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401-0102-00L 5 Credits BSC , MSC , WBZ D-USYS , D-MATH , D-INFK , D-BIOL , D-ITET
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Applied Multivariate Statistics

Lecturers & Examiners: Dr. Fabio Sigrist
VVZ CR n/a

Last Updated: 2026-02-05 15:41:55

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. An emphasis is on applications and solving problems with the statistical software R.

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 the statistical software R 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, 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.

General Information

Language
English
Levels
BSC , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Closed book; simple pocket calculator with no communication capability

Course Components

Type Title Time & Place Hours
lecture Applied Multivariate Statistics
  • Mon 15:00-17:00 (ER SA TZ)
  • Mon 15:15-17:00 (HG F 3)
2 h weekly
exercise Applied Multivariate Statistics
  • Mon 08:00-10:00 (ER SA TZ)
  • Mon 08:15-10:00 (HG D 1.1)
1 h weekly

Offered In