Multivariate Statistics for Understanding Complex Data
Duration: 40.0 hours
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 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
Live Instructor Dates 
