Stefan Th. Gries 
Teaching at the University of California, Santa Barbara 
Ling 204: Statistical methodology (W2024)
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 mixedeffects (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 . 


 

