In this lesson, students visually identify clusters in data and then do a card sort to distinguish linear and non-linear associations. Students will not study non-linear associations or clustering using quantitative tools. Instead they will rely on visual patterns in scatter plots (MP7). Next, they bring everything they have studied in the unit so far to analyze and interpret bivariate data in context (MP4). They create a scatter plot, identify outliers, fit a line, and determine and interpret the slope of the line. They compare actual and predicted values. They reflect on what they have learned about modeling bivariate data.
- Categorize data sets, and describe (orally) the properties used to create categories.
- Create a scatter plot and draw a line to fit bivariate data, and identify (orally and in writing) outliers that appear in the data.
- Describe (orally) features of data on scatter plots, including “linear” and “nonlinear association” and “clustering” using informal language.
- Interpret (orally and in writing) features of a scatter plot with a line of fit, including outliers, slope of the line, and clustering.
Let’s look for other patterns in data.
Print and cut up cards from the Scatter Plot City blackline master. Prepare 1 set of cards for every student.
- I can analyze a set of data to determine associations between two variables.
- I can pick out clusters in data from a scatter plot.
- I can use a scatter plot to decide if two variables have a linear association.
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