 Basic Statistics1- Ass_Q21b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Q21b) Basic Stats1- Assignment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Q21b) Check Whether the Adipose Tissue (AT) and Waist Circumference(Waist) from wc-at data set follows Normal Distribution\n",
    "\n",
    "  Dataset: wc-at.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:07:10.713679Z",
     "start_time": "2021-09-21T15:07:10.692636Z"
    }
   },
   "source": [
    "To check whether the AT& Waist data follows a normal distribution we plot the density plot and boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:11:10.074076Z",
     "start_time": "2021-09-21T15:10:38.275768Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:12:57.018902Z",
     "start_time": "2021-09-21T15:12:56.856294Z"
    }
   },
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc=pd.read_csv(\"wc-at.csv\")\n",
    "wc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:13:40.940043Z",
     "start_time": "2021-09-21T15:13:40.851280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.89403669724771"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.AT.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:14:23.602109Z",
     "start_time": "2021-09-21T15:14:23.569105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.54"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.AT.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:17:14.671784Z",
     "start_time": "2021-09-21T15:17:14.661602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.584869324127853"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.AT.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:18:18.990001Z",
     "start_time": "2021-09-21T15:18:18.631771Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chetana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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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FZB7eS1elwFcD9R7MJx2uaiI7KZYoj9vpUMLevOxEXt1zgvcP15CXZmd4JngEdHZcX1fZDb22re1xX4F7/K3r2/7FEQ7T+KmlvYtjdS1BORtuOPK4XRTmJLPxUBUNLR0kxFhnBBMcbFp147cj1U0oDPvbb6hMdR5MLsxJ5t2DVRSV1nLV+ROdDscYwKYcMUNQXNVEpNtFdrKtLT5aksdFkj8xji2ltdY11wQNSxzGb4dPnSEnNZYIl31sRtPCnBROt3Zy4ESj06EYA1jiMH5qaOmgqqmNadZIO+qmp8czPjqCD0trnA7FGMASh/HTkWrvNCNTLXGMOrdLuDAnmUMnm6g90+50OMZY4jD+Ka1uJtrjIj0h2ulQxqTCnGQAtpTWOhyJMZY4jJ9Ka84wOTkWl63254iEGA/nTYxne1kd3TaS3DjMEocZVHNbJ6ca28ix0eKOKpiSxOnWzo9nJzbGKZY4zKCO1jYDMMUSh6POT48nxuNma5lNt26cZYnDDOpozRncLiErycZvOCnC7WJOVgJ7j5/mdGuH0+GYMcwShxlUaU0zmYkxeNz2cXHagilJdHYrf95V6XQoZgyz/wRmQB1d3Ryra7H2jSCRmRjDhPgontla4XQoZgyzxGEGVFHXQpcqOSmxTodiABGhYHISW4/WccSmWzcOscRhBlRa4/3nNNkSR9CYl52IS+BZO+swDrHEYQZ0tOYME8dHERtpEykHi/ExHi49L43ntlXQbRMfGgdY4jD96lblaE2zdcMNQjcWZHG8oZX3S2z+KjP6LHGYfp1oaKWts9vaN4LQ1TMnEh8dYY3kxhGWOEy/ys4O/Eu2M45gE+1xc+3cSby0u5JGG9NhRpklDtOv8tpm4qMiSIy1JUuD0Y0FWbR2dPPy7hNOh2LGGL8Sh4g8KyKfERFLNGNIeV0zWcmxiE1sGJQKJieSkxLL89uPOR2KGWP8TQQPAbcCh0TkRyIyI4AxmSDQ3N5JdVM7k22akaAlInx2fibvl9RQ2dDidDhmDPErcajq66r6BaAAKAVeE5G/iMhdItLvdQwRWSYiB0SkWETu7WO/iMjPfft3iUjBEOr+o4ioiKT68x7M0FTUef8RZSVbw3gwu35+JqrwwvbjTodixhC/Lz2JSApwJ/AVYDtwP95E8lo/5d3Ag8ByYCZwi4jM7FVsOZDvu92N98xm0Loikg1cDZT5G78ZmrLaZgTISrQzjmA2JWUchVOSeH57BWrrdJhR4m8bx3PARiAWuFZVr1PVp1X160B/a4kuBIpVtURV24F1wMpeZVYCT6jXB0CiiGT4Ufc+4J8B+0sJkIq6ZiaOjybK43Y6FDOI6wsyOXiyiT3HTzsdihkj/D3jeERVZ6rqD1W1EkBEogBUtbCfOplAeY/HFb5t/pTpt66IXAccU9WdAwUsIneLSJGIFFVVVQ345sxfU1XKa1tsGvUQcc0Fk4h0u3humzWSm9Hhb+L4QR/b3h+kTl9dcXqfIfRXps/tIhILfBf4t0FeG1V9WFULVbUwLS1tsOKmh5qmdlo6uphs7RshISHWw5UzJrB+53E6u7qdDseMAQMmDhFJF5EFQIyIzBeRAt/tcryXrQZSAWT3eJwF9G7B669Mf9vzgFxgp4iU+rZvE5H0QWIxQ1Be5x34Zw3joeP6gkyqm9rYWFztdChmDBhs5rqleBvEs4Cf9djeCPzLIHW3APkikgscA1bh7dLb03pgjYisAxYBDapaKSJVfdVV1T3AhLOVfcmjUFXtr2UEldU2ExXhYkJ8lNOhGD9dMX0CibEent92jCumTxi8gjHnYMDEoaq/AX4jIjeq6rNDeWJV7RSRNcArgBt4TFX3iMhq3/61wAZgBVAMNAN3DVR3aG/NDFdFXQuZSTG4bOBfyIiMcHHNnAye2VpBU1sncVE2m7EJnAE/XSJym6r+FsgRkX/ovV9Vf9ZHtZ77N+BNDj23re1xX4F7/K3bR5mcgfaboevo6qayoYVL861dKNRcPz+L335QxksfVXJTYfbgFYwZpsEax8/ObhcHxPdxM2HmeH0L3QrZ1r4RcmwKEjNaBrtU9Uvfz++PTjjGaWdnxLWuuKHn7BQk979xiMqGFjIS7HdoAsPfAYA/FpHxIuIRkTdEpFpEbgt0