107.348 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, UE, 1.0h, 2.0EC


  • Semester hours: 1.0
  • Credits: 2.0
  • Type: UE 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

Theoretical and practical examples using R.

Teaching methods

Theoretical and practical examples using R.

Mode of examination


Additional information

The prerequisite for the course is 

105.596 VO Econometrics 1: Linear Models




Examination modalities

Continuous assessment via oral examination and regular homework tasks throughout the semester. A data analysis project, that will be presented at the end of the semester, will count for 1/3 of the grade.

Course registration

Begin End Deregistration end
24.02.2020 09:00 31.03.2020 23:59 31.03.2020 23:59


Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified


No lecture notes are available.

Previous knowledge

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

Preceding courses


  • Attendance Required!