Linear Regression 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

Use Desmos to generate a linear regression model. What is the residual for the point (5, 96)? Round to the nearest hundredth.

• 2.34

• -0.31

• -0.53

• 3.67

Use Desmos to determine the linear regression model for this data set. What is the residual for the point (6, 368)? Round to the nearest hundredth.

• -2.21

• 6.31

• -2.91

• -0.73

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

The equation of the line of best fit for the points on a scatterplot is ŷ = 5.3-0.23x. What is the residual for the point (5, 4)?

• -0.15

• 0.15

• 1

• 0.62

• 0.85

• 
• 
• 
• 

After performing analyses on a set of data, Mrs. Schoeneck examined the scatter plot of the residual values for each analysis. Which scatter plot indicated the best linear fit for the data?

• A

• B

• C

The equation of the LSRL for the points on a scatterplot is ŷ = 5.3-0.23x. What is the residual for the point (5, 4)?

• -0.15

• 0.15

• 1

• 0.62

• 0.85

Shown is a residual plot.
Would a linear regression model be a good fit?

• YES

• NO