Question 15: The dataset used in this part of the case analysis, Set A data, comes from Airline Pricing, Price Dispersion, and Ticket Characteristics on and off the Internet by Anirban Sengupta and Steven N. Wiggins. These are the variables we will be using:

The linear regression model we will be using is listed below. The rtrip_fare is the Y intercept also shown as (_con).

Please answer the following and provide detailed answers: Propose two additional independent variables that you think should be included in this regression. Explain why you think they should be included and provide your rationale on the potential signs (positive or negative) for each additional variable. Avoid abstract ideas or variables that cannot be easily quantified or measured.

Variables Name carrier roundtrip online rtrip_fare advance travelrestriction busclass mshare hub distance Label Operating carrier Equals 1 if roundtrip travel, Source:Sabre Equals 1 if purchased online Roundtrip fare, Source:Sabre Number of days in advance ticket purchased Equals 1 if travel day restricted, Source: Amadeus Equals 1 if individual traveled on business/full coach fare class, Source:Sabre Market share of a carrier on the route, Source:T-100 Equals 1 if one or both end-points on a route involves a hub airport for the ope Distance between endpoint airports generate rtponline = roundtrip*online regress rtrip_fare advance roundtrip online rtponline travelrestriction mshare hub Source SS df MS Model Residual 4815685.5 25243966.2 7 687955.071 445 56728.0139 Number of obs F(7, 445) Prob > F R-squared Adj R-squared Root MSE 453 12.13 0.0000 0.1602 0.1470 238.18 Total 30059651.7 452 66503.6542 rtrip_fare Coefficient Std. err. t P>t1 [95% conf. interval] advance roundtrip online rtponline travelrestriction mshare hub -.5769832 -112.5671 -190.9661 96.11274 -134.6873 99.67903 -2.699468 476.5464 .5482856 29.36254 70.95156 80.75897 23.01637 46.59757 26.70554 36.23287 -1.05 -3.83 -2.69 1.19 -5.85 2.14 -0.10 13.15 0.293 0.000 0.007 0.235 0.000 0.033 0.920 0.000 -1.654534 -170.2736 -330.4079 -62.60361 -179.9216 8.100403 -55.18411 405.3376 .5005676 -54.86063 -51.52437 254.8291 -89.45305 191.2577 49.78517 547.7552 _cons Variables Name carrier roundtrip online rtrip_fare advance travelrestriction busclass mshare hub distance Label Operating carrier Equals 1 if roundtrip travel, Source:Sabre Equals 1 if purchased online Roundtrip fare, Source:Sabre Number of days in advance ticket purchased Equals 1 if travel day restricted, Source: Amadeus Equals 1 if individual traveled on business/full coach fare class, Source:Sabre Market share of a carrier on the route, Source:T-100 Equals 1 if one or both end-points on a route involves a hub airport for the ope Distance between endpoint airports generate rtponline = roundtrip*online regress rtrip_fare advance roundtrip online rtponline travelrestriction mshare hub Source SS df MS Model Residual 4815685.5 25243966.2 7 687955.071 445 56728.0139 Number of obs F(7, 445) Prob > F R-squared Adj R-squared Root MSE 453 12.13 0.0000 0.1602 0.1470 238.18 Total 30059651.7 452 66503.6542 rtrip_fare Coefficient Std. err. t P>t1 [95% conf. interval] advance roundtrip online rtponline travelrestriction mshare hub -.5769832 -112.5671 -190.9661 96.11274 -134.6873 99.67903 -2.699468 476.5464 .5482856 29.36254 70.95156 80.75897 23.01637 46.59757 26.70554 36.23287 -1.05 -3.83 -2.69 1.19 -5.85 2.14 -0.10 13.15 0.293 0.000 0.007 0.235 0.000 0.033 0.920 0.000 -1.654534 -170.2736 -330.4079 -62.60361 -179.9216 8.100403 -55.18411 405.3376 .5005676 -54.86063 -51.52437 254.8291 -89.45305 191.2577 49.78517 547.7552 _cons