Basic_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.3.5)Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (1.22.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.22.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.7.3)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scipy) (1.22.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install scipy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (3.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (3.0.6)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (4.28.5)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (9.0.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.22.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.11.2)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.7.3)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.3.5)\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (3.5.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from seaborn) (1.22.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (3.0.6)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (9.0.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (4.28.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting statsmodels\n",
      "  Downloading statsmodels-0.13.2-cp310-cp310-win_amd64.whl (9.1 MB)\n",
      "     ---------------------------------------- 9.1/9.1 MB 242.1 kB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from statsmodels) (1.22.0)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from statsmodels) (21.3)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from statsmodels) (1.3.5)\n",
      "Requirement already satisfied: scipy>=1.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from statsmodels) (1.7.3)\n",
      "Collecting patsy>=0.5.2\n",
      "  Downloading patsy-0.5.2-py2.py3-none-any.whl (233 kB)\n",
      "     ------------------------------------ 233.7/233.7 KB 386.6 kB/s eta 0:00:00\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from packaging>=21.3->statsmodels) (3.0.6)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.25->statsmodels) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: six in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n",
      "Installing collected packages: patsy, statsmodels\n",
      "Successfully installed patsy-0.5.2 statsmodels-0.13.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 22.0.2; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install statsmodels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>likeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>170.688</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>165.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>171.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>173.000</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>164.000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>31</td>\n",
       "      <td>160.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Pakora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>26</td>\n",
       "      <td>172.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>40</td>\n",
       "      <td>178.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>25</td>\n",
       "      <td>5.700</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>33</td>\n",
       "      <td>157.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Samosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>245 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age   height  weight  gender likeness\n",
       "0     27  170.688    76.0    Male  Biryani\n",
       "1     41  165.000    70.0    Male  Biryani\n",
       "2     29  171.000    80.0    Male  Biryani\n",
       "3     27  173.000   102.0    Male  Biryani\n",
       "4     29  164.000    67.0    Male  Biryani\n",
       "..   ...      ...     ...     ...      ...\n",
       "240   31  160.000    60.0    Male   Pakora\n",
       "241   26  172.000    70.0    Male  Biryani\n",
       "242   40  178.000    80.0    Male  Biryani\n",
       "243   25    5.700    65.0    Male  Biryani\n",
       "244   33  157.000    56.0  Female   Samosa\n",
       "\n",
       "[245 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"ml_data.csv\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9904/3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age        28.653061\n",
       "height    147.896996\n",
       "weight     70.743837\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9904/2270059322.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age        28.0\n",
       "height    169.1\n",
       "weight     70.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median\n",
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>likeness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  height  weight gender likeness\n",
       "0  27.0   165.0    60.0   Male  Biryani\n",
       "1   NaN     NaN    62.0    NaN      NaN\n",
       "2   NaN     NaN    65.0    NaN      NaN\n",
       "3   NaN     NaN    68.0    NaN      NaN\n",
       "4   NaN     NaN    70.0    NaN      NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \n",
      "1 \n",
      "2 \n",
      "3 \n",
      "4 \n",
      "5 \n",
      "6 \n",
      "7 \n",
      "8 \n",
      "9 \n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i,end=\" \")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 "
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    print(i, end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inter Quartile Range (IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        25.0\n",
       "height    158.5\n",
       "weight     60.0\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p25 = df.quantile(0.25)\n",
    "p25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        31.0\n",
