Description
A great way for kids to experiment with correlation among variables, as well as plotting scatter plots and drawing lines of best fit.
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Highlights
Digital downloads
Grades
8th
Subjects
Standards
CCSS8.SP.A.1
CCSS8.SP.A.2
CCSS8.SP.A.3
Tags
Pages
1
Answer Key
Rubric only
Teaching Duration
90 minutes
Description
A great way for kids to experiment with correlation among variables, as well as plotting scatter plots and drawing lines of best fit.
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.
Reviews
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My students loved the project. It was a great help for my students.
Can't wait to use it
good
There was not a preview of what this activity was about. I took a chance on it and it is not worth the money for it! This is a 1/2 sheet list of requirements for students to make their own scatter plot from their own topic. I would like my money back please.
It is my understanding that it is not possible to refund. I'm sorry that this was not what you were looking for. I use it as a nice activity to have the kids complete at the tail end of learning about scatter plots. It allows them a bit of ownership and exploration.
good product
Thanks! Glad you found this useful!
Good project
Thanks! Glad you found this useful!
Questions & Answers
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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.
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