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Scatter Plot Project with Real World Sports Data

Rated 4.7 out of 5, based on 61 reviews
4.7 (61 ratings)
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Rise over Run
23.9k Followers
Grade Levels
8th - 9th
Resource Type
Standards
Formats Included
  • PDF
Pages
29 pages
$4.00
$4.00
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Rise over Run
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What educators are saying

I had originally skipped the scatter plot unit in our curriculum so I needed to go back and cover it before the end of the school year. This resource was easy to use and worked great with my Algebra 1 class.
This was a great activity to use with my class as extra practice creating and analyzing scatter plots. It was super helpful that it was already differentiated for the different levels I have in my classroom.
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Description

Practice creating and analyzing scatter plots with these six differentiated real world projects. Students are provided statistics for a sport and must graph the data. Then, they write an analysis describing correlation, whether or not the data is linear, any outliers, and how the scatter plot might be used to make predictions.

The scatterplots range from 38 points to 149 points, so they can be used to differentiate. The scatter plots will take a little time to plot, but they result in real world graphs along with meaningful and relevant discussions.

Topics include basketball, football, racecars, baseball, and horseracing. A simple rubric is included.

Completed scatterplots are also included if you want your class to practice analyzing without graphing all the points themselves.

These can be used as in-class group projects or take-home individual projects for 8th grade math or Algebra 1. Check out the preview!

Total Pages
29 pages
Answer Key
Included
Teaching Duration
N/A
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Standards

to see state-specific standards (only available in the US).
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
Fit a function to the data; use functions fitted to data to solve problems in the context of the data.

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