Drivethru Scatter Plots - Line of Best Fit Activity
Jessica Wilkerson
790 Followers
Grade Levels
8th - 9th
Subjects
Resource Type
Standards
CCSS8.SP.A.1
CCSS8.SP.A.2
CCSS8.SP.A.3
Formats Included
- PDF
Pages
4 pages
Jessica Wilkerson
790 Followers
What educators are saying
I used this activity to introduce "line of best fit" with my students and prediction of future values. Students were very engaged!
My students were really interested in this activity. They enjoyed it. I thought it related to the real world and was fun.
Description
This activity is a fun way to work with scatter plots in a realistic situation. Students will compare the fat and calories in items from a fictional fast food restaurant by creating a scatter plot. They will also draw a line of best fit, approximate the slope of that line, and make predictions based on the line.
Included:
*Drive-Thru Scatterplots Activity
*Answer Key
Please leave feedback with any suggestions for improvements! :)
Included:
*Drive-Thru Scatterplots Activity
*Answer Key
Please leave feedback with any suggestions for improvements! :)
Total Pages
4 pages
Answer Key
Included
Teaching Duration
N/A
Report this resource to TPT
Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT’s content guidelines.
Standards
to see state-specific standards (only available in the US).
CCSS8.SP.A.1
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.
CCSS8.SP.A.2
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.
CCSS8.SP.A.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.