The Task Statement provides data students can analyze for the remainder of this lesson. It gives the average high temperature in September in different locations across North America. This is only one possible choice for data to analyze. If appropriate, students can instead use the data collected for locations from the earlier lesson. If the class-collected data set is used, the instructions are the same. As students interpret the data they are reasoning about the situation (MP2).

Make sure students understand the information listed in the table. Invite students to share their answers to the question about association. In the next activity, students will plot their data to see if the association they see in the table would be modeled well by a linear function.

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 (MP4). 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 .) Even though different answers will have slightly different slopes and intercepts, they will be close to each other.

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.