Lesson Objective

Construct scatter plots using bivariate quantitative data, and analyze them to identify linear or non-linear associations, positive or negative trends, clustering, and outliers.

How does a scatter plot help us visualize the relationship between two different quantitative measurements?

What is the difference between a positive, negative, and non-linear association in a real-world scenario?

Why do certain data points group together in clusters, and what does a lone outlier tell us about the overall trend?

Bivariate data
Quantitative variable
Scatter plot
Positive/Negative association
Linear vs. Non-linear trend
Cluster
Outlier

CCSS.MATH.CONTENT.8.SP.A.1

Description: Students transition from plotting isolated coordinate pairs to evaluating global data behaviors. They learn to identify trends and diagnose patterns like tight clusters or distinct outliers.

DOK Level: 3 (Strategic Thinking & Analysis)

Students often try to connect data points sequentially like a line graph instead of treating them as discrete bivariate pairs.

Assuming a "negative association" implies an error in data collection rather than an inverse relationship.

Provide pre-labeled or color-coded coordinate grids to assist with spatial organization.

Unit Test

Exit Tickets