Consider the following Stata output for the model of house prices: lhprice=β0+β1bdrms+β2llotsize+β3lsqrft+u estimated on a random sample of 86 houses. You may assume the Gauss Markov Assumptions hold. The variables are defined as: lhprice = the natural log of the house price bedrms = the number of bedrooms llotsize = the natural log of the land or lot size lsqrft = the natural log of the floor space of the house in square feet. Source SS df MS Model Residual 4.65621742 2.09289629 3 82 1.55207247 0.025523126 Total 6.74911372 85 0.079401338 lprice coef. std. err. t P > |t| [95% Conf. Interval] bdrms 0.0581214 -0.0055282 0.121771 llotsize 0.1494716 0.0616548 0.2372884 lsqrft 0.636171 0.421989 0.850353 _cons -0.7083136 -2.221521 0.8048935 Number of obs = 86 F (3, 82) = 60.81 Prob > F = 0.0000 R-squared = 0.6899 Adj R-squared = 0.6786 Root MSE = 0.15976 Does the number of bedrooms have a statistically significant effect on the house price at the 1% significance level? a) No, all else equal, the effect of the number of bedrooms is not statistically significant at the 1% level. b) Yes, all else equal, the effect of the number of bedrooms is statistically significant at the 1% level.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

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Question

Consider the following Stata output for the model of house prices:

lhprice=β₀+β₁bdrms+β₂llotsize+β₃lsqrft+u

estimated on a random sample of 86 houses. You may assume the Gauss Markov Assumptions hold.

The variables are defined as:

lhprice = the natural log of the house price
bedrms = the number of bedrooms
llotsize = the natural log of the land or lot size
lsqrft = the natural log of the floor space of the house in square feet.

Source

Model

Residual

4.65621742

2.09289629

1.55207247

0.025523126

Total

6.74911372

0.079401338

lprice	coef.	std. err.	t	P > \|t\|	[95% Conf. Interval]
bdrms	0.0581214				-0.0055282	0.121771
llotsize	0.1494716				0.0616548	0.2372884
lsqrft	0.636171				0.421989	0.850353
_cons	-0.7083136				-2.221521	0.8048935

Number of obs = 86

F (3, 82) = 60.81

Prob > F = 0.0000

R-squared = 0.6899

Adj R-squared = 0.6786

Root MSE = 0.15976

Does the number of bedrooms have a statistically significant effect on the house price at the 1% significance level?

a) No, all else equal, the effect of the number of bedrooms is not statistically significant at the 1% level.

b) Yes, all else equal, the effect of the number of bedrooms is statistically significant at the 1% level.

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