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Modulbezeichnung:

Bayesian Regression Models: Theory and Applications in Marketing (and other Disciplines)

Modulbezeichnung (englisch):

Bayesian Regression Models: Theory and Applications in Marketing (and other Disciplines)

Modulnummer:

06-021-DS16-H-0518

Niveau:

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Geberstudiengang:

Typ:

Modul

Federführende Fakultät/Sprachenzentrum:

Wirtschaftswissenschaftliche Fakultät

Modulverantwortliche/r:

Büschken, Joachim

Prüfende:

Leistungspunkte (ECTS-Punkte):

Kompetenzen:

- To obtain a general understanding of Bayesian regression models and their applications to typical analytical problems in marketing (and other disciplines).
- To be able to apply Bayesian regression models to various data types in a meaningful way, to be able to assess result and model performance

Inhalte/Themen:

- Introduction to Bayesian modeling in general and Bayesian regression models in particular
- Application to standard data, ordinal data and binary data
- Model-based data analysis, assessment of performance of models

Formale Voraussetzungen für die Teilnahme:

Empfohlene Voraussetzungen:

- Participants have good knowledge of multivariate statistics
- Participants have basic knowledge of R (or an equivalent programming language/environment such as Python, Matlab or GAUSS).

Lehr- und Lernformen/Lehrveranstaltungstypen:

- Lectures
- In-class data analysis
- Project presentations

Voraussetzungen für die Vergabe von ECTS-Punkten:

- Each participant is assigned an empirical project to work on. This project is concerned with the application of a models to data
- Participants document results from this application in a draft of research paper.
- Key results from the analysis are presented in class by each participant and discussed.

Zeitaufwand/Verteilung der ECTS-Punkte innerhalb des Moduls:

21 h = Class hours (participation is mandatory, absence is to be accounted for)
49 h = Literature/reading
80 h = Work on assigned project (data analysis, paper, presentation, discussion)
150 h = total

Modulnote:

- Presentation of a project in which topic models are applied to text data (30%)
- Project documentation (70%)

Lehr- und Lernmethode:

Polyvalenz mit anderen Studiengängen/Hinweise zur Zugänglichkeit:

Turnus des Angebots:

Beteiligte Fachgebiete:

Bemerkung: