In column (4), does the elasticity of price with respect to size depend on the size...

 

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In column (4), does the elasticity of price with respect to size depend on the size of the house? Use a 5% level of significance for the test. The critical value is (two decimal places) The test statistic is (two decimal places) Is there sufficient evidence to conclude that the elasticity of price with respect to size depends on the size of the house? (Type Yes or No) QUESTION 5 What can we conclude about the interaction term between pool and view in column (5)? a. The interaction term is significant at the 10% level and its sign indicates that the impact of installing a pool has a smaller effect on price when the house has a view b. The interaction term is significant at the 10% level and its sign indicates that the impact of installing a pool has a larger effect on price when the house has a view. O c. The sign of the interaction term implies that the impact of installing a pool has a smaller effect on price when the house has a view, but the estimate is not statistically significant at the 10% level. d. The sign of the interaction term implies that the impact of installing a pool has a larger effect on price when the house has a view, but the estimate is not statistically significant at the 10% level QUESTION 6 Regardless of your conclusion in Question 5, and continuing to use column (5), answer the following questions: Find the approximate percent effect of adding a pool on the price of a house with a view. Find the approximate percent effect of adding a pool on the price of a house without a view. Report both answers to one decimal place without a percentage sign. QUESTION 2 Suppose a different family purchases a 2,500 square foot house, and also plans to undertake extensions that will increase the total size of the house by 500 square feet. Again, use the results in column (1), and assume the house has a pool, is in excellent condition, but does not have a view (which will not change after the extensions). Without performing any further calculations, what are you able to conclude about the expected impact of the extensions on this house? O a. Both the estimated dollar increase and the estimated percentage increase in price would be the same as for the house described in Question 1. O b. The estimated percentage increase in price would be the same as for the house described in Question 1, but the estimated dollar increase would be higher. O c. Both the estimated dollar increase and the estimated percentage increase in price would be higher than for the house described in Question 1. O d. The estimated percentage increase in price would be the same as for the house described in Question 1, but the estimated dollar increase would be lower. QUESTION 3 Using column (4), is the overall effect of size on price statistically significant? How would you go about testing this hypothesis? Match each question to the correct option. What is the null hypothesis for this test? A. t-test What is the alternative hypothesis for this test? What is the appropriate test? What is the appropriate critical value? Assume a 5% level of significance. B. H : B B # 2 C. H : B + B = 1 D. 1.64 E. =. H : B = 0 0 1 F. 2.30 G. H: At least one of B and/or B #0 # #0 H. H : B = P I. 1.96 J. F-test K. 3.00 L. H Ho =0 At least one of Band/or B0 M. 3.84 N. . H : B# 0 B. QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor Size In(Size) In(Size) Bedrooms Pool View Pool X View Condition Intercept (1) 0.00042 (0.000038) 0.082 (0.032) 0.037 (0.029) 0.13 (0.045) 10.97 (0.069) 0.102 (2) 0.72 0.69 (0.054) 0.071 (0.034) 0.027 (0.028) 0.12 (0.035) 6.60 (0.39) (3) 0.098 0.68 (0.087) 0.0036 (0.037) 0.071 (0.034) 0.026 (0.026) 0.12 (0.035) 6.63 (0.53) (4) 0.099 0.57 (2.03) 0.0078 (0.14) 0.071 (0.036) 0.027 (0.029) 0.12 (0.036) 7.02 (7.50) 0.099 (5) 0.73 0.69 (0.055) 0.071 (0.035) Summary Statistics SER R 0.74 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binary variable (1 if house has a nice view, 0 otherwise); Condition = binary variable (1 if real estate agent reports house is in excellent condition, 0 otherwise). 0.027 (0.030) 0.0022 (0.10) 0.12 (0.035) 6.60 (0.40) 0.099 A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions? (Report your answer to the nearest dollar and do not include any commas or $ signs.) In column (4), does the elasticity of price with respect to size depend on the size of the house? Use a 5% level of significance for the test. The critical value is (two decimal places) The test statistic is (two decimal places) Is there sufficient evidence to conclude that the elasticity of price with respect to size depends on the size of the house? (Type Yes or No) QUESTION 5 What can we conclude about the interaction term between pool and view in column (5)? a. The interaction term is significant at the 10% level and its sign indicates that the impact of installing a pool has a smaller effect on price when the house has a view b. The interaction term is significant at the 10% level and its sign indicates that the impact of installing a pool has a larger effect on price when the house has a view. O c. The sign of the interaction term implies that the impact of installing a pool has a smaller effect on price when the house has a view, but the estimate is not statistically significant at the 10% level. d. The sign of the interaction term implies that the impact of installing a pool has a larger effect on price when the house has a view, but the estimate is not statistically significant at the 10% level QUESTION 6 Regardless of your conclusion in Question 5, and continuing to use column (5), answer the following questions: Find the approximate percent effect of adding a pool on the price of a house with a view. Find the approximate percent effect of adding a pool on the price of a house without a view. Report both answers to one decimal place without a percentage sign. QUESTION 2 Suppose a different family purchases a 2,500 square foot house, and also plans to undertake extensions that will increase the total size of the house by 500 square feet. Again, use the results in column (1), and assume the house has a pool, is in excellent condition, but does not have a view (which will not change after the extensions). Without performing any further calculations, what are you able to conclude about the expected impact of the extensions on this house? O a. Both the estimated dollar increase and the estimated percentage increase in price would be the same as for the house described in Question 1. O b. The estimated percentage increase in price would be the same as for the house described in Question 1, but the estimated dollar increase would be higher. O c. Both the estimated dollar increase and the estimated percentage increase in price would be higher than for the house described in Question 1. O d. The estimated percentage increase in price would be the same as for the house described in Question 1, but the estimated dollar increase would be lower. QUESTION 3 Using column (4), is the overall effect of size on price statistically significant? How would you go about testing this hypothesis? Match each question to the correct option. What is the null hypothesis for this test? A. t-test What is the alternative hypothesis for this test? What is the appropriate test? What is the appropriate critical value? Assume a 5% level of significance. B. H : B B # 2 C. H : B + B = 1 D. 1.64 E. =. H : B = 0 0 1 F. 2.30 G. H: At least one of B and/or B #0 # #0 H. H : B = P I. 1.96 J. F-test K. 3.00 L. H Ho =0 At least one of Band/or B0 M. 3.84 N. . H : B# 0 B. QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor Size In(Size) In(Size) Bedrooms Pool View Pool X View Condition Intercept (1) 0.00042 (0.000038) 0.082 (0.032) 0.037 (0.029) 0.13 (0.045) 10.97 (0.069) 0.102 (2) 0.72 0.69 (0.054) 0.071 (0.034) 0.027 (0.028) 0.12 (0.035) 6.60 (0.39) (3) 0.098 0.68 (0.087) 0.0036 (0.037) 0.071 (0.034) 0.026 (0.026) 0.12 (0.035) 6.63 (0.53) (4) 0.099 0.57 (2.03) 0.0078 (0.14) 0.071 (0.036) 0.027 (0.029) 0.12 (0.036) 7.02 (7.50) 0.099 (5) 0.73 0.69 (0.055) 0.071 (0.035) Summary Statistics SER R 0.74 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binary variable (1 if house has a nice view, 0 otherwise); Condition = binary variable (1 if real estate agent reports house is in excellent condition, 0 otherwise). 0.027 (0.030) 0.0022 (0.10) 0.12 (0.035) 6.60 (0.40) 0.099 A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions? (Report your answer to the nearest dollar and do not include any commas or $ signs.)

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QUESTION 2 Based on the information provided and assuming the results in column 1 we can conclude th... View the full answer