Modeling the number of air passengers correctly is essential for management policy in the global world. Based on seasonality (depending on the season of the year), data about the number of air passengers are heteroscedastic. Heteroscedasticity violates “Homoscedastici-ty” which is one of the central assumptions of linear regression analysis. In this study, a new weighting approach called “Weighting Absolute Centered External Variable” (WCEV) is ap-plied to the Turkish total monthly air passenger’s data to obtain correct statistical inference and forecasting. Besides scatter plot months vs. studentized residuals, the homoscedastici-ty assumption is checked with the studentized RCEV test as well. Consequently, the WCEV method is shown superior performance against multiple linear regressions and exponential weighted moving average (EWMA) methods. The study also provides insights into the sea-sonal patterns of air passenger demand in Turkey, with passenger mobility increasing in the last quarter of each year and the lowest demand in January and February. This information can be used to optimize airport and airplane maintenance schedules and increase capacity during peak months.