105.725 General Regression Models
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VU, 3.0h, 5.0EC


  • Semester hours: 3.0
  • Credits: 5.0
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to (i) apply modern regression/statistical learning methods to build predictive models, (ii) select and validate statistical learning models, (iii) assess model fit and error and (iv) use the R language for modern regression and data analysis.

Subject of course

Simple Linear Regression, Multiple Regression, Regression Diagnostics, Generalized Linear Models, Nonparametric Regression, Penalized Regression, Model Selection and Dimension Reduction.

Teaching methods

The material will be presented in lecture slides in conjunction with derivations on board. Course related information and material, including notes and details about course assignments and exams will be posted in TUWEL.

Lecture notes by Dr. Gurker can be downloaded from the class website. Reference books are Applied Linear Statistical Models, 5th Edition by Kutner et al.; Practical Regression and ANOVA Using R at ftp://cran.r-project.org/pub/R/doc/contrib/Faraway-PRA.pdf ; Introduction to Statistical Learning with applications in R by James, Witten, Tibshirani & Hastie; Elements of Statistical Learning by Hastie, Tibshirani & Friedman.

The statistical software we will use is R. It can be downloaded from the R home page . RStudio offers a GUI R platform.


Mode of examination


Additional information

The prerequisite for the course is 

105.596 VO Econometrics 1: Linear Models



Course dates

Thu09:00 - 11:3012.03.2020FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Allgemeine Regressionsmodelle

Examination modalities

The final exam will be written and will cover all topics presented in class.

Course registration

Begin End Deregistration end
05.03.2020 15:00 25.03.2020 23:59 25.03.2020 23:59



No lecture notes are available.

Previous knowledge

Basic probability and statistics; Linear algebra; Econometrics 1: Linear Models.

Preceding courses


  • Attendance Required!