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Analysis of variance (view source)

Revision as of 02:51, May 25, 2007

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JEB BERKELEY IS WHITE
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{{statistics}}
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'''Analysis of variance''' or '''ANOVA''' is a statistical method for comparing different models. Models are built with explanatory parameters that attempt to describe the variance of a given data set. The amount of the variance explained by two models are compared and a corresponding value is calculated for how well one model explains the data relative to the other compared models. This value is called the [[Fisher distribution | F-value]] (named for [[R.A. Fisher]] who developed the first ANOVA model). The larger the F-value the better one model explains the variance than the others. In order to determine [[statistical significance]] the f-value is compared to a specific [[Fisher distribution]] based on the [[degrees of freedom]]. The Fisher distribution can give the probability that a given data set was derived from one model relative to another.
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The ANOVA test makes several assumptions about the data including that the error is independent, that the data is [[Normal distribution | normally distributed]], and [[homogeneity of variance]].
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The one-way ANOVA is probably one of the most used statistical methods for comparing data sets. Today most scientists calculate it using a statistical package, of which several are available.
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[[category:Probability and Statistics]]
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