Vorlesung: 35621 Computational Statistics - Regression in R - Details

Vorlesung: 35621 Computational Statistics - Regression in R - Details

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Veranstaltungsname Vorlesung: 35621 Computational Statistics - Regression in R
Untertitel
Veranstaltungsnummer 35621
Semester WiSe 23/24
Aktuelle Anzahl der Teilnehmenden 98
Heimat-Einrichtung Lehreinheit für Computergestützte Statistik und Mathematik
beteiligte Einrichtungen Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Veranstaltungstyp Vorlesung in der Kategorie Lehre (mit Prüfung)
Erster Termin Dienstag, 17.10.2023 10:00 - 12:00 Uhr
Art/Form online
Voraussetzungen
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'.
Lernorganisation
Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained R-exercises and by presenting/explaining code snippets.
Leistungsnachweis
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)
Sonstiges
Course is taught in English.
ECTS-Punkte
3

Räume und Zeiten

Keine Raumangabe
Dienstag: 10:00 - 12:00, wöchentlich

Studienbereiche

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Modulzuordnungen

Kommentar/Beschreibung

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.