       "height    176.0\n",
       "weight     80.0\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p75 = df.quantile(0.75)\n",
    "p75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        6.0\n",
       "height    17.5\n",
       "weight    20.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = p75-p25\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 4., 0., 0., 2., 0., 2., 0., 1., 1.]),\n",
       " array([ 77. ,  85.3,  93.6, 101.9, 110.2, 118.5, 126.8, 135.1, 143.4,\n",
       "        151.7, 160. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blood_sugar = [113,85,90,160,149,88,93,115,135,80,77,92,129]\n",
    "plt.xlabel(\"Sugar Level\")\n",
    "plt.ylabel(\"Number of Patient\")\n",
    "plt.title(\"BLood Sugar Chart\")\n",
    "plt.hist(blood_sugar,rwidth=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# load dataset\n",
    "t= sns.load_dataset(\"titanic\")\n",
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.barplot(x=\"sex\",y=\"survived\",hue=\"class\",data=t,color=\"green\",ci=None,palette=\"Spectral\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tip = sns.load_dataset(\"tips\")\n",
    "tip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot= sns.boxplot(x=\"total_bill\",y=\"day\",data=tip,showmeans=True,meanprops={\"marker\":\"+\",\"markersize\":\"10\",\"markeredgecolor\":\"yellow\"},dodge=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carat', ylabel='price'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 468x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds = sns.load_dataset(\"diamonds\")\n",
    "f,ax = plt.subplots(figsize=(6.5,6.5))\n",
    "sns.despine(f,left=True,bottom=True)\n",
    "clarity_ranking = [\"I1\",\"SI2\",\"SI1\",\"VS2\",\"VS1\",\"VVS2\",\"VVS1\",\"IF\"]\n",
    "sns.scatterplot(x=\"carat\",y=\"price\",\n",
    "                hue=\"clarity\",size=\"depth\",\n",
    "                palette=\"ch:r=-.2,d=.3_r\",\n",
    "                hue_order=clarity_ranking,\n",
    "                sizes=(1, 8),linewidth=0,\n",
    "                data=diamonds,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flower = sns.load_dataset(\"iris\")\n",
    "flower.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style=None,rc=None) # set style\n",
    "plot = sns.lineplot(x=\"sepal_length\",y=\"petal_length\",hue=\"species\",data=flower)\n",
    "plot.set_title(\"Flower Lineplot\")\n",
    "plt.xlim(4)\n",
    "sns.set_style(\"dark\")\n",
    "plt.figure(figsize=(8,9)) # size of figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Sample Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-1.4812257933030548, pvalue=0.1726850896295392)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "data = [14,14,16,13,12,17,15,13,15,14]\n",
    "# perform one sample t-test\n",
    "stats.ttest_1samp(a=data,popmean=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Sample Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.7372383130242788, 0.0823451148638443)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.weightstats import ztest as ztest\n",
    "cityA = [82,84,85,89,91,92,94,99,99,105,109,109,109,110,112,113,114,114]\n",
    "cityB = [90,91,91,91,95,95,99,99,108,109,114,115,116,117,117,128,129,130,113]\n",
    "ztest(cityA,cityB,value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paired Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-2.9732484231168796, pvalue=0.01007144862643272)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre = [88,82,84,93,75,78,84,87,95,91,83,89,77,68,91]\n",
    "post = [91,84,88,90,79,80,88,90,90,96,88,89,81,74,92]\n",
    "stats.ttest_rel(pre,post)\n",
    "\n",
    "# h0: The mean pre test and post test scores are equal\n",
    "\n",
    "# h1: The mean pre test and post test scores are not equal\n",
    "\n",
    "# Since the p value 0.0101 is less than 0.05, we reject the null hypothesis\n",
    "# We have sufficient evidence to say that the true mean test score\n",
    "# is different for students before and after participating in the study program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pooled Standard Deviation 3.2071349029490928\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import statistics\n",
    "sample1=[4,5,6]\n",
    "# Computing sample standard deviation for sample1\n",
    "SD1= statistics.stdev(sample1)\n",
    "sample2 = [10,12,14,16,18,20]\n",
    "#  Computing sample standard deviation for sample2\n",
    "SD2 = statistics.stdev(sample2)\n",
    "\n",
    "# Calculate length of 1st sample\n",
    "n1 = len(sample1)\n",
    "\n",
    "# Calculate length of 2nd sample\n",
    "n2 = len(sample2)\n",
    "\n",
    "pooled_standard_derivation = math.sqrt(((n1 -1)*SD1 * SD1 + (n2 -1)*SD2 * SD2) / (n1+n2-2))\n",
    "\n",
    "print(\"Pooled Standard Deviation\",pooled_standard_derivation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing of Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.236067977499793, pvalue=0.04170979503207775)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Welch_Test\n",
    "# define two arrays of data\n",
    "booklet=[90,85,88,89,94,91,79,83,87,88,91,90]\n",
    "no_booklet=[67,90,71,95,88,83,72,66,75,86,93,84]\n",
    "\n",
    "# perform welch's test\n",
    "stats.ttest_ind(booklet,no_booklet,equal_var=False)\n",
    "\n",
    "# Since this p_values is less than 0.05,we can reject null hypothesis of the test and conclude that there is a\n",
    "# statistically significant difference in mean exam scores between the two groups\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exam score of each group\n",
    "from scipy.stats import f_oneway\n",
    "group1 = [85,86,88,75,78,94,98,79,71,80]\n",
    "group2 = [91,92,93,85,87,84,82,88,95,96]\n",
