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Enhancing Educational Approaches in Teaching Regression Techniques

Abstract Number:

2340 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Sierra Merkes (1), Anne Driscoll (2)

Institutions:

(1) Virginia Tech Statistics Department, N/A, (2) N/A, N/A

Co-Author:

Anne Driscoll  
N/A

First Author:

Sierra Merkes  
Virginia Tech Statistics Department

Presenting Author:

Sierra Merkes  
Virginia Tech Statistics Department

Abstract Text:

This presentation highlights the impact of the COS Instructional Grant in enhancing the educational experience of undergraduate and graduate students in Regression Analysis courses. Our project aimed to improve students' statistical skills, encourage critical thinking in real-world problem-solving, and help them communicate complex concepts clearly. Students explored data by applying regression analysis to scenarios like studying the relationship between physical characteristics and systolic blood pressure while identifying challenges like outliers. While students mastered technical skills, the project revealed a need to improve critical thinking and data interpretation, especially in recognizing anomalies. We will discuss current and future efforts, including earlier integration of crucial thinking through visualizations, creating separate course sections for different student backgrounds, and funding undergraduate research to develop statistical tools like an R package for mixed models. Our initiatives aim to boost student engagement, improve course alignment, and equip students with technical and transferable skills for careers in data analysis and beyond.

Keywords:

Statistical Education|Regression Analysis|Course Enhancement| | |

Sponsors:

Section on Statistics and Data Science Education

Tracks:

Miscellaneous

Can this be considered for alternate subtype?

No

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

I understand