Assignment-1-Q21_b (Basic Statistics Level-1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-1-Q21_b (Basic Statistics Level-1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Q21-b)\tCheck Whether the Adipose Tissue (AT) and Waist Circumference(Waist)  from wc-at data set  follows Normal Distribution \n",
    "       Dataset: wc-at.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Waist</th>\n",
       "      <th>AT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.75</td>\n",
       "      <td>25.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.60</td>\n",
       "      <td>25.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.80</td>\n",
       "      <td>42.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83.95</td>\n",
       "      <td>42.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.65</td>\n",
       "      <td>29.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>100.10</td>\n",
       "      <td>124.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>93.30</td>\n",
       "      <td>62.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>101.80</td>\n",
       "      <td>133.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>107.90</td>\n",
       "      <td>208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>108.50</td>\n",
       "      <td>208.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Waist      AT\n",
       "0     74.75   25.72\n",
       "1     72.60   25.89\n",
       "2     81.80   42.60\n",
       "3     83.95   42.80\n",
       "4     74.65   29.84\n",
       "..      ...     ...\n",
       "104  100.10  124.00\n",
       "105   93.30   62.20\n",
       "106  101.80  133.00\n",
       "107  107.90  208.00\n",
       "108  108.50  208.00\n",
       "\n",
       "[109 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcat=pd.read_csv('Database/wc-at.csv')\n",
    "wcat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hitesh Koli\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting distribution for Waist Circumference (Waist) \n",
    "sns.distplot(wcat.Waist)\n",
    "plt.ylabel('density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hitesh Koli\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting distribution for Adipose Tissue (AT) \n",
    "sns.distplot(wcat.AT)\n",
    "plt.ylabel('density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91.90183486238533, 90.8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# WC\n",
    "wcat.Waist.mean() , wcat.Waist.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101.89403669724771, 96.54)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AT\n",
    "wcat.AT.mean() , wcat.AT.median()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Inference: Both the Adipose Tissue (AT) and Waist Circumference(Waist) data set do follow the normal distribution approximately (as mean and median of both the data are approximately same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}