Represents a test that a sample comes from a specified distribution.
SystemObject Extreme.Statistics.TestsHypothesisTest Extreme.Statistics.TestsOneSampleTestDouble Extreme.Statistics.TestsChiSquareGoodnessOfFitTest
Namespace:
Extreme.Statistics.Tests
Assembly:
Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
public sealed class ChiSquareGoodnessOfFitTest : OneSampleTest<double>
Public NotInheritable Class ChiSquareGoodnessOfFitTest
Inherits OneSampleTest(Of Double)
public ref class ChiSquareGoodnessOfFitTest sealed : public OneSampleTest<double>
[<SealedAttribute>]
type ChiSquareGoodnessOfFitTest =
class
inherit OneSampleTest<float>
endThe ChiSquareGoodnessOfFitTest type exposes the following members.
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| Name | Description |
|---|
 | ChiSquareGoodnessOfFitTest(HistogramDouble, ContinuousDistribution) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution using histogram data.
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| ChiSquareGoodnessOfFitTest(HistogramInt32, DiscreteDistribution) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution using histogram data.
|
| ChiSquareGoodnessOfFitTest(ICategoricalVector, VectorDouble) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution.
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| ChiSquareGoodnessOfFitTest(VectorDouble, VectorDouble) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution using histogram data.
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| ChiSquareGoodnessOfFitTest(VectorDouble, Distribution) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution.
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| ChiSquareGoodnessOfFitTest(HistogramDouble, ContinuousDistribution, Int32) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution using histogram data.
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| ChiSquareGoodnessOfFitTest(HistogramInt32, DiscreteDistribution, Int32) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution using histogram data.
|
| ChiSquareGoodnessOfFitTest(VectorDouble, Distribution, Int32) |
Constructs a new chi-squared goodness-of-fit test
for the specified distribution.
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Use the ChiSquareGoodnessOfFitTest class
to test whether a sample comes from a specific distribution.
The sample can be supplied as either a vector or
a vector.
The distribution can be specified as a probability distribution (a class that inherits from Distribution)
or a vector. The distribution can be discrete or continuous.
If the sample and distribution are both supplied as a vector, the boundaries
must be identical.
A chi-square test uses binned data. A drawback is that the results of the test depend
on how the data is binned. The test also needs a suffient sample size for the chi-square approximation
to be valid.
The AndersonDarlingTest and OneSampleKolmogorovSmirnovTest tests
are alternatives to the chi-square goodness-of-fit test, but they are valid only for continuous distributions.
Reference