Syllabus and overview

This course is a more advanced course on statistical modeling with an emphasis on various kinds of regression modeling; it presupposes a good understanding of the third edition (2021) of my textbook Statistics for Linguistics with R: [...]. We begin with a first recap of linear and generalized linear regression modeling. We then discuss the use of contrasts and general linear hypothesis tests for linear and generalized linear regression models, followed by some ideas on how to explore curvature in data (regressions with breakpoints, polynomial regressions, and generalized additive models). This is followed by a larger chunk on linear and generalized linear mixed-effects (or multilevel) modeling, where we reanalyze published data and discuss numerical and visual exploration of regression results. The last session is then devoted to random forests. Obviously, we use the open source software tool R .

Course downloads

Folder for the whole course

HTML for session 01
HTML for session 02
HTML for session 03
HTML for session 04
HTML for session 06
HTML for session 08
HTML for session 09
HTML for session 10

Software

R (from CRAN) (make sure you have at least version 4.3.2)
RStudio (make sure you have at least version 2023.09.1-494)