Describing Trends in Scatter Plots
5 min
Teacher Prep
Setup
Narrative
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.
Launch
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.
Student Task
Which three go together? Why do they go together?
Sample Response
Sample responses:
- A, B, and C go together because they have a line that models a connection between the variables.
- A, B, and D go together because the points are fairly close together.
- A, C, and D go together because all the points have a positive y-coordinate.
- B, C, and D go together because the data show a negative trend.
Activity Synthesis (Teacher Notes)
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:
- A positive association is a relationship between 2 quantities where one tends to increase as the other increases.
- A negative association is a relationship between 2 quantities where one tends to decrease as the other increases.
Standards
- 8.SP.1·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.
- 8.SP.2·Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
- 8.SP.A.1·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.
- 8.SP.A.2·Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
20 min
10 min
Knowledge Components
All skills for this lesson
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Describing Trends in Scatter Plots
5 min
Teacher Prep
Setup
Narrative
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.
Launch
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.
Student Task
Which three go together? Why do they go together?
Sample Response
Sample responses:
- A, B, and C go together because they have a line that models a connection between the variables.
- A, B, and D go together because the points are fairly close together.
- A, C, and D go together because all the points have a positive y-coordinate.
- B, C, and D go together because the data show a negative trend.
Activity Synthesis (Teacher Notes)
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:
- A positive association is a relationship between 2 quantities where one tends to increase as the other increases.
- A negative association is a relationship between 2 quantities where one tends to decrease as the other increases.
Standards
- 8.SP.1·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.
- 8.SP.2·Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
- 8.SP.A.1·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.
- 8.SP.A.2·Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.