }
   ],
   "source": [
    "female_prob = gender_vs_intent_2_levels.loc['Female', 'Total'] / gender_vs_intent_2_levels.loc['Total', 'Total']\n",
    "yes_intent_prob = gender_vs_intent_2_levels.loc['Total', 'Yes'] / gender_vs_intent_2_levels.loc['Total', 'Total']\n",
    "yes_intent_and_female = gender_vs_intent_2_levels.loc['Female', 'Yes'] / gender_vs_intent_2_levels.loc['Total', 'Total']\n",
    "\n",
    "if yes_intent_and_female == (female_prob * yes_intent_prob):\n",
    "    print(\"Hence using the above mentioned formula we find that the graduate intention and being female are independent events.\")\n",
    "else:\n",
    "    print(\"Hence using the above mentioned formula we find that the graduate intention and being female are not independent events.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559cb0eb-c423-4b98-abdf-5a7eb5c77384",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 2.7. Note that there are four numerical (continuous) variables in the data set, GPA, Salary, Spending, and Text Messages.<br/>Answer the following questions based on the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e43871d-5c1a-430c-b3ed-1efd7fad081a",
   "metadata": {},
   "source": [
    "### 2.7.1. If a student is chosen randomly, what is the probability that his/her GPA is less than 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "266dc5d2-32dc-40ab-be16-6b55aeebc5df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.732804Z",
     "start_time": "2023-01-10T13:52:42.718806Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 62 students, 17 students have GPA less than 3.\n",
      "Hence if a student is chosen randomly, the probability that his/her GPA is less than 3 is 0.274.\n"
     ]
    }
   ],
   "source": [
    "gpa_less_than_3_prob = survey_df[survey_df['GPA'] < 3].shape[0] / survey_df.shape[0]\n",
    "print(f\"Out of the 62 students, 17 students have GPA less than 3.\\n\"\n",
    "      f\"Hence if a student is chosen randomly, the probability that his/her GPA is less than 3 is {round(gpa_less_than_3_prob, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5e5fae-742b-4811-befa-53c615656629",
   "metadata": {},
   "source": [
    "### 2.7.2. Find the conditional probability that a randomly selected male earns 50 or more. Find the conditional probability that a randomly selected female earns 50 or more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "81c80973-f9c3-40d6-96fb-a804ccb7a715",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.748807Z",
     "start_time": "2023-01-10T13:52:42.734810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 29 male students, 14 students have salaries more than or equal to 50.\n",
      "Hence the conditional probability that a randomly selected male earns 50 or more is 0.483.\n"
     ]
    }
   ],
   "source": [
    "male_salary_more_50 = survey_df[(survey_df['Gender'] == 'Male') & (survey_df['Salary'] >= 50)].shape[0] / survey_df[survey_df['Gender'] == 'Male'].shape[0]\n",
    "print(f\"Out of the 29 male students, 14 students have salaries more than or equal to 50.\\n\"\n",
    "      f\"Hence the conditional probability that a randomly selected male earns 50 or more is {round(male_salary_more_50, 3)}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "674fdc6c-4441-422c-872b-4593e24468f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:42.764807Z",
     "start_time": "2023-01-10T13:52:42.750807Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 33 female students, 18 students have salaries more than or equal to 50.\n",
      "Hence the conditional probability that a randomly selected female earns 50 or more is 0.545.\n"
     ]
    }
   ],
   "source": [
    "female_salary_more_50 = survey_df[(survey_df['Gender'] == 'Female') & (survey_df['Salary'] >= 50)].shape[0] / survey_df[survey_df['Gender'] == 'Female'].shape[0]\n",
    "print(f\"Out of the 33 female students, 18 students have salaries more than or equal to 50.\\n\"\n",
    "      f\"Hence the conditional probability that a randomly selected female earns 50 or more is {round(female_salary_more_50, 3)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10cd352-69bb-4b24-b033-42c29f724c28",
   "metadata": {},
   "source": [
    "## 2.8. Note that there are four numerical (continuous) variables in the data set, GPA, Salary, Spending, and Text Messages. For each of them comment whether they follow a normal distribution. Write a note summarizing your conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73564454-d97c-4a9e-b3af-a73cef78f989",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:43.623118Z",
     "start_time": "2023-01-10T13:52:42.766810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = survey_df.hist(column=['GPA', 'Salary', 'Spending', 'Text Messages'], \n",
