Germán Rodríguez

Generalized Linear Models
Princeton University
The notes are offered in two formats: HTML and PDF.
I expect most of you will want to *print* the notes, in which case you can use the links
below to access the PDF file for each chapter. If you are *browsing* use the table of contents
to jump directly to each chapter and section in HTML format. For more details on these formats
please see the discussion below.

The list above has two extensions to the original notes: an addendum on Over-Dispersed Count Data, which describes models with extra-Poisson variation and negative binomial regression, and a brief discussion of models for longitudinal and clustered data. Because of these additions we now skip Chapter 5.

No, there is no Chapter 1 ... yet. One day I will write an introduction to the course and that will be Chapter 1.

It turns out that making the lecture notes available on the web was a bit of a challenge because web browsers were designed to render text and graphs but not equations, which are often shown using bulky graphs or translated into text with less than ideal results.

The notes were written using LaTeX, which produces postscript or PDF, so the simplest solution was to post the generated PDF files, one per chapter. This format is best for printing the notes. Our PDF files are now smaller and look better on the screen that before. To view the files you will need Adobe Reader, unless you use a browser like Chrome that can render PDF natively.

I also wanted to offer a version in HTML, which is best for browsing. Fortunately, there is now an excellent Javascript library called MathJax that does a superb job of rendering mathematics on the web. I combined this library with a custom program that translates the rest of the LaTeX source into HTML, leaving all equations to MathJax. This approach supersedes the original pages, which had been generated using TtH, a program that worked much better than alternatives such as LaTeX2Html and served us well for many years.

RodrÃguez, G. (2007). *Lecture Notes on Generalized Linear Models*.
URL: http://data.princeton.edu/wws509/notes/