SUMMARY OUTPUT Regression Statistics Multiple R 0.77903578 R Square 0.60689675 Adjusted R 0.60412283 Square Standard 259438.815 Error Observations 1000 ANOVA SS MS Significance Regression 1.0308E+14 1.4726E+13 218.787148 3.593E-196 Residua 992 6.677E+13 6.7308E+10 Tota 999 1.6985E+14 Coefficients Standard t Sta P-value Lower 95% Upper 95% Lower Upper Error 95.0% 95.0% Intercept 7054720.89 683768.44 10.3174123 8.8997E-24 5712922.25 8396519.54 5712922.25 8396519.54 X Variable 1 -75321.264 11820.4204 -6.3721307 2.8496E-10 -98517.163 -52125.364 -98517.163 -52125.364 X Variable 2 14373.5738 19807.498 0.72566327 0.46821638 -24495.833 53242.981 -24495.833 53242.981 X Variable 3 346.230484 14.654962 23.6254781 2.9322E-98 317.472198 374.98877 317.472198 374.98877 X Variable 4 65434.7022 19486.4084 3.35796626 0.00081503 27195.3876 103674.017 27195.3876 103674.017 X Variable 5 881588.401 93986.6257 9.37993459 4.3476E-20 697152.97 1066023.83 697152.97 1066023.83 X Variable 6 22291.7317 13586.4609 1.64073131 0.10117021 -4369.772 48953.2354 -4369.772 48953.2354 X Variable 7 -3650.7958 343.49314 -10.628439 4.6153E-25 -4324.8524 -2976.7392 -4324.8524 -2976.7392Estimate the following simple regression model for all the observations in your sample: log(PRICE) = 0 + lSQFT + u State and interpret the estimated coefficients