Applied multivariate statistics for the social sciences
This book was written for those who will be using, rather than developing, advanced statistical methods. It focuses on a conceptual understanding of the material rather than proving results. It is a graduate level textbook with abundant examples.
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TWO GROUP MULTIVARIATE ANALYSIS
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05 level analysis of variance assumption BEGIN DATA Bonferroni inequality canonical correlation canonical variates cell chapter coefficients column components analysis contrasts control lines correlation matrix covariance matrix critical value cross validity DATA LIST data set degrees of freedom dependent variables determine discriminant analysis discriminant function drug effect size eigenvalues END DATA equal error term estimate example factor GPID group sizes illustrate indicate interaction interpretation kurtosis linear combination Mahalanobis distance main effect methods multiple correlation multiple regression multivariate analysis multivariate test multivariate test statistics null hypothesis number of variables obtained outliers overall posttest prediction equation printout probability problem ratio Recall regression analysis repeated measures design residuals rotation sample SAS and SPSSX scores set of predictors set of variables SPSSX MANOVA standard deviations stepdown stepwise subcommand sum of squares treatment Tukey type I error univariate univariate tests varimax vector