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Sleep and academic performance

Full Metadata

Overview

Year:
2015
Contributor:
Bugbee, Erin (creator)
Hodder, Jordan (creator)
Loor, Cristian (creator)
Nimmagadda, Raghu (creator)
Peters, Anmol Emmanuel (creator)
Wang, Hsin-Lei Charlene (creator)
Yoon, Chris (creator)
Genre:
photographs
Subject:
Cofounding variables
Sleep
Academic achievement

Files

Views

Description

Abstract:
This project aims to display the relationship between the amount of sleep a student ets and the student's academic performance. The visualization combines multiple data sets to show the relationship between hours of sleep and GPA and to bring attention to possible confounding variables, such as caffeine consumption, video game usage, and time spent watching television

Content

Access Conditions

Use and Reproduction
This work is licensed under a Creative Commons Attribution 4.0 International License

Citation

Bugbee, Erin, Hodder, Jordan, Loor, Cristian, et al., "Sleep and academic performance" (2015). APMA0110: What’s the Big Deal with Data Science?. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:697470/

Relations

Has Parts:

  • Child thumbnail
    Layout of Sleep and Academic Performance installation
    • Type: Page-Image
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    Coffee chat
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    Coffee chat
    • Type: Page-Image
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    Confounding variables
    • Type: Page-Image
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  • Child thumbnail
    Sleeping box
    • Type: Page-Image
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    Sleeping box
    • Type: Page-Image
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    Take a nap
    • Type: Page-Image
    • Order: 7
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  • Child thumbnail
    TV watching habits vs. academic performance
    • Type: Page-Image
    • Order: 8
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  • Child thumbnail
    Sleep vs. grades
    • Type: Page-Image
    • Order: 9
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  • Child thumbnail
    Team Sleep display tags
    • Type: Pdf
    • Order: 10
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Collection:

  • APMA0110: What’s the … Thumbnail

    APMA0110: What’s the Big Deal with Data Science?

    This collection highlights student projects created in the course APMA0110:What's the Big Deal with Data Science? The seminar, taught by Dr. Katharine M. Kinnaird (2016-2018), serves as a practical introduction to the interdisciplinary field of data science and exposes students …
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