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(a) Write estimated regression equation model. (b) What is the estimated increase in price for a house in $ with one more bedroom, holding everything else constant? (c) Does having an additional bedroom significantly impact house prices?

(a) Write estimated regression equation model. (b) What is the estimated increase in price for a house in $ with one more bedroom, holding everything else constant? (c) Does having an additional bedroom significantly impact house prices?

MATLAB: An Introduction with Applications
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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V Empirical 5. Following is the regression output for a data from a random sample of house prices and the attributes that drive the prices. In the model below, price is the house price in $1000s, sqrft is size of house in square feet, and bdrms is number of bedrooms Dependent Variable: PRICE Method: Least Squares Variable C SQRFT BDRMS R-squared Adjusted R-squared Coefficient -19.315 0.128436 15.19819 0.631918 0.623258 63.04484 337845.4 -487.999 72.96353 S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0 (a) Write estimated regression equation model. Sample: 1 88 Included observations: 88 Std. Error 31.04662 0.013824 9.483517 (d) What does 0.631918 mean here? Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat (c) Does having an additional bedroom significantly impact house prices? t-Statistic Prob. -0.62213 0.5355 9.290506 0.000 1.60259 0.1127 (b) What is the estimated increase in price for a house in $ with one more bedroom, holding everything else constant? 293.546 102.7134 11.15907 11.24352 11.19309 1.858074 (e) The first house in the sample has sqrft of 2,438 and bdrms 5. Find the predicted selling price for this house from the OLS regression line. (f) The actual selling price of the first house in the sample was $300,000. Find the residual for this house. Does it suggest that the buyer underpaid or overpaid for the house?
Transcribed Image Text:V Empirical 5. Following is the regression output for a data from a random sample of house prices and the attributes that drive the prices. In the model below, price is the house price in $1000s, sqrft is size of house in square feet, and bdrms is number of bedrooms Dependent Variable: PRICE Method: Least Squares Variable C SQRFT BDRMS R-squared Adjusted R-squared Coefficient -19.315 0.128436 15.19819 0.631918 0.623258 63.04484 337845.4 -487.999 72.96353 S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0 (a) Write estimated regression equation model. Sample: 1 88 Included observations: 88 Std. Error 31.04662 0.013824 9.483517 (d) What does 0.631918 mean here? Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat (c) Does having an additional bedroom significantly impact house prices? t-Statistic Prob. -0.62213 0.5355 9.290506 0.000 1.60259 0.1127 (b) What is the estimated increase in price for a house in $ with one more bedroom, holding everything else constant? 293.546 102.7134 11.15907 11.24352 11.19309 1.858074 (e) The first house in the sample has sqrft of 2,438 and bdrms 5. Find the predicted selling price for this house from the OLS regression line. (f) The actual selling price of the first house in the sample was $300,000. Find the residual for this house. Does it suggest that the buyer underpaid or overpaid for the house?
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