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.
Literature
- "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.
Exam
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.
Announcements
-
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
|
Literature:
- 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 |