cGb0lde1kBTrIT7aZsQNRTYFiRkN/o7j+LSqngauwdtV9jzgnwIWlXFMeW2zXaYKYVNSxrHApiAxAeZv4jj79XMF8JSq1gYoHuOg0y0dNLR0kJ1kiSOUXT/fpiAxgeVv4viTiOwHCoE3RCQNaA1cWMYJZwf+2RlHaLtmTgaRbpc1kpuA8Xda9XuBJXgH23UAZ/jkhIUmxJXXtuAWISMh2ulQzDlIjI3kihlp/HGHTUFiAmMoK/qdD9wsIrcDnwM+HZiQjFPK65rJSIzG47aFHkPdDQVZVDe1scmmIDEB4G+vqv8GfgJcDFzou/U3K64JQV3dyrG6FmvfCBMfT0Fil6tMAPg7L0EhMFOtm0bYOtXYSntXt7VvhInICBfXzpnE74vKaWjpICHGulebkePvNYndgM1AG8bKa71LxWbbwL+w8fnCbNo6u1m/08Z0mJHlb+JIBfaKyCsisv7sLZCBmdFVXttMbKSb5HGRTodiRsjszPGcnzGePxSVD17YmCHw91LV9wIZhHFeeV0z2UmxiM2IGzZEhM8XZvH9P+1lX+Vpzs8Y73RIJkz42x33HaAU8PjubwG2BTAuM4paO7qoamyz9o0w9Nl5mUS6XfzezjrMCPK3V9XfAs8Av/RtygReCFBMZpRV1LWgQHaytW+Em6RxkVw9ayLPbz9GW2eX0+GYMOFvG8c9wEXAaQBVPUSPlfhMaPt4RtxEO+MIR58vzKa+uYPX955yOhQTJvxNHG2q2n72gYhEANY1N0xU1DWTFh9FTKTb6VBMAFw8LZVJCdGs21LmdCgmTPibON4RkX8BYkTkauAPwJ8CF5YZLapKWW0zk23gX9hyu4SbCrPZVFxNWU2z0+GYMOBvr6p7gS8DHwFfxbuk6yOBCsqMnrrmDprbu8iy9o2Q8uTmoZ09RHvcCPCvL3zEstkZQ369WxdNHnIdE778Shyq2i0iLwAvqGpVYEMyo+ls+8Zk61EV1hJiPMxIH0/R0TquOn+izUdmzsmAnx7x+p6IVAP7gQMiUiUi/zY64ZlAK69rxuMWJsTbjLjhbvHUFJrbu9h9rMHpUEyIG+xrx7fw9qa6UFVTVDUZWARcJCJ/P9iTi8gyETkgIsUicm8f+0VEfu7bv0tECgarKyL/4Su7Q0ReFZFJ/r5Z80nltc1kJcXidtnAv3A3NW0cKeMi2XzE1mEz52awxHE7cIuqHjm7QVVLgNt8+/olIm7gQWA5MBO4RURm9iq2HMj33e4GHvKj7v9R1TmqOg94EbCzn2Hq7OqmsqHV5qcaI1wiLMpNpqy2mcqGFqfDMSFssMThUdVPTOjva+cYbLrNhUCxqpb4uvKu45OLP60EnlCvD4BEEckYqK5v7fOzxmHdgofteEMrXd1qI8bHkIIpSUS4hM0ldtZhhm+wxNE+zH3gHV3ec56DCt82f8oMWFdE/peIlANfoJ8zDhG5W0SKRKSoqsra8/tS7msYtzU4xo7YyAjmZCWyo7yelnYbSW6GZ7DEMVdETvdxawQuGKRuXxfNe58d9FdmwLqq+l1VzQZ+B6zp68VV9WFVLVTVwrS0tEFCHZvK65pJiPEw3tZqGFMumpZCe1c3H5baWYcZngETh6q6VXV8H7d4VR3sv00FkN3jcRbQe2GA/sr4UxfgSeDGQeIw/Sivbbb2jTEoIyGGvLRxvH+4ms5uW5PcDF0gO3NvAfJFJFdEIoFVQO81PNYDt/t6Vy0GGlS1cqC6IpLfo/51eLsJmyGqbmqjrrnD2jfGqIunpXG6tZOPKqxrrhk6f0eOD5mqdorIGuAVwA08pqp7RGS1b/9avCPQVwDFQDNw10B1fU/9IxGZDnQDR4HVgXoP4WxHWT1g7Rtj1XkT45gQH8Wm4mrmZSfaOixmSAKWOABUdQPe5NBz29oe9xXvzLt+1fVtt0tTI2B7eR0ugUmJdqlqLBIRLp6WynPbj1FSfYa8tDinQzIhxOYdGKN2lNeTnhBNZIR9BMaqudmJjIuKYNOhT/S4N2ZA9l9jDOrqVnaWN9hlqjHO43axZGoyB042crzeBgQa/1