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

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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30 The following figure computed from SQFT data is given:  ∑(? − ?̅)² = 128237398.86    
  Find standard error of the slope coefficient, se(?₁) = ______.        
  The margin of error to build a 95% confidence interval for the slope coefficient that relates the price response to each additional square foot is _______.
 
a 0.005                  
b 0.009                  
c 0.016                  
d 0.028                  
                     
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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