The researcher estimated a nonlmear regression by adding the variable STORIESA2 (i.e., STORIES2). log(seiltng price) bo b:BEDS + + b4STORIES + bsSTORIES2 + b6VACANT + b7Age + e Dependent Variable Method Least Squares Date: 08/0301 Time. 16:58 sample: 1 6660 IF YEAR-2001 Included observations: 1746 Variable LOG(SFLA) BEDS BATHS STORIES STORIESA2 VACANT R-squared Adjusted R-squared SE. of regression Sum squared resid Log likelihood Prob(F-statistic) Coefficient 5.898568 0.925893 -0.069610 0.033590 0471502 0.149252 -0.037820 -0.004177 0.709350 0.708179 0201018 7022965 327.7548 6059567 o_oooooo Std Error 0208468 0126756 0009518 0015419 c 182371 0059597 0011269 0100265 t-Statlstic 2829477 3460462 -7313708 2178491 -2585392 2S04330 -3356101 -1578847 Mean dependent var SD dependent var Aka*e info criterion Schwarz criterion Hannan-Quinn citer Durbin-Watson stat p rob coooo coooo coooo 00295 00098 01124 c0008 coooo 1201860 0372115 -0 366271 -0341231 -0 357014 1 329441 QI 0. Between equations (3) and which model better fits the data? Explain. [2] QI 1 _ What is the number of stories associated with the minimum log(selling_price)? Give your answer up to the first decimal place. [2]