niGIMOVzXR1NZpDeOGJVNTifa4eHO/LfJk/GeJYww62zBua3CYmEg3n8pLZW/laZuGxPjNEscYtL28joQYDylxkU6HYoLARXmpREXYWYfxnyWOMWh7Wb11wTQfO3vWsef4aU40tDodjgkBljjGmKa2Tg6ebGRedqLToZggctG0FO9ZxwE76zCDs8Qxxuwoq6dbYcGUJKdDMUEkNjKCJXkp7D7WwDHrYWUGYYljjNlSWotLYP7kRKdDMUHm0vw0YiPdvPRRJd6xucb0zRLHGLP1aB0z0scTH20z4pq/Fu1xc9WMCZRUn+HAyUanwzFBzBLHGNLZ1c32sjoKc+wylenbwtwUUsZF8tLuE3R121mH6ZsljjFk/4lGzrR3WfuG6ZfbJSyfnU5VYxtFR229DtM3SxxjSJFv4Z7CnGSHIzHB7PyM8eSkxPL6vlO0dtgqgeaTLHGMIUVH68hIiCbTZsQ1AxARPnPBJJrbOnl17wmnwzFByBLHGKGqFJXW2dmG8UtmUgyL81LYXFL78dr0xpxliWOMOFbfwonTrRRa+4bx09XnTyQ+OoLntx+jo8uWmDX/nyWOMWLr0TrABv4Z/0V73Fw7dxInTrfy+HtHnA7HBBFLHGPEltJaxkW6mZEe73QoJoTMzBjP+enx3PfaIY7WnHE6HBMkLHGMEUWldcyfnESE237lxn8iwnXzMvG4hb9/egeddsnKYIljTDjd2sGBk412mcoMS0KMhx9cfwHbyur5r7cPOx2OCQKWOMaALUdqUYVFudajygzPdXMnsXLeJO5/4xA7yuudDsc4LKCJQ0SWicgBESkWkXv72C8i8nPf/l0iUjBYXRH5PyKy31f+eRFJDOR7CAfvH64h0u2iwM44zDn495WzmRgfxd8/vYMzbZ1Oh2McFLDEISJu4EFgOTATuEVEZvYqthzI993uBh7yo+5rwGxVnQMcBL4TqPcQLt4vqWH+5ESiPW6nQzEhLCHGw89unsfRmjN8+9ldNoPuGBbIM46FQLGqlqhqO7AOWNmrzErgCfX6AEgUkYyB6qrqq6p69uvOB0BWAN9DyKtvbmdv5WmW5KU4HYoJA4unpvCPS6fz4q5KHt1kXXTHqkAmjkygvMfjCt82f8r4UxfgS8BLfb24iNwtIkUiUlRVVTXE0MPHh772jSVTLXGYkfG1y/JYOmsiP3xpP+8frnE6HOOAQCaOvha07n1u21+ZQeuKyHeBTuB3fb24qj6sqoWqWpiWluZHuOHp/ZIaoiJczLOFm8wIERF+ctNcclJi+fpT2zhuKwaOOYFMHBVAdo/HWcBxP8sMWFdE7gCuAb6gdqF1QO8frqEwJ4moCGvfMCMnPtrDL7+4gLaObu56fAunWzucDsmMokAmji1AvojkikgksApY36vMeuB2X++qxUCDqlYOVFdElgHfBq5TVZt9bQC1Z9rZf6KRxbl2mcqMvGkT4nnotgUcrmri7367zeazGkMiAvXEqtopImuAVwA38Jiq7hGR1b79a4ENwAqgGGgG7hqoru+pHwCigNdEBOADVV0dqPcRyjaXeK8/W8O4OVdPbi7rd9/KeZk8u62Cm3/5ATcWZOL7uzwnty6afM7PYQInYIkDQFU34E0OPbet7XFfgXv8revbPm2Ewwxb75fUEONxMycr0elQTBhbMCWJuuZ23tx/ivjoCJbOSnc6JBNgAU0cxlkflHjbNyIjbIIAE1hXzZhAY2sn7xysIjLCxRXTJzgdkgkgSxxhqrqpjYMnm/js/L56MRszskSElfMm0dHVzWt7TxLpdnHRtFSnwzIBYokjTG085B27crH98ZpR4hLhxoIsOrq6+fNHlbhcYuOHwpRdwwhTbx+oIjUuktmTEpwOxYwhbpdw84XZnJ8ez592Hv/4C4wJL5Y4wlBXt/LuwSouzU/D5Tr3Hi7GDEWEy8Wti6YwOzOBl3af4M39p5wOyYwwu1QVhnZW1FPX3MFl08fuiHnjLLdLuLkwG49LeH3fSVo7ulg2Ox3XCHTVNc6zxBGG3j5QhUvg0nxLHMY5bpdw44IsIiNcbCqupqGlg88tyMJjq1CGPEscYeidA6eYm51I0rhIp0MxY5xLhOvmTiIpNpKX95ygsbWTLy6eQkykTYETyiz1h5mapjZ2HWvg8vOsH70JDiLCpeel8fnCbMprm1n77mHqmtudDsucA0scYebdQ1WowuXWvmGCzLzsRO66KIfG1g7Wvn2YozVnnA7JDJMljjDz9oEqUsZFckGmdcM1wWdqWhxfvTQPT4SLRzYeYfORGltJMARZ4ggjZ7vhXnaedcM1wWvi+GjuuXwaeRPG8ccdx3lu+zGbWTfEWOIII9YN14SKmEg3ty/J4YrpaWw9WsevNpZQb+0eIcMSRxh5Zc8JIlzC5TbBnAkBLhGunpnObYsmU9XYxi/eLGbP8QanwzJ+sMQRJlSVV3afYEleCgkxHqfDMcZvMyclcM8V00geF8nvNpfxwvZjtLR3OR2WGYAljjBx8GQTpTXNLJttayGY0JMaF8VXL5vKJfmpfFhay3UPbGJf5WmnwzL9sMQRJl7efQIRuHrmRKdDMWZYIlwuls/O4EsX5VLf0sHKB97jobcP02kN50HHEkeYeGl3JQsmJzEhPtrpUIw5J9MmxPHyNy/hyhkT+M+X93PjQ3/hwIlGp8MyPVjiCAMHTzay/0Qj186d5HQoxoyIlLgoHrqtgAdunU95XQvX/GIjv3jjkHXbDRKWOMLAn3YexyWw4oIMp0MxZsSICNfMmcRrf38pS2el89PXDnLdA++xq6Le6dDGPEscIU5VWb/zOJ/KSyUtPsrpcIwZcSlxUTxwawFrb1tATVMbn33wPX7w4l6a2zudDm3MCmjiEJFlInJARIpF5N4+9ouI/Ny3f5eIFAxWV0RuEpE9ItItIoWBjD8U7Kpo4GhNM9fZZSoT5pbNTue1f7iMVQsn88imI3z6vnd596CtMOiEgE2rLiJu4EHgaqAC2CIi61V1b49iy4F8320R8BCwaJC6u4EbgF8GKvZQ8szWCqIiXCy1brgmjDy5uazffbMnJfC3l0zl+e3HuP2xD5mfnciKCzIYFzX8f2e3Lpo87LpjUSDPOBYCxapaoqrtwDpgZa8yK4En1OsDIFFEMgaqq6r7VPVAAOMOGa0dXfxxxzGWzkq3QX9mTMlNHcfXr5zGFdPT2FlRz32vH2R7WZ1NmDhKApk4MoHyHo8rfNv8KeNP3QGJyN0iUiQiRVVV4Xk6+9rek5xu7eSmwiynQzFm1HncLq6emc6aK/NJGRfJH7ZW8Ou/lFJ7xua8CrRAJo6+pmft/XWgvzL+1B2Qqj6sqoWqWpiWFp6T/v1hawWTEqL5VF6q06EY45j08dF89bI8rp2TwdHaZu5/4yCbDlXR1W1nH4ESyMRRAWT3eJwFHPezjD91x7TS6jO8e7CKmwqzcdsU6maMc4mwJC+Vb12VT15aHBt2n2DtO4epbGhxOrSwFMjEsQXIF5FcEYkEVgHre5VZD9zu6121GGhQ1Uo/645pv/3gKBEusUY9Y3pIjI3ki4unsOrCbOpbOnjwrWJe2XPCBg6OsID1qlLVThFZA7wCuIHHVHWPiKz27V8LbABWAMVAM3DXQHUBROR64BdAGvBnEdmhqksD9T6CUUt7F78vKmfp7HQmjrcpRozpSUSYk5XItAlxvLT7BO8crGL3sQY+Oz+TvLQ4p8MLCwFLHACqugFvcui5bW2P+wrc429d3/bngedHNtLQ8vz2Y5xu7eT2xVOcDsWYoBUbGcGNBVnMy07k+e3HeHTTERZMSWL57HRiIwP6ry/s2cjxENPVrfxqYwlzshJYmJvsdDjGBL28tDi+eVU+l52XxvayOu57/RC7Kuqt6+45sMQRYl7dc4Ij1WdYfVkeItYobow/PG4XS2el83eXTyMxxsO6LeX8vqic1g5bMGo47HwthKgqD71zmJyUWJbOspHixgzVpMQYVl+Wx7uHqnhj30nKapu5uTB78Irmr9gZRwh5fd8pdlU08LXL86wLrjHD5HYJV0yfwN2XTAXg4Y0l/PyNQzbuYwgscYSI7m7lp68eIDd1HDcW2EhxY87V5JRxfP3KfC7ITOBnrx1k1cPvc6zexn34wxJHiPjTruPsP9HIt/4mnwi3/dqMGQnRHjc3XziZ+26ey97jp1n2f9/lz7sqnQ4r6Nl/oBDQ3N7JDzfsZ3bmeK6ZY9OnGzPSrp+fxYZvXsLUtDjueXIb9z67y9b7GIAljhDwX28d5sTpVr537Sxr2zAmQKakjOOZ1Uv42uV5PF1UzrW/2MTe46edDisoWeIIcodONvLwuyWsnDeJwhwbt2FMIHncLr69bAa//fIiGls7+eyD7/HYpiM25qMXSxxBrKtb+adndjEuys3/uGam0+EYM2ZcNC2Vl791KZfkp/LvL+7ly78poqapzemwgoYljiC29p3D7Civ5/srZ5MaZ+uJGzOaksdF8sgdhXz/ullsKq5m2f0b2XgoPNf2GSpLHEFqc0kNP331ANfOncS1czKcDseYMUlEuONTOfzxnotIiPHwxUc/5F9f+IimtrHdcG6JIwhVNrTw9ae2k5Myjh/ecIFNLWKMw87PGM+LX7+Yv70kl99tLmPpfe+y6VC102E5xhJHkGls7eCux7fQ3N7FQ7ctIC7KZoUxJhhEe9x89zMzeWb1p4jyuLjt0c1857ldnG7tcDq0UWeJI4icaevky78uovhUEw/dVsD09HinQzLG9LJgShIbvnEJX71sKk9vKefKn7zDH4rK6R5DU5ZY4ggSDc0d3Pn4h2wtq+O+m+dxSX54rpNuTDiI9rj5zvLz+eM9F5OdHMM/PbOLG9f+hW1ldU6HNioscQSBozVnuOGh99hRXs/9q+Zx7VwbHW5MKLggK4FnV3+Kn9w0l/LaFm74r7/wld8Usa8yvAcO2gV0h/1p53H+5bmPcLmE//7yIhZPTXE6JGPMELhcwucWZLF8djqPv3eEX75bwoqfb2TZrHS+cslUFkxJcjrEEWeJwyEVdc18/097eW3vSQomJ3L/qvlkJ8c6HZYxZpjGRUWw5sp8vrg4h19tLOG/PzjKS7tPMH9yIncsyWHprHRiIt1OhzkiLHGMsvLaZh7ddIQnN5fhcsG9y2fw5Ytz8diMt8aEhYRYD/+4dDp/d0Uez2yt4NFNR/jW0zuIi4pgxQXprJyXyYU5yURGhO7fvCWOUdDQ0sHre0+y4aNK3jpwChHhpgVZfOOqfCYlxjgdnjEmAGIjI7h9SQ63LZrC5iO1PLutghd3VfL7ogrioyK4dHoal+WncWFuMjkpsSE1XiugiUNElgH3A27gEVX9Ua/94tu/AmgG7lTVbQPVFZFk4GkgBygFPq+qQdOVQVWpOdPOR8ca2Ha0jq1H69hSWktHlzIpIZqvXpbHFxdPsYRhzBjhcglL8lJYkpfCv6+cxaZD1by5/xRv7D/18dofqXGRzMtO4vyMeKanx5M/IZ6spBjGBek4roBFJSJu4EHgaqAC2CIi61V1b49iy4F8320R8BCwaJC69wJvqOqPRORe3+NvB+I9tHZ00dzeRUtHFy3tXbR2eG8tHV2caeuk5kw7NU3t1DS1UX2mnYraZo5Un+F0q3c6Apd4R5zesSSHFXMymJ+dGFLfKowxIys2MoJPz0rn07PS6e5WSqqb2FJax5Yjtew61sBbB0791RK2ibEeMhNjyEyMISUuiqRYD4mxHhJjIxkf7SE20k20x020x0WMx3s/yuPC43IR4RYiXC6iIly4Rng5hkCms4VAsaqWAIjIOmAl0DNxrASeUO+cxR+ISKKIZOA9m+iv7krgcl/93wBvE6DE8R8v7uV3m8sGLTc+OoKUuCgyE2NYOS+T3NRxzMiIZ25WYtB+YzDGOMvlEqZNiGfahHhuWTgZ8H5ZLT7VxOGqJo7Vt3CsroVj9S0cqT7DtrI66ps76BziQMPH77qQK6ZPGNHYA/lfLRMo7/G4Au9ZxWBlMgepO1FVKwFUtVJE+jwiInI3cLfvYZOIHBjOm+hHKhDME9UEe3wQ/DEGe3wQ/DEGe3zgi/ELTkfRv3M+hlf+5zm9/pS+NgYycfR1btQ7VfZXxp+6A1LVh4GHh1LHXyJSpKqFgXjukRDs8UHwxxjs8UHwxxjs8UHwxxis8QWyP1gFkN3jcRZw3M8yA9U96buche/nqRGM2RhjzCACmTi2APkikisikcAqYH2vMuuB28VrMdDguww1UN31wB2++3cAfwzgezDGGNNLwC5VqWqniKwBXsHbpfYxVd0jIqt9+9cCG/B2xS3G2x33roHq+p76R8DvReTLQBlwU6DewwACcglsBAV7fBD8MQZ7fBD8MQZ7fBD8MQZlfGKLsBtjjBmK0B3zbowxxhGWOIwxxgyJJQ4/icj3ROSYiOzw3Vb02PcdESkWkQMistThOJf54ij2jax3nIiUishHvuNW5NuWLCKvicgh389RnXtaRB4TkVMisrvHtn5jGu3fcT/xBc1nUESyReQtEdknIntE5Ju+7cF0DPuLMSiOo4hEi8iHIrLTF9/3fduD5hj2S1Xt5scN+B7wj31snwnsBKKAXOAw4HYoRrfv9acCkb64ZgbBsSsFUntt+zFwr+/+vcB/jnJMlwIFwO7BYnLid9xPfEHzGQQygALf/XjgoC+OYDqG/cUYFMcR73i1ON99D7AZWBxMx7C/m51xnLuVwDpVbVPVI3h7iC10KJaPp3lR1Xbg7FQtwWgl3ilj8P387Gi+uKq+C9T6GdOo/477ia8/TsRXqb4JSVW1EdiHd8aHYDqG/cXYn1GNUb2afA89vpsSRMewP5Y4hmaNiOzyXUY4e/rY37QpTgimWHpS4FUR2eqbCgZ6TR0DjOxkOsPTX0zBdFyD7jMoIjnAfLzfmIPyGPaKEYLkOIqIW0R24B3I/JqqBu0x7MkSRw8i8rqI7O7jthLvzL15wDygEvjp2Wp9PJVTfZyDKZaeLlLVAryzId8jIpc6HdAQBctxDbrPoIjEAc8C31LVgRbaDqYYg+Y4qmqXqs7DOzvGQhGZPUDxYPkc2kJOPanq3/hTTkR+Bbzoe+jP1CqjJZhi+ZiqHvf9PCUiz+M9vT4pIhnqnagyWKaO6S+moDiuqnry7P1g+AyKiAfvP+Tfqepzvs1BdQz7ijHYjqMvpnoReRtYRpAdw77YGYeffL/As64HzvZ2WQ+sEpEoEcnFu7bIh6Mdn48/07yMKhEZJyLxZ+8Dn8Z77IJx6pj+YgqK33EwfQZFRIBHgX2q+rMeu4LmGPYXY7AcRxFJE5FE3/0Y4G+A/QTRMeyXEy3yoXgD/hv4CNjl+wVm9Nj3Xbw9HA4Ayx2OcwXe3iOHge8GwXGbircnyE5gz9mYgBTgDeCQ72fyKMf1FN7LFB14v8l9eaCYRvt33E98QfMZBC7Ge5lkF7DDd1sRZMewvxiD4jgCc4Dtvjh2A//m2x40x7C/m005YowxZkjsUpUxxpghscRhjDFmSCxxGGOMGRJLHMYYY4bEEocxxpghscRhzCgSketFREVkhohs9s3OWiYiVT1ma81xOk5jBmIjx40ZXbcAm4BVqroIQETuBApVdY2TgRnjLzvjMGaU+OZMugjvYL5VDodjzLBZ4jBm9HwWeFlVDwK1IlLgcDzGDIslDmNGzy1410jB9/MWB2MxZtisjcOYUSAiKcCVwGwRUbyrNaqI/LOzkRkzdHbGYczo+BzwhKpOUdUcVc0GjuCdiM+YkGKJw5jRcQvwfK9tzwK3OhCLMefEZsc1xhgzJHbGYYwxZkgscRhjjBkSSxzGGGOGxBKHMcaYIbHEYYwxZkgscRhjjBkSSxzGGGOG5P8BVA46ePUAx6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(wc.AT, label = 'AT');\n",
    "plt.xlabel('AT');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:18:28.161065Z",
     "start_time": "2021-09-21T15:18:27.969614Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chetana\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\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": [
