This course focuses on understanding the basic methods underlying multivariate analysis through computer applications using R. Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting. Through class exercises and a project, students apply these methods to real data and learn to think critically about data analysis and research findings.