Bivariate Class

public static class Bivariate

<ExtensionAttribute>
Public NotInheritable Class Bivariate

[ExtensionAttribute]
public ref class Bivariate abstract sealed

[<AbstractClassAttribute>]
[<SealedAttribute>]
[<ExtensionAttribute>]
type Bivariate = class end

Inheritance

Object    Bivariate

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 multiple-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).

Another common operation on bivariate data is to try to predict one variable from the other. There are many types of models you can construct to make such predictions, including LinearRegression(IReadOnlyListDouble, IReadOnlyListDouble), PolynomialRegression(IReadOnlyListDouble, IReadOnlyListDouble, Int32), and NonlinearRegression(IReadOnlyListDouble, IReadOnlyListDouble, FuncIReadOnlyListDouble, Double, Double, IReadOnlyListDouble). If the dependent variable is Boolean, you can use LinearLogisticRegression(IReadOnlyListBoolean, IReadOnlyListDouble) instead.

Reference