Universität Passau
35621 Lecture: Computational Statistics - Regression in R - Details
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General information

Course number 35621
Semester SoSe 21
Current number of participants 138
expected number of participants 20
Home institute Lehreinheit für Computergestützte Statistik und Mathematik
participating institutes Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Courses type Lecture in category Teaching
First date Tue., 13.04.2021 10:00 - 12:00 Uhr, Room: (Online (Zoom))
The course aims at students with a basic knowledge in statistics and complements some of the topics treated in 'Methods in Econometrics I and II'.
Learning organization
Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained R-exercises and by presenting/explaining code snippets.
Performance record
Final exam (60 minutes); R-skills are certified via a certificate when the final exam is passed.
  • Kleiber, C. & A. Zeileis (2008), Applied Econometrics with R, Springer.
  • Field, A. & Miles, J. & Field, Z. (2012), Discovering Statistics using R, SAGE.
  • Wooldridge, J. (2013), Introductory Econometrics, 5Ed., South Western.
  • Greene, W.H. (2012), Econometric Analysis, Pearson.
  • Ligges, U. (2008), Programmieren mit R, Springer.
Usually each winter term
The course aims at providing students with a basic understanding, which regression models to employ for certain types of variables and data structures. A further course objective is to enable students to choose between competing model specifications and to judge if a given model is (severely) misspecified.
2 SWS (30 h attendance, 45-60 h self-study)
Course is taught in English.
ECTS points

Course location / Course dates

(Online (Zoom)) Tuesday: 10:00 - 12:00, weekly (13x)

Fields of study

This information on acceptance for credit of modules for individual degree programmes is not binding; please check the module catalogue at the Faculty of Computer Science and Mathematics to confirm that this module can be counted towards your degree.


The course focuses on estimating regression models and evaluating the estimated specifications with the statistical software R. Model evaluation procedures discussed in class range from graphical methods, classic validation techniques and tests to simulation-based approaches. The effects of variables being measured on different scales and variable transformations are discussed. Dealing with different data structures such as cross-sections, time series, and panel data is also covered in class.