STAT 661 - Categorical Data Analysis

Units: 2
Applied methods for discrete data. Topics may include: proportions and counts, contingency tables, loglinear models, logistic regression, Poisson regression, generalized linear models. Data integrity. Computing techniques and analysis of discrete data. Use of SAS. Report writing.

Prerequisites: STAT 631 and STAT 632.
Possible Instructional Methods: On-ground.
Grading: A-F grading only.
Cross-listed: BSTA 661.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply appropriate methods to describe and present categorical data in summaries, tables and/or graphs.
Formulate solutions to problems and develop statistical models involving categorical data.
Program appropriate SAS procedures to produce appropriate graphs, reports, and statistical output.
Communicate results to others regarding categorical data analysis.