Using Multivariate StatisticsThis text takes a practical approach to multivariate data analysis, with an introductionto the most commonly encountered statistical and multivariate techniques. 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 ofhigherlevel mathematics. *A new chapter on survival analysis (Ch. 15) allows students to analyze data where the outcome is time until something happens. This is very popular in biomedical research. *A new chapter on time series analysis (Ch. 16) encourages students to learn to model patterns in data gathered over many trials and to test for the effectiveness on an intervention ( 
What people are saying  Write a review
User ratings
5 stars 
 
4 stars 
 
3 stars 
 
2 stars 
 
1 star 

User Review  Flag as inappropriate
This book is written for students in the social sciences. The authors provide many insightful details that go beyond the theory.
Review: Using Multivariate Statistics
User Review  GoodreadsThis used to be one of the best references in the field. However, it is somehow outdated now. A wellupdated edition is definitely needed.. Read full review
Contents
Combining Variables  10 
Using the Book  17 
Review of Univariate and Bivariate Statistics  31 
Copyright  
23 other sections not shown
Common terms and phrases
adjusted ARIMA assessed ATTDRUG ATTHOUSE ATTROLE autocorrelation canonical correlation canonical variates cell Chapter chisquare classification confidence intervals correlation matrix covariance matrix data set degrees of freedom deleted differences DISCRIM distribution eigenvalues equation evaluated expected frequencies factors FIT INDEX groups homoscedasticity hypothesis interaction kurtosis labeled levels linear logistic regression Mahalanobis distance main effects MANOVA mean measured missing data missing values multicollinearity multiple regression multivariate outliers normality orthogonal PACF parameter estimates partial autocorrelations plots predicted predictors procedure programs ratio READTYP regression coefficients relationship reliable researcher residuals rotation scatterplots scores Section sequential significant skewness smallsample example SPSS SPSS MANOVA standard errors statistical strength of association sum of squares survival survival analysis Syntax and Selected SYSTAT Table TIMEDRS tion transformation treatment Type I error univariate variables variance Wald test withinsubjects Yes No Yes Yes Yes Yes