Use the following information to answer Q10-Q11. The researcher estinmated a nonlinear regression by adding the variable STORIES^2 (i.e., STORIES²). log (selling price) = bo + b̟ log (SFLA) + b,BEDS + b,BATHS + b,STORIES + b;STORIES + B6VACANT + b,Age +e Dependent Variable: LOG(SELLING PRICE) Method Least Squares Date: 08/03/21 Time: 16:58 Sample: 1 6660 IF YEAR=2001 Included observations: 1746 Variable Coefficient Std. Error t-Statistic Prob. C LOG(SFLA) BEDS BATHS STORIES STORIES 2 VACANT AGE 5.898568 0.925893 -0.069610 0.033590 -0.471502 0.149252 -0.037820 -0.004177 28.29477 34.60462 -7.313708 2.178491 -2.585392 2.504330 -3.356101 -15.78847 0.0000 0.0000 0.0000 0.0295 0.0098 0.0124 0.0008 0.0000 0.208468 0.026756 0.009518 0.015419 0.182371 0.059597 0.011269 0.000265 R-squared Adjusted R-squared S.É of regression Sum squared resid Loglikelihood F-statistic Prob(F-statistic) 0.709350 0.708179 0.201018 70.22965 327.7548 605.9567 0.000000 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat 12.01860 0.372115 -0.366271 -0.34 1231 -0.357014 1.329441 Q10. Between equations (3) and (4), which model better fits the data? Explain. Q11. What is the number of stories associated with the minimum log(selling_price)? Give your answer up to the first decimal place +
Use the following information to answer Q10-Q11. The researcher estinmated a nonlinear regression by adding the variable STORIES^2 (i.e., STORIES²). log (selling price) = bo + b̟ log (SFLA) + b,BEDS + b,BATHS + b,STORIES + b;STORIES + B6VACANT + b,Age +e Dependent Variable: LOG(SELLING PRICE) Method Least Squares Date: 08/03/21 Time: 16:58 Sample: 1 6660 IF YEAR=2001 Included observations: 1746 Variable Coefficient Std. Error t-Statistic Prob. C LOG(SFLA) BEDS BATHS STORIES STORIES 2 VACANT AGE 5.898568 0.925893 -0.069610 0.033590 -0.471502 0.149252 -0.037820 -0.004177 28.29477 34.60462 -7.313708 2.178491 -2.585392 2.504330 -3.356101 -15.78847 0.0000 0.0000 0.0000 0.0295 0.0098 0.0124 0.0008 0.0000 0.208468 0.026756 0.009518 0.015419 0.182371 0.059597 0.011269 0.000265 R-squared Adjusted R-squared S.É of regression Sum squared resid Loglikelihood F-statistic Prob(F-statistic) 0.709350 0.708179 0.201018 70.22965 327.7548 605.9567 0.000000 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat 12.01860 0.372115 -0.366271 -0.34 1231 -0.357014 1.329441 Q10. Between equations (3) and (4), which model better fits the data? Explain. Q11. What is the number of stories associated with the minimum log(selling_price)? Give your answer up to the first decimal place +
Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter24: Forecasting Models
Section24.8: Multiple Regression
Problem 9P
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