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Tukey's test is either: Tukey's range test, or Tukey's test of additivity More information...

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    • In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance is partitioned into components due to different explanatory variables. In its simplest form ANOVA gives a statistical test of whether the means of several groups are all equal, and therefore generalizes Student's two-sample t-test to more than two groups. ANOVAs are helpful because they possess a certain advantage over a two-sample t-test.
    • An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact F-tests mainly arise when the models have been fit to the data using least squares. The name was coined by George W. Snedecor, in honour of Sir Ronald A. Fisher.
    • (^(d_1+d_2}}}} \!</math>| cdf =| mean = for | median =| mode = for | variance = for | skewness =for | kurtosis =see text| entropy =| mgf =does not exist, raw moments defined elsewhere | char =defined elsewhere| }} In probability theory and statistics, the F-distribution is a continuous probability distribution. It is also known as Snedecor's F distribution or the Fisher-Snedecor distribution .
      http://en.wikipedia.org/wiki/File:F_distributionCDF.png
    • Analysis of covariance (ANCOVA) is a general linear model with one continuous outcome variable (quantitative) and one or more factor variables (qualitative). ANCOVA is a merger of ANOVA and regression for continuous variables. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative predictors account. The inclusion of covariates can increase statistical power because it accounts for some of the variability.
    • In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses. The presence of interactions can have important implications for the interpretation of statistical models.
      http://commons.wikipedia.org/wiki/File:Statistical_interaction.jpg
    • Multivariate analysis of variance (MANOVA) is a generalized form of univariate analysis of variance (ANOVA). It is used in cases where there are two or more dependent variables. As well as identifying whether changes in the independent variable(s) have significant effects on the dependent variables, MANOVA is also used to identify interactions among the dependent variables and among the independent variables.
    • In statistics, a covariate is a variable that is possibly predictive of the outcome under study. A covariate may be of direct interest or it may be a confounding or interacting variable. The alternative terms explanatory variable, independent variable, or predictor, are used in a regression analysis. In econometrics, the term "control variable" is usually used instead of "covariate".
    • In statistics, Bartlett's test is used to test if k samples are from populations with equal variances. Equal variances across samples is called homoscedasticity or homogeneity of variances. Some statistical tests, for example the analysis of variance, assume that variances are equal across groups or samples. The Bartlett test can be used to verify that assumption. Bartlett's test is sensitive to departures from normality.
    • In statistics, a confounding variable (also confounding factor, lurking variable, a confound, or confounder) is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable.

     

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