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    • AndersonDarlingDistribution Class
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  • KruskalWallisTest Class
    • KruskalWallisTest Constructors
    • Properties
    • Methods
KruskalWallisTest ClassExtreme Optimization Numerical Libraries for .NET Professional
Represents the Kruskal-Wallis test.
Inheritance Hierarchy

SystemObject
  Extreme.Statistics.TestsHypothesisTest
    Extreme.Statistics.TestsMultiSampleTestDouble
      Extreme.Statistics.TestsKruskalWallisTest

Namespace: Extreme.Statistics.Tests
Assembly: Extreme.Numerics.Net40 (in Extreme.Numerics.Net40.dll) Version: 6.0.16073.0 (6.0.16312.0)
Syntax

C#
VB
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public sealed class KruskalWallisTest : MultiSampleTest<double>
Public NotInheritable Class KruskalWallisTest
	Inherits MultiSampleTest(Of Double)
public ref class KruskalWallisTest sealed : public MultiSampleTest<double>
[<SealedAttribute>]
type KruskalWallisTest =  
    class
        inherit MultiSampleTest<float>
    end

The KruskalWallisTest type exposes the following members.

Constructors

  NameDescription
Public methodKruskalWallisTest
Constructs a new KruskalWallisTest.
Public methodKruskalWallisTest(IDataFrame)
Constructs a new KruskalWallisTest for the samples in a data frame.
Public methodKruskalWallisTest(NumericalVariable)
Constructs a new KruskalWallisTest for the specified NumericalVariable array.
Public methodKruskalWallisTest(VariableCollection)
Constructs a new KruskalWallisTest for the samples in a VariableCollection.
Public methodKruskalWallisTest(VectorDouble)
Constructs a new KruskalWallisTest for the specified VectorT array.
Public methodKruskalWallisTest(NumericalVariable, CategoricalVariable)
Constructs a new KruskalWallisTest for observations grouped by the specified grouping variable.
Public methodKruskalWallisTest(VectorDouble, ICategoricalVector)
Constructs a new KruskalWallisTest for observations grouped by the specified grouping variable.
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Properties

  NameDescription
Public propertyChiSquarePValue
Gets the probability that the test statistic would take on the calculated value under the null hypothesis assuming the distribution of the statistic is approximated by a chi-square distribution.
Public propertyCorrectForTies
Gets or sets whether the Kruskal-Wallis statistic should be corrected for ties.
Public propertyDistribution
Gets the probability distribution used in the hypothesis test.
(Inherited from HypothesisTest.)
Public propertyGrouping
Gets the grouping that divides Sample into groups.
(Inherited from MultiSampleTestT.)
Public propertyHypothesisType
Gets or sets whether the test is one or two-tailed.
(Inherited from HypothesisTest.)
Public propertyName
Gets the name of the hypothesis test.
(Inherited from HypothesisTest.)
Public propertyPValue
Gets the probability that the test statistic would take on the calculated value under the alternate hypothesis.
(Inherited from HypothesisTest.)
Public propertySample
Gets the vector that contains the sample data.
(Inherited from MultiSampleTestT.)
Public propertySamples
Gets the collection of samples for the test.
(Inherited from MultiSampleTestT.)
Public propertySamples
Gets the collection of samples for the test.
(Inherited from MultiSampleTest.)
Public propertySignificanceLevel
Gets the significance level used to test the null hypothesis.
(Inherited from HypothesisTest.)
Public propertyStatistic
Gets the value of the test statistic.
(Inherited from HypothesisTest.)
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Methods

  NameDescription
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Public methodGetConfidenceInterval
Returns the confidence interval for the test parameter for the default confidence level.
(Inherited from HypothesisTest.)
Public methodGetConfidenceInterval(Double)
Returns the confidence interval for the test parameter for the specified confidence level.
(Inherited from HypothesisTest.)
Public methodGetCount
Returns the number of observations in the specified group.
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetLowerCriticalValue
Gets the lower critical value for the hypothesis test's current significance level.
(Inherited from HypothesisTest.)
Public methodGetLowerCriticalValue(Double)
Gets the lower critical value for the hypothesis test at the specified significance level.
(Inherited from HypothesisTest.)
Public methodGetMeanRank
Returns the mean of the ranks of the observations from the specified group.
Public methodGetPValue
Gets the probability that the test statistic would take on the calculated value under the specified alternate hypothesis.
(Inherited from HypothesisTest.)
Public methodGetSumOfRanks
Returns the sum of the ranks of the observations from the specified group.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodGetUpperCriticalValue
Gets the upper critical value for the test statistic at the hypothesis test's current significance level.
(Inherited from HypothesisTest.)
Public methodGetUpperCriticalValue(Double)
Gets the upper critical value for the test statistic at the specified significance level.
(Inherited from HypothesisTest.)
Public methodReject
Returns whether the null hypothesis is rejected using the default significance level.
(Inherited from HypothesisTest.)
Public methodReject(Double)
Returns whether the null hypothesis is rejected using the specified significance level.
(Inherited from HypothesisTest.)
Public methodSummarize
Returns a string containing a human-readable summary of the object.
(Inherited from HypothesisTest.)
Public methodSummarize(SummaryOptions)
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from HypothesisTest.)
Public methodToString
Returns a string that represents the current object.
(Inherited from HypothesisTest.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Remarks

Use the KruskalWallisTest class to test whether a number of samples come from the same population. This test can be thought of as a non-parametric variation of one-way analysis of variance that doesn't assume the data is normally distributed.

This class approximates the distribution of the test statistic by a beta distribution. This approximation has been found to be more accurate than the traditional chi-square approximation. Still, for small sample sizes, critical values and tail probabilities may differ significantly from their exact values.

Version Information

Numerical Libraries

Supported in: 6.0, 5.x, 4.x
See Also

Reference

Extreme.Statistics.Tests Namespace

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