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‎Measure_of_Variability.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"collapsed_sections": [],
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"authorship_tag": "ABX9TyOV87J8C5fXRut2bqYuzsIz",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/DeepthiTabithaBennet/Python_AppliedStatistics/blob/main/Measure_of_Variability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CL891ro7yhxS"
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},
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"source": [
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"**MEASURE OF VARIABILITY**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "p3JwtRYEdkiC"
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},
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"source": [
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"# Written by Deepthi Tabitha Bennet\n",
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"\n",
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"import numpy as np\n",
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"import math\n",
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"\n",
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"arr = np.array([32, 56, 47, 87, 98, 36, 87, 38, 97, 4, 26, 94, 95, 90, 24, 7, 90, 63])\n",
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"arr.sort()"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "p4n01pG0o4Ms"
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},
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"source": [
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"**Range**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "_cQd7VrXo97b",
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"outputId": "825db697-58e9-42bb-d783-6aa473fc0775"
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},
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"source": [
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"range = arr[len(arr)-1] - arr[0]\n",
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"print(range)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"94\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "P9wy5d8jibQE"
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},
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"source": [
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"**Inter Quartile Range**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lJM23sdpe7ne",
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"outputId": "83bee92b-d957-489f-fde3-e047158cb506"
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},
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"source": [
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"Q1 = np.quantile(arr,.25)\n",
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"Q3 = np.quantile(arr,.75)\n",
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"\n",
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"IQR = Q3 - Q1\n",
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"print(IQR)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"57.0\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-od66TYhpBgb"
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},
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"source": [
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"**Variance**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "9VPJgK-kpGDT",
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"outputId": "c6da936f-bd43-404d-bc7e-46102af40d63"
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},
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"source": [
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"sum = arr.sum()\n",
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"mean = sum / len(arr)\n",
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"\n",
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"difference_squared = 0\n",
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"\n",
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"for i in arr:\n",
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" difference_squared += (i - mean) ** 2\n",
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"\n",
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"variance = (difference_squared / (len(arr)) - 1)\n",
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"print(variance)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"1048.0277777777778\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CfDqx8FSpHIK"
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},
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"source": [
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"**Standard Deviation**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "nP7IHJI0pKeL",
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"outputId": "432a7890-57f1-49f4-8625-4165f6a71b40"
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},
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"source": [
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"sum = arr.sum()\n",
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"mean = sum / len(arr)\n",
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"\n",
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"difference_squared = 0\n",
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"\n",
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"for i in arr:\n",
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" difference_squared += (i - mean) ** 2\n",
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"\n",
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"SD = math.sqrt(difference_squared / (len(arr)) - 1)\n",
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"print(SD)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"32.373257138844984\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "QPp6ZuddinHD"
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},
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"source": [
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"**Coefficient of Variance**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "zu_vI8A7kBhj",
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"outputId": "b4861865-5dac-4248-bc0b-cd36f06d52b5"
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},
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"source": [
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"COV = (SD / mean) * 100\n",
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"print(COV)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"54.408835527470565\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3Ll6eh-lxIsb"
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},
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"source": [
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"**Z - Score**"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "xE_JeW51xM7i",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "d0977237-ca73-4f22-e2da-4062fbce2440"
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},
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"source": [
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"q = int(input(\"Enter the Index of the number, whose Z - Score is to be calculated : \"))\n",
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"z = (arr[q-1] - mean) / SD\n",
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"print(z)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Enter the Index of the number, whose Z - Score is to be calculated : 3\n",
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"-1.0965841295407748\n"
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]
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}
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]
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}
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]
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}

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