Sample Class

Represents a set of data points, where each data point consists of a single real number.

Inheritance Hierarchy

SystemObject
Meta.Numerics.StatisticsSample

Namespace: Meta.Numerics.Statistics
Assembly: Meta.Numerics (in Meta.Numerics.dll) Version: 4.1.4

Syntax

C++

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public sealed class Sample : ICollection<double>, 
	IEnumerable<double>, IEnumerable, IReadOnlyCollection<double>

Public NotInheritable Class Sample
	Implements ICollection(Of Double), IEnumerable(Of Double), 
	IEnumerable, IReadOnlyCollection(Of Double)

public ref class Sample sealed : ICollection<double>, 
	IEnumerable<double>, IEnumerable, IReadOnlyCollection<double>

[<SealedAttribute>]
type Sample =  
    class
        interface ICollection<float>
        interface IEnumerable<float>
        interface IEnumerable
        interface IReadOnlyCollection<float>
    end

The Sample type exposes the following members.

Constructors

	Name	Description
	Sample	Initializes a new, empty sample.
	Sample(IEnumerableDouble)	Initializes a new sample from a list of values.
	Sample(Double)	Initializes a new sample from a list of values.
	Sample(String)	Initializes a new, empty sample with the given name.

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Properties

	Name	Description
	CorrectedStandardDeviation	Gets the Bessel-corrected standard deviation.
	Count	Gets the number of values in the sample.
	InterquartileRange	Gets the interquartile range of sample measurements.
	IsReadOnly	Gets a value indicating whether the sample is read-only.
	Maximum	Gets the largest value in the sample.
	Mean	Gets the sample mean.
	Median	Gets the sample median.
	Minimum	Gets the smallest value in the sample.
	Name	Gets or sets the name of the sample.
	PopulationMean	Gets an estimate of the population mean from the sample.
	PopulationStandardDeviation	Gets an estimate of the population standard deviation from the sample.
	PopulationVariance	Gets an estimate of the population variance from the sample.
	Skewness	Gets the sample skewness.
	StandardDeviation	Gets the sample standard deviation.
	Variance	Gets the sample variance.

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Methods

	Name	Description
	Add(IEnumerableDouble)	Adds multiple values to the sample.
	Add(Double)	Adds a value to the sample.
	Add(Double)	Adds multiple values to the sample.
	CentralMoment	Computes the given sample central moment.
	Clear	Remove all values from the sample.
	Contains	Determines whether the sample contains the given value.
	Copy	Copies the sample.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	FisherFTest	Tests whether the variances of two samples are compatible.
	GetEnumerator	Gets an enumerator of sample values.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InverseLeftProbability	Gets the sample value corresponding to a given percentile score.
	KolmogorovSmirnovTest(ContinuousDistribution)	Tests whether the sample is compatible with the given distribution.
	KolmogorovSmirnovTest(Sample, Sample)	Tests whether the sample is compatible with another sample.
	KruskalWallisTest(Sample)	Performs a Kruskal-Wallis test on the given samples.
	KruskalWallisTest(IReadOnlyListSample)	Performs a Kruskal-Wallis test on the given samples.
	KuiperTest	Tests whether the sample is compatible with the given distribution.
	LeftProbability	Gets the fraction of values equal to or less than the given value.
	MannWhitneyTest	Tests whether one sample median is compatible with another sample median.
	MaximumLikelihoodFit	Performs a maximum likelihood fit.
	OneWayAnovaTest(Sample)	Performs a one-way analysis of variance (ANOVA).
	OneWayAnovaTest(IReadOnlyCollectionSample)	Performs a one-way analysis of variance (ANOVA).
	PopulationCentralMoment	Estimates the given population central moment from the sample.
	PopulationRawMoment	Estimates the given population raw moment from the sample.
	RawMoment	Computes the given sample raw moment.
	Remove	Removes a given value from the sample.
	ShapiroFranciaTest	Performs a Shapiro-Francia test of normality on the sample.
	SignTest	Tests whether the sample median is compatible with the given reference value.
	StudentTTest(Double)	Tests whether the sample mean is compatible with the reference mean.
	StudentTTest(Sample, Sample)	Tests whether one sample mean is compatible with another sample mean.
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform	Transforms all values using a user-supplied function.
	TwoWayAnovaTest	Performs a two-way analysis of variance.
	ZTest	Performs a z-test.

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Extension Methods

	Name	Description
	CentralMoment	Computes the given sample central moment. (Defined by Univariate.)
	CorrectedStandardDeviation	Computes the Bessel-corrected standard deviation. (Defined by Univariate.)
	Maximum	Finds the maximum value. (Defined by Univariate.)
	Mean	Computes the sample mean. (Defined by Univariate.)
	Minimum	Finds the minimum value. (Defined by Univariate.)
	PopulationCentralMoment	Estimates the given central moment of the underlying population. (Defined by Univariate.)
	PopulationMean	Estimates the mean of the underlying population. (Defined by Univariate.)
	PopulationRawMoment	Estimates the given raw moment of the underlying population. (Defined by Univariate.)
	PopulationStandardDeviation	Estimates of the standard deviation of the underlying population. (Defined by Univariate.)
	PopulationVariance	Estimates of the variance of the underlying population. (Defined by Univariate.)
	RawMoment	Computes the given sample raw moment. (Defined by Univariate.)
	SignTest	Tests whether the sample median is compatible with the given reference value. (Defined by Univariate.)
	Skewness	Computes the sample skewness. (Defined by Univariate.)
	StandardDeviation	Computes the sample standard deviation. (Defined by Univariate.)
	StudentTTest	Tests whether the sample mean is compatible with the reference mean. (Defined by Univariate.)
	Variance	Computes the sample variance. (Defined by Univariate.)
	ZTest	Performs a z-test to test whether the given sample is compatible with the given normal reference population. (Defined by Univariate.)

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Remarks

A univariate sample is a data set which records one number for each independent observation. For example, data from a study which measured the weight of each subject could be stored in the Sample class. The class offers descriptive statistics for the sample, estimates of descriptive statistics of the underlying population distribution, and statistical tests to compare the sample distribution to other sample distributions or theoretical models.

Reference

Meta.Numerics.Statistics Namespace