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.
Lernen Sie, wie Sie / 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.
Zielgruppe / Who should attend
Business analysts, social science researchers, marketers, and statisticians who want to use SAS to make sense of highly dimensional multivariate data
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.
In diesem Kurs wird mit folgenden Software Modulen gearbeitet / This course addresses SAS/STAT Software
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
