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https://github.com/matackett/modernize-regression

Supplemental materials for the article "Three principles for modernizing an undergraduate regression analysis course"
https://github.com/matackett/modernize-regression

data-science education r regression statistics

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Supplemental materials for the article "Three principles for modernizing an undergraduate regression analysis course"

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## Supplemental materials for the article "Three principles for modernizing an undergraduate regression analysis course"



## Abstract



As data has 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 the 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 majors and non-majors. Three principles guiding the modernization of the course are presented, along with how these principles align with the necessary skills of statistical practice outlined in recent statistics curriculum guidelines. The paper includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for modern statistical practice into the curriculum alongside the 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.



## STA 210: Regression Analysis course websites



💻 [Fall 2021](https://sta210-fa21.netlify.app)



💻 [Fall 2022](https://sta210-fa22.netlify.app)



-   The materials for this semester utilize the **tidymodels** framework and are based on the [Spring 2022](https://sta210-s22.github.io/website/) iteration of STA 210 taught by Dr. Mine Çetinkaya-Rundel.



## Sample activities & assignments



📝 [Simple Linear Regression: 2020 United States Election](slr-us-election)



📝 [Multiple Linear Regression: LEGOs in-class activity](lego-activity)



## Project + teamwork



👩‍💻 ["Get to Know You" Survey](get-to-know-you-survey.pdf)



🤝 [Team agreement](team-agreement)



💻 [Project instructions (Fall 2021)](https://sta210-fa21.netlify.app/project/)



✅ [Project peer review](https://github.com/matackett/peer-feedback)



✅ [Project report rubric](project-report-rubric.csv)



## Resources for data



Resources used to find data for the course:



-   [TidyTuesday](https://github.com/rfordatascience/tidytuesday)

-   [OpenIntro](https://www.openintro.org/data/)

-   [Data is Plural](https://www.data-is-plural.com/)

-   [FiveThirtyEight](https://data.fivethirtyeight.com/)



These resources have been useful, because they have typically have good documentation on the original source of the data and the variable definitions. In the case of the OpenIntro resources, these data sets have been curated specifically to use for regression exercises. Some resources such as FiveThirtyEight data sets from TidyTuesday, have accompanying articles, so class activities and assignments can include a comparison of the students' analysis approach and conclusions to those of the original authors'.



## Acknowledgements



The pedagogy and computing infrastructure used in STA 210: Regression Analysis are largely inspired by the introductory data science curriculum [Data Science in a Box](https://datasciencebox.org) by Dr. Mine Çetinkaya-Rundel.