Using multivariate statistics
"Using Multivariate Statistics" provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set - when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.
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Review of Univariate and Bivariate
Screening Data Prior
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adjusted analysis of variance assessed association ATTDRUG ATTHOUSE attitudes ATTROLE between-subjects BMDP BMDP7M canonical correlation canonical variates cell Chapter classification combination comparisons correlation matrix covariates data set degrees of freedom deleted deviations differences DISCRIM discriminant function analysis distribution eigenvalues equation error term evaluated example expected frequencies F-STATISTIC factor scores groups HAPHOUSE hierarchical homoscedasticity interaction interpretation INTEXT kurtosis labeled Lambda levels linear loadings LTIMEDRS Mahalanobis distance main effect MANOVA marginal MASC means MENHEAL missing data missing values multicollinearity multiple regression multivariate outliers NEVER YES normality null hypothesis number of factors orthogonal parameter estimates plots predicted predictors procedures profile analysis programs READTYP regression coefficients relationship reliable residuals saturated model scatterplots Section SETUP AND SELECTED skewness solution SPSS stepdown analysis stepwise sum of squares SYSTAT SYSTAT MGLH Table transformation treatment Type I error univariate within-subjects women Yes No Yes Yes Yes Yes