Multivariate Statistics for Understanding Complex Data

Course code: MULT42

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.
1 800 EUR

2 178 EUR including VAT

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Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request Upon request 21 hours en 1 800 EUR Register
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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


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.

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