Statistical Modelling
Autumn semester 2019
General information
Lecturer  Christina HeinzeDeml 

Assistants  Jinzhou Li, Drago Plecko 
Lectures  Wed 810 HG G 5 
Thu 1315 HG D 1.2  
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, highdimensional 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 handson experience with this. You will also learn to interpret and critique regression analyses done by others.
Announcements
 October 31st 2019:
In December, there will be some deviations from the usual schedule: December 5th: Lecture instead of exercise class
 December 11th + 18th: No class
 December 12th: Exercise class
 December 19th: Question hour  questions can be sent to the assistants beforehand
 September 17th 2019:
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 if you bring a laptop to class. 
August 23rd 2019:
Starting on September 26th, 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. You need to bring your own laptop for solving the R questions. On Wednesdays there will be a lecture every week and the class on Thursday will alternate between lectures and exercise sessions (exceptions will be announced). Please check this course website regularly for announcements regarding the schedule. The first lecture will be on September 18th.
Course materials
The datasets used in the R scripts shown during the lectures can be found here.
Week  Topic 

Week 1  I  Introduction

Week 1  II  Classical linear model

Week 2  Classical linear model

Week 3  I  Classical linear model

Week 3  II  Classical linear model

Week 4  Hypothesis testing

Week 5  I  Hypothesis testing and confidence intervals

Week 5  II  Confidence intervals and model selection

Week 6  Model selection

Week 7  I  Model diagnostics

Week 7  II  Model diagnostics

Week 8  General linear model, weighted least squares and instrumental variables 
Week 9  I  Instrumental variables and penalized regression

Week 9  II  Penalized regression

Week 10  Robust regression 
Week 11  I  Generalized linear models

Week 11  II  Generalized linear models

Week 12  I  Generalized linear models and nonparametric regression 
Week 12  II  Nonparametric regression

Software
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 26th will feature an R tutorial with some exercises. Please install R and RStudio and bring your laptop to the exercise classes.
Series
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 email your solutions to the assistants or place them in the corresponding tray in HG J 68. Students who need ECTS credit points have to take the exam.
Exercises  Solutions  Due date 

Series 1  Solutions 1  08.10.2019 
Series 2  Solutions 2  20.10.2019 
Series 3  Solutions 3  5.11.2019 
Series 4  Solutions 4  20.11.2019 
Series 5  Solutions 5  11.12.2019 
Series 6  Solutions 6  19.12.2019 
Literature
 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 by Peter Bühlmann, Nicolai Meinshausen and HansRudolf Künsch.
 S. Weisberg (2005). Applied Linear Regression. 3rd edition, Wiley.