Datos dados en una array. Encuentre la asimetría de la distribución de datos.
La asimetría es una medida de la asimetría de la distribución de datos. La asimetría es una asimetría en una distribución estadística, en la que la curva aparece distorsionada o sesgada hacia la izquierda o hacia la derecha. La asimetría se puede cuantificar para definir hasta qué punto una distribución difiere de una distribución normal. La asimetría se puede calcular como
Where gamma is called skewness sigma is called standard deviation and sigma square can be calculated as
N is number of population and mu is called mean of data.
Ejemplos:
Input : arr[] = {2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2} Output : 0.777001 Input : arr[] = {5, 20, 40, 80, 100} Output : 0.0980392
Para obtener más información sobre la asimetría
https://en.wikipedia.org/wiki/Skewness
https://www.universalclass.com/articles/math/statistics/skewness-in-statistical-terms.htm
C++
// CPP code to find skewness // of statistical data. #include<bits/stdc++.h> using namespace std; // Function to calculate // mean of data. float mean(float arr[], int n) { float sum = 0; for (int i = 0; i < n; i++) sum = sum + arr[i]; return sum / n; } // Function to calculate standard // deviation of data. float standardDeviation(float arr[], int n) { float sum = 0; // find standard deviation // deviation of data. for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return sqrt(sum / n); } // Function to calculate skewness. float skewness(float arr[], int n) { // Find skewness using above formula float sum = 0; for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return sum / (n * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n)); } // Driver function int main() { float arr[] = {2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2}; // calculate size of array. int n = sizeof(arr)/sizeof(arr[0]); // skewness Function call cout << skewness(arr, n); return 0; }
Java
// java code to find skewness // of statistical data. import java.io.*; class GFG { // Function to calculate // mean of data. static double mean(double arr[], int n) { double sum = 0; for (int i = 0; i < n; i++) sum = sum + arr[i]; return sum / n; } // Function to calculate standard // deviation of data. static double standardDeviation(double arr[], int n) { double sum = 0 ; // find standard deviation // deviation of data. for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return Math.sqrt(sum / n); } // Function to calculate skewness. static double skewness(double arr[], int n) { // Find skewness using // above formula double sum = 0; for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return sum / (n * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n)); } // Driver function public static void main (String[] args) { double arr[] = { 2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2 }; // calculate size of array. int n = arr.length; // skewness Function call System.out.println(skewness(arr, n)); } } //This code is contributed by vt_m
Python3
# Python3 code to find skewness # of statistical data. from math import sqrt # Function to calculate # mean of data. def mean(arr, n): summ = 0 for i in range(n): summ = summ + arr[i] return summ / n # Function to calculate standard # deviation of data. def standardDeviation(arr,n): summ = 0 # find standard deviation # deviation of data. for i in range(n): summ = (arr[i] - mean(arr, n)) *(arr[i] - mean(arr, n)) return sqrt(summ / n) # Function to calculate skewness. def skewness(arr, n): # Find skewness using above formula summ = 0 for i in range(n): summ = (arr[i] - mean(arr, n))*(arr[i] - mean(arr, n))*(arr[i] - mean(arr, n)) return summ / (n * standardDeviation(arr, n) *standardDeviation(arr, n) *standardDeviation(arr, n) * standardDeviation(arr, n)) # Driver function arr = [2.5, 3.7, 6.6, 9.1,9.5, 10.7, 11.9, 21.5,22.6, 25.2] # calculate size of array. n = len(arr) # skewness Function call print('%.6f'%skewness(arr, n)) # This code is contributed by shubhamsingh10
C#
// C# code to find skewness // of statistical data. using System; class GFG { // Function to calculate // mean of data. static float mean(double []arr, int n) { double sum = 0; for (int i = 0; i < n; i++) sum = sum + arr[i]; return (float)sum / n; } // Function to calculate standard // deviation of data. static float standardDeviation(double []arr, int n) { double sum = 0 ; // find standard deviation // deviation of data. for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return (float)Math.Sqrt(sum / n); } // Function to calculate skewness. static float skewness(double []arr, int n) { // Find skewness using // above formula double sum = 0; for (int i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return (float)sum / (n * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n)); } // Driver function public static void Main () { double []arr = { 2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2 }; // calculate size of array. int n = arr.Length; // skewness Function call Console.WriteLine(skewness(arr, n)); } } // This code is contributed by vt_m
PHP
<?php // PHP code to find skewness // of statistical data. // Function to calculate // mean of data. function mean( $arr, $n) { $sum = 0; for ($i = 0; $i < $n; $i++) $sum = $sum + $arr[$i]; return $sum / $n; } // Function to calculate standard // deviation of data. function standardDeviation($arr, $n) { $sum = 0; // find standard deviation // deviation of data. for ($i = 0; $i < $n; $i++) $sum = ($arr[$i] - mean($arr, $n)) * ($arr[$i] - mean($arr, $n)); return sqrt($sum / $n); } // Function to calculate skewness. function skewness($arr, $n) { // Find skewness using above formula $sum = 0; for ($i = 0; $i < $n; $i++) $sum = ($arr[$i] - mean($arr, $n)) * ($arr[$i] - mean($arr, $n)) * ($arr[$i] - mean($arr, $n)); return $sum / ($n * standardDeviation($arr, $n) * standardDeviation($arr, $n) * standardDeviation($arr, $n) * standardDeviation($arr, $n)); } // Driver Code $arr = array(2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2); // calculate size of array. $n = count($arr); // skewness Function call echo skewness($arr, $n); // This code is contributed by vt_m ?>
Javascript
<script> // JavaScript code to find skewness // of statistical data. // Function to calculate // mean of data. function mean(arr, n) { let sum = 0; for (let i = 0; i < n; i++) sum = sum + arr[i]; return sum / n; } // Function to calculate standard // deviation of data. function standardDeviation(arr, n) { let sum = 0 ; // find standard deviation // deviation of data. for (let i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return Math.sqrt(sum / n); } // Function to calculate skewness. function skewness(arr, n) { // Find skewness using // above formula let sum = 0; for (let i = 0; i < n; i++) sum = (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)) * (arr[i] - mean(arr, n)); return sum / (n * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n) * standardDeviation(arr, n)); } let arr = [ 2.5, 3.7, 6.6, 9.1, 9.5, 10.7, 11.9, 21.5, 22.6, 25.2 ]; // calculate size of array. let n = arr.length; // skewness Function call document.write(skewness(arr, n).toFixed(6)); </script>
Producción:
0.777001
Publicación traducida automáticamente
Artículo escrito por Dharmendra_Kumar y traducido por Barcelona Geeks. The original can be accessed here. Licence: CCBY-SA