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This warm-up prompts students to compare four scatter plots. It gives students a reason to use language precisely (MP6). It gives the teacher an opportunity to hear how students use terminology and talk about characteristics of the items in comparison to one another.
It also introduces students to positive and negative associations by comparing scatter plots with best-fit lines.
Arrange students in groups of 2–4. Display the scatter plots for all to see. Give students 1 minute of quiet think time and ask them to indicate when they have noticed 3 plots that go together and can explain why. Next, tell students to share their response with their group, and then together find as many sets of three as they can.
Which three go together? Why do they go together?
Invite each group to share one reason why a particular set of 3 go together. Record and display the responses for all to see. After each response, ask the class if they agree or disagree. Since there is no single correct answer to the question of which three go together, attend to students’ explanations and ensure the reasons given are correct.
During the discussion, ask students to explain the meaning of any terminology they use, such as “trend,” “model,” or “variable.” Also, press students on unsubstantiated claims.
During the discussion, introduce new vocabulary:
In this activity, students draw their own linear model to fit the data in a scatter plot. In one scatter plot, the data points are nearly linear, and in another there is much more variation in the data. A discussion follows about what makes some lines a better fit than others (MP3).
In the digital version of the activity, students use an applet to fit a linear model to the data. The applet allows students to drag two points on a line around the graph to find a good linear model. Use the digital version if available to allow students to try different models easily.
Monitor for groups who use these strategies:
Arrange students in groups of 2. Provide each student a piece of dried pasta and a straightedge.
Tell students that they may use the pasta to try different lines to see what might fit the data best before actually drawing a line with their straightedge and pencil.
Select students with different strategies, such as those described in the Activity Narrative, to share later.
Your teacher will give you a piece of pasta and a straightedge.
Here are two copies of the same scatter plot. Experiment with drawing lines to fit the data. Draw the line that you think best fits the data. Compare it with a partner’s.
Here are two copies of another scatter plot. Experiment with drawing lines to fit the data. Draw the line that you think best fits the data. Compare it with a partner’s.
The purpose of this discussion is to look at some strategies for drawing a line that fits the data well.
Invite previously selected groups to share how they found a good linear model. Sequence the discussion of the strategies in the order listed in the Activity Narrative. If possible, record and display their work for all to see.
Connect the different responses to the learning goals by asking questions such as:
If desired and time allows, demonstrate this procedure:
Optional
If students understand what makes for a good fit from the previous activity, then this activity may be considered optional. The next activity will give students less scaffolded practice deciding if a line fits the data well.
Students have seen linear models for data in a previous lesson. In this activity, students begin to determine what makes a good model for data. They compare two different lines with the same data set to determine which model fits the data better. A formal, quantitative discussion of lines that best fit data will come in later grades. At this stage, students are only asked to informally determine whether the line fits the data well based on how close the points are to the line.
Display the scatter plot for all to see and ask students, “What do you notice? What do you wonder?”
Students might notice:
Students might wonder:
Tell students that these are all prices of used cars that are all the same make and model that are for sale. For each car, the scatter plot shows its year of manufacture and the price at which it is being sold. Ask students a few questions to familiarize themselves with the graph, like:
Tell students that in this task, they are going to see 2 different models for this set of data.
The scatter plots both show the year and price for the same 13 used cars. However, each scatter plot shows a different model for the relationship between year and price.
For how many cars does the model in Diagram A make a good prediction of its price?
For how many cars does the model underestimate the price?
For how many cars does it overestimate the price?
For how many cars does the model in Diagram B make a good prediction of its price?
For how many cars does the model underestimate the price?
For how many cars does it overestimate the price?
For how many cars does the prediction made by the model in Diagram A differ by more than \$3,000? What about the model in Diagram B?
The purpose of this discussion is for students to see some strategies for evaluating the fit of a model.
Some questions for discussion:
We say that Model A “fits the data” better than Model B, or that model A is a “better fit.”
This activity gives students additional practice finding linear models that match the association of the data. In the first scatter plot, students are given a linear model that has a good slope, but is shifted up from the center of the data. In the second set of data, students are given a linear model that goes through the middle of the data, but has a slope that is too steep. Students are given the opportunity to correct these issues by drawing their own linear models on the same scatter plots.
Arrange students in groups of 2. Give students 2 minutes quiet work time followed by partner discussion and whole-class discussion.
Ask students why a line might be added to a scatter plot (to help predict additional values, to show a positive or negative association). Tell students that they will have a chance to practice adding lines to scatter plots by first critiquing a given line and then improving the linear model by drawing their own line for the same scatter plot.
Is this line a good fit for the data? Explain your reasoning.
Draw a line that fits the data better.
Is this line a good fit for the data? Explain your reasoning.
Draw a line that fits the data better.
The purpose of the discussion is for students to recognize the important aspects of a linear model for a set of data.
Consider asking some of the following questions.
Display the scatter plot for all to see.
To highlight the main ideas from today's lesson about associations and trend lines, ask:
When a linear function fits data well, we say there is a “linear association” between the variables. For example, the relationship between height and weight for 25 dogs with the linear function whose graph is shown in the scatter plot.
We say there is a positive association between dog height and dog weight because knowledge about one variable helps predict the other variable, and when one variable increases, the other tends to increase as well.
What do you think the association between the weight of a car and its fuel efficiency is?
We say that there is a negative association between fuel efficiency and weight of a car because knowledge about one variable helps predict the other variable, and when one variable increases, the other tends to decrease.