Regression and Residuals

• 50 miles

• -50 miles

• 8 miles

• -25 miles

When using midterm exam scores to predict a student's final grade in the class, the student would prefer to have

• positive residual, because that means the student's final grade is higher than we would predict with the model

• positive residual, because that means the student's final grade is lower than we would predict with the model

• residual equal to zero, because that means the student’s final grade is exactly what we would predict with the model

• negative residual, because that means the student’s final grade is higher than we would predict with the model

• 617.51 miles

• 621.09 miles

• 100 miles

• 0.19 miles

• We predict that for every mile driven, 61.93 hours has passed

• We predict that for every mile driven, 1.79 hours has passed

• We predict that for every hour driven, 1.79 miles is covered

• We predict that for every hour driven, 61.93 miles is covered

A negative residual means...

• that the predicted value is too big

• that the predicted value is too small

• that the x value is too big

• that the x value is too small

What is the "error" or the how off your predicted value is from your actual value?

• Residual

• Correlation

• Causation

• Error

Use the dropdowns to complete the statements about the graph:

The strength of the linear relationship is ​ (a)   (strong/weak) because the residual values are relatively ​ (b)   (small/large) . The residuals on the graph ​ (c)   (show/do not show) a pattern.

• weak

• strong

• large

• small

• show

• do not show

Use the dropdowns to complete the statements about the graph: The strength of the linear relationship is ​ (a)   (strong/weak) because the residual values are relatively ​ (b)   (small/large) . The residuals on the graph ​ (c)   (show/do not show) a pattern.

• weak

• strong

• large

• small

• show

• do not show

Question 9

• 1

• 2

• 3

• 4

A negative residual means...

• that the predicted value is too big

• that the predicted value is too small

• that the x value is too big

• that the x value is too small