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401-0649-00L 5 Credits BSC , DR , MSC , WBZ D-HEST , D-MATH , D-BIOL , D-INFK , D-ERDW , D-ITET , D-PHYS , D-USYS
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Applied Statistical Regression

Lecturers & Examiners: Dr. Marcel Dettling
VVZ CR n/a

Last Updated: 2026-06-01 11:30:39

Abstract

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.

Objective

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

Content

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.

Resources

Lecture Notes

A script will be available.

Literature

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

General Information

Language
English
Levels
BSC , DR , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Written aid of max. 20 pages with arbitrary content (format A4, 10 sheets printed on front and back or 20 pages only front-printed, can be machine-written), and a pocket calculator without communication possibilities.
Mathematics Bachelor / Mathematics Master: see notice in the catalogue data.

Course Components

Type Title Time & Place Hours
lecture Applied Statistical Regression
  • Mon 08:15-10:00 (HG E 1.2)
2 h weekly
exercise Applied Statistical Regression
Mon 10-12 might not work for all different programmes where this course is offered. However, the relevant parts of the exercises will be live-streamed and recorded for later viewing.
  • Mon 10:15-12:00 (HG E 1.2)
1 h weekly

Offered In