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Different Aspects of Regression Analysis

Dr. Andreas Groll

Schedule and Venue

Dr. Andreas Groll
Mon 14:00-16:00
Tue 10:00-12:00 (every two weeks)
Quant Lab, Room B 120
Dr. Andreas Groll
Tue 10:00-12:00 (every other week) Quant Lab, Room B 120
Final Exam TBA TBA



The lectures start in the 2nd week of the semester, i.e. on Monday, 20.04.2015.

Course Description

Regression analysis is one of the most used statistical methods for the analysis of empirical problems in econonimc, social and other sciences. A variety of model classes and inference concepts exists, reaching from the classical linear regression to modern non- and semiparamtric regression. The aim of this course is to give an overview of the most important concepts of regression and to give an impression of its flexibility. The following main topics will be covered:

  • Linear regression models
  • Random effects models (mixed models)
  • Time series analysis
  • Generalized linear models


[1] Fahrmeir, L., T. Kneib, and S. Lang (2007). Regression. Berlin: Springer.
[2] Fahrmeir, L. and G. Tutz (2001). Multivariate Statistical Modelling Based on Generalized Linear Models (2nd ed.). New York: Springer.
[3] J. D. Hamilton (1994). Time Series Analysis. Princeton University Press.
Further literature will be announced in the course.

For whom is this course?

Target Participants: Students of the business mathematics bachelor programm.

Pre-requisites:  Stochastic; knowledge in probability theory recommended.

Applicable credits:  business mathematics bachelor programm (WP13).

Lecture Notes

Notes (PDF, 875KB)


The solutions of exercise sheets is not obligatory, but it is highly recommended. Correcting your answers and thinking through the exercises is the best preparation for the exam.

Problem and Answer Sheets

Problem Sheet 1 (PDF, 109KB),  

Final Exams