In a study of housing demand, a county assessor is interested in developing a regression model to estimate the selling price of residential properties within her jurisdiction. She randomly selects 15 houses and records the selling price in addition to the following values: the size of the house (in hundreds of square feet), the total number of rooms in the house, the age of the house, and an indication of whether the house has an attached garage.

1.Estimate a multiple regression equation using all of the available explanatory variables to predict the selling price of residential properties.

2.Do you see any evidence of multicollinearity in this model? Explain.

3.Now run a simple linear regression using the variable that has the highest correlation with the selling price.

4.Compare the results of both models and summarize your findings.

House:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Selling Price:

240800

215200

199200

182400

144800

126400

312000

185600

176800

162400

304000

256000

222400

159200

130400

Size:

3070

2660

2390

2240

1500

1440

3720

2520

2160

2140

3000

3000

2700

2020

1200

Number of Rooms:

7

6

7

6

7

7

9

7

7

8

8

8

7

7

6

Age:

23

23

20

9

17

8

31

15

8

20

15

18

17

18

17

Attached Garage:

1

1

1

0

0

0

1

1

0

1

1

1

1

0

0