Syllabus and overview

This course is a selective introduction to predictive modeling applications in linguistics. We will work our way through a variety of regression modeling applications: linear regression, binary logistic regression, multinomial, and ordinal regression models. Then, one session will be concerned with model diagnostics and, perhaps, model validation. Finally, there are sessions on similarity-based modeling and on classification and regression trees. Like its prerequisite course Ling 201/104, this course is based on the third edition of my textbook Statistics for linguistics with R: a practical introduction (2021) and uses the open source programming language R.

Downloads for class sessions
(files will be made available when appropriate)

Folder for the whole course
Navigator

Additional files to be added to that folder per session:
For session 01: the answer key
For session 03: the answer key
For session 04: the answer key
For session 04: the answer key
For session 06: the answer key
For session 07: the answer key
For session 08: the qmd file for the practice session, the corresponding Google doc, and the answer key
For session 09: the qmd file for the practice session, the corresponding Google doc, and the answer key
For session 10: the qmd file for the practice session and the answer key

Assignments

Graded assignments: Pick two of these 10 assignments and analyze the data comprehensively (as if they were your own); note the difficulty levels, which also correspond to weights: If you do equally well on two assignments with different difficulty levels, you'll get more points for the one with the higher difficulty level.
Deadline for final submission: 17 June 2025, 23:59:59 PDST (no extensions!)

Links to relevant software and sites

R (at least version 4.4.3 and make sure you update all packages before the course starts)
RStudio (at least version 2025.x); installing Quarto might also be useful
my 2021 statistics textbook, its companion website, and its StatForLing with R newsgroup, which I moderate.