This article examines the problem of accounting for and planning consumables in clinical diagnostic laboratories today. It presents the results of using approximation methods (polynomial, exponential, Fourier series, Gaussian function, power function, rational polynomials, and sine sum) and linear regression for predictive planning of biochemical reagent consumption. Data from five years of biochemical research at a local medical facility was used for calculations. The calculations were performed using the MATLAB software environment. A comparative analysis of the methods used was conducted, including the calculation of the determination coefficient (reliability coefficient). Gaussian approximation is the best statistical model for predicting reagent consumption.

Keywords: regression, approximation, study, criterion, polynomial, centroid, probability, determination, reagent, metric