| ShapiroWilkTest Class |
Class ShapiroWilkTest tests the null hypothesis that the sample comes from a normally distributed population.
Inheritance Hierarchy Namespace: CenterSpace.NMath.CoreAssembly: NMath (in NMath.dll) Version: 7.4
Syntax [SerializableAttribute]
public class ShapiroWilkTest : ICloneable
<SerializableAttribute>
Public Class ShapiroWilkTest
Implements ICloneable
[SerializableAttribute]
public ref class ShapiroWilkTest : ICloneable
[<SerializableAttribute>]
type ShapiroWilkTest =
class
interface ICloneable
end
The ShapiroWilkTest type exposes the following members.
Constructors | Name | Description |
---|
| ShapiroWilkTest(Double) |
Constructs a ShapiroWilkTest intance with the provided sample data, default alpha and
default hypothesis type.
|
| ShapiroWilkTest(DoubleVector) |
Constructs a ShapiroWilkTest intance with the provided sample data, default alpha and
default hypothesis type.
|
| ShapiroWilkTest(IDFColumn) |
Constructs a ShapiroWilkTest intance with the provided sample data, default alpha and
default hypothesis type.
|
| ShapiroWilkTest(Int32) |
Constructs a ShapiroWilkTest intance with the provided sample data, default alpha and
default hypothesis type.
|
| ShapiroWilkTest(Double, HypothesisType) |
Constructs a ShapiroWilkTest intance with the provided sample, provided
hypothesis type and default alpha.
|
| ShapiroWilkTest(Double, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
default hypothesis type.
|
| ShapiroWilkTest(DoubleVector, HypothesisType) |
Constructs a ShapiroWilkTest intance with the provided sample, provided
hypothesis type and default alpha.
|
| ShapiroWilkTest(DoubleVector, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
default hypothesis type.
|
| ShapiroWilkTest(IDFColumn, HypothesisType) |
Constructs a ShapiroWilkTest intance with the provided sample, provided
hypothesis type and default alpha.
|
| ShapiroWilkTest(IDFColumn, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
default hypothesis type.
|
| ShapiroWilkTest(Int32, HypothesisType) |
Constructs a ShapiroWilkTest intance with the provided sample, provided
hypothesis type and default alpha.
|
| ShapiroWilkTest(Int32, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
default hypothesis type.
|
| ShapiroWilkTest(Double, HypothesisType, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
hypothesis type.
|
| ShapiroWilkTest(DoubleVector, HypothesisType, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
hypothesis type.
|
| ShapiroWilkTest(IDFColumn, HypothesisType, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
hypothesis type.
|
| ShapiroWilkTest(Int32, HypothesisType, Double) |
Constructs a ShapiroWilkTest intance with the provided sample, alpha and
hypothesis type.
|
TopProperties | Name | Description |
---|
| Alpha |
Gets and sets the alpha level associated with this hypothesis test.
|
| Data |
Data used to calculate the Shapiro Wilk test statistic. The data is modified for calculation
by removing NaNs and sorting.
|
| DefaultAlpha |
Gets and sets the default alpha level associated with the PearsonChiSquareTest. Used
to determine whether or not to reject the null hypothesis.
|
| DefaultType |
Gets and sets the default alpha level associated with the PearsonChiSquareTest. Used
to determine whether or not to reject the null hypothesis.
|
| N |
Gets the sample size.
|
| P |
Gets the p-value associated with the test statistic.
|
| Reject |
Returns true if the null hypothesis can be rejected, using the current
hypothesis type and alpha level; otherwise, false.
|
| S |
Gets the sample standard deviation.
|
| Statistic |
Shapiro Wilk Test statistic, generally referred to as W
|
| Type |
Hypothesis type
|
| Xbar |
Sample mean
|
TopMethods Remarks
ShapiroWilkTest comes from Shapiro, S. S.; Wilk, M. B. (1965). "An analysis of variance test for normality (complete samples)". Biometrika 52 (3-4): 591–611.
Implementation based on Royson R94.
The sample data provided must be of size between 3 and 5000. If the size becomes too large, then the test begins to
perform poorly.
See Also