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Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept DISTANCE PAX SWA YES VACATION YES HI 0.8567 0.7339 0.7318 39.3716 638 df 5 632 637 Coefficients Standard Error 7.08935 0.00269 0.00012 0.3485 3.54152 17.3438 2.0809E-55 3.57526 14.6041 7.8163E-42 0.00099 7.3950 4.5121E-13 a) What percent of total variation in FARE does the model overall explain/determine SS MS 2701807.711 540361.542 979676.999 1550 122 3681484.709 86.67589 0.07751 -0.00011 -61.42324 -52.21338 0.00735 F 348.5929 Significance F 5.5794E-179 It Stot P-value 12.2262 5.2751E-31 28.8351 2,4522E-117 -0.9381 Lower 95% 72 7544 0.0722 -0.0004 -68 3778 -59,2342 0.0054 Upper 95% b) Does the model overall fit the dati NO YES c) Should one interpret the estimated value for the intercept? NO 0.25 pf d) Interpret the value for the estimated coefficient for DISTANCE e) Which explanatory variable has the least statistically significant relationship with FARE? YES (CIRCLE one; f) Predict the FARE (fare price in Ss) for an airline ticket with point-to-point DISTANCE of 1000 miles, with 15,000 flown passengers (PAX) on a route, whose passengers fly on SWA, whose passengers fly non-vacation routes, and with market concentration index of 4,500. 100.5974 0.0828 0.0001 -54,4687 -45,1926 0.0093

Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept DISTANCE PAX SWA YES VACATION YES HI 0.8567 0.7339 0.7318 39.3716 638 df 5 632 637 Coefficients Standard Error 7.08935 0.00269 0.00012 0.3485 3.54152 17.3438 2.0809E-55 3.57526 14.6041 7.8163E-42 0.00099 7.3950 4.5121E-13 a) What percent of total variation in FARE does the model overall explain/determine SS MS 2701807.711 540361.542 979676.999 1550 122 3681484.709 86.67589 0.07751 -0.00011 -61.42324 -52.21338 0.00735 F 348.5929 Significance F 5.5794E-179 It Stot P-value 12.2262 5.2751E-31 28.8351 2,4522E-117 -0.9381 Lower 95% 72 7544 0.0722 -0.0004 -68 3778 -59,2342 0.0054 Upper 95% b) Does the model overall fit the dati NO YES c) Should one interpret the estimated value for the intercept? NO 0.25 pf d) Interpret the value for the estimated coefficient for DISTANCE e) Which explanatory variable has the least statistically significant relationship with FARE? YES (CIRCLE one; f) Predict the FARE (fare price in Ss) for an airline ticket with point-to-point DISTANCE of 1000 miles, with 15,000 flown passengers (PAX) on a route, whose passengers fly on SWA, whose passengers fly non-vacation routes, and with market concentration index of 4,500. 100.5974 0.0828 0.0001 -54,4687 -45,1926 0.0093

MATLAB: An Introduction with Applications
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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Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept DISTANCE PAX SWA=YES VACATION=YES HI 0.8567 0.7339 0.7318 39.3716 638 df 5 632 637 86.67589 0.07751 -0.00011 -61.42324 -52.21338 0.00735 t Stat P-value 7.08935 12.2262 5.2751E-31 0.00269 28.8351 2.4522E-117 0.00012 -0.9381 0.3485 3.54152 -17.3438 2.0809E-55 3.57526 -14.6041 7.8163E-42 0.00099 7.3950 4.5121E-13 a) What percent of total variation in FARE does the model overall explain/determine SS MS 2701807.711 540361.542 979676.999 1550.122 3681484.709 Coefficients Standard Error F 348.5929 Significance F 5.5794E-179 Upper 95% 100.5974 72.7544 0.0722 0.0828 -0.0004 0.0001 -68.3778 -54.4687 -59.2342 0.0054 -45.1926 0.0093 Lower 95% b) Does the model overall fit the dat? NO YES c) Should one interpret the estimated value for the intercept? NO 0.25 pt) d) Interpret the value for the estimated coefficient for DISTANCE e) Which explanatory variable has the least statistically significant relationship with FARE? YES (CIRCLE one; f) Predict the FARE (fare price in Ss) for an airline ticket with point-to-point DISTANCE of 1000 miles, with 15,000 flown passengers (PAX) on a route, whose passengers fly on SWA, whose passengers fly non-vacation routes, and with market concentration index of 4,500.0
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept DISTANCE PAX SWA=YES VACATION=YES HI 0.8567 0.7339 0.7318 39.3716 638 df 5 632 637 86.67589 0.07751 -0.00011 -61.42324 -52.21338 0.00735 t Stat P-value 7.08935 12.2262 5.2751E-31 0.00269 28.8351 2.4522E-117 0.00012 -0.9381 0.3485 3.54152 -17.3438 2.0809E-55 3.57526 -14.6041 7.8163E-42 0.00099 7.3950 4.5121E-13 a) What percent of total variation in FARE does the model overall explain/determine SS MS 2701807.711 540361.542 979676.999 1550.122 3681484.709 Coefficients Standard Error F 348.5929 Significance F 5.5794E-179 Upper 95% 100.5974 72.7544 0.0722 0.0828 -0.0004 0.0001 -68.3778 -54.4687 -59.2342 0.0054 -45.1926 0.0093 Lower 95% b) Does the model overall fit the dat? NO YES c) Should one interpret the estimated value for the intercept? NO 0.25 pt) d) Interpret the value for the estimated coefficient for DISTANCE e) Which explanatory variable has the least statistically significant relationship with FARE? YES (CIRCLE one; f) Predict the FARE (fare price in Ss) for an airline ticket with point-to-point DISTANCE of 1000 miles, with 15,000 flown passengers (PAX) on a route, whose passengers fly on SWA, whose passengers fly non-vacation routes, and with market concentration index of 4,500.0
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