Statistical Modelling

Autumn semester 2021

General information

Lecturer Christina Heinze-Deml
Assistant Juan Gamella
Lectures Mon 10-12 ML D 28
Thu 14-16 HG E 1.1
Course catalogue data >>

Course content

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 17th 2021:
    • The lectures will be held in hybrid mode: The lectures will be held in presence and also be livestreamed and recorded via zoom. The zoom details can be found on Moodle.
    • Starting on September 30th, the exercise classes will take place every second Thursday. 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. On Mondays there will be a lecture every week and the class on Thursday will alternate between lectures and exercise sessions (exceptions will be announced).
    • We will be using the ETH EduApp during the lectures for clicker questions. Please install it on your phone or tablet (iOS, Android). If this is not possible, you can also access the EduApp via the Web App.

Course materials

  • Zoom link and lecture recordings: See Moodle.
  • The handwritten notes made during the lectures can be found here. If you have trouble accessing these, please let us know.
  • The datasets used in the R scripts shown during the lectures can be found here.
  • Two old exams are made available here. Note: The course was then taught by a different lecturer and some of the covered material differs.
Week Topic
Week 1 Introduction
  • Slides
  • R code
  • Reading: Fahrmeir et al.: 1.2, 2.1 – 2.2.1, 3.1 – 3.1.1
Week 2 Classical linear model
Week 2 - exercise Introduction to R
Week 3 - I Classical linear model
Week 3 - II Classical linear model
Week 4 - I Classical linear model
Week 4 - exercise Exercise
Week 5 - I Hypothesis testing
Week 5 - II Hypothesis testing and confidence intervals
Week 6 Multiple testing I
Week 6 - exercise Exercise
Week 7 - I Multiple testing II
Week 7 - II Prediction intervals and model selection
Week 8 - I Model selection
Week 8 - exercise Tutorial - Linear algebra review: Positive-definite matrices
Week 9 - I Model diagnostics
Week 9 - II Model diagnostics
Week 10 - I General linear model, weighted least squares and robust regression
Week 10 - II Tutorial - clicker questions on multiple testing
Week 11 - I Cancelled - no class
Week 11 - II Robust regression and generalized linear models
Week 12- I Generalized linear models
Week 12 - II Tutorial: ROC curves
Week 13 - I Generalized linear models and penalized regression
Week 13 - II Penalized regression
Week 14 - I Knockoffs
Week 14 - II Tutorial: Subdifferentials, exponential families


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

Exercise classes will take place every other week on Thursdays. The first exercise class on September 30th will feature an R tutorial with some exercises. Please install R and RStudio and bring your laptop to the exercise classes.

If you would like the TA to go over a particular topic or exercise during the tutorial, please write an email or post your question here in advance.

Would you like something done differently? You can leave anonymous feedback here.


Exercises Solutions Due date
R Series R Solutions None
Series 1 Solutions 1 October 14th 2021
Series 2 Solutions 2 October 28th 2021
Series 3 Solutions 3 (updated Nov 17) 14:00, November 9th 2021
Series 4 (updated Nov 12) Solutions 4 14:00, November 23rd 2021
Series 5 Solutions 5 14:00, November 30th 2021
Series 6 Solutions 6 14:00, December 21st 2021
Series 7 Solutions 7 None