Applied Multivariate Statistics
Spring semester 2022
|Lectures||Mondays 14.15-16.00 (HG E 1.2.)|
|Exercises||Mondays 10.15-12.00 (HG E 1.1., bi-weekly irregular schedule)|
|Course catalogue data||>>|
|Course Moodle page||>>|
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.
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
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.
January 18th, 2022:
More information about this course will be published soon.
February 18th, 2022:
Lectures start on Monday February 21st. Exercise classes start on Monday March 14th.
The schedule is subject to minor modifications. The slides will be uploaded before each lecture.
|Week 1||21.02.2022||Introduction & visualization
|Week 2||28.02.2022||Visualization & outliers
|Week 3||07.03.2022||Principal component analysis 1
|Week 4||14.03.2022||Principal component analysis 2
|Week 5||21.03.2022||Multidimensional scaling
|Week 6||28.03.2022||Multidimensional scaling continued / Cluster analysis
|Week 7||11.04.2022||Cluster analysis continued
|Week 8||25.04.2022||Factor Analysis
|Week 9||02.05.2022||Classification 1: discriminant analysis & logistic regression
|Week 10||09.05.2022||Extending univariate methods
|Week 11||16.05.2022||Classification 2: trees & random forest
|Week 12||23.05.2022||Models for repeated measures data
- 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
Exercises will be held in person in HG E7 bi-weekly, but on an irregular schedule (see blow and/or Moodle).
Series and solutions
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 discussion in the exercise class.
|Exercises||Solutions||Date of exercise class||R code|
|<!a href="https://polybox.ethz.ch/index.php/s/hfw3hDwr4uxsZY2/download">Series 1||Solutions 1||14.03.2022|
|Series 2||Solutions 2||28.03.2022||
|Series 3||Solutions 3||11.04.2022||
|Series 4||Solutions 4||02.05.2022||
|Series 5||Solutions 5||16.05.2022||
|Series 6||Solutions 6||30.05.2022||