﻿Bivariate Class  # Bivariate Class

Contains methods for analyzing on bivariate samples. Inheritance Hierarchy
SystemObject
Meta.Numerics.StatisticsBivariate

Namespace:  Meta.Numerics.Statistics
Assembly:  Meta.Numerics (in Meta.Numerics.dll) Version: 4.1.4 Syntax
`public static class Bivariate`

The Bivariate type exposes the following members. Methods
NameDescription  CorrelationCoefficient
Computes the correlation coefficient between the two variables.  Covariance
Computes the covariance of the two variables.  CrosstabsR, C
Produces a cross-tabulation.  KendallTauTest
Performs a Kendall concordance test for association.  LinearLogisticRegression
Computes the best-fit linear logistic regression from the data.  LinearRegression
Computes the best-fit linear regression from the data.  NonlinearRegression(IReadOnlyListDouble, IReadOnlyListDouble, FuncIReadOnlyDictionaryString, Double, Double, Double, IReadOnlyDictionaryString, Double)
Finds the parameterized function that best fits the data.  NonlinearRegression(IReadOnlyListDouble, IReadOnlyListDouble, FuncIReadOnlyListDouble, Double, Double, IReadOnlyListDouble)
Finds the parameterized function that best fits the data.  PairedStudentTTest
Performs a paired Student t-test.  PearsonRTest
Performs a Pearson correlation test for association.  PolynomialRegression
Computes the polynomial of given degree which best fits the data.  PopulationCovariance
Estimates the covariance of the two variables in the population.  SpearmanRhoTest
Performs a Spearman rank-order test of association between the two variables.  WilcoxonSignedRankTest
Performs a Wilcoxon signed rank test.
Top Remarks

A bivariate sample is a sample in which each observation contains measurements of two quantities. A data set with height and weight measured for each person in the sample, for example, is bivariate. A data set with height measured for each person in two different groups, for example, is not bivariate. The first data set can be analyzed with the methods here. The second, which is just two independent univariate samples, should be analyzed with the multi-sample methods of the Univariate class.

One common task with bivariate data is to determine whether some association exists between the two measured quantities. This can be accomplished with the PearsonRTest, the SpearmanRhoTest, or the KendallTauTest. Simpler than testing for the statistical significance of any association is simply to measure it by reporting the CorrelationCoefficient(IReadOnlyListDouble, IReadOnlyListDouble). See Also