    "group3 = [79,78,88,94,92,85,83,85,82,81]\n",
    "\n",
    "f_oneway(group1,group2,group3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(water)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.533333</td>\n",
       "      <td>8.533333</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>0.000527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(sun)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>24.866667</td>\n",
       "      <td>12.433333</td>\n",
       "      <td>23.3125</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(water):C(sun)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>2.3125</td>\n",
       "      <td>0.120667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>24.0</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   df     sum_sq    mean_sq        F    PR(>F)\n",
       "C(water)          1.0   8.533333   8.533333  16.0000  0.000527\n",
       "C(sun)            2.0  24.866667  12.433333  23.3125  0.000002\n",
       "C(water):C(sun)   2.0   2.466667   1.233333   2.3125  0.120667\n",
       "Residual         24.0  12.800000   0.533333      NaN       NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "# Create a dataframe\n",
    "df = pd.DataFrame({'water': np.repeat(['daily', 'weekly'], 15),\n",
    "                          'sun': np.tile(np.repeat(['low', 'med','high'], 5),2),\n",
    "                          'height': [6,6,6,5,6,5,5,6,4,5,\n",
    "                                     6,6,7,8,7,3,4,4,4,5,\n",
    "                                     4,4,4,4,4,5,6,6,7,8]})\n",
    "\n",
    "\n",
    "# Performing two-way ANOVA\n",
    "model = ols('height ~ C(water) + C(sun) +\\\n",
    "C(water):C(sun)',\n",
    "            data=df).fit()\n",
    "sm.stats.anova_lm(model, type=2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pooled Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pooled Standard Deviation =  3.2071349029490928\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import statistics\n",
    "sample1 = [4, 5, 6]\n",
    "\n",
    "# Computing sample standard deviation for sample1\n",
    "SD1 = statistics.stdev(sample1)\n",
    "sample2 = [10, 12, 14, 16, 18, 20]\n",
    "\n",
    "# Computing sample standard deviation for sample2\n",
    "SD2 = statistics.stdev(sample2)\n",
    "\n",
    "# calculate length of 1st sample\n",
    "n1 = len(sample1)\n",
    "\n",
    "# calculate length of 2nd sample\n",
    "n2 = len(sample2)\n",
    "\n",
    "\n",
    "pooled_standard_deviation = math.sqrt(\n",
    "    ((n1 - 1)*SD1 * SD1 +\n",
    "     (n2-1)*SD2 * SD2) /\n",
    "    (n1 + n2-2))\n",
    "print(\"Pooled Standard Deviation = \",\n",
    "      pooled_standard_deviation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing of Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=50.0, pvalue=0.21138945901258455)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mann-whitney U test\n",
    "group1 = [20,23,21,25,18,17,18,24,20,24,23,19]\n",
    "group2 = [24,25,21,22,23,18,17,28,24,27,21,23]\n",
    "\n",
    "stats.mannwhitneyu(group1,group2,alternative='two-sided')\n",
    "#H0 The npg is equal between two groups\n",
    "\n",
    "#HA The npg is not equal between two groups\n",
    "\n",
    "# Sine the p_value 0.2114 is not less than 0.05 we fall to reject the null hypothesis\n",
    "\n",
    "# This means we do not have sufficient evidence to say that the true mean npg is different between the two groups\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=6.287801578353988, pvalue=0.043114289703508814)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kruskal-Wallis Test\n",
    "group1 = [7,14,14,13,12,9,6,14,12,8]\n",
    "group2 = [15,17,13,15,15,13,9,12,10,8]\n",
    "group3 = [6,8,8,9,5,14,13,8,10,9]\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "stats.kruskal(group1,group2,group3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.6708203932499369, 0.5023349543605021)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run Test\n",
    "from statsmodels.sandbox.stats.runs import runstest_1samp\n",
    "\n",
    "# create dataset\n",
    "data = [12,16,16,15,14,18,19,21,13,13]\n",
    "\n",
    "# Perform run test\n",
    "runstest_1samp(data,correction=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tukey Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import f_oneway\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=5.167774552944481, pvalue=0.012582197136592609)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#enter data for three groups\n",
    "a = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
    "b = [91, 92, 93, 90, 97, 94, 82, 88, 95, 96]\n",
    "c = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
    "\n",
    "#perform one-way ANOVA\n",
    "f_oneway(a, b, c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     a      b      8.4 0.0159   1.4259 15.3741   True\n",
      "     a      c      1.3 0.8894  -5.6741  8.2741  False\n",
      "     b      c     -7.1 0.0454 -14.0741 -0.1259   True\n",
      "-----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#create DataFrame to hold data\n",
    "df = pd.DataFrame({'score': [85, 86, 88, 75, 78, 94, 98, 79, 71, 80,\n",
    "                             91, 92, 93, 90, 97, 94, 82, 88, 95, 96,\n",
    "                             79, 78, 88, 94, 92, 85, 83, 85, 82, 81],\n",
    "                   'group': np.repeat(['a', 'b', 'c'], repeats=10)})\n",
    "\n",
    "# perform Tukey's test\n",
    "tukey = pairwise_tukeyhsd(endog=df['score'],\n",
    "                          groups=df['group'],\n",
    "                          alpha=0.05)\n",
    "\n",
    "#display results\n",
    "print(tukey)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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