| Syllabus and overview |
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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 two sessions are devoted to random forests and the exploratory method of hierarchical cluster analysis. Obviously, we use the open source software tool R  . |
Course downloads |
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Folder to download & unzip
Session 01: the Google doc, the qmd file for class, and the HTML report
Session 02: the Google doc, the qmd file for class, and the HTML report
Session 03: the Google doc, the qmd file for class, and the HTML report
Session 04: the Google doc, the qmd file for class, and the HTML report
Session 05: the Google doc, the qmd file for class, and the HTML report
Session 06: the Google doc, the qmd file for class, and the HTML report
Session 07: the Google doc, the qmd file for class, and the HTML report
Session 09: the Google doc, the qmd file for class, and the HTML report
Session 10: the Google doc, the qmd file for class, and the HTML report
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Software to have installed |
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R (from CRAN) (make sure you have at least version 4.5.x)
RStudio (make sure you have at least version 2025.x)
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