Instructor	Dipankar Bandyopadhyay Professor Department of Biostatistics, School of Medicine, Virginia Commonwealth University One Capitol Square [OCS], Room # 737 E-mail: dbandyop at vcu dot edu
Teaching Assistants	None
Class Schedule	Tuesday/Thursday 10:00 AM - 11:50 AM OCS, # 5009 (Classroom)
Office Hours	Thursdays [OCS, # 737] @ 2:00 PM - 3:00 PM, or by appointment
Required Texts	A. Agresti: Categorical Data Analysis, 3rd Edition, Wiley, 2013 Errata sheet Anette J. Dobson & Adrian G. Barnett: An Introduction to Generalized Linear Models, 3rd Edition, CRC Press/Chapman & Hall, 2008
General Class Outline and Reading Blocks	Course Outline Introduction (Agresti Chapter 1) Contingency Tables (Agresti Chapters 2, 3) Generalized Linear Models (Agresti Chapter 4)
Grading	Homework assignments + Other Assignments + class participation (40% total) Two midterm exams (20% each)                                          Final exam (20%)

Date	Lecture Title
01/16, 01/18	Class Introduction Lecture 1: Introduction
01/23, 01/25	Lecture 2: Inference on Binomial parameters Lecture 3: Inference on Multinomial Parameters Reading Assignment Lecture 4: Measures of Association and Variance Estimation Computing: Exercise 1; SAS1                  Exercise 2; SAS2                  Program1; Program2
01/30, 02/01	Lecture 5: Contingency table - I Lecture 6: Contingency table - II [Following the book:Notes1, Notes2]
02/06, 02/08, 02/13	Lecture 7: Inference for Contingency Tables - I Lecture 8: Inference for Contingency Tables - II
02/15, 02/20	Testing Independence Ordinal Measures Partitioning On Chi-square partitioning Midterm samples Midterm 1 [on 03/01],
02/22	Lecture 9: Generalized Linear Models - I
02/27, 03/01	Lecture 10: Generalized Linear Models - II, Generate Random Exponentials Look at this interesting paper on overdispersion
03/06, 03/08	Spring Break
03/13	Lecture 11: Logistic Regression - I SAS codes: Crabs, Crabs1, Grouping Example Reading material: Binomial Link functions                          A recourse on the cloglog link.
03/15	Midterm-I Solutions Lecture 12: Logistic Regression -II Reading material: Separation problem in logistic regression. Ready SAS stuffs                           Bayesian suggestions [for more advanced readers]
03/20, 03/22,03/27	Lecture 13: Building, Checking and Applying Logistic Regression Peduzzi The SAS Power procedure Lasso in 1 page; Original lasso paper Penalized Likelihood:Intro Crabs (W+D): SAS code Aggregate statement
03/29	Lecture 14: Alternative Binary Response Models More on FIRTH EXACT logistic regression SAS Notes: Exact Conditional Logistic Regression, Codes Bayesian Analysis in SAS: Manual GAM in SAS; More on GAM
04/03	Lecture 15: Multinomial Responses SAS Count Data Proc NLMIXED Intro gator.sas; impair.sas; Leuk.sas Note: Midterm 2 [Takehome], April 20 (Friday, 4:30 PM) -- April 24 (Tuesday, 5 PM)
04/05, 04/10	Lecture 16: Loglinear Models; Read Brown's test Datasets from HW # 5: vaso.sas; marijuana.txt; heart.sas Cohen paper [HW # 5]
04/12	Lecture 17: Matched pairs, Generalized McNemar test
04/17, 04/19	Lecture 18: Marginal Models; depress dataset; QIC SAS documentation for Proc GENMOD Short Intro in Informative Cluster Size Goodness-of-fit for the GEE model [Paper1, Paper 2]; SAS code from Paper 1 HW6 ; Efron's Statistical Science paper
04/24, 04/26	Lecture 19: Random Effects Model - I Bridge Density Another popular read on Conditional vs Marginal models A nice primer on Importance Sampling
05/03	No class on 05/01 Lecture 20: Random Effects Model - II Cumulative link models: Interpretation & other details SAS codes exploring Zero Inflation, Hurdle Regression and FMM.
Extras	Bayesian Models for Categorical Data [Agresti's notes] Download WinBUGS here Download for Mac/Linux here Steps to run WinBUGS Some examples [categorical data]: Seeds, Salmonella, Epilepsy
	Final Takehome: May 4 [4:30 PM] - May 9 [5:00 PM]

* Printing instruction: The .pdf files can be printed more than one slide to a page by accessing the advanced printer options on your printers. Check which ones in 
   the Department are capable of doing so.
* Acknowledgements to Dr. Haitao Chu [Univ. of Minnesota] and Dr. Tim Hanson [University of South Carolina]