Regression Analysis II – Linear Models



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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

The goal is to develop an applied and intuitive (not purely theoretical or mathematical) understanding of the topics and procedures, so that participants can use them in their own research and also understand the work of others. Whenever possible presentations will be in “Words,” “Picture,” and “Math” languages in order to appeal to a variety of learning styles. Advanced regression topics will be covered only after the foundations have been established. The ordinary least squares multiple regression topics that will be covered include: -Various F‑tests (e.g., group significance test; Chow test; relative importance of variables and groups of variables; comparison of overall model performance) -Categorical independent variables (e.g., new tests for “Intervalness” and “Collapsing”) -Dichotomous dependent variables: Logit and Probit analysis -Outliers, influence, and leverage -Advanced diagnostic plots and graphical techniques -Matrix algebra: A quick primer (Optional) -Regression models… now from a matrix perspective -Heteroskedasticity: Definition, consequences, detection, and correction -Autocorrelation: Definition, consequences, detection, and correction -Generalized Least Squares (GLS) and Weighted Least Squares (WLS).