The model predicts that the price of a residential property with a size of 2400 square feet would be ______ thousand. a 318.219                   b 303.066                   c 288.634                   d 274.890

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 5E
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27 The model predicts that the price of a residential property with a size of 2400 square feet would be ______ thousand.
a 318.219                  
b 303.066                  
c 288.634                  
d 274.890                  
                     
Questions 27-30 are basedon the following regression summary output
You are assisting the county assessor (whose job is to assess the value of properties for property tax purposes) in
determining a model where the price of a single-family residential property in a given neighborhood can be predicted
based on the size of the property (square feet). You obtain a sample of residential properties. You run a regression and
obtain the following regression output from Microsoft Excel.
y = PRICE
x = SQFT
Square feet
Regression Statistics
Multiple R
0.4404
R Square
Adjusted R Square
0.1931
Standard Error
Observations
1000
ANOVA
df
SS
MS
F
Significance F
Regression
1.
586664.2
240.08
1.1E-48
Residual
998 2438732.6
Total
999
3025397
Coefficients
Std Error
t Stat
P-value
Lower 95% Upper 95%
Intercept
126.3042
11.1587
11.3189
0.0000
104.4069
148.2014
SOFT
0.0676
1.1E-48
Transcribed Image Text:Questions 27-30 are basedon the following regression summary output You are assisting the county assessor (whose job is to assess the value of properties for property tax purposes) in determining a model where the price of a single-family residential property in a given neighborhood can be predicted based on the size of the property (square feet). You obtain a sample of residential properties. You run a regression and obtain the following regression output from Microsoft Excel. y = PRICE x = SQFT Square feet Regression Statistics Multiple R 0.4404 R Square Adjusted R Square 0.1931 Standard Error Observations 1000 ANOVA df SS MS F Significance F Regression 1. 586664.2 240.08 1.1E-48 Residual 998 2438732.6 Total 999 3025397 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 126.3042 11.1587 11.3189 0.0000 104.4069 148.2014 SOFT 0.0676 1.1E-48
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