Assembly: Meta.Numerics (in Meta.Numerics.dll) Version: 22.214.171.124 (126.96.36.199)
|C#||Visual Basic||Visual C++||F#|
public sealed class BivariateSample
Public NotInheritable Class BivariateSample
public ref class BivariateSample sealed
[<SealedAttribute>] type BivariateSample = class end
XNA Framework Only
.NET Compact Framework Only
Initializes a new bivariate sample.
Initializes a new bivariate sample with the given variable names.
Adds a data point to the sample.
Removes all data points from the sample.
Determines whether the sample contains a given data point.
Copies the bivariate sample.
Gets the number of data points.
Gets the covariance of the two variables.
Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.(Inherited from Object.)
Serves as a hash function for a particular type.(Inherited from Object.)
Gets the Type of the current instance.(Inherited from Object.)
Performs a Kendall concordance test for association.
Computes the best-fit linear logistic regression from the data.
Computes the best-fit linear regression from the data.
|Load(IDataReader, Int32, Int32)|
Loads values from a data reader.
Creates a shallow copy of the current Object.(Inherited from Object.)
Performs a paired Student t-test.
Performs a Pearson correlation test for association.
Computes the polynomial of given degree which best fits the data.
Estimates of the population covariance of two variables.
Removes a data point from the sample.
Performs a Spearman rank-order test of association between the two variables.
Swaps the X and Y variables in the bivariate sample.
Gets a read-only univariate sample consisting of the x-values of the data points.
Gets a read-only univariate sample consisting of the y-values of the data points.
A bivariate sample consists of pairs of real numbers, where each pair is an independent measurement. For example, if you measure the height and weight of a sample of people, the data could be stored as a bivariate sample. The class can compute various descriptive statistics for the sample, perform appropriate statistical tests on the sample data, and fit the sample data to various models.