STA 221 - Regression Analysis: Theory and Applications
Spring 2026 - Dr. Maria Tackett
Overview
In STA 221, students will learn how linear and logistic regression models are used to explore multivariable relationships, apply these methods to answer relevant and engaging questions using a data-driven approach, and learn the mathematical underpinnings of the models. Students will develop computing skills to implement a reproducible data analysis workflow and gain experience communicating statistical results. Throughout the semester, students will work on a team project where they will develop a research question, answer it using methods learned in the course, and share results through a written report and presentation.
Topics include applications of linear and logistic regression, analysis of variance, model diagnostics, and model selection. Regression parameter estimation via maximum likelihood least squares will also be discussed. Students will gain experience using the computing tools R and GitHub to analyze real-world data from a variety of fields.
Pre-requisites
Either any STA 100-level course or STA 230, 231, or 240L and MATH 216, 218, or 221. The recommended co-requisite is STA 230, 231, or 240L. Interested students with different backgrounds should seek instructor consent.
Class meetings
See Canvas for class meeting times and location.
Teaching team
Instructor
Maria Tackett is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. Her work focuses on understanding how active learning strategies can be used to promote engagement and student motivation in undergraduate statistics courses. She also studies how classroom practices in introductory math and statistics courses impact students’ sense of community, self-efficacy, and learning outcomes.
Office hours time and location on Canvas.
Teaching assistants
| Name | Role |
| Cathy Lee | Lab 01 leader |
| Krish Bansal | Lab 01 helper |
| Xueyan Hu | Lab 02 leader |
| Allison Yang | Lab 02 helper |
Office hours times and locations on Canvas.
License
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