Regression statistics Multiple R 0.7428 R Square 0.5518 Adjusted R Square 0.5128 Standard Error 60591.9567 Observations 26 ANOVA df SS MS Regression 2 1.03955E+11 Residual 51977265516 23 84441860122 3671385223 Total 25 1.88396E+11 F 14.15739901 Significance F 9.81929E-05 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept Sq Ft -5882.7622 65587.6835 -0.0897 0.9293 -141561.2229 59.7331 54309.2083 21.2707 22101.6231 2.8082 2.4572 Beds 0.0100 0.0220 15.7313 8588.5174 129795.6985 103.7349 100029.8991 Compare the fit for this simpler model to that of the model that also includes number of bathrooms as an independent variable. The adjusted R2 for the simpler model is of the model in part a. 0.49 (to 2 decimals) that is higher than the adjusted R2 Multiple R 0.7429 R Square 0.5519 Adjusted R Square 0.4907 Standard Error 61948.6931 Observations 26 ANOVA SS 1.0397E+11 MS 3.4656E+10 F 9.0306E+00 22 8.4428E+10 3.8376E+09 df Regression 3 Residual Total Significance F 4.3455E-04 25 1.8840E+11 Coefficients Standard Error t Stat P-value Lower 95% Intercept Baths -5531.0144 -1386.2100 67312.9506 23143.8052 -0.0822 0.9353 -145129.5298 -0.0599 0.9528 -49383.5243 Upper 95% 134067.5011 46611.1044 Sq Ft Beds 60.2793 54797.0778 23.5813 24019.7592 2.5562 0.0180 11.3748 109.1838 2.2813 0.0326 4983.1461 104611.0095 Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R2 is greater than 45%, the estimated regression equation provides a good fit. The estimated regression equation does provide a reasonable fit because the adjusted R2 is 0.55

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