We conduct a study to explain house prices (in thousands of dollars) in Montreal. We run the following multiple regression model using 100 observations: House prices = 0 + 1(Areai) + 2(Number of Bedroomsi) + 3(Year of Constructioni) + i

The explanatory variables are: (i) Area, a dummy variable (where Downtown = 1, and Other = 0); (ii) Number of Bedrooms; and (iii) Year of Construction. We obtained the following results:

ANOVA

df SS MS

Regression 3 5523.709 1841.57

Residual A 335.5119 3.505333

Total 99 5861.221

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 39.75415 31.3013 1.270048 0.207137 22.3784 101.8867

Area B 0.374814 13.72036 0.000000 4.398583 5.886582

Year of Construction 0.03474 0.015665 2.2176 C 0.003644 0.065835

Number of Bedrooms 5.000415 0.133277 37.51899 0.000000 4.735863 D

1. (2 points) Find the value of A in the above table.

Answer: (A) 90 (B) 92 (C) 94 (D) 96 (E) 98

2. (2 points) Find the value of B in the above table.

Answer: (A) 0.5142 (B) 5.1420 (C) 0.1382 (D) 1.3820

3. (2 points) Find the value of C in the above table (a reasonably small range is OK if the exact value is not available from tables):

Answer: (A) 0.01 P-Value 0.02 (B) 0.04 P-Value 0.08 (C) 0.02 P-Value 0.04 (D) 0.05 P-Value 0.10

4. (2 points) Find the value of D in the above table

Answer: (A) Without the standard error of the model, the upper bound cannot be computed. (B) 4.935863 (C) 6.378671 (D) 5.264957