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In statistics, Bartlett's test, named after Maurice Stevenson Bartlett, [1] is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances. [2] Some statistical tests, such as the analysis of variance , assume that variances are equal across groups or samples, which can be checked with Bartlett's test.
Homogeneity can be studied to several degrees of complexity. For example, considerations of homoscedasticity examine how much the variability of data-values changes throughout a dataset. However, questions of homogeneity apply to all aspects of the statistical distributions, including the location parameter.
In statistics, Levene's test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. [1] This test is used because some common statistical procedures assume that variances of the populations from which different samples are drawn are equal. Levene's test assesses this assumption.
The Cochran's Q test is an extension of the McNemar's test for more than two "treatments". The Liddell's exact test is an exact alternative to McNemar's test. [10] [11] The Stuart–Maxwell test is different generalization of the McNemar test, used for testing marginal homogeneity in a square table with more than two rows/columns. [12] [13] [14]
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In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data. It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [ 1 ]
Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test .The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.
When there is homogeneity of variance, sphericity of the covariance matrix will occur, because for between-subjects independence has been maintained. [5] [page needed] For the within-subject effects, it is important to ensure normality and homogeneity of variance are not being violated. [5] [page needed]