Regression, Residuals, r-squared

How do you calculate a residual?

• Observed - Predicted
• Predicted - Observed

Residuals are . . . 

• possible models not explored by the researcher
• variation in the response variable that is explained by the model
• the difference between the observed response and the values predicted by the model
• data collected from individuals that is not consistent with the rest of the group

Shown is a residual plot. Would a linear regression model of the data be most appropriate?

• YES
• NO

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CCSS.HSS.ID.B.6

Estimate the correlation coefficient.

• r = -0.8
• r = -0.5
• r = 0.5
• r = 0.8

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

Estimate the correlation coefficient for this scatterplot.

• r = 1.2
• r = 0.89
• r = 0
• r = -0.89

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

Shown is a residual plot. Would a linear regression model of the data be most appropriate?

• YES
• NO

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CCSS.HSS.ID.A.3

In a scatterplot r is called

• Coefficient of Determination
• Correlation coefficient
• Regression Line
• Slope

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CCSS.HSS.ID.C.8

A scatter plot and residual plot are the same thing.

• True
• False

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CCSS.HSS.ID.B.6

LSRL stands for

• Least Sum Regression Line
• Least Squares Regression Line
• Lower Standard Regression Line
• Line Scatterplot Regression Line

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CCSS.HSS.ID.C.7

CCSS.HSS.ID.C.8

The Coefficient of Determination is 

• r squared
• r 
• r times the standard deviation of y over the standard deviation of x
• a + bx

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CCSS.HSS.ID.C.8