Package org.cicirello.math.stats

Class Statistics

  • public final class Statistics
extends Object
    Utility class of basic statistics.

    • Method Summary

      All Methods Static Methods Concrete Methods 
      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 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.

    • Method Detail

      • 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.

      • 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).