This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs.

Learn How To

make sense of the math behind many multivariate statistical analyses reduce dimensionality with principal components analysis identify latent variables with exploratory factor analysis and factor rotation understand individual preferences with qualitative preference analysis explain associations among many categories with correspondence analysis finds patterns of association among different sets of continuous variables with canonical correlation analysis explain differences among groups in terms of many predictor variables through canonical discriminant analyses classify observations into groups with linear and quadratic discriminant analyses fit complex multivariate predictive models with partial least squares regression analysis.Who Should Attend

Business analysts, social science researchers, marketers, and statisticians who want to use SAS to make sense of highly dimensional multivariate data

Prerequisites

Before attending this course, you should be familiar with statistical concepts such as hypothesis testing, linear models, and collinearity concepts in regression. You should have an understanding of the topics taught in Statistics 2: ANOVA and Regression or equivalent.

SAS Products Covered

SAS/STAT

Course Outline

Overview of Multivariate Methods

examples of multivariate analyses matrix algebra conceptsPrincipal Components Analysis Using the PRINCOMP procedureprincipal component analysis for dimension reductionExploratory Factor Analysis Using the FACTOR Procedurefactor analysis for latent variable measurement factor rotationMultidimensional Preference Analysis Using the PRINQUAL and TRANSREG proceduresplotting high-dimensional preference data mapping preferences to other characteristicsCorrespondence Analysis Using the CORRESP Procedureunderstanding complex associations among categorical variablesCanonical Variate Analysis Using the CANCORR and CANDISC Proceduresmultivariate dimensions reduction for two sets of variablesDiscriminant Function Analysis Using the DISCRIM Procedureclassification into groups linear discriminant analysis quadratic discriminant analysis empirical validationPartial Least Squares Regression Using the PLS ProcedurePLS for one target variable PLS for many targets PLS for predictive modeling