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What is the model if we use Floor Area and Offices as predictors? Predict Assessed Value ($’000) Suppose the final model is: Assessed Value ($'000) = 145.921 + 0.307 * Floor Area What would be the assessed value of a medical office building with a floor area of 3500 sqft, two offices, and built 15 years ago? Is this assessed value consistent with actual values in the database?

What is the model if we use Floor Area and Offices as predictors? Predict Assessed Value ($’000) Suppose the final model is: Assessed Value ($'000) = 145.921 + 0.307 * Floor Area What would be the assessed value of a medical office building with a floor area of 3500 sqft, two offices, and built 15 years ago? Is this assessed value consistent with actual values in the database?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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What is the model if we use Floor Area and Offices as predictors?

 

Predict Assessed Value ($’000)

Suppose the final model is: Assessed Value ($'000) = 145.921 + 0.307 * Floor Area

What would be the assessed value of a medical office building with a floor area of 3500 sqft, two offices, and built 15 years ago?

Is this assessed value consistent with actual values in the database?

SUMMARY OUTPUT Regression Statistics Multiple R 0.973781 R Square 0.94825 Adjusted R 0.946048 Standard E 86.98889 Observatic 50 ANOVA df SS MS F ignificance F Regressior 2 6516856 3258428 430.6065 5.98E-31 Residual 47 355652.1 7567.066 Total 49 6872508 Coefficientsandard Erre t Stat P-value Lower 95%Upper 95%ower 95.09/pper 95.0% Intercept 103.8676 40.5244 2.563089 0.013636 22.34306 185.3922 22.34306 185.3922 Floor Area 0.272087 0.017338 15.69338 2.59E-20 0.237208 0.306966 0.237208 0.306966 Offices 67.07839 26.42672 2.538279 0.014513 13.91469 120.2421 13.91469 120.2421
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R 0.973781 R Square 0.94825 Adjusted R 0.946048 Standard E 86.98889 Observatic 50 ANOVA df SS MS F ignificance F Regressior 2 6516856 3258428 430.6065 5.98E-31 Residual 47 355652.1 7567.066 Total 49 6872508 Coefficientsandard Erre t Stat P-value Lower 95%Upper 95%ower 95.09/pper 95.0% Intercept 103.8676 40.5244 2.563089 0.013636 22.34306 185.3922 22.34306 185.3922 Floor Area 0.272087 0.017338 15.69338 2.59E-20 0.237208 0.306966 0.237208 0.306966 Offices 67.07839 26.42672 2.538279 0.014513 13.91469 120.2421 13.91469 120.2421
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