Overall Notes

• There is one version of this assessment provided in the Teacher Resources Materials (TRM). Use this assessment as-is or use it as a model to create your own. 

• If you are making your own assessment, consider using the test bank and the ExamView software.

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Common Student Errors

• Students will often flip the calculation for a residual by subtracting the actual y from the predicted y. Remind them that residual = When students are asked to describe the relationship between two quantitative variables, Actual – Predicted = A – P.  

• When interpreting the slope of the equation for the least-squares regression line, students will often forget to include “predicted” when referring to the y variable. 

• When interpreting the y-intercept of the equation for the least-squares regression line, students will often forget to include “predicted” when referring to the y variable value when x = 0. 

• Students often think that outliers in a scatterplot must always have a big impact on the slope of the least-squares regression line. Remember that an outlier with an x-value close to the mean of x will pull the least-squares regression line towards itself, but without changing the slope much. 

• When justifying why a linear model is appropriate for a set of two-variable data, students often want to give one single reason (the residual plot has no leftover pattern). Urge them to provide additional justification (r2 close to 1, small s). 

• Students might have trouble memorizing the word-for-word interpretations of s and r2. We think this is OK as long as the meaning of what they write down is correct.