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Analyze a linear regression line by using residual analysis

teaches Common Core State Standards CCSS.Math.Practice.MP1 http://corestandards.org/Math/Practice/MP1
teaches Common Core State Standards CCSS.Math.Practice.MP4 http://corestandards.org/Math/Practice/MP4
teaches Common Core State Standards CCSS.Math.Practice.MP5 http://corestandards.org/Math/Practice/MP5
teaches Common Core State Standards CCSS.Math.Content.HSS-ID.B.6 http://corestandards.org/Math/Content/HSS/ID/B/6
teaches Common Core State Standards CCSS.Math.Content.HSS-ID.B.6b http://corestandards.org/Math/Content/HSS/ID/B/6/b
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Big Ideas: A least squares regression equation predicts a linear association between two quantitative variables, based on a scatter plot. A residual plot analysis assesses the linearity of the relationship. This lesson builds on students previous work in 8th grade, where students informally fit a regression line to a set of data and assessed the fit by measuring how close data points were to the line. In this lesson students formally fit a least squares regression line to a set of data thought to be linearly associated. After determining the least squares regression line, students find residuals and create a residual plot to assess the linearity of the relationship between two quantitative variables. Vocabulary: least squares regression line, residual, observed value, predicted value, residual plot
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