The article combines traditional multiple linear regression models with fully connected linear regression models to improve data analysis. By using both types of regression, the researchers were able to overcome the limitations of each and create a more effective model. They applied this approach to study passenger rail traffic in the Irkutsk region, successfully managing multicollinearity and ensuring all input variables were protected in the model. The results show that a combination of multiple and fully connected regressions can be very useful in solving complex data analysis problems.