Lehrende: Prof. Dr. Martin Elff
Veranstaltungsart: Seminar
Orga-Einheit: Graduate School | ZUGS
Anzeige im Stundenplan:
Semesterwochenstunden: 16
Credits: 4,0
Standort: Campus der Zeppelin Universität
Unterrichtssprache: Englisch
Min. | Max. Teilnehmerzahl: 3 | 12
Inhalte: The course introduces to and discusses regression beyond the ordinary linear model and their application in analyses using the statistical software R. The course contents will be adapted to the needs and interests of course participants discussed before the course, ideally at the beginning of the semester. Potential topics are: • A review of linear regression in R • Visualising regression results • Coding of categorical predictors • Wald-tests and other multi-parameter tests • Logistic regression models in R • Regression models for durations in R • Regression models for event counts in R • Regression models for categorical dependent variables in R • Discrete choice models in R • Multilevel regression models in R • Regression models for panel data in R • Regression models for time series data in R Note that the two-day format of the course allows discussion only a subset of these topics. This is why it is important to discuss your priorities with the course instructor beforehand. Learning objectives After successful completion of the course, participants are expected to be able to • select generalised regression models relevant for their research questions based on the type of dependent variables • apply these models in practical analysis using the software for data analysis and statistical graphics R • interpret output created with this software Literature Agresti, Alan. 2002. Categorical Data Analysis. 2nd ed. New York: Wiley. Fox, John. 2018. An R Companion to Applied Regression. Thousand Oaks und London und New Delhi: Sage. Fox, John. 1997. Applied Regression Analysis, Linear Models, and Related Methods. Thousand Oaks und London und New Delhi: Sage. Ruppert, David, M. P Wand, and R. J Carrol. 2003. Semiparametric Regression. Cambridge: Cambridge University Press. Course assessment • Completion of a practical exercise sheet. 100% attendance
