Module org.cicirello.rho_mu
Package org.cicirello.math.stats

Class Statistics

java.lang.Object
org.cicirello.math.stats.Statistics
public final class Statistics extends Object
Utility class of basic statistics.

  • Method Summary

    Modifier and Type
    Method
    Description
    static double
    correlation(double[] X, double[] Y)
    Computes correlation coefficient for a pair of random variables.
    static double
    correlation(int[] X, int[] Y)
    Computes correlation coefficient for a pair of random variables.
    static double[][]
    correlationMatrix(double[][] data)
    Computes correlation matrix.
    static double[][]
    correlationMatrix(int[][] data)
    Computes correlation matrix.
    static double
    covariance(double[] X, double[] Y)
    Computes covariance for a pair of random variables.
    static double
    covariance(int[] X, int[] Y)
    Computes covariance for a pair of random variables.
    static double
    mean(double[] data)
    Computes mean of a dataset.
    static double
    mean(int[] data)
    Computes mean of a dataset.
    static double
    stdev(double[] data)
    Computes the sample standard deviation.
    static double
    stdev(int[] data)
    Computes the sample standard deviation.
    static double
    tTestUnequalVariances(double[] data1, double[] data2)
    Welch's t-test, also known as t-test with unequal variances.
    static double
    tTestUnequalVariances(int[] data1, int[] data2)
    Welch's t-test, also known as t-test with unequal variances.
    static Number[]
    tTestWelch(double[] data1, double[] data2)
    Welch's t-test, also known as t-test with unequal variances.
    static Number[]
    tTestWelch(int[] data1, int[] data2)
    Welch's t-test, also known as t-test with unequal variances.
    static double
    variance(double[] data)
    Computes variance of a population.
    static double
    variance(int[] data)
    Computes variance of a population.
    static double
    varianceSample(double[] data)
    Computes variance of a sample.
    static double
    varianceSample(int[] data)
    Computes variance of a sample.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Method Details

    • mean

      public static double mean(int[] data)
      Computes mean of a dataset.
      Parameters:
      data - The dataset.
      Returns:
      the mean of the data.

    • mean

      public static double mean(double[] data)
      Computes mean of a dataset.
      Parameters:
      data - The dataset.
      Returns:
      the mean of the data.

    • variance

      public static double variance(int[] data)
      Computes variance of a population.
      Parameters:
      data - The dataset.
      Returns:
      the variance of the data.

    • variance

      public static double variance(double[] data)
      Computes variance of a population.
      Parameters:
      data - The dataset.
      Returns:
      the variance of the data.

    • varianceSample

      public static double varianceSample(int[] data)
      Computes variance of a sample.
      Parameters:
      data - The dataset.
      Returns:
      the variance of the data.

    • varianceSample

      public static double varianceSample(double[] data)
      Computes variance of a sample.
      Parameters:
      data - The dataset.
      Returns:
      the variance of the data.

    • stdev

      public static double stdev(int[] data)
      Computes the sample standard deviation.
      Parameters:
      data - The dataset.
      Returns:
      the sample standard deviation.

    • stdev

      public static double stdev(double[] data)
      Computes the sample standard deviation.
      Parameters:
      data - The dataset.
      Returns:
      the sample standard deviation.

    • covariance

      public static double covariance(int[] X, int[] Y)
      Computes covariance for a pair of random variables.
      Parameters:
      X - Array of samples of first variable.
      Y - Array of samples of second variable.
      Returns:
      the covariance of X and Y.

    • covariance

      public static double covariance(double[] X, double[] Y)
      Computes covariance for a pair of random variables.
      Parameters:
      X - Array of samples of first variable.
      Y - Array of samples of second variable.
      Returns:
      the covariance of X and Y.

    • correlation

      public static double correlation(int[] X, int[] Y)
      Computes correlation coefficient for a pair of random variables.
      Parameters:
      X - Array of samples of first variable.
      Y - Array of samples of second variable.
      Returns:
      the correlation coefficient of X and Y.

    • correlation

      public static double correlation(double[] X, double[] Y)
      Computes correlation coefficient for a pair of random variables.
      Parameters:
      X - Array of samples of first variable.
      Y - Array of samples of second variable.
      Returns:
      the correlation coefficient of X and Y.

    • correlationMatrix

      public static double[][] correlationMatrix(int[][] data)
      Computes correlation matrix.
      Parameters:
      data - The data with random variables in rows and samples in columns.
      Returns:
      the correlation matrix, M, where M[i][j] is the correlation coefficient of data[i] and data[j].

    • correlationMatrix

      public static double[][] correlationMatrix(double[][] data)
      Computes correlation matrix.
      Parameters:
      data - The data with random variables in rows and samples in columns.
      Returns:
      the correlation matrix, M, where M[i][j] is the correlation coefficient of data[i] and data[j].

    • tTestUnequalVariances

      public static double tTestUnequalVariances(double[] data1, double[] data2)
      Welch's t-test, also known as t-test with unequal variances. The Welch's t-test can be used when variances are unequal and is also applicable if sample sizes differ.
      Parameters:
      data1 - First dataset.
      data2 - Second dataset.
      Returns:
      The t statistic.

    • tTestUnequalVariances

      public static double tTestUnequalVariances(int[] data1, int[] data2)
      Welch's t-test, also known as t-test with unequal variances. The Welch's t-test can be used when variances are unequal and is also applicable if sample sizes differ.
      Parameters:
      data1 - First dataset.
      data2 - Second dataset.
      Returns:
      The t statistic.

    • tTestWelch

      public static Number[] tTestWelch(double[] data1, double[] data2)
      Welch's t-test, also known as t-test with unequal variances. The Welch's t-test can be used when variances are unequal and is also applicable if sample sizes differ. This method computes both the t statistic, as well as the approximate degrees of freedom.
      Parameters:
      data1 - First dataset.
      data2 - Second dataset.
      Returns:
      An array, a, of length 2 such that a[0] is the t statistic (as a Double object), and a[1] is the degrees of freedom (as an Integer object).

    • tTestWelch

      public static Number[] tTestWelch(int[] data1, int[] data2)
      Welch's t-test, also known as t-test with unequal variances. The Welch's t-test can be used when variances are unequal and is also applicable if sample sizes differ. This method computes both the t statistic, as well as the approximate degrees of freedom.
      Parameters:
      data1 - First dataset.
      data2 - Second dataset.
      Returns:
      An array, a, of length 2 such that a[0] is the t statistic (as a Double object), and a[1] is the degrees of freedom (as an Integer object).