Here, you can find code for in-person tutorials, .pdfs for practice, as well as problem sets.

This course extends what you did in the previous term by focusing on non-linear model forms for the outcome variable. These are typically called ”generalized linear models” (GLMs), although for historical reasons people in the social sciences call them ”maximum likelihood models”. The principle we will care about is how to adapt the standard linear model that you know so that a broader class of outcome variables can be accommodated. These include: counts, dichotomous outcomes, bounded variables, and more. There is a strong theoretical basis for the models that we will use. Also, the bulk of the learning in the course will take place outside of the classroom by reading, practicing using statistical software, replicating the work of others, and doing problem sets.

• Jeffrey Ziegler, Office Hours: W/Th 13:00-14:00 Zoom
• Martyn Egan

• Computer with Windows/Mac/Linux OS (no Chrome books)
• Required software:
  • R (version 4+) - statistical programming language
  • RStudio - integrated development environment for R
  • Latex - typesetting programming language
  • TexStudio - integrated development environment for Latex
  • Git - version control system
  • GitHub - git-based online platform for code hosting

• Course website
• Course repository