gov.sandia.cognition.statistics.method

Class AnalysisOfVarianceOneWay.Statistic

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic

All Implemented Interfaces:

ConfidenceStatistic, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

AnalysisOfVarianceOneWay

public static class AnalysisOfVarianceOneWay.Statistic
extends AbstractConfidenceStatistic

Returns the confidence statistic for an ANOVA test

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

nullHypothesisProbability

Constructor Summary

Constructors

Constructor and Description
Statistic(double F, double DFbetween, double DFwithin) Creates a new instance of Statistic

Method Summary

Modifier and Type	Method and Description
AnalysisOfVarianceOneWay.Statistic	clone() This makes public the clone method on the Object class and removes the exception that it throws.
double	getDFbetween() Getter for DFbetween
double	getDFwithin() Getter for DFwithin
double	getF() Getter for F
double	getTestStatistic() Gets the statistic from which we compute the null-hypothesis probability.
protected void	setDFbetween(double DFbetween) Setter for DFbetween
protected void	setDFwithin(double DFwithin) Setter for DFwithin
protected void	setF(double F) Setter for F

Methods inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

getNullHypothesisProbability, setNullHypothesisProbability, toString

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

Statistic

public Statistic(double F,
                 double DFbetween,
                 double DFwithin)

Creates a new instance of Statistic

Parameters:

F - Input to the Snedecor F-distribution

DFbetween - Degrees of freedom between the treatments

DFwithin - Degrees of freedom within the treatments

Method Detail

clone

public AnalysisOfVarianceOneWay.Statistic clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

getF

public double getF()

Getter for F

Returns:

Input to the Snedecor F-distribution

setF

protected void setF(double F)

Setter for F

Parameters:

F - Input to the Snedecor F-distribution

getDFbetween

public double getDFbetween()

Getter for DFbetween

Returns:

Degrees of freedom between the treatments

setDFbetween

protected void setDFbetween(double DFbetween)

Setter for DFbetween

Parameters:

DFbetween - Degrees of freedom between the treatments

getDFwithin

public double getDFwithin()

Getter for DFwithin

Returns:

Degrees of freedom within the treatments

setDFwithin

protected void setDFwithin(double DFwithin)

Setter for DFwithin

Parameters:

DFwithin - Degrees of freedom within the treatments

getTestStatistic

public double getTestStatistic()

Description copied from interface: ConfidenceStatistic

Gets the statistic from which we compute the null-hypothesis probability. In an ANOVA, this would be the "F" statistic. In a t-test, this would be the "t" value. And so forth.

Returns:

Confidence statistic used to compute the null-hypothesis probability.