    "sn.boxplot(wc.AT),\n",
    "plt.xlabel('AT');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that the AT column distribution is more skewed towards the right since mean>median ,skew value is +ve  and also boxplot is longer on the right side of the median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:16:26.771158Z",
     "start_time": "2021-09-21T15:16:26.763171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.90183486238533"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.Waist.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:16:43.986578Z",
     "start_time": "2021-09-21T15:16:43.978897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.Waist.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:16:56.105819Z",
     "start_time": "2021-09-21T15:16:56.092381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1340560824786468"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc.Waist.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:19:05.567386Z",
     "start_time": "2021-09-21T15:19:05.244399Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chetana\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": [
    "sn.distplot(wc.Waist, label = 'Waist');\n",
    "plt.xlabel('Waist');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:19:28.665821Z",
     "start_time": "2021-09-21T15:19:28.387776Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Chetana\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\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": [
    "sn.boxplot(wc.Waist),\n",
    "plt.xlabel('Waist');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-21T15:19:33.606116Z",
     "start_time": "2021-09-21T15:19:33.592155Z"
    }
   },
   "source": [
    "We can say that the Waist column distribution is slightly skewed towards the right since mean>median ,skew value is +ve  and also boxplot is slightly longer on the right side of the median"
   ]
  }
 ],
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