Data were collected from a random sample of 380 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor." An estimated regression yields: P/ric\\e = 116.8 + 0.475307? + 22.98ath + 0.153Hsize + 0.004Lsize (23.4) (2.66) (8.76) (0.011) (0.00047) + 0.088Age 47.8Poor, F12 = 0.71, SER = 40.7 (0.305) (10.3) The tstatistic for the coefcient on BDR is .179 . (Round your response to three decimal places.) Is the coefcient on BDR statistically signicantly different from zero? Since the tstatistic 0.05, the coefficient on BDR is not statistically signicantly different from zero. Since the tstatistic > 1.96, the coefficient on BDR is statistically significantly different from zero. Typically fivebedroom houses sell for much more than two-bedroom houses. Is this consistent with the regression? Yes, the coefficients on BDR and Hsize accurately take into account that each additional bedroom changes not only the total number of bedrooms but the total house size and thus price. V No, the coefficient on BDR measures the partial effect of the number of bedrooms, holding house size constant, and thus significantly underestimates the price of five-bedroom houses. A homeowner purchases 1960 square feet from an adjacent lot. Construct a confidence interval of 80% for the change in the value of her house. The 80% confidence interval for the effect of lot size on price is ( , ) (in thousands of dollars). (Round your responses to two decimal places.) 5. Lot size is measured in square feet. Do you think that another scale might be more appropriate? a) Yes, if the lot size were measured in thousands of square feet, the estimate coefficient would be 4 instead of 0.0040, thus making the regression results easy to read and interpret. b) No, if the lot size were measured in thousands of square feet, the estimate coefficient would be 0.000004 instead of 0.004 thus making the regression results more difficult to read and interpret. c) Yes, if the lot size were measured in thousands of square feet, the estimate coefficient would be 1,000 instead of 0.004 thus normalizing the regression results. d) No, choosing another scale would not affect the regression results because the estimate coefficient would remain unaffected. 6. The degree of freedom to test if the coefficients on BDR and Age are statistically different from zero at the 10% level is