1) The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop three regression models (in excel) to predict the selling price based upon each of the other factors individually. Which of these is best?
|
SELLING PRICE ($)
|
SQUARE FOOTAGE
|
BEDROOMS
|
AGE (YEARS)
|
|
84,000
|
1,670
|
2
|
30
|
|
79,000
|
1,339
|
2
|
25
|
|
91,500
|
1,712
|
3
|
30
|
|
120,000
|
1,840
|
3
|
40
|
|
127,500
|
2,300
|
3
|
18
|
|
132,500
|
2,234
|
3
|
30
|
|
145,000
|
2,311
|
3
|
19
|
|
164,000
|
2,377
|
3
|
7
|
|
155,000
|
2,736
|
4
|
10
|
|
168,000
|
2,500
|
3
|
1
|
|
172,500
|
2,500
|
4
|
3
|
|
174,000
|
2,479
|
3
|
3
|
|
175,000
|
2,400
|
3
|
1
|
|
177,500
|
3,124
|
4
|
0
|
|
184,000
|
2,500
|
3
|
2
|
|
195,500
|
4,062
|
4
|
10
|
|
195,000
|
2,854
|
3
|
3
|
2) Use the data in Problem 1 and develop a regression model to predict selling price based on the square footage and number of bedrooms. Use this to predict the selling price of a 2,000-square-foot house with 3 bedrooms. Compare this model with the models in Problem 1. Should the number of bedrooms be included in the model? Why or why not?