THE REGRESSION MODEL CONSTRUCTION OF THE SUNFLOWER OIL ROAD TRANSPORTATION SYSTEM IN THE ODESA – IVANO-FRANKIVSK DIRECTION - Nefyodov, V.; Cherepakha, O. (Municipal economy of cities. Series: «Economy science», 2025)
Ukrainian sunflower oil shipments in March 2024 amounted to 608 thousand tons - at the level of the previous two months and 16% more than last year. In total, for the first seven months of the 2024 season, Ukrainian companies exported 3.8 million tons of sunflower oil, compared to 3.2 million tons a year ago (+18.7%). One of the ways of transporting goods is delivery using logistics centers. The advantage of this option is that it is possible to reduce the stock of finished products of the enterprise - manufacturer by transporting all finished products to the center and with low transportation costs. The disadvantages of this option are the high direct costs that suppliers of goods bear to numerous consumers - customers of goods. On the basis of the developed experimental plan, painted according to the determined maximum and minimum values of the arguments, the linear influence of factors on the time of sunflower oil transportation was considered. The R-squared coefficient is one of the most effective estimates of the adequacy of the regression model. In the case of a linear model without an independent variable, the R2 coefficient = 0.993, in the case of a linear model with an independent variable, R2 = 0.991, which means that the linear model without an independent variable is more accurate. The results obtained indicate a more rational use of a linear model without an independent variable for determining the costs of road transportation of sunflower oil in the direction Odesa - Ivano-Frankivsk. As a result of the experiment, it was established that there is a functional relationship between the inputs of the system of road transportation of sunflower oil in the direction Odesa - Ivano-Frankivsk and the outputs. This confirms the correctness of the previous analysis and the choice of the type of regression model.