gov.sandia.cognition.statistics.method

## Class AnalysisOfVarianceOneWay

• All Implemented Interfaces:
BlockExperimentComparison<java.lang.Number>, NullHypothesisEvaluator<java.util.Collection<? extends java.lang.Number>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

```@ConfidenceTestAssumptions(name="One-Way Analysis of Variance",
alsoKnownAs={"1-way ANOVA","Fixed-effects 1-way ANOVA","F test"},
description={"ANOVA tests to determine if the means between the various treatments are equal.","ANOVA is a generalization of the paired Student t-test, where there can be multiple treatments.","When there are two groups, a control group and a treatment group, ANOVA is equivalent to the unpaired t-test."},
assumptions={"The data are sampled from a Gaussian distribution.","The variance within the different groups is equal.","The data from each group is collected independently of each other."},
nullHypothesis="The means from all groups are equal.",
dataPaired=false,
dataSameSize=false,
distribution=SnedecorFDistribution.CDF.class,
reference=@PublicationReference(author="Wikipedia",title="Analysis of Variance",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Analysis_of_variance"))
public class AnalysisOfVarianceOneWay
extends AbstractCloneableSerializable
implements BlockExperimentComparison<java.lang.Number>```
Analysis of Variance single-factor null-hypothesis testing procedure, usually called "1-way ANOVA". ANOVA evaluates the probability of the null hypothesis for a Collection of treatment cases. Each "treatment" is an experiment with a Collection of results from a given population. You can have different population sizes in each treatment. The null hypothesis is that there are no differences between the populations and that observed differences are due to chance.
Since:
2.0
Author:
Kevin R. Dixon
Serialized Form
• ### Nested Class Summary

Nested Classes
Modifier and Type Class and Description
`static class ` `AnalysisOfVarianceOneWay.Statistic`
Returns the confidence statistic for an ANOVA test
• ### Field Summary

Fields
Modifier and Type Field and Description
`static AnalysisOfVarianceOneWay` `INSTANCE`
Default instance.
• ### Constructor Summary

Constructors
Constructor and Description
`AnalysisOfVarianceOneWay()`
Creates a new instance of AnalysisOfVarianceOneWay
• ### Method Summary

All Methods
Modifier and Type Method and Description
`AnalysisOfVarianceOneWay.Statistic` `evaluateNullHypothesis(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)`
Evaluates the null hypothesis for the given block-design treatments
`AnalysisOfVarianceOneWay.Statistic` ```evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1, java.util.Collection<? extends java.lang.Number> data2)```
Evaluates the ANOVA statistics for the two given treatments, each treatment can have a different number of samples
• ### Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

`clone`
• ### Methods inherited from class java.lang.Object

`equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

`clone`
• ### Field Detail

• #### INSTANCE

`public static final AnalysisOfVarianceOneWay INSTANCE`
Default instance.
• ### Constructor Detail

• #### AnalysisOfVarianceOneWay

`public AnalysisOfVarianceOneWay()`
Creates a new instance of AnalysisOfVarianceOneWay
• ### Method Detail

• #### evaluateNullHypothesis

```@PublicationReference(author={"Frederick J. Gravetter","Larry B. Wallnau"},
title="Statistics for the Behavioral Sciences",
type=Book,
year=2003,
pages={406,412},
notes="Chapter 13.3")
public AnalysisOfVarianceOneWay.Statistic evaluateNullHypothesis(java.util.Collection<? extends java.util.Collection<? extends java.lang.Number>> data)```
Description copied from interface: `BlockExperimentComparison`
Evaluates the null hypothesis for the given block-design treatments
Specified by:
`evaluateNullHypothesis` in interface `BlockExperimentComparison<java.lang.Number>`
Parameters:
`data` - Collection of treatments for the block-design experiment, where each treatment contains
Returns:
The confidence for the null hypothesis.
• #### evaluateNullHypothesis

```public AnalysisOfVarianceOneWay.Statistic evaluateNullHypothesis(java.util.Collection<? extends java.lang.Number> data1,
java.util.Collection<? extends java.lang.Number> data2)```
Evaluates the ANOVA statistics for the two given treatments, each treatment can have a different number of samples
Specified by:
`evaluateNullHypothesis` in interface `NullHypothesisEvaluator<java.util.Collection<? extends java.lang.Number>>`
Parameters:
`data1` - First treatment
`data2` - Second treatment
Returns:
ANOVA Confidence statistics