# Analysis of variance

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means.

- F-test of equality of variances
- In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance.
Notionally, any F-test can be regarded as a comparison of two variances, but the specific case being discussed in this
- Journal of Modern Applied Statistical Methods
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- Shlomo Sawilowsky
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- ANOVA on ranks
- In statistics, one purpose for the analysis of variance (ANOVA) is to analyze differences in means between groups. The test statistic, F, assumes independence of observations, homogeneous variances, and population normality. ANOVA on ranks is a statistic
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