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Abstract
Multivariate statistics analyzes data on several random variables simultaneously. This course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics including visualization, dimension reduction, supervised and unsupervised learning for multivariate data.
Objective
After the course, you are able to: - describe the various methods and the concepts behind them - identify adequate methods for a given statistical problem - use statistical software to efficiently apply these methods - interpret the output of these methods
Content
Visualization, multivariate outliers, the multivariate normal distribution, dimension reduction, principal component analysis, multidimensional scaling, factor analysis, independent component analysis, cluster analysis, classification, multivariate tests and multiple testing
Resources
Lecture Notes
None
Literature
1) "An Introduction to Applied Multivariate Analysis with R" (2011) by Everitt and Hothorn 2) "An Introduction to Statistical Learning: With Applications in R" (2013) by Gareth, Witten, Hastie and Tibshirani Electronic versions (pdf) of both books can be downloaded for free from the ETH library.