    "                  figsize=(10, 10), bins=8, layout=(2, 2))\n",
    "for ax in axs.flatten():\n",
    "    ax.set_ylabel(\"Frequency\", size=15)\n",
    "plt.suptitle('Distribution of Continous Variables in Survey dataset', size=17)\n",
    "plt.tight_layout();\n",
    "# plt.savefig('images/Figure5.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cff36bf-41b9-4929-bf05-7b3c47782e87",
   "metadata": {},
   "source": [
    "Theoretically the mean, median and mode of a normal distribution are equal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b3fae1-b765-43cb-9e04-7e08668cbeaa",
   "metadata": {},
   "source": [
    "### Distribution of GPA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9eb424cb-915e-4171-a28d-09900ad0ad6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:43.639202Z",
     "start_time": "2023-01-10T13:52:43.626201Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the GPA variable:\n",
      "Mean = 3.1\n",
      "Median = 3.2\n",
      "Mode = [3.0, 3.1, 3.4]\n"
     ]
    }
   ],
   "source": [
    "gpa_mean = survey_df['GPA'].mean()\n",
    "gpa_median = survey_df['GPA'].median()\n",
    "gpa_mode = survey_df['GPA'].mode()\n",
    "gpa_std = survey_df['GPA'].std()\n",
    "print(f\"For the GPA variable:\\n\"\n",
    "      f\"Mean = {round(gpa_mean, 1)}\\n\"\n",
    "      f\"Median = {round(gpa_median, 1)}\\n\"\n",
    "      f\"Mode = [{gpa_mode[0]}, {gpa_mode[1]}, {gpa_mode[2]}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a07786c-c65e-4ed6-87fc-9b30659c1551",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:43.829215Z",
     "start_time": "2023-01-10T13:52:43.642203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "plt.axis('equal')\n",
    "stats.probplot(survey_df['GPA'], plot=sns.mpl.pyplot, sparams=(gpa_mean, gpa_std), fit=False)\n",
    "plt.plot(plt.ylim(), plt.ylim())\n",
    "plt.title('QQ-Plot for GPA');\n",
    "# plt.savefig('images/Figure6.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd41c959",
   "metadata": {},
   "source": [
    "From the above outputs we can conclude that the GPA variable in the Survey dataset approximately follows a\n",
    "normal distribution. This can also be verified from the histogram in Figure 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ec6192-bffe-43d7-bc83-9fe05fdc267f",
   "metadata": {},
   "source": [
    "### Distribution of Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d146622b-adac-482b-8b04-7319ca0c471c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:43.845215Z",
     "start_time": "2023-01-10T13:52:43.831216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Salary variable:\n",
      "Mean = 48.5\n",
      "Median = 50.0\n",
      "Mode = 40.0\n"
     ]
    }
   ],
   "source": [
    "salary_mean = survey_df['Salary'].mean()\n",
    "salary_median = survey_df['Salary'].median()\n",
    "salary_mode = survey_df['Salary'].mode()\n",
    "salary_std = survey_df['Salary'].std()\n",
    "print(f\"For the Salary variable:\\n\"\n",
    "      f\"Mean = {round(salary_mean, 1)}\\n\"\n",
    "      f\"Median = {round(salary_median, 1)}\\n\"\n",
    "      f\"Mode = {salary_mode[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e8217c9c-a68d-47ef-a105-761097e3a294",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.014231Z",
     "start_time": "2023-01-10T13:52:43.847221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "plt.axis('equal')\n",
    "stats.probplot(survey_df['Salary'], plot=sns.mpl.pyplot, sparams=(salary_mean, salary_std), fit=False)\n",
    "plt.plot(plt.ylim(), plt.ylim())\n",
    "plt.title('QQ-Plot for Salary');\n",
    "# plt.savefig('images/Figure7.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7923cd",
   "metadata": {},
   "source": [
    "The mean, median and mode of Salary are very different from each other but its QQ-Plot is close to the\n",
    "straight line. Hence the Salary variable approximately follows a normal distribution. This can also be verified\n",
    "from the histogram in Figure 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f030f77-78ea-4ba0-b035-cd546a41918d",
   "metadata": {},
   "source": [
    "### Distribution of Spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7aee5519-e2a7-40c7-a4ae-86f9878806b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.029230Z",
     "start_time": "2023-01-10T13:52:44.016232Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Spending variable:\n",
      "Mean = 482.0\n",
      "Median = 500.0\n",
      "Mode = 500\n"
     ]
    }
   ],
   "source": [
    "spend_mean = survey_df['Spending'].mean()\n",
    "spend_median = survey_df['Spending'].median()\n",
    "spend_mode = survey_df['Spending'].mode()\n",
