NY Next Generation Math NY-8.SP.1
The Standard
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.
New York State Next Generation Mathematics Learning Standards
Teacher's field guide
What This Standard Means
What Students Need to Do
- Students plot paired measurements for two variables on a scatter plot. They describe visible clusters, outliers, direction of association, and whether the pattern is linear or nonlinear.
What Mastery Looks Like
- A student chooses sensible scales, labels both axes, and plots each ordered pair accurately. The student describes the association using evidence from the overall pattern rather than one point.
Common Misconceptions
- Students may connect points in data order, reverse the coordinates, or choose a scale that hides the pattern. They may also mistake association for proof that one variable causes the other.
How to Assess It
- Give students (1, 62), (2, 68), (3, 72), (4, 78), (5, 81), and (6, 87). Ask them to graph the pairs and describe the direction and form of the association.
Lesson moves
Ways to Teach It
Tape two axes on the floor, give students paired-data cards, and have each student stand at the matching coordinate.
Ask, "What evidence makes this pattern positive and linear rather than merely a scattered group of points?"
Match scatter-plot cards to descriptions such as negative, clustered, nonlinear, or no clear association, then defend each match.
Collect classmates' arm spans and heights, graph the pairs, and describe the association without claiming that one measurement causes the other.
Keep exploring
Related Standards
- NY-8.SP.2
Understand that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, i...
- NY-8.SP.3
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept.
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