SUMMARY OUTPUT Regression Statistics Multiple R 0.77903578 R Square 0.60689675 0.60412283 Adjusted R Square Standard Error...
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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 + B₁SQFT + u State and interpret the estimated coefficients. 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 + B₁SQFT + u State and interpret the estimated coefficients.
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The estimated simple regression model for the observations in the sample is logPRICE Po B1SQFT u The estimated coefficient for Po is 705472089 and for B1SQFT is 68376844 The coefficient for Po is the ... View the full answer
Related Book For
Financial Algebra advanced algebra with financial applications
ISBN: 978-0538449670
1st edition
Authors: Robert K. Gerver
Posted Date:
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