Regression Analysis II – Linear Models

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).

Course leader

Timothy McDaniel

Target group

Master | PhD | Postdoc | Professional