Analysis of Categorical Data (STA 5853) Spring 99
		   Time: TR 7:00 - 8:15 pm, Room: HSS 3.03.18


Instructor:		Dr. Ming-Ying Leung

Office Hours:		TR  10 am - 12 noon, or by appointment
			Office:	SB 4.01.22
			Phone: 	458-5535
			Fax:	458-4439
			Email:	leung@math.utsa.edu
			Web:	http://www.math.utsa.edu/~leung/

Course Objectives: To introduce statistical methods for analyzing multifactor
categorical data using contingency tables and models.  

Scope: This course will introduce techniques for analyzing multifactor 
contingency tables, builing linear and loglinear models, and estimating 
model paramters. Strategies of model selection, assessment of goodness 
of fit, and measures of association will be discussed. 

Text:			Categorical Data Analysis by Alan Agresti.

Prerequisite:		STA 5103 or STA 5503 

Syllabus:		Chapter 1	Introduction
			Chapter 2	Describing Two-Way Contingency Tables
			Chapter 3	Inference for Two-Way Contingency Tables 
			Chapter 4	Models for Binary Response Variables
			Chapter 5	Loglinear Models
			Chapter 6	Fitting Loglinear and Logit Models
			Chapter 7	Building and Applying Loglinear Models
						
Calculator:	Texas Instrument TI-83

Software:	MINITAB, Student Edition; PC SAS.

Grading:	There will be 10 homework assignments, two in class tests, 
		and a final project. They will contribute to your overall 
		grade according to the following percentages.

		Homework: 	40% (Due in class every Thursday except on test dates) 
		Test I: 	20% (In class Thursday, 2/25)
		Test II:	20% (In class Thursday, 4/8)
		Final Project:	20% (Due in my office, Thursday, 5/13, by 10:45 pm)

NO MAKEUP EXAM will be given except for emergency or medical reasons. In such cases, the 
student should submit a written request accompanied by official documents to arrange for a makeup 
test. Overdue assignments will only be accepted for a good reason. However, the instructor 
reserves the right to discount part or all of the credit for any late homework.

LAST DAY TO DROP an individual course is 3/19. After this date, no individual course can be 
dropped without withdrawing from the University.