At the end of the course, the successful student should be able 
to do the following:
1. Modeling and inference for contingency table using log-
linear  models;
2. Using graphical models and related ideas to organize log-
linear modeling;
3. Perform logistic regression analyses with multiple 
predictors;
4. Compare different models with respect to their predictive 
power;
5. Use graphical and other methods for assessing the adequacy 
of  the fitted model;
6. Interpret each coefficient in the model;
7. Describe the methods and results to a non-statistical reader.

Analysis of contingency tables; log-linear models; logistic 
regression; probit model; Goodness-of-fit tests; model 
selection; zero-inflated counts; graphical models; causal 
inference