Data Assumption: Multicollinearity 5.8k views.Data Assumptions: Univariate Normality 5.9k views.Data Assumptions: Its about the residuals, and not the variables’ raw data 6.3k views.Outlier cases – univariate outliers 6.5k views.Which Test: Factor Analysis (FA, EFA, PCA, CFA) 6.8k views.Analysis of Covariance (ANCOVA) 6.9k views.Outlier cases – bivariate and multivariate outliers 9.1k views.Getting the hang of z-scores 10.1k views.Data Assumption: Homogeneity of regression slopes (test of parallelism) 11.8k views.Data Assumption: Homogeneity of variance (Univariate Tests) 12.1k views.Which Test: Logistic Regression or Discriminant Function Analysis 13.8k views.Repeated Measures ANOVA versus Linear Mixed Models.One-Sample Kolmogorov-Smirnov goodness-of-fit test 15.6k views.Data Assumption: Homogeneity of variance-covariance matrices (Multivariate Tests) 19.1k views.Which Test: Chi-Square, Logistic Regression, or Log-linear analysis 20.4k views.The “compare means function” also allows for an easy comparison of variance across groups. Another easy visual is to compare the mean, variance, and skewness of groups in your statistics programme’s “explore” or “descriptives” command.Do side-by-side box-plots of each group and if the width of the boxes does not vary markedly by group, it suggests no violation of the assumption. Simple box-plots is easy to grasp the graphical way of checking for the lack of homogeneity of variances.The plot should have no obvious pattern (so random data points is evidence of no violation of the assumption). The GLM procedures provide a scatterplot of “spread versus level plot”.
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