Set+1_Descriptive+statistics+Probability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c27a523",
   "metadata": {},
   "source": [
    "#### Q1. Look at the data given below. Plot the data, find the outliers and find out  μ,σ,σ^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6fa0c449",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statistics as stat\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4d0338d",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_of_company = ['Allied Signal','Bankers Trust','General Mills','ITT Industries','J.P.Morgan & Co.','Lehman Brothers',\n",
    "      'Marriott','MCI','Merrill Lynch','Microsoft','Morgan Stanley','Sun Microsystems','Travelers','US Airways',\n",
    "      'Warner-Lambert']\n",
    "Measure_x = [24.23,25.53,25.41,24.14,29.62,28.25,25.81,24.39,40.26,32.95,91.36,25.99,39.42,26.71,35.00]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f806d729",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# boxplot\n",
    "plt.boxplot(Measure_x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49cd14fd",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# barplot\n",
    "sns.barplot(x=name_of_company,y=Measure_x)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ab64edc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Outlier in the boxplot: 10    91.36\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Outliers\n",
    "Measure_x = pd.Series(Measure_x)\n",
    "q1 = np.quantile(Measure_x,0.25)\n",
    "q3 = np.quantile(Measure_x,0.75)\n",
    "q2 = np.median(Measure_x)\n",
    "IQR = q3 - q1\n",
    "upper_bond = q3+(1.5*IQR)\n",
    "lower_bond = q1-(1.5*IQR)\n",
    "outliers = Measure_x[(Measure_x <= lower_bond) | (Measure_x >= upper_bond)]\n",
    "print('The Outlier in the boxplot:',outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97e40856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean : 33.27133333333333\n",
      "Variance : 287.1466123809524\n",
      "Standard Deviation : 16.945400921222028\n"
     ]
    }
   ],
   "source": [
    "print('Mean :',stat.mean(Measure_x))\n",
    "print('Variance :',stat.variance(Measure_x))\n",
    "print('Standard Deviation :',stat.stdev(Measure_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb5739a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}