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