notes.pdf: pages 1-8 (especially the part on estimability)

The required textbook is:
McCullagh and Nelder, Generalized Linear Models, 2nd.~ed. (M&N)

M&N: chapter 1: This is a simple overview and introduction.

M&N: chapter 3: This is material covered in BIOS 262 - there is nothing new here. Think of
this as a BIOS 262 refresher. Use this chapter to become familiar with the "language" and
tone of M&N. (Yes, you can read chapter 3 before chapter 2!)

M&N: chapter 2: This is an introduction to generalized linear models. It is the basic
foundation for everything that follows.

M&N Chapter 4: Read section 4.6 before 4.5. Overdispersion is not well-covered in M&N, so 
I'll supplement it with additional material.

notes.pdf: pages 1, 9-12 

Familiarize yourself with the documentation of SAS PROC GENMOD and PROC LOGISTIC.
Manuals are available online:
  http://support.sas.com/documentation/onlinedoc/sas9doc.html
For those who use R, do the same for function "glm", help(glm).

M&N Chapters 5 and 6: 
These two chapters can be confusing. Except for sections 6.2 and 6.3, they cover multinomial 
response models. 
Here's how to think of the various sections:
5.1 the systematic component: can be difficult when taken alone without reference to 
examples - will become clear after we cover chapter 6!
5.3 the random component (the multinomial distribution)
5.4, 4.5 estimation
5.6 examples
6.4-6.6 more multinomial models

6.2-6.3 models for counts and generally for variance proportional to the mean.
These two sections are not part of the multinomial story.


em.pdf: The EM algorithm (description and an example).

Chapter 7

Chapter 9

ee3a.pdf 

ld05a.pdf

ghq.pdf [optional]


Analysis of Longitudinal Data (Optional Textbook: Diggle, Heagerty, Liang \& Zeger, Analysis
of Longitudinal Data, 2nd.~ed.)
   Chapter 1: Introduction
   Chapter 2: Design
   Chapter 3: Exploration
   Chapter 4: General Linear Models
   Chapter 6: ANOVA
   Chapter 7: Generalized Linear Models
   Chapter 8: Marginal models
   Chapter 9: Random effects models