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
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
Related questions
Question
27 | The model predicts that the |
|||||||||
a | 318.219 | |||||||||
b | 303.066 | |||||||||
c | 288.634 | |||||||||
d | 274.890 | |||||||||

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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