Lecture: 35621 Computational Statistics - Regression in R - Details

Lecture: 35621 Computational Statistics - Regression in R - Details

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General information

Course name Lecture: 35621 Computational Statistics - Regression in R
Subtitle
Course number 35621
Semester WiSe 24/25
Current number of participants 84
Home institute Lehreinheit für Computergestützte Statistik und Mathematik
participating institutes Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Courses type Lecture in category Lehre (mit Prüfung)
Next date Tuesday, 28.01.2025 10:00 - 12:00 Uhr
Type/Form online
Pre-requisites
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 organisation
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.
SWS
2
Literatur
  • 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.
Qualifikationsziele
The course focuses on estimating and evaluating regression models 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 course includes model selection (i.e., finding the best model from a large number of possible models), model validation (i.e., checking whether the presumed best specification satisfies the model assumptions), and model interpretation (for linearly and/or nonlinearly transformed variables). Additionally, different data structures such as cross-sections, time series, and panel data are shortly discussed.
Workload
2 SWS (30 h attendance, 45-60 h self-study)
Miscellanea
Course is taught in English.
ECTS points
3

Rooms and times

No room preference
Tuesday: 10:00 - 12:00, weekly

Fields of study

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Module assignments

Comment/Description

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.