options: Summarize and describe distributions.

Understand and evaluate random processes underlying statistical experiments.

Develop understanding of statistical variability.

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

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

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

Use random sampling to draw inferences about a population.

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

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

Use probability to evaluate outcomes of decisions.

Draw informal comparative inferences about two populations.

Interpret linear models.

Investigate patterns of association in bivariate data.

Calculate expected values and use them to solve problems.

Summarize, represent, and interpret data on two categorical and quantitative variables.
problem_activity: Activity
The mathematical purpose of this activity is for students to:
distinguish between linear and nonlinear relationships in bivariate, numerical data
informally assess the fit of a linear model
compare the slope and the vertical intercepts of different linear models
describe the relationship between two variables.
Students are given cards showing scatter plots and a linear model. They sort the cards based on how well the lines fit the data as well as by slope and intercept in increasing order.
A sorting task gives students opportunities to analyze representations, statements, and structures closely and make connections (MP2, MP7).
Launch
Arrange students in groups of 2. Give students a chance to familiarize themselves with what is on the cards. For example, you might ask them to sort the cards into categories of their choosing, and explain their categories to their partner.
Conversing: MLR8 Discussion Supports.
In pairs, ask students to take turns sorting the cards and explaining their reasoning to their partner. Display the following sentence frames for all to see: “ _____ should be before _____ because . . . .”, and “I noticed _____ , so I . . . .” Encourage students to challenge each other when they disagree. This will help students clarify their reasoning about linear models.
Design Principle(s): Support sense-making; Maximize meta-awareness
Action and Expression: Internalize Executive Functions.
Provide students with a template for organizing their observations. Provide a template or invite students to fold a blank piece of paper in thirds, and label with three headers of “
\(x\)
,” “
\(y\)
” and “linear model fits?” to collect their answers. Explain that in the first column, they will always write increasing, since they will be reading each graph from left to right, then have them fill in the behavior of the y values in the next column, and in the last column, their conclusions about whether a linear model fits well.
Supports accessibility for: Language; Organization
Student Facing
Your teacher will give you a set of cards that show scatter plots.
Arrange all the cards in three different ways. Ensure that you and your partner agree on the arrangement before moving on to the next one. Sort all the cards in order from:
best to worst for representing with a linear model
least to greatest slope of a linear model that fits the data well
least to greatest vertical intercept of a linear model that fits the data well
For each card, write a sentence that describes how
\(y\)
changes as
\(x\)
increases and whether the linear model is a good fit for the data or not.
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
The purpose of this discussion is for students to discuss the goodness of fit for linear models.
Here are some questions for discussion.
“How are scatter plots of A and F the same? How are they different?” (They have the same slope and the linear model for each scatter plot are equally well fit. They are different because they have a different vertical intercept.)
“How do you know if a linear model is a good fit?” (You need to look at the scatter plot and the line of best fit and make a decision about whether or not the data follows a linear trend.)
“Why is the goodness fit for the linear model in scatter plot B better than the fit for the linear model in scatter plot A?” (The data in B falls on or very close to the linear model. The data in A is scattered around the line of best fit and has roughly the same number of values below that line of best fit as it does above the line of best fit.)


correct_option_index: 11