    "spend_std = survey_df['Spending'].std()\n",
    "print(f\"For the Spending variable:\\n\"\n",
    "      f\"Mean = {round(spend_mean, 1)}\\n\"\n",
    "      f\"Median = {round(spend_median, 1)}\\n\"\n",
    "      f\"Mode = {spend_mode[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7f36b198-5a38-4051-b898-c3f0b864f970",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.219245Z",
     "start_time": "2023-01-10T13:52:44.031233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "plt.axis('equal')\n",
    "stats.probplot(survey_df['Spending'], plot=sns.mpl.pyplot, sparams=(spend_mean, spend_std), fit=False)\n",
    "plt.plot(plt.ylim(), plt.ylim())\n",
    "plt.title('QQ-Plot for Spending');\n",
    "# plt.savefig('images/Figure8.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a86981",
   "metadata": {},
   "source": [
    "Even though the mean, median and mode of Spending are close to each other, its QQ-Plot varies a lot from the\n",
    "straight line. Hence the Spending variable does not follow a normal distribution. This can also be verified from\n",
    "the histogram in Figure 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7907847-2bf7-4be5-bc94-e16a0ef3be3a",
   "metadata": {},
   "source": [
    "### Distribution of Text Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a99249c4-6d09-4656-bfb0-c6754a6866cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.234247Z",
     "start_time": "2023-01-10T13:52:44.221247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the Text Messages variable:\n",
      "Mean = 246.2\n",
      "Median = 200.0\n",
      "Mode = 300\n"
     ]
    }
   ],
   "source": [
    "sms_mean = survey_df['Text Messages'].mean()\n",
    "sms_median = survey_df['Text Messages'].median()\n",
    "sms_mode = survey_df['Text Messages'].mode()\n",
    "sms_std = survey_df['Text Messages'].std()\n",
    "print(f\"For the Text Messages variable:\\n\"\n",
    "      f\"Mean = {round(sms_mean, 1)}\\n\"\n",
    "      f\"Median = {round(sms_median, 1)}\\n\"\n",
    "      f\"Mode = {sms_mode[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7c308586-8002-4615-afdc-3872df15753a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.409260Z",
     "start_time": "2023-01-10T13:52:44.236251Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "plt.axis('equal')\n",
    "stats.probplot(survey_df['Text Messages'], plot=sns.mpl.pyplot, sparams=(sms_mean, sms_std), fit=False)\n",
    "plt.plot(plt.ylim(), plt.ylim())\n",
    "plt.title('QQ-Plot for Text Messages');\n",
    "# plt.savefig('images/Figure9.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa5a5ff",
   "metadata": {},
   "source": [
    "As the mean, median and mode of Text Messages variable are very different from each other and its QQ-Plot is\n",
    "not close to the straight line, the Text Messages variable does not follow a normal distribution. This can also be\n",
    "verified from the histogram in Figure 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13d3c2e-daac-4a21-a70a-43d99ee37747",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Problem 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb931120",
   "metadata": {},
   "source": [
    "An important quality characteristic used by the manufacturers of ABC asphalt shingles is the amount of moisture the shingles contain when they are packaged. Customers may feel that they have purchased a product lacking in quality if they find moisture and wet shingles inside the packaging.   In some cases, excessive moisture can cause the granules attached to the shingles for texture and coloring purposes to fall off the shingles resulting in appearance problems. To monitor the amount of moisture present, the company conducts moisture tests. A shingle is weighed and then dried. The shingle is then reweighed, and based on the amount of moisture taken out of the product, the pounds of moisture per 100 square feet are calculated. The company would like to show that the mean moisture content is less than 0.35 pounds per 100 square feet.\n",
    "\n",
    "The file (A & B shingles.csv) includes 36 measurements (in pounds per 100 square feet) for A shingles and 31 for B shingles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65c3619",
   "metadata": {},
   "source": [
    "## Sample of the A & B Shingles Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7c192d9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.440265Z",
     "start_time": "2023-01-10T13:52:44.412262Z"
    }
   },
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B\n",
       "0  0.44  0.14\n",
       "1  0.61  0.15\n",
       "2  0.47  0.31\n",
       "3  0.30  0.16\n",
       "4  0.15  0.37"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shingles_df = pd.read_csv('datasets/A+&+B+shingles.csv')\n",
