SUMWRYOUTPUT Regression Statistics Multiple A RSquare Adusted R Square 0sa2107045 Standard Eror 0.777020675 0603761129 87111.55191 Observations ANOVA df MS Signécance F 4E11 4E11 Regression Residual Total SEN01 3E08 34 E11 E:09 36 E11 Coeticients Standard Error Staf Pvalue 48909 96 Leper 9% Lower 960% Upper 9.0% 27072846 22972 Lower S% Intercept Sqft 171331.46 3.50 0.00 71934.48 270728.46 12856 71934.45 179.14 2489 720 0.00 229.72 128.56

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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A Realtor examines the factors that influence the price of a house in Arlington, Massachusetts.  He collects data on recent house sales (Price) and notes each house’s square footage (Sqft) as well as its number of bedrooms (Beds) and number of bathrooms (Baths).  

A regression analysis between sales (in $1000) and advertising (in $) resulted in the following least squares line: yhat = 80,000 + 5x. This implies that an:

SUMWRYOUTPUT
Regression Statistics
Multiple A
RSquare
Adusted R Square 0sa2107045
Standard Eror
0.777020675
0603761129
87111.55191
Observations
ANOVA
df
MS
Signécance F
4E11 4E11
Regression
Residual
Total
SEN01
3E08
34
E11 E:09
36
E11
Coeticients Standard Error Staf Pvalue
48909 96
Leper 9% Lower 960% Upper 9.0%
27072846
22972
Lower S%
Intercept
Sqft
171331.46
3.50
0.00
71934.48 270728.46
12856
71934.45
179.14
2489
720
0.00
229.72
128.56
Transcribed Image Text:SUMWRYOUTPUT Regression Statistics Multiple A RSquare Adusted R Square 0sa2107045 Standard Eror 0.777020675 0603761129 87111.55191 Observations ANOVA df MS Signécance F 4E11 4E11 Regression Residual Total SEN01 3E08 34 E11 E:09 36 E11 Coeticients Standard Error Staf Pvalue 48909 96 Leper 9% Lower 960% Upper 9.0% 27072846 22972 Lower S% Intercept Sqft 171331.46 3.50 0.00 71934.48 270728.46 12856 71934.45 179.14 2489 720 0.00 229.72 128.56
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