APPLIED CATEGORICAL DATA ANALYSIS
"Which is more popular, Apple, PC, or Linux?" "Rate your satisfaction with this product on a scale from 1 to 5." Organizing information into categories is natural! But, canonical statistical approaches like standard linear regression are not designed for analyzing such data. Develop techniques appropriate for analyzing categorical response data, with an emphasis on their application. Topics include: multidimensional contingency tables, inference for measures of association between categorical variables, and regression modeling for categorical data - specifically, logistic Poisson regression. Utilizes the R programming language.
Topics
- multidimensional contingency tables
- inference for measures of association between categorical variables
- and regression modeling for categorical data - specifically
- logistic Poisson regression
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: STAT 333 , 340 , graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Construct and interpret contingency tables for the purposes of summarizing, displaying, exploring, and analyzing data classified by one or more categorical variables. Audience: Undergraduate 2. Calculate and interpret summary measures of association between categorical variables, such as odds ratios. Audience: Undergraduate 3. Perform inference for parameters of interest arising from models of categorical data by using point estimators, confidence intervals, and hypothesis tests, and interpreting their results. Audience: Undergraduate 4. Fit logistic and Poisson regression models where appropriate; assess the goodness of the model fits; perform model selection; and interpret/make inferences from well-fitting models. This includes the important subcases of fitting logistic and loglinear models to contingency tables. Audience: Undergraduate 5. Utilize a statistical computing package (such as R) to carry out analyses of categorical data. Audience: Undergraduate Learning Outcomes: 1. Construct and interpret contingency tables for the purposes of summarizing, displaying, exploring, and analyzing data classified by one or more categorical variables. Audience: Undergraduate 2. Calculate and interpret summary measures of association between categorical variables, such as odds ratios. Audience: Undergraduate 3. Perform inference for parameters of interest arising from models of categorical data by using point estimators, confidence intervals, and hypothesis tests, and interpreting their results. Audience: Undergraduate 4. Fit logistic and Poisson regression models where appropriate; assess the goodness of the model fits; perform model selection; and interpret/make inferences from well-fitting models. This includes the important subcases of fitting logistic and loglinear models to contingency tables. Audience: Undergraduate 5. Utilize a statistical computing package (such as R) to carry out analyses of categorical data. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027