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Statistics: Bivariate Data Project
Statistics: Bivariate Data Project
Statistics: Bivariate Data Project
Statistics: Bivariate Data Project
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Description

This project has students investigate a bivariate data relationship: the number of pages in a textbook and its price using bivariate data analysis techniques.

Concepts that students need to be familiar with prior to completing this project include:

  • scatter plots
  • correlation relationships
  • prediction vs. residuals
  • least squares regression lines
  • correlation coefficient
  • interpreting linear models
  • correlation vs. causation

The project is in a Google Doc so that you can modify it to fit your class needs.

A data set included.

Easel assessment included.

Full, detailed rubric is included.

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.

Statistics: Bivariate Data Project

Above Average Math
7 Followers
$3.75

Highlights

Digital downloads
Grades icon
Grades
11th - 12th
Standards icon
Standards
Pages
7
Answer Key
Rubric only
Teaching Duration
50 minutes

Description

This project has students investigate a bivariate data relationship: the number of pages in a textbook and its price using bivariate data analysis techniques.

Concepts that students need to be familiar with prior to completing this project include:

  • scatter plots
  • correlation relationships
  • prediction vs. residuals
  • least squares regression lines
  • correlation coefficient
  • interpreting linear models
  • correlation vs. causation

The project is in a Google Doc so that you can modify it to fit your class needs.

A data set included.

Easel assessment included.

Full, detailed rubric is included.

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.

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Questions & Answers

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Standards

to see state-specific standards (only available in the US).
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
Informally assess the fit of a function by plotting and analyzing residuals.
Fit a linear function for a scatter plot that suggests a linear association.
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