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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

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

ENGR.ECONOMIC ANALYSIS
ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
Section: Chapter Questions
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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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