Three principles for modernizing an undergraduate regression analysis course

TL;DR

This paper presents three guiding principles and pedagogical strategies for modernizing an undergraduate regression analysis course to better equip students with practical data analysis skills aligned with current curriculum guidelines.

Contribution

It introduces a set of three principles for updating regression courses, integrating modern data science skills with traditional statistical concepts, supported by practical activities and resources.

Findings

Improved student engagement with modern data analysis techniques

Successful integration of practical skills into the curriculum

Positive feedback from diverse student populations

Abstract

As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This paper describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with non-majors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The paper includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The paper concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the paper, with full sample assignments and resources for finding data in the supplemental materials.