STAT 358
Introduction to Categorical Data Analysis Spring 2022
Division III Quantative/Formal Reasoning
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Class Details

This course focuses on methods for analyzing categorical response data. In contrast to continuous data, categorical data consist of observations classified into two or more categories. Traditional tools of statistical data analysis (such as linear regression) are not designed to handle such data and pose inappropriate assumptions. We will develop methods specifically designed for modeling categorical data, with applications in the social and biological sciences as well as in medical research, engineering and economics. This course has two parts. The first part will discuss statistical inference for parameters of categorical distributions (Bernoulli, Binomial, Multinomial, Poisson) and for measures of association arising in contingency tables (difference and ratio of proportions and odds ratios). Inferential methods covered include Wald, score and likelihood ratio tests and confidence intervals, as well as the bootstrap. The longer second part will focus on statistical modeling of categorical response data via generalized linear models, with a heavy focus on logistic regression models with both quantitative and categorical predictors and their interactions. Model fitting and inference will be based on maximum likelihood and carried out via R.
The Class: Format: lecture
Limit: 15
Expected: 15
Class#: 3971
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: Weekly homework assignments consisting of exercises from the textbook as well as data analysis problems, carried out using R. Occasional short in-class quizzes at the beginning of class. One Midterm (with both in-class and take-home component). Final Project with presentation. Final exam. Homework accounts for roughly 15% of the grade, quizzes for another 15%, midterm (in-class and take-home combined) and final for about 30% each, and project for the remaining 10%.
Prerequisites: STAT 346: Regression and Forecasting
Enrollment Preferences: stats majors
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: Students learn how to analyze data and communicate results.

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