    "shingles_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e6cbde",
   "metadata": {},
   "source": [
    "## Checking the types of variables in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "966d271e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.455269Z",
     "start_time": "2023-01-10T13:52:44.442270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A       36 non-null     float64\n",
      " 1   B       31 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 704.0 bytes\n"
     ]
    }
   ],
   "source": [
    "shingles_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163b7004",
   "metadata": {},
   "source": [
    "## Checking the distribution of both variables in the Shingles dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b1496b91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.802471Z",
     "start_time": "2023-01-10T13:52:44.458271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = shingles_df.hist(figsize=(10, 5))\n",
    "for ax in axs.flatten():\n",
    "    ax.set_ylabel(\"Frequency\", size=15)\n",
    "plt.suptitle('Distribution of Variables in Shingles dataset', size=17)\n",
    "plt.tight_layout();\n",
    "# plt.savefig('images/Figure10.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7109fcab",
   "metadata": {},
   "source": [
    "## 3.1 Do you think there is evidence that mean moisture contents in both types of shingles are within the permissible limits? State your conclusions clearly showing all steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c19087de",
   "metadata": {},
   "source": [
    "### Hypothesis test for A shingles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0f8c0e88",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.817479Z",
     "start_time": "2023-01-10T13:52:44.805479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic = -1.4735046253382782 and p_value = 0.9252236685509249\n"
     ]
    }
   ],
   "source": [
    "# using \"alternative = 'greater'\" for right-tailed t-test.\n",
    "t_stat, p_value = stats.ttest_1samp(shingles_df['A'], popmean=0.35, alternative='greater')\n",
    "print(f\"t_statistic = {t_stat} and p_value = {p_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b31f588",
   "metadata": {},
   "source": [
    "### Hypothesis test for B shingles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39ad37b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.833481Z",
     "start_time": "2023-01-10T13:52:44.819482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic = -3.1003313069986995 and p_value = 0.9979095225996808\n"
     ]
    }
   ],
   "source": [
    "# using \"alternative = 'greater'\" for right-tailed t-test.\n",
    "t_stat, p_value = stats.ttest_1samp(shingles_df['B'].dropna(), popmean=0.35, alternative='greater')\n",
    "print(f\"t_statistic = {t_stat} and p_value = {p_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1439ff45",
   "metadata": {},
   "source": [
    "## 3.2 Do you think that the population mean for shingles A and B are equal? Form the hypothesis and conduct the test of the hypothesis. What assumption do you need to check before the test for equality of means is performed?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c330b5",
   "metadata": {},
   "source": [
    "To perform a 2-sample t-test, we assume that the sample variance of both the samples are equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e5bc45ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.849481Z",
     "start_time": "2023-01-10T13:52:44.835483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ratio of the variance of both samples is 0.9773231765154546.\n",
      "As the ratio is close to 1 we can proceed we the 2-sample t-test.\n"
     ]
    }
   ],
   "source": [
    "A_var = shingles_df['A'].var(skipna=True)\n",
    "B_var = shingles_df['B'].var(skipna=True)\n",
    "print(f'The ratio of the variance of both samples is {A_var / B_var}.\\n'\n",
    "      f'As the ratio is close to 1 we can proceed we the 2-sample t-test.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232ff77e",
   "metadata": {},
   "source": [
    "### Hypothesis Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4cb03000",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-10T13:52:44.865485Z",
     "start_time": "2023-01-10T13:52:44.851483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic = 1.2896282719661123 and p_value = 0.20174965718353277\n"
     ]
    }
   ],
   "source": [
    "t_stat, p_value = stats.ttest_ind(shingles_df['A'], shingles_df['B'], nan_policy='omit')\n",
    "print(f\"t_statistic = {t_stat} and p_value = {p_value}\")"
   ]
  }
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