A realtor in a local area is interested in being able to predict the selling price for a newly listed home or for someone considering listing their home. This realtor would like to attempt to predict the selling price by using the size of the home (in square feet), the number of rooms, the age of the home (in years) and if the home has an attached garage. Use the Excel output below to determine if this realtor will be able to use this information to predict the selling price (in $1000). Summary measures Multiple R 0.9439 R-Square 0.8910 Adj. R-Square 0.8474 StErr of Estimate 22.241 Regression coefficients Coefficient Std Err t-value p-value Constant -19.026 54.769 -0.3474 0.7355 Size 7.494 1.529 4.9010 0.0006 Number of Rooms 7.153 9.211 0.7767 0.4553 Age -0.673 0.992 -0.6789 0.5126 Attached Garage 0.453 20.192 0.0224 0.9826 Which statements are true? ( )
A.
T he only variable that is significant in this model is the size of the home in square feet
B.
The attached garage is a dummy (0, 1) variable
C.
R 2 = 0.8910; This represents 89.1% of the variation in the selling price can be explained by this regression equation.
D.
You should not recommend that the realtor use this model to predict the selling price of a home