Akademik Veri Yönetim Sistemi
y_BIS2019, İstanbul, Türkiye, 25 - 28 Eylül 2019, ss.84
Air transportation is a major contributor to the development of countries. To achieve that, the
airports should have crucial features such as sufficient capacity, accessibility and meeting the
needs of passengers and airways, etc. In 2003, air transport sector has developed tremendously
with opening of the domestic lines to the competition. This conversion leads decreasing in ticket
prices. Also, the convenience of airway transportation and the speed of transportation have led to
large increases in the number of passengers. With the effect of the building new airports and
increasing in the domestic connecting flights, international airports, especially Atatürk Airport,
started to provide services beyond capacity. Hence, to prevent overcrowding, the increase in the
number of passengers has to be considered. This is only possible with correct modeling. In this
study, Atatürk Airport's total number of passengers was modeled by multiple linear regression
analysis. According to analysis results, some of the basic regression assumptions like
homoscedastic residuals was not provided. Also, it can be seen that, the residuals show a special
type of the heteroskedastic distribution named butterfly type distribution. The heteroscedasticity
detected by RCEV test which is developed in 2017. It is obvious that modeling errors will cause
many economic and social losses The analysis was repeated with the Weighted Least Squares
Regression method (WCEV Regression) due to many economic and social losses caused by
modeling errors in the heteroscedasticity examination detected using RCEV test. End of the
analysis, it can be concluded that all assumptions are achieved and butterfly distributed residual
problem had solved. Adjusted R 2 of the weighted model is found 92.1%. Finally, total air
passenger of the Atatürk Airport successfully modelled with a new weighted regression model.
As a future direction, we are planning to model new Istanbul Airport’s service and capacity
schedule in the context of econometric framework.