Target group
Business analysts, social science researchers, marketers, and statisticians who want to use SAS to make sense of highly dimensional multivariate data
Course structure
Overview of Multivariate Methods
- examples of multivariate analyses
- matrix algebra concepts
Principal Components Analysis Using the PRINCOMP procedure
- principal component analysis for dimension reduction
Exploratory Factor Analysis Using the FACTOR Procedure
- factor analysis for latent variable measurement
- factor rotation
Multidimensional Preference Analysis Using the PRINQUAL and TRANSREG procedures
- plotting high-dimensional preference data
- mapping preferences to other characteristics
Correspondence Analysis Using the CORRESP Procedure
- understanding complex associations among categorical variables
Canonical Variate Analysis Using the CANCORR and CANDISC Procedures
- multivariate dimensions reduction for two sets of variables
Discriminant Function Analysis Using the DISCRIM Procedure
- classification into groups
- linear discriminant analysis
- quadratic discriminant analysis
- empirical validation
Partial Least Squares Regression Using the PLS Procedure
- PLS for one target variable
- PLS for many targets
- PLS for predictive modeling
