options: Summarize, represent, and interpret data on a single count or measurement variable.

Summarize, represent, and interpret data on two categorical and quantitative variables.

Use the rules of probability to compute probabilities of compound events in a uniform probability model.

Understand independence and conditional probability and use them to interpret data.

Use random sampling to draw inferences about a population.

Draw informal comparative inferences about two populations.

Interpret linear models.

Use probability to evaluate outcomes of decisions.

Calculate expected values and use them to solve problems.

Investigate chance processes and develop, use, and evaluate probability models.

Understand and evaluate random processes underlying statistical experiments.

Develop understanding of statistical variability.

Summarize and describe distributions.

Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Investigate patterns of association in bivariate data.
problem_activity: Warm-up
The mathematical purpose of this activity is for students to match bivariate data with its context. Students should think about whether they might expect a strong correlation or not as well as whether the relationship has a positive or negative correlation. Monitor for students who discuss linear relationships or variability in the data.
Launch
Arrange students in groups of two to four. Provide access to the scatter plots and contexts for the warm-up.
Student Facing
Match the variables to the scatter plot you think they best fit. Be prepared to explain your reasoning.
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A
[IMG]
B
[IMG]
C
[IMG]
D
[IMG]
Student Response
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Anticipated Misconceptions
Students may struggle with matching the pairs of variables with a scatter plot. Encourage students to think about how related the variables are and how the
\(y\)
variable might change as the
\(x\)
variable increases.
Activity Synthesis
The goal of this discussion is for students to discuss how the characteristics of the scatter plots allowed them to determine the context. For each context, select groups to share their match and reasoning. Select groups who used linear model and variability in their small group discussions.
Here are some questions for discussion.
“How did the concept of linear relationships help you to make a match?” (For the height in inches and the height in feet, I knew that the data would be very linear. I chose scatter plot A because it was the only one that appeared linear. I did wonder why scatter plot A was not perfectly linear. Maybe it had to do with rounding or measurement error.)
“How did you use the concept of linearity to help you to make a match?” (First, I expected the temperature and cereal context relationship to be totally random rather than linear at all, so that fit with scatter plot C. Second, I knew that problem 3 matched with scatter plot A, because I knew it should be almost perfectly linear. For a given height in feet, there is only one height in inches.)
“Which matches were the most difficult? What helped you figure them out?” (It was hardest to find the matches for scatter plots B and D because they were pretty scattered. I noticed that the data in scatter plot D displayed a negative relationship, so I looked for a context that would have a negative relationship. It makes sense that customer satisfaction decreases as wait time increases.)


correct_option_index: 1