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Data were collected from a random sample of 290 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number o 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 ag 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: Price = 116.8 + 0.475BDR+22.9Bath + 0.153Hsize + 0.004Lsize (23.4) (2.69) (8.76) (0.011) (0.00047) + 0.088Age - 47.8Poor, R =0.71, SER=40.7 (0.305) (10.3) The t-statistic for the coefficient on BDR is (Round your response to three decimal places.) Is the coefficient on BDR statistically significantly different from zero?

Data were collected from a random sample of 290 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number o 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 ag 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: Price = 116.8 + 0.475BDR+22.9Bath + 0.153Hsize + 0.004Lsize (23.4) (2.69) (8.76) (0.011) (0.00047) + 0.088Age - 47.8Poor, R =0.71, SER=40.7 (0.305) (10.3) The t-statistic for the coefficient on BDR is (Round your response to three decimal places.) Is the coefficient on BDR statistically significantly different from zero?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Data were collected from a random sample of 290 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: Price = 116.8 + 0.475BDR+ 22.9Bath + 0.153Hsize + 0.004Lsize II (23.4) (2.69) (8.76) (0.011) (0.00047) + 0.088Age – 47.8Poor, R´ = 0.71, SER=40.7 (10.3) (0.305) The t-statistic for the coefficient on BDR is (Round your response to three decimal places.) Is the coefficient on BDR statistically significantly different from zero? O A. Since the t-statistic > 0.05, the coefficient on BDR is not statistically significantly different from zero. B. Since the t-statistic < 1.96, the coefficient on BDR is not statistically significantly different from zero. O C. Since the t-statistic < 0.05, the coefficient on BDR is statistically significantly different from zero. O D. Since the t-statistic > 1.96, the coefficient on BDR is statistically significantly different from zero. Typically five-bedroom houses sell for much more than two-bedroom houses. Is this consistent with the regression? O A. 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. O B. 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. 1 A homeowner purchases 1960 square feet from an adjacent lot. Construct a confidence interval of 90% for the change in the value of her house.
Transcribed Image Text:Data were collected from a random sample of 290 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: Price = 116.8 + 0.475BDR+ 22.9Bath + 0.153Hsize + 0.004Lsize II (23.4) (2.69) (8.76) (0.011) (0.00047) + 0.088Age – 47.8Poor, R´ = 0.71, SER=40.7 (10.3) (0.305) The t-statistic for the coefficient on BDR is (Round your response to three decimal places.) Is the coefficient on BDR statistically significantly different from zero? O A. Since the t-statistic > 0.05, the coefficient on BDR is not statistically significantly different from zero. B. Since the t-statistic < 1.96, the coefficient on BDR is not statistically significantly different from zero. O C. Since the t-statistic < 0.05, the coefficient on BDR is statistically significantly different from zero. O D. Since the t-statistic > 1.96, the coefficient on BDR is statistically significantly different from zero. Typically five-bedroom houses sell for much more than two-bedroom houses. Is this consistent with the regression? O A. 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. O B. 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. 1 A homeowner purchases 1960 square feet from an adjacent lot. Construct a confidence interval of 90% for the change in the value of her house.
A homeowner purchases 1960 square feet from an adjacent lot. Construct a confidence interval of 90% for the change in the value of her house. The 90% confidence interval for the effect of lot size on price is ( (in thousands of dollars). (Round your responses to two decimal places.) Lot size is measured in square feet. Do you think that another scale might be more appropriate? O A. 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. O 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. O C. No, choosing another scale would not affect the regression results because the estimate coefficient would remain unaffected. O D. Yes, if the lot size were measured in thousands of square feet, the estimate coefficient would be 4 instead of 0.00N, thus making the regression results easy to read and interpret. The degree of freedom to test if the coefficients on BDR and Age are statistically different from zero at the 1% level is . The critical value for the preceding test using the Fm co distribution table is . (Enter your values exactly as they appear in the table.) The F-statistic for omitting BDR and Age from the regression is F= 0.08. Are the coefficients on BDR and Age statistically different from zero at the 1% level? A. Because 0.08 is greater than the critical value, the coefficients are not jointly significant at the 1% level. O B. Because 0.08 is less than the critical value, the coefficients are not jointly significant at the 1% level. O C. Because 0.08 is greater than the critical value, the coefficients are jointly significant at the 1% level. O D. Because 0.08 is less than the critical value, the coefficients are jointly significant at the 1% level.
Transcribed Image Text:A homeowner purchases 1960 square feet from an adjacent lot. Construct a confidence interval of 90% for the change in the value of her house. The 90% confidence interval for the effect of lot size on price is ( (in thousands of dollars). (Round your responses to two decimal places.) Lot size is measured in square feet. Do you think that another scale might be more appropriate? O A. 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. O 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. O C. No, choosing another scale would not affect the regression results because the estimate coefficient would remain unaffected. O D. Yes, if the lot size were measured in thousands of square feet, the estimate coefficient would be 4 instead of 0.00N, thus making the regression results easy to read and interpret. The degree of freedom to test if the coefficients on BDR and Age are statistically different from zero at the 1% level is . The critical value for the preceding test using the Fm co distribution table is . (Enter your values exactly as they appear in the table.) The F-statistic for omitting BDR and Age from the regression is F= 0.08. Are the coefficients on BDR and Age statistically different from zero at the 1% level? A. Because 0.08 is greater than the critical value, the coefficients are not jointly significant at the 1% level. O B. Because 0.08 is less than the critical value, the coefficients are not jointly significant at the 1% level. O C. Because 0.08 is greater than the critical value, the coefficients are jointly significant at the 1% level. O D. Because 0.08 is less than the critical value, the coefficients are jointly significant at the 1% level.
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