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

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It is the variable of description