options: Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?

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.

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.

Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.
problem_activity: Activity
A scatter plot is shown and the points interpreted in context. Later, a linear model is graphed with the scatter plot to help students see an obvious outlier (MP7). In the discussion, the term outlier is introduced.
Digital
Launch
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Ask students, “What do you notice? What do you wonder?” Ideally, they will notice that these are pictures of a foot of three different people. All three feet are approximately the same length but different widths. Tell students that all of these feet are a size 8. However, they wouldn’t all necessarily find the same shoe equally comfortably, because of the varying widths. (Besides the numerical size, some shoes also come in different widths.) Students should understand that human feet can vary in both length and width.
If using the digital activity, after this discussion, clarify for students that a scatter plot has been created for them to anaylze feet dimensions, using technology. Students can then work to complete the digital task which includes using a line of best fit to identify outliers.
Representation: Internalize Comprehension.
Provide appropriate reading accommodations and supports to ensure students access to written directions, word problems and other text-based content.
Supports accessibility for: Language; Conceptual processing
Speaking: MLR8 Discussion Supports.
Use this routine to clarify the meaning of “width” and “length” in the context of the task. Connect the terms width and length multi-modally by utilizing different types of sensory inputs, such as demonstrating the width and length on the images of each foot using gestures or inviting students to do so. Invite students to chorally repeat phrases that include these words in context. This will help students produce and make sense of the language needed to understand the context of the task.
Design Principle(s): Support sense-making
Student Facing
Here is a scatter plot that shows lengths and widths of 20 different left feet. Use the double arrows to show or hide the expressions list.
Estimate the widths of the longest foot and the shortest foot.
Estimate the lengths of the widest foot and the narrowest foot.
Click on the gray circle next to the words “The Line” in the expressions list. The graph of a linear model should appear. Find the data point that seems weird when compared to the model. What length and width does that point represent?
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Student Response
Teachers with a valid work email address can
click here to register or sign in
for free access to Student Response.
Print
Launch
[IMG]
Ask students, “What do you notice? What do you wonder?” Ideally, they will notice that these are pictures of a foot of three different people. All three feet are approximately the same length but different widths. Tell students that all of these feet are a size 8. However, they wouldn’t all necessarily find the same shoe equally comfortably, because of the varying widths. (Besides the numerical size, some shoes also come in different widths.) Students should understand that human feet can vary in both length and width.
If using the digital activity, after this discussion, clarify for students that a scatter plot has been created for them to anaylze feet dimensions, using technology. Students can then work to complete the digital task which includes using a line of best fit to identify outliers.
Speaking: MLR8 Discussion Supports.
Use this routine to clarify the meaning of “width” and “length” in the context of the task. Connect the terms width and length multi-modally by utilizing different types of sensory inputs, such as demonstrating the width and length on the images of each foot using gestures or inviting students to do so. Invite students to chorally repeat phrases that include these words in context. This will help students produce and make sense of the language needed to understand the context of the task.
Design Principle(s): Support sense-making
Student Facing
Here is a scatter plot that shows lengths and widths of 20 different left feet.
[IMG]
Estimate the widths of the longest foot and the shortest foot.
Estimate the lengths of the widest foot and the narrowest foot.
Here is the same scatter plot together with the graph of a model for the relationship between foot length and width.
[IMG]
Circle the data point that seems weird when compared to the model. What length and width does that point represent?
Student Response
Teachers with a valid work email address can
click here to register or sign in
for free access to Student Response.
Activity Synthesis
Introduce the term
outlier
. An outlier is a point that is separated from the rest of the data. Sometimes data sets have outliers. Sometimes that’s because there really is a data point that is very different than the others. Sometimes it is because there was an error in collecting the data. Sometimes it is because there was an error in entering the data. When there are outliers, one has to make a judgement about whether to include it in the analysis or not.


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