Click or drag to resize

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: 4.0.5.0 (4.0.5.0)
Syntax
public static class Bivariate

The Bivariate type exposes the following members.

Methods
  NameDescription
Public methodStatic memberCorrelationCoefficient
Computes the correlation coefficient between the two variables.
Public methodStatic memberCovariance
Computes the covariance of the two variables.
Public methodStatic memberCrosstabsR, C
Produces a cross-tabulation.
Public methodStatic memberKendallTauTest
Performs a Kendall concordance test for association.
Public methodStatic memberLinearLogisticRegression
Computes the best-fit linear logistic regression from the data.
Public methodStatic memberLinearRegression
Computes the best-fit linear regression from the data.
Public methodStatic memberNonlinearRegression(IReadOnlyListDouble, IReadOnlyListDouble, FuncIReadOnlyDictionaryString, Double, Double, Double, IReadOnlyDictionaryString, Double)
Finds the parameterized function that best fits the data.
Public methodStatic memberNonlinearRegression(IReadOnlyListDouble, IReadOnlyListDouble, FuncIReadOnlyListDouble, Double, Double, IReadOnlyListDouble)
Finds the parameterized function that best fits the data.
Public methodStatic memberPairedStudentTTest
Performs a paired Student t-test.
Public methodStatic memberPearsonRTest
Performs a Pearson correlation test for association.
Public methodStatic memberPolynomialRegression
Computes the polynomial of given degree which best fits the data.
Public methodStatic memberPopulationCovariance
Estimates the covariance of the two variables in the population.
Public methodStatic memberSpearmanRhoTest
Performs a Spearman rank-order test of association between the two variables.
Public methodStatic memberWilcoxonSignedRankTest
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).

Another common operation on bivariate data is to try to predict one variable on the basis of 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.

See Also