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.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Transcribed Image Text: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 Multiple R R Square ANOVA Adjusted R Square Standard Error Observations 0.7429 0.5519 0.4907 61948.6931 Regression statistics Regression Residual Total df Intercept Sq Ft Beds Lower 95% 0.9353 -145129.5298 Intercept Baths 0.9528 -49383.5243 0.0180 Sq Ft Beds 0.0326 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 Standard Error -5531.0144 67312.9506 -1386.2100 23143.8052 23.5813 60.2793 54797.0778 24019.7592 Significance F 3 SS MS F 1.0397E+11 3.4656E+10 9.0306E+00 4.3455E-04 8.4428E+10 3.8376E+09 22 25 1.8840E+11 df 0.7428 0.5518 0.5128 60591.9567 26 SS 2 1.03955E+11 23 84441860122 25 1.88396E+11 Coefficients Standard Error -5882.7622 59.7331 54309.2083 Stat 65587.6835 21.2707 22101.6231 -0.0822 -0.0599 2.5562 2.2813 P-value MS F 51977265516 14.15739901 3671385223 t Stat -0.0897 2.8082 2.4572 P-value 11.3748 4983.1461 Significance F 9.81929E-05 Lower 95% 0.9293 -141561.2229 15.7313 0.0100 0.0220 8588.5174 Upper 95% 134067.5011 46611.1044 109.1838 104611.0095 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 ✔ than the adjusted R² of the model in part a.

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