Module_Statistics_Others

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Statistics module — Statistics Module

Other statistics functions — Others Statistics Functions

Vorcyc.Mathematics.Statistics.Others provides additional statistical functions including covariance, correlation coefficient, and linear regression analysis.

All methods below are on class: Vorcyc.Mathematics.Statistics.Others

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📒Contents

• 🔖 Covariance method
• 🔖 CorrelationCoefficient method
• 🔖 LinearRegression method

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Vorcyc.Mathematics.Statistics.Others class

Vorcyc.Mathematics.Statistics.Others provides additional statistical functions including covariance, correlation coefficient, and linear regression analysis.

Methods

1. Covariance method

• public static T Covariance<T>(this Span<T> x, Span<T> y) where T : INumber<T>
  • Computes the covariance of two data sets.
  • Parameters:
    • x: First data set.
    • y: Second data set.
  • Returns: Covariance of the two data sets.

2. CorrelationCoefficient method

• public static T CorrelationCoefficient<T>(this Span<T> x, Span<T> y) where T : IFloatingPointIeee754<T>
  • Computes the correlation coefficient of two data sets.
  • Parameters:
    • x: First data set.
    • y: Second data set.
  • Returns: Correlation coefficient of the two data sets.

3. LinearRegression method

• public static (T Slope, T Intercept) LinearRegression<T>(this Span<T> x, Span<T> y) where T : IFloatingPointIeee754<T>
  • Performs simple linear regression analysis.
  • Parameters:
    • x: Independent variable data.
    • y: Dependent variable data.
  • Returns: Tuple of slope and intercept.

Code example

Example using multiple methods from the Others class, with comments:

using System;
using Vorcyc.Mathematics.Statistics;

public class OthersExample
{
    public static void Main()
    { 
        // Define two data sets
        Span<double> x = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0 }; 
        Span<double> y = new double[] { 2.0, 4.0, 6.0, 8.0, 10.0 };

      // Compute covariance
        double covariance = x.Covariance(y);
        Console.WriteLine("Covariance: " + covariance);

        // Compute correlation coefficient
        double correlationCoefficient = x.CorrelationCoefficient(y);
        Console.WriteLine("Correlation Coefficient: " + correlationCoefficient);

        // Linear regression
        var (slope, intercept) = x.LinearRegression(y);
        Console.WriteLine($"Linear Regression: Slope = {slope}, Intercept = {intercept}");
    }
}