Questions 27-30 are based on 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. a b С d 28 a b с d 29 a b Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations с ANOVA Regression Error Total Intercept SQFT df 39.348 38.209 Coefficients 7.0340 0.1320 y = PRICE x = SQFT 0.7442 0.5534 SS 978 1 976 1511142.6 977 3387132.24 Std Error 27 The model predicts that the price of a residential property with a size of 2200 square feet would be 315.572 309.385 303.318 297.371 5.5404 MS F Significance F 1875989.6 1211.6433 3.00623E-17. t Stat The regression model estimates that by the size of the properties. 0.5094 0.5539 0.6019 0.6542 Square feet 1.2696 P-value 0.2045 3.00623E-17: The standard error of estimate, se(e), for the regression is: 41.751 40.535 Lower 95% Upper 95% -3.8384 17.9065 ($1,000). fraction of the variation in the price of the residential properties is explained
Questions 27-30 are based on 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. a b С d 28 a b с d 29 a b Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations с ANOVA Regression Error Total Intercept SQFT df 39.348 38.209 Coefficients 7.0340 0.1320 y = PRICE x = SQFT 0.7442 0.5534 SS 978 1 976 1511142.6 977 3387132.24 Std Error 27 The model predicts that the price of a residential property with a size of 2200 square feet would be 315.572 309.385 303.318 297.371 5.5404 MS F Significance F 1875989.6 1211.6433 3.00623E-17. t Stat The regression model estimates that by the size of the properties. 0.5094 0.5539 0.6019 0.6542 Square feet 1.2696 P-value 0.2045 3.00623E-17: The standard error of estimate, se(e), for the regression is: 41.751 40.535 Lower 95% Upper 95% -3.8384 17.9065 ($1,000). fraction of the variation in the price of the residential properties is explained
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Questions 27-30 are based on 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.
27
a
b
с
d
28
a
b
с
d
29
a
b
с
d
30
a
b
с
d
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations.
ANOVA
Regression
Error
Total
Intercept
SQFT
df
Coefficients
7.0340
0.1320
y = PRICE
x = SQFT
0.7442
0.0074
0.0128
0.0221
0.0381
0.5534
SS
978
1
976
1511142.6
977 3387132.24
Std Error
5.5404
MS
1875989.6
The regression model estimates that
by the size of the properties.
0.5094
0.5539
0.6019
0.6542
t Stat
1.2696
Square feet
Significance F
F
1211.6433 3.00623E-17.
The model predicts that the price of a residential property with a size of 2200 square feet would be
315.572
309.385
303.318
297.371
P-value Lower 95% Upper 95%
0.2045 -3.8384 17.9065
3.00623E-17:
The standard error of estimate, se(e), for the regression is:
41.751
40.535
39.348
38.209
($1,000).
fraction of the variation in the price of the residential properties is explained
The following figure computed from SQFT data is given: Σ(x-x)²= 107713904.30
Find standard error of the slope coefficient, se(b,) =
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
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