About this course

Course code TPMULT94
Duration 3 Days

  • This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. Strong emphasis is on understanding the results of the analysis and presenting your conclusions with graphs.


  • 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.

Who should attend

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

Delegates will 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.


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 modelling

This course addresses

  • SAS/STAT software

3 Days

Delivery Method

Delivery method


Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.

Find dates and prices

Online booking is currently not available for this course, to find out more please call us on 0113 220 7150 or email us at info@qa.com to discuss how we can help.

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