Applied Statistical Regression

Autumn semester 2016

General information

Lecturer Marcel Dettling
Assistants Marco Eigenmann, Niklas Pfister
Lectures Mon 08-10 HG E 1.2 >>
Exercises Mon 10-12 (bi-weekly) HG D 7.2 >>
Course catalogue data >>

Faraway (2005): Linear Models with R
Faraway (2006): Extending the Linear Model with R
Draper & Smith (1998): Applied Regression Analysis
Fox (2008): Applied Regression Analysis and GLMs
Montgomery et al. (2006): Introduction to Linear Regression Analysis

Course content


This course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis.


The students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling.


The course starts with the basics of linear modeling, and then proceeds to parameter estimation, tests, confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, multicollinearity problems and model interpretation, as well as general modeling strategies.

The last third of the course is dedicated to an introduction to generalized linear models: this includes the generalized additive model, logistic regression for binary response variables, binomial regression for grouped data and poisson regression for count data.


The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held.


  • September 1st, 2016:
    Beginning of lecture: Monday, 26/09/2016. (Exercises start on 26/09/2016 at 10:15 in room HG D 7.2, with a special introduction to the "R" software)
  • Question hour:
    On January 13th & 20th, 2017, 2 - 3 pm in HG G 19.1
  • Exam Review:
    On March 3rd, 2017, 12 - 1 pm in HG G 19.2

Course materials

Lecture notes

The lecture notes are available here.

Course organisation

The course outline can be found here. Further detail is given in the following table.

Week Topic
Week 1 (19.09.2016) No lecture
Week 2 (26.09.2016) Linear Modeling and Smoothing
Week 3 (03.10.2016) Simple Linear Regression: Fitting and Inference
Week 4 (10.10.2016) Curvilinear Models, Variable Transformations
Week 5 (17.10.2016) Multiple Linear Regression: Model and Fitting
Week 6 (24.10.2016) Multiple Linear Regression: Inference and Prediction
Week 7 (31.10.2016) Extensions: Categorical Variables, Interactions
Week 8 (07.11.2016) Model Diagnostics: Standard Residual Plots
Week 9 (14.11.2016) Model Diagnostics: Advanced Techniques
Week 10 (21.11.2016) Multicollinearity and Variable Selection
Week 11 (28.11.2016) Modeling Strategies, Cross Validation
Week 12 (05.12.2016) Generalized Additive Modeling (GAM)
Week 13 (12.12.2016) Generalized Linear Modeling (GLM)
  • Slides
Week 13 (19.12.2016) Grouped Data, Poisson Regression
  • Slides

Exercise classes

Exercises will be held roughly bi-weekly, see below. On these dates, the exercise classes will take place from 10:15 to 11:55 in HG D 7.2

  • September 26, 2016
  • October 10, 2016
  • October 24, 2016
  • November 7, 2016
  • November 21, 2016
  • December 5, 2016
  • December 19, 2016

Series and solutions

The solved exercises should be placed in the corresponding tray in HG J68 on the due date by 4pm at the latest. Only solutions to the exercises with your most important findings and answers shall be handed in, but no R script files and lengthy compilations of output or figures.

Exercises Solutions Date
Series 1 Solutions 1 NA
Series 2 Solutions 2 October 3, 2016
Series 3 Solutions 3 October 17, 2016
Series 4 Solutions 4 October 31, 2016
Series 5 Solutions 5 November 14, 2016
Series 6 Solutions 6 November 28, 2016
Series 7 Solutions 7 December 12, 2016
Series 8 Solutions 8 NA

Help with R

On Monday, September 26st 2016, there will be an introduction to the statistical software R during the exercise class from 10:15 to 11:55. The tutorial can be downloaded here. In addition, you will work on Series 1 during this class which further introduces you to R.

R homepage
R studio homepage
Getting help with R
Online R course (in German)
Try R