In regression analysis, we examine the relationship between a random response variable and several other explanatory variables. In this class, we consider the theory of linear regression with one or more explanatory variables. Moreover, we also study robust methods, generalized linear models, model choice, high-dimensional linear models, nonlinear models and nonparametric methods. Several numerical examples will illustrate the theory. You will learn to perform a regression analysis and interpret the results correctly. We will use the statistical software R to get hands-on experience with this.
September 13th 2022
First lecture on September 22nd
The first lecture is on Thursday, September 22nd, starting at 14:15 in HG E 1.1.
Frequency of lectures and exercises
There will be a lecture every Monday at 10:15 in ML D 28. Lectures and exercises alternate on Thursdays at 14:15 in HG E 1.1. Exercises and will take place each second week, starting on on September 29th. Exceptions will be announced during the lecture and on this website.
Content of the first exercises session
The first exercise session will include an introduction to the statistical programming language R with some exercises. In the exercise sessions, you can solve the R problems, the series and ask questions. You need to bring your own laptop for solving the R questions.
Recording of lectures
The lectures will not be streamed via Zoom. The lectures will be recorded and uploaded to the ETH video portal after each lecture.
- First lecture on September 22nd
October 8th 2022
No class and no exercise class on October 10th and October 13th.
Please read chapters 1.6.1, 1.6.3, and 1.6.4 of the script and solve the exercise series 2. If you have any questions regarding the exercises, use the Moodle Overflow forum or send an email to Malte Londschien. The reading material will be discussed in the next lecture on October 17th.
- No class and no exercise class on October 10th and October 13th.
November 8th 2022
Switch of lecture and exercise Thursday, November 10th and Monday, November 14th.
There will be a lecture on the next Thursday, November 10th. The exercise class will take place on Monday, November 14th. The rooms remain the same.
- Switch of lecture and exercise Thursday, November 10th and Monday, November 14th.
December 5th 2022
Two more exams, both from 2021, have been made available.
- Two more exams, both from 2021, have been made available.
- Lecture recordings: See the ETH video portal.
- Moodle: Please use the Q&A Moodle Overflow Forum to ask questions.
- The datasets used in the R scripts shown during the lectures can be found here (old).
- Four old exams are made available from 2018/2019 and 2021. Note: Some of the covered material may differ.
- Scans of the visualizer will be uploaded here.
- Slides will be uploaded here.
|Week 2||Classical linear model|
|Week 3||Classical linear model|
|Week 4||Classical linear model
|Week 5||Classical linear model|
|Week 6||Classical linear model|
|Week 7||Classical linear model|
|Week 8||Classical linear model|
|Week 9||Classical linear model|
|Week 10||Generalized linear models|
|Week 11||Generalized linear models|
|Week 12||Nonparametric models|
Examples in the lecture as well as solutions to the exercises will be based on the statistical software R. R 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. An R Tutorial can be found here. The most commonly used editor for R is RStudio which can be downloaded from here.
Exercise classes will take place every other week on Thursdays. The first exercise class on September 29th will feature an R tutorial with some exercises. Please install R and RStudio and bring your laptop to the exercise classes.
If you are a PhD student who needs ETH credit points, the submission of four exercise series is mandatory. If this applies to you, please contact the assistant.
|R Series||R Solutions|
|Series 1||Solutions 1|
|Series 2||Solutions 2|
|Series 3||Solutions 3|
|Series 4||Solutions 4|
|Series 5||Solutions 5|
- L. Fahrmeir, T. Kneib, S. Lang and B. Marx (2013), Regression - Models, Methods and Applications. Springer.
- T. Hastie, R. Tibshirani, and J. Friedman (2009), The Elements of Statistical Learning [ESL]. 2nd edition, Springer.
- G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning: with Applications in R [ISLR]. Springer.
- Script (to be updated) by Peter Bühlmann, Nicolai Meinshausen and Hans-Rudolf Künsch.
- additional Notes by Peter Bühlmann on Heteroscedastic errors and robust inference.
- S. Weisberg (2005). Applied Linear Regression. 3rd edition, Wiley.