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Linear regression Project College Values AP Statistics Distance learning

Grade Levels
9th - 12th
Formats Included
  • PDF
  • Activity
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Easel Activity Included
This resource includes a ready-to-use interactive activity students can complete on any device. Easel by TpT is free to use! Learn more.


Use linear regression for college selection with this linear regression project. Great for AP Statistics. Use the data provided in the resource or have students generate their own data to analyze, calculate residuals, and more on the value of college as related to rankings or career earnings. This project is part of the AP Statistics curriculum.

Included in this resource:

  • Linear Regression project examining relationship between college tuition costs and college rankings
  • Linear Regression project examining relationship between college tuition costs and after college earnings
  • EDITABLE Rubric
  • Examples
  • PDF of all of the above for those who do not have powerpoint or access to google slides.

*NOTE: You can add due dates, points, additional instructions to any of the student pages in powerpoint or google slides.

Topics included in this project:

  • Linear regression
  • Residuals
  • Linear regression analysis

This resource can be found in the following bundles:

Get students ready for the AP Statistics exam with the full curriculum.

Questions? Leave your questions in the Q & A tab, and I will get back to you within 24 hours.

This purchase is for one teacher only. This resource is not to be shared with colleagues or used by an entire grade level, school, or district without purchasing the proper number of licenses. If you are interested in a site license, please contact me for a quote. This resource may not be uploaded to the internet in any form, including classroom/personal websites or network drives.

Total Pages
Answer Key
Included with rubric
Teaching Duration
3 days
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to see state-specific standards (only available in the US).
Compute (using technology) and interpret the correlation coefficient of a linear fit.
Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
Fit a linear function for a scatter plot that suggests a linear association.
Informally assess the fit of a function by plotting and analyzing residuals.


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