Determining the sale price of a home is an important task for city assessors, as it helps the city project future tax revenue. Regression mo

Determining the sale price of a home is an important task for city assessors, as it helps the city project future tax revenue. Regression models using the physical characteristics of a home to predict the sale price is standard practice for many assessors. A random sample of 724 homes sold in Ames, Iowa, between 2006 and 2010 was obtained to build such a model for the city of Ames. The assessor considered the following variables in their initial model:
Variable Description
LotArea Lot size (in thousands of square feet)
LivingArea Living space (in thousands of square feet)
Bedrooms Number of bedrooms
Rooms Number of rooms
Fireplaces Number of fireplaces
Bath Number of bathrooms
Age Age of the home (in years)
Price Sale price of the home (in thousands of dollars)
Below is the output obtained from the statistical software.
Estimate Std Error t value Pr(>|t|)
(Intercept) 100.55 6.167 16.31 < 0.0001
LotArea 0.99 0.144 6.86 < 0.0001
LivingArea 112.97 5.600 20.17 <0 .0001
Bedrooms −16.35 2.366 −6.91 < 0.0001
Rooms −0.51 1.667 −0.30 0.7613
Fireplaces 12.80 2.206 5.80 < 0.0001
Bat −13.60 3.459 −3.93 < 0.0001
Age−0.96 0.054 −17.86 < 0.0001
Number of Observations Residual Std Error R2 Adjusted R2
724 33.69 0.7686 0.7663
What is the response variable?
a. lot area
b. living area
c. age
d. sale price
What proportion of the variation in sale price does this multiple regression model explain?
a. .3369
b. .7686
c. .7663
d. 0.2314