A real estate investor has devised a model to estimate home prices in a new suburban development. Data for a random sample of 30 homes were gathered on the selling price of the home (in units of $1,000), the home size (square feet), the lot size (in units of 1,000 square feet), and the number of bedrooms.
The following multiple regression output was generated:
Multiple R 0.9647
R Square 0.9307
Adjusted R Square 0.9227
Standard Error 26.0389
Coefficients Standard Error
Intercept -34.6165 38.3735 -0.9021 0.3753
X1 (Sq ft) 0.1532 0.0184 8.3122 0.0000
X2 (Lot size) 9.0024 1.7120 5.2583 0.0002
X3 (Bedrooms) 17.3903 6.8905 2.5238 0.1259
a. Why is the coefficient for lot size appositive number?
b. Which is the most statistically significant variable? What evidence shows this?
c. Which is the least statistically significant variable? What evidence shows this?
d. For a 0.05 level of significance, should any variable be dropped from this model? Why or why not?
e. Predict the sales price of a 2,134-square foot home with a lot size of 13,400 square feet and three bedrooms.
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