6 Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year andobtains the regression results in the table shown below.Dependent variable: In(Price)RegressorSizeIn(Size)In(Size)²BedroomsPoolViewPool x ViewConditionInterceptSummary Statistics2R²N(1)0.00044(0.000041)0.087(0.035)0.041(0.033)0.19(0.047)12.17(0.073)(2)0.730.77(0.058) (0.089)0.073(0.035)0.026(0.029)(3)0.15(0.036)6.62(0.43)0.0039(0.041)0.072(0.036)0.026(0.028)0.63(2.06)0.0085(0.17)0.075(0.039)0.026(0.032)0.150.13(0.038) (0.039)6.727.07(0.53)(7.53)(5)0.72(0.062)0.077(0.037)0.026(0.031)0.0032(0.14)0.22(0.037)6.64(0.42)0.07150.07690.08175005005005005000Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = numberof bedrooms; Pool = binary variable (1 if house has a swimming pool, 0 otherwise); View = binaryvariable (1 if house has a nice view, 0 otherwise); Condition = binary variable (1 if real estateagent reports house is in excellent condition, 0 otherwise). Is the coefficient on condition significant in column (4)?OA. The coefficient is not statistically significant at the 5% significance level.OB. The coefficient is not statistically significant at the 10% significance level.Oc. The coefficient is statistically significant at the 1% significance level.OD. The coefficient is not statistically significant at the 1% significance level.Is the interaction term between Pool and View statistically significant in column (5)?OA. The difference is statistically significant at the 1% significance level.OB. The interaction term is not statistically significant at the 1% significance level.Oc. The difference is statistically significant at the 5% significance level.OD. The difference is statistically significant at the 10% significance level.Use the regression in column (5) to compute the effect of adding a view on the price of a house without a pool.The house price is estimated to increase by%(Express your response as a percentage and round to two decimal places)Use the regression in column (5) to compute the effect of adding a view on the price of a house with a pool.%(Express your response as a percentage and round to two decimal places)

Standard errors are reported in brackets. Coefficient is significant if null hypothesis is rejected.…

Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year and obtains the regression results in the table shown below. Dependent variable: In(Price) Regressor Size In(Size) In(Size)² Bedrooms Pool View Pool View Condition Intercept Summary Statistics -2 (1) 0.00044 (0.000041) R N 0.087 (0.035) 0.041 (0.033) 0.19 (0.047) 12.17 (0.073) (2) (3) 0.73 0.77 (0.058) (0.089) 0.073 (0.035) 0.026 (0.029) 0.0039 (0.041) 0.072 (0.036) 0.026 (0.028) (4) 0.63 (2.06) 0.0085 (0.17) 0.075 (0.039) 0.026 (0.032) 0.15 0.15 0.13 (0.036) (0.038) (0.039) 6.62 6.72 7.07 (0.43) (0.53) (7.53) (5) 0.72 (0.062) 0.077 (0.037) 0.026 (0.031) 0.0032 (0.14) 0.22 (0.037) 6.64 (0.42) 0.0715 0.0769 0.0817 500 500 500 500 5000 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 pont reports house is in excellent condition, 0 otherwise).

Suppose that a researcher collects data on houses that have sold in a particular neighborhood over the past year and obtains the regression results in the table shown below. Dependent variable: In(Price) Regressor Size In(Size) In(Size)² Bedrooms Pool View Pool View Condition Intercept Summary Statistics -2 (1) 0.00044 (0.000041) R N 0.087 (0.035) 0.041 (0.033) 0.19 (0.047) 12.17 (0.073) (2) (3) 0.73 0.77 (0.058) (0.089) 0.073 (0.035) 0.026 (0.029) 0.0039 (0.041) 0.072 (0.036) 0.026 (0.028) (4) 0.63 (2.06) 0.0085 (0.17) 0.075 (0.039) 0.026 (0.032) 0.15 0.15 0.13 (0.036) (0.038) (0.039) 6.62 6.72 7.07 (0.43) (0.53) (7.53) (5) 0.72 (0.062) 0.077 (0.037) 0.026 (0.031) 0.0032 (0.14) 0.22 (0.037) 6.64 (0.42) 0.0715 0.0769 0.0817 500 500 500 500 5000 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 pont reports house is in excellent condition, 0 otherwise).

MACROECONOMICS FOR TODAY

10th Edition

ISBN:9781337613057

Author:Tucker

Publisher:Tucker

Chapter1: Introducing The Economic Way Of Thinking

Section1.A: Applying Graphics To Economics

Problem 1SQ

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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 (1) (2) (3) (4) (5) 0.00042 (0.000038) Size In(Size) 0.57 (2.03) 0.69 0.68 0.69 (0.055) (0.054) (0.087) In(Size)² 0.0078 (0.14) Bedrooms 0.0036 (0.037) Рol 0.082 0.071 0.071 0.071 0.071 (0.032) (0.034) (0.034) (0.036) (0.035) 0.037 0.027 0.026 0.027 0.027 (0.030) View (0.029) (0.028) (0.026) (0.029) Pool x View 0.0022 (0.10) 0.12 (0.035) Condition 0.13 0.12 0.12 (0.035) 0.12 (0.045) (0.035) (0.036) 6.63 (0.53) Intercept 10.97 6.60 7.02 6.60 (0.069) (0.39) (7.50) (0.40) Summary Statistics SER 0.102 0.098 0.099 0.099 0.099 R? 0.72 0.74 0.73 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…
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