standard_description: Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.
problem_activity: Optional activity
In this activity, students use the data from the previous activity and draw a scatter plot and a line that fits the data. The given data show a clear linear association, so it is appropriate to model the data with a line. Students can use a piece of dried linguine pasta or some other rigid, slim, long object (for example, wooden skewer) to eyeball the line that best fits the data. (The line of best fit has a variance of
\(R^2=0.94\)
.) Even though different answers will have slightly different slopes and intercepts, they will be close to each other.
Launch
Students in same groups of 3–4. Provide access to pieces of dried linguine pasta.
Representation: Internalize Comprehension.
Provide a range of examples and counterexamples for a best fit line. Consider displaying charts and examples from previous lessons to aide in memory recall.
Supports accessibility for: Conceptual processing
Student Facing
Make a scatter plot of the data.
[IMG]
Describe any patterns of association that you notice.
Draw a line that fits the data. Write an equation for this line.
Student Response
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Anticipated Misconceptions
If a student is stuck on making the scale on the graph, remind them to look at the range from the previous problem.
Activity Synthesis
At the start of the discussion, make sure students agree that there seems to be a negative association that looks like a line would fit the data nicely before moving on.
Invite several groups to share the equations they came up with and how they found them. They will likely have slightly different slopes and intercepts. Ask students to explain where those differences come from. (Not everyone chose the same points to guide the trendline they drew for the data.) The differences should be small and the different models give more or less the same information. In the next activity, students will be using the model (equation and graph) to make predictions.
Representing, Conversing: MLR8 Discussion Supports.
After students complete their scatter plots from the data, they should meet with a small group of 3–4 students to share and compare. While each student shares, circulate and encourage students to look for commonalities and to discuss their differences in displays. Tell students to generate an agreed on response for the equation and the visual display of the line even though they may have differences. Tell students that one member will share out, but they won’t know who, so they all need to be prepared to share and explain. During the whole-class discussion, amplify use of mathematical language such as “slope”, “intercept”, “equation”, “graph”, and “negative association”. Use this to help students develop their explanations of associations and differences that they see in how the data is interpreted.
Design Principle(s): Cultivate conversation; Maximize meta-awareness


aligns: no