In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that important variables affecting selling price (Y, measured in thousands of dollars) are the size of a house (X1, measured in hundreds of square feet), the total number of rooms (X2), age (X3), and whether or not the house has an attached garage (X₁ No = 0, Yes = 1). Y = a+B₁X₁+ẞ₁₂X₂+ß₂X3+ß«X₁+ε Now suppose that the estimate of the model produces following results: a = 166.048, b₁ = 3.459, b₂ = 8.015, b = -0.319, b₁ = 1.186. 8b1 = 1.079, 862 = 5.288. 863=0.789, 864 = 12.252, R² = 0.838. F-statistic = 12.919, and se= 13.702. Note that the sample consists of 15 randomly selected observations. According to the estimated model, holding all else constant, an additional room means the market value by approximately Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining the market value of residential property? Check all that apply. Size of the house (X1) Total number of rooms (X2) Age (X2) Having an attached garage (X4) What proportion of the total variation in sales is explained by the regression equation? ○ 0.789 0.129 0.838 The given F-value shows that the assessor the 0.05 level) proportion of the variation in income. reject the null hypothesis that neither of the independent variables explains a significant (at Which of the following is an approximate 95 percent prediction interval for the selling price of a 15-year-old house having 18 hundred sq. ft., 3 rooms, and an attached garage? (186.964, 214.368) (221.352, 276.160) O (173.262, 228.070)
In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that important variables affecting selling price (Y, measured in thousands of dollars) are the size of a house (X1, measured in hundreds of square feet), the total number of rooms (X2), age (X3), and whether or not the house has an attached garage (X₁ No = 0, Yes = 1). Y = a+B₁X₁+ẞ₁₂X₂+ß₂X3+ß«X₁+ε Now suppose that the estimate of the model produces following results: a = 166.048, b₁ = 3.459, b₂ = 8.015, b = -0.319, b₁ = 1.186. 8b1 = 1.079, 862 = 5.288. 863=0.789, 864 = 12.252, R² = 0.838. F-statistic = 12.919, and se= 13.702. Note that the sample consists of 15 randomly selected observations. According to the estimated model, holding all else constant, an additional room means the market value by approximately Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining the market value of residential property? Check all that apply. Size of the house (X1) Total number of rooms (X2) Age (X2) Having an attached garage (X4) What proportion of the total variation in sales is explained by the regression equation? ○ 0.789 0.129 0.838 The given F-value shows that the assessor the 0.05 level) proportion of the variation in income. reject the null hypothesis that neither of the independent variables explains a significant (at Which of the following is an approximate 95 percent prediction interval for the selling price of a 15-year-old house having 18 hundred sq. ft., 3 rooms, and an attached garage? (186.964, 214.368) (221.352, 276.160) O (173.262, 228.070)
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
Chapter4: Estimating Demand
Section: Chapter Questions
Problem 1E
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Transcribed Image Text:In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of
residential property within her jurisdiction. The assessor suspects that important variables affecting selling price (Y, measured in thousands of dollars)
are the size of a house (X1, measured in hundreds of square feet), the total number of rooms (X2), age (X3), and whether or not the house has an
attached garage (X₁ No = 0, Yes = 1).
Y = a+B₁X₁+ẞ₁₂X₂+ß₂X3+ß«X₁+ε
Now suppose that the estimate of the model produces following results: a = 166.048, b₁ = 3.459, b₂ = 8.015, b = -0.319, b₁ = 1.186.
8b1 = 1.079, 862 = 5.288. 863=0.789, 864 = 12.252, R² = 0.838. F-statistic = 12.919, and se= 13.702. Note that the sample consists of 15
randomly selected observations.
According to the estimated model, holding all else constant, an additional room means the market value
by approximately
Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining the market value of residential
property? Check all that apply.
Size of the house (X1)
Total number of rooms (X2)
Age (X2)
Having an attached garage (X4)
What proportion of the total variation in sales is explained by the regression equation?
○ 0.789
0.129
0.838
The given F-value shows that the assessor
the 0.05 level) proportion of the variation in income.
reject the null hypothesis that neither of the independent variables explains a significant (at
Which of the following is an approximate 95 percent prediction interval for the selling price of a 15-year-old house having 18 hundred sq. ft., 3 rooms,
and an attached garage?
(186.964, 214.368)
(221.352, 276.160)
O (173.262, 228.070)
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