Red Light Reaction – A Statistics Project with Real Life Application
We describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
|
| Series: | Journal of Statistics and Data Science Education |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26939169.2024.2407781 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850251401888792576 |
|---|---|
| author | Silvia Heubach Tuyetdong Phan-Yamada |
| author_facet | Silvia Heubach Tuyetdong Phan-Yamada |
| author_sort | Silvia Heubach |
| collection | DOAJ |
| description | We describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without distraction and measure the time it takes to apply brakes after the red brake lights of the car ahead appear. Students use the collected data to practice a range of statistical techniques, such as assessing data for outliers, creating a hypothesis to be tested, performing an appropriate test, and then translating their results to determine a safe driving distance. In the second part of the project, students work in groups outside of class. Each group recruits a category of test subjects (e.g., athletes, video gamers, STEM majors) of their choosing, collects data, and performs statistical analysis. Finally, students develop hypotheses as to whether different categories of drivers have better or worse reaction times, collect additional relevant data, and perform the appropriate statistical test. The applets, the project guide, the dataset, and supporting videos can be downloaded from https://osf.io/h8yxw/files/osfstorage. For users of the Canvas LMS, we also provide an export cartridge that contains the various project components. Supplementary materials for this article are available online. |
| format | Article |
| id | doaj-art-642826b96b86431eb2408e9d36ca5b17 |
| institution | OA Journals |
| issn | 2693-9169 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Statistics and Data Science Education |
| spelling | doaj-art-642826b96b86431eb2408e9d36ca5b172025-08-20T01:57:54ZengTaylor & Francis GroupJournal of Statistics and Data Science Education2693-91692025-01-01331465110.1080/26939169.2024.2407781Red Light Reaction – A Statistics Project with Real Life ApplicationSilvia Heubach0Tuyetdong Phan-Yamada1Department of Mathematics, California State University Los Angeles, Los Angeles, CADepartment of Mathematics, California State University Los Angeles, Los Angeles, CAWe describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without distraction and measure the time it takes to apply brakes after the red brake lights of the car ahead appear. Students use the collected data to practice a range of statistical techniques, such as assessing data for outliers, creating a hypothesis to be tested, performing an appropriate test, and then translating their results to determine a safe driving distance. In the second part of the project, students work in groups outside of class. Each group recruits a category of test subjects (e.g., athletes, video gamers, STEM majors) of their choosing, collects data, and performs statistical analysis. Finally, students develop hypotheses as to whether different categories of drivers have better or worse reaction times, collect additional relevant data, and perform the appropriate statistical test. The applets, the project guide, the dataset, and supporting videos can be downloaded from https://osf.io/h8yxw/files/osfstorage. For users of the Canvas LMS, we also provide an export cartridge that contains the various project components. Supplementary materials for this article are available online.https://www.tandfonline.com/doi/10.1080/26939169.2024.2407781Distracted drivingHands-on data collectionIntroductory statisticsReaction time |
| spellingShingle | Silvia Heubach Tuyetdong Phan-Yamada Red Light Reaction – A Statistics Project with Real Life Application Journal of Statistics and Data Science Education Distracted driving Hands-on data collection Introductory statistics Reaction time |
| title | Red Light Reaction – A Statistics Project with Real Life Application |
| title_full | Red Light Reaction – A Statistics Project with Real Life Application |
| title_fullStr | Red Light Reaction – A Statistics Project with Real Life Application |
| title_full_unstemmed | Red Light Reaction – A Statistics Project with Real Life Application |
| title_short | Red Light Reaction – A Statistics Project with Real Life Application |
| title_sort | red light reaction a statistics project with real life application |
| topic | Distracted driving Hands-on data collection Introductory statistics Reaction time |
| url | https://www.tandfonline.com/doi/10.1080/26939169.2024.2407781 |
| work_keys_str_mv | AT silviaheubach redlightreactionastatisticsprojectwithreallifeapplication AT tuyetdongphanyamada redlightreactionastatisticsprojectwithreallifeapplication |