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