Lesson 8
Using the Correlation Coefficient
Lesson Narrative
The mathematical purpose of this lesson is to compute (using technology) and interpret the correlation coefficient for a bivariate, numerical data set. The work of this lesson connects to previous work because students learned how to read and interpret the correlation coefficient. The work of this lesson connects to upcoming work because students will learn to distinguish between correlation and causation.
When students use the value of the correlation coefficient to describe the relationship between two variables, they are looking for and making use of structure (MP7). To make sense of the relationship between variables, students reason abstractly and quantitatively (MP2). When students examine relationships to think about correlations, they also consider additional variables that might have an influence on any trends they see. Deciding which variables need to be included is a part of the process of modeling with mathematics (MP4).
Learning Goals
Teacher Facing
 Describe (orally and in writing) the strength and sign of the relationship between variables based on the correlation coefficient.
 Use technology to calculate the correlation coefficient and describe the strength of a relationship based on that value.
Student Facing
 Let’s look closer at correlation coefficients.
Required Materials
Required Preparation
Students should have access to graphing technology that can compute the leastsquares regression line and correlation coefficient from a set of bivariate data. Acquire devices that can run Desmos (recommended) or other graphing technology. It is ideal if each student has their own device. (Desmos is available under Math Tools.)
Learning Targets
Student Facing
 I can describe the strength of a relationship between two variables.
 I can use technology to find the correlation coefficient and explain what the value tells me about a linear model in everyday language.
CCSS Standards
Addressing
Building Towards
Glossary Entries

negative relationship
A relationship between two numerical variables is negative if an increase in the data for one variable tends to be paired with a decrease in the data for the other variable.

positive relationship
A relationship between two numerical variables is positive if an increase in the data for one variable tends to be paired with an increase in the data for the other variable.

strong relationship
A relationship between two numerical variables is strong if the data is tightly clustered around the best fit line.

weak relationship
A relationship between two numerical variables is weak if the data is loosely spread around the best fit line.
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