Stefan Th. Gries 
Teaching at the University of California, Santa Barbara 
Ling 204: Statistical methodology (W2020)
Syllabus and overview 
This course is a more advanced course on statistical modeling with an emphasis on more sophisticated aspects of regression modeling and other multivariate methods; it presupposes a good understanding of the second edition (2013) of my 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 parts are then devoted to influential data points and validation approaches as well as classification/regression trees and random forests. We use the open source software tool R . 


 

