Linked from

The 28 pages that link to Analysis of variance, each with the reason it gives.

• Null hypothesisRelated: Its standard omnibus null states that all group means are equal.

• Ronald FisherRelated: Fisher introduced this framework for analyzing experiments and comparing group means.

• Design of experimentsRelated: It evaluates whether designed factor effects explain response variation beyond experimental error.

• Fisher informationRelated: Fisher developed both this method and information-based theory within his broader statistical work.

• Degrees of freedomRelated: Its sums of squares are paired with degrees of freedom to form mean squares and F tests.

• Ordinary least squaresRelated: In its linear-model formulation, OLS estimates effects and decomposes sums of squares.

• Sample meanRelated: It evaluates differences among sample means relative to within-group variation.

• Management accountingRelated: Managers use variances to locate departures from budgets and operating expectations.

• BiometricsRelated: It separates group differences from within-group biological variation.

• Chi-squared distributionRelated: Under normal sampling, residual sums of squares scaled by variance have chi-squared distributions.

• Sum of squaresRelated: It separates total variation into between-group and within-group sums of squares.

• Factorial experimentRelated: ANOVA commonly tests main and interaction effects in factorial data.

• Coefficient of determinationRelated: Its sums-of-squares decomposition explains the classical R² calculation.

• F-distributionRelated: ANOVA uses an F statistic to compare between-group variation with within-group variation.

• Law of total varianceRelated: Its between-group and within-group sums of squares mirror the identity’s two components.

• Parametric statisticsRelated: Classical analyses often model observations with normally distributed errors and finite parameters.

• Statistical analysisRelated: It tests whether observed group differences exceed expected sampling variation.

• Cochran's theoremRelated: Its sums of squares are independent chi-squared components under the normal-model assumptions.