Applied Multivariate Statistics

Spring semester 2023

General information

Lecturer Fabio Sigrist
Assistant Maybritt Schillinger
Lectures Mondays 14.15-16.00 (HG E 1.2.)
Exercises Mondays 10.15-12.00 (HG E 1.1., bi-weekly, see Moodle for the schedule)
Course catalogue data >>
Course Moodle page >>

Important announcement

This website contains only basic info about the course. All materials will be added to the course Moodle. The forums where you can ask questions can be found there.

Course content

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.


  • "An Introduction to Applied Multivariate Analysis with R" (2011) by Everitt and Hothorn
  • "An Introduction to Statistical Learning: With Applications in R" (2013) by Gareth, Witten, Hastie and Tibshirani
  • "Introductory Statistics with R" (2008) by Dalgaard

Additional information

Examples in the lecture and hints as well as solutions to the exercises will be based on the statistical software R. This is a freely available open source program that works on all platforms and has become worldwide standard for data analysis. It can be downloaded from CRAN. A R Tutorial can be found here.


There will be a 120-minute written exam during the regular ETH exam sessions. The exam is a "closed book" exam, a simple pocket calculator with no communication capability is permitted. The exam covers all topics which were discussed and/or applied during either the lectures or the exercises. Upon passing the exam, the course will be awarded 5 ECTS credit points.

PhD students who would like to obtain credit points but do not need to take the exam and obtain a grade need to sign up with the lecturer at the beginning of the semester and hand in 5 well-solved exercises.


  • February 13th, 2023:
    Lectures start on Monday February 20th. Exercise classes start on 13th of March.

Course schedule

The schedule is subject to minor modifications. The slides will be uploaded before each lecture.

Week Date Topic
Week 1 20.02.2023 Introduction & visualization
Week 2 27.02.2023 Visualization & outliers
Week 3 06.03.2023 Principal component analysis 1
Week 4 13.03.2023 Principal component analysis 2
Week 5 20.03.2023 Multidimensional scaling
Week 6 27.03.2023 Multidimensional scaling continued / Cluster analysis
Week 7 03.04.2023 Cluster analysis continued
Week 8 24.04.2023 Factor Analysis
Week 9 08.05.2023 Classification 1: discriminant analysis & logistic regression
Week 10 15.05.2023 Extending univariate methods
Week 11 22.05.2023 Classification 2: trees & random forest


  • Everitt and Hothorn (2011), An Introduction to Applied Multivariate Analysis with R, Springer
  • James, Witten, Hastie, and Tibshirani (2013), An Introduction to Statistical Learning, Springer
  • P. Dalgaard (2008), Introductory Statistics with R, Springer

Exercise classes

Exercises will be held in person in HG E1.1 bi-weekly, but on an irregular schedule (see blow and/or Moodle).

There is no testat requirement for students who take the exam. PhD students who do not take the exam but would like to obtain a testat should send their solutions to the assistants by email no later than one week after the exercise class.

Exercises Date of exercise class
Series 1 13.03.2023
Series 2 27.03.2023
Series 3 17.04.2023
Series 4 08.05.2023
Series 5 15.05.2023
Series 6 22.05.2023