Lesson 13: Course Summary and Additional Topics II
Course Summary
Lesson 1  Overview and ReviewOverview of probability and inference(ref: Wasserman(2004)) The basic problem we study in probability: Given a data generating process, what are the properties of the outcomes? The basic problem of statistical inference: Given the outcomes, what can we say about the process that generated the data? For example, given the observed cell counts, what are the true cell probabilities? Discrete probability & Statistical Inference (Lecture 1 )
We applied these three basic inferential problems to significance testing and modeling of oneway, twoway, threeway and kway tables, and discrete response as a function of both discrete and continuous data. 
Lesson 2  Oneway Tables

Lesson 3  Twoway Tables

Lesson 5  Threeway Tables

Lesson 6  Logistic Regression

Lesson 8  Multinomial Logistic Regression ModelsForming Logits BaslineLogit Model Adjacent Logit Model Proportional Odds Cumulative Logit Model 
Lesson 9  Poisson RegressionIntroduction to Generalized Linear Model (GLM) Poisson Regression for Count Data Poisson Regression for Rate Data Negative Binomial Model – an alternative to Poisson Regression when data are more dispersed 
Lesson 10  Loglinear modelsWhen discussing models, we need to keep in mind

Lesson 12  Advanced Topics IOther modeling relevant to categorical data are
Introduction to GEE is covered in Lesson 12 
Lesson 13  Summary & Additional Topics IICourse SummaryReview of Model SelectionRef. Ch. 9 (Agresti), and more advanced topics on model selection with ordinal data are in Sec. 9.4 and 9.5. One response variable:
Two or more response variables:
Model selection strategies with Loglinear models
Classes of loglinear models:
Introduction to Graphical Models References for Causal Inference 