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  2. Analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_variance

    The use of ANOVA to study the effects of multiple factors has a complication. In a 3-way ANOVA with factors x, y and z, the ANOVA model includes terms for the main effects (x, y, z) and terms for interactions (xy, xz, yz, xyz). All terms require hypothesis tests.

  3. Mixed-design analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Mixed-design_analysis_of...

    For example, if participants completed a specific measure at three time points, C = 3, and df WS = 2. The degrees of freedom for the interaction term of between-subjects by within-subjects term(s), df BSXWS = (R – 1)(C – 1), where again R refers to the number of levels of the between-subject groups, and C is the number of within-subject tests.

  4. G*Power - Wikipedia

    en.wikipedia.org/wiki/G*Power

    Additionally, the user must determine which of the many contexts this test is being used, such as a one-way ANOVA versus a multi-way ANOVA. In order to calculate power, the user must know four of five variables: either number of groups, number of observations, effect size, significance level (α), or power (1-β). G*Power has a built-in tool ...

  5. Multivariate analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Multivariate_analysis_of...

    The image above depicts a visual comparison between multivariate analysis of variance (MANOVA) and univariate analysis of variance (ANOVA). In MANOVA, researchers are examining the group differences of a singular independent variable across multiple outcome variables, whereas in an ANOVA, researchers are examining the group differences of sometimes multiple independent variables on a singular ...

  6. F-test - Wikipedia

    en.wikipedia.org/wiki/F-test

    The formula for the one-way ANOVA F-test statistic is =, or =. The "explained variance", or "between-group variability" is = (¯ ¯) / where ¯ denotes the sample mean in the i-th group, is the number of observations in the i-th group, ¯ denotes the overall mean of the data, and denotes the number of groups.

  7. Analysis of covariance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_covariance

    Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable (DV) are equal across levels of one or more categorical independent variables (IV) and across one or more continuous variables.

  8. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    This algorithm can easily be adapted to compute the variance of a finite population: simply divide by n instead of n − 1 on the last line.. Because SumSq and (Sum×Sum)/n can be very similar numbers, cancellation can lead to the precision of the result to be much less than the inherent precision of the floating-point arithmetic used to perform the computation.

  9. Two-way analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Two-way_analysis_of_variance

    In statistics, the two-way analysis of variance (ANOVA) is an extension of the one-way ANOVA that examines the influence of two different categorical independent variables on one continuous dependent variable. The two-way ANOVA not only aims at assessing the main effect of each independent variable but also if there is any interaction between them.