Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website) Click on the datafile logo to reference the data. DATA file a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house: SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression statistics Regression Residual Total ANOVA Multiple R R Square Adjusted R Square Standard Error Observations 0.7429 0.5519 0.4907 61948.6931 Regression Residual Total df Regression statistics Intercept Sq Ft Beds 26 Standard Error Coefficients -5531.0144 67312.9506 -1386.2100 23143.8052 23.5813 60.2793 54797.0778 24019.7592 SS 3 1.0397E+11 22 8.4428E+10 25 1.8840E+11 df 0.7428 0.5518 0.5128 60591.9567 Intercept Baths Sq Ft Beds Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R 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 (to 2 decimals). b. The Excel output for the estimated regression equation that can be used to predict selling price given square footage and the number of bedrooms: SUMMARY OUTPUT 26 Coefficients MS 3.4656E+10 3.8376E+09 t Stat Standard Error 65587.6835 21.2707 22101.6231 -5882.7622 59.7331 54309.2083 -0.0822 -0.0599 2.5562 2.2813 SS MS F Significance F 2 1.03955E+11 51977265516 14.15739901 9.81929E-05 3671385223 23 84441860122 25 1.88396E+11 F Significance F 9.0306E+00 4.3455E-04 P-value t Stat Upper 95% 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 (to 2 decimals) that is lower Lower 95% 0.9353 -145129.5298 0.9528 -49383.5243 0.0180 11.3748 0.0326 4983.1461 -0.0897 2.8082 2.4572 Lower 95% 0.9293 -141561.2229 0.0100 0.0220 8588.5174 15.7313 Upper 95% 134067.5011 46611.1044 109.1838 104611.0095 P-value ✓than the adjusted R2 of the model in part a.
Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website) Click on the datafile logo to reference the data. DATA file a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house: SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression statistics Regression Residual Total ANOVA Multiple R R Square Adjusted R Square Standard Error Observations 0.7429 0.5519 0.4907 61948.6931 Regression Residual Total df Regression statistics Intercept Sq Ft Beds 26 Standard Error Coefficients -5531.0144 67312.9506 -1386.2100 23143.8052 23.5813 60.2793 54797.0778 24019.7592 SS 3 1.0397E+11 22 8.4428E+10 25 1.8840E+11 df 0.7428 0.5518 0.5128 60591.9567 Intercept Baths Sq Ft Beds Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R 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 (to 2 decimals). b. The Excel output for the estimated regression equation that can be used to predict selling price given square footage and the number of bedrooms: SUMMARY OUTPUT 26 Coefficients MS 3.4656E+10 3.8376E+09 t Stat Standard Error 65587.6835 21.2707 22101.6231 -5882.7622 59.7331 54309.2083 -0.0822 -0.0599 2.5562 2.2813 SS MS F Significance F 2 1.03955E+11 51977265516 14.15739901 9.81929E-05 3671385223 23 84441860122 25 1.88396E+11 F Significance F 9.0306E+00 4.3455E-04 P-value t Stat Upper 95% 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 (to 2 decimals) that is lower Lower 95% 0.9353 -145129.5298 0.9528 -49383.5243 0.0180 11.3748 0.0326 4983.1461 -0.0897 2.8082 2.4572 Lower 95% 0.9293 -141561.2229 0.0100 0.0220 8588.5174 15.7313 Upper 95% 134067.5011 46611.1044 109.1838 104611.0095 P-value ✓than the adjusted R2 of the model in part a.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section: Chapter Questions
Problem 4SGR
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