APPLICATION OF MATHEMATICAL STATISTICS METHODS FOR THE ANALYSIS AND FORECASTING OF ENTERPRISE REVENUES

Abstract

The article examines the theoretical and applied aspects of using mathematical statistics methods for analyzing and forecasting enterprise revenues. It is substantiated that the revenues of economic entities are formed under the influence of a complex set of internal and external factors, which determines their complex dynamics and the need for scientifically grounded evaluation methods. It is shown that mathematical statistics serves as a key tool for formalizing economic processes, enabling a transition from descriptive analysis of financial indicators to their factor-based and predictive modeling. The essence of the main statistical approaches used in the analysis of enterprise revenues is revealed, in particular descriptive statistics, time series analysis, correlation and regression analysis, and time series methods. It is established that descriptive statistics provide a summary of initial data and assessment of the level and stability of revenues, while dynamic analysis allows identifying trends in their changes over time. Correlation and regression analysis is used to determine the strength and direction of the impact of factors on revenue formation, while time series models make it possible to take into account trend, seasonal, and random components of economic processes. Special attention is given to statistical forecasting methods based on the extrapolation of identified patterns. The application of trend models, exponential smoothing, moving average methods, multivariate regression models, and ARIMA approaches is considered. It is emphasized that forecast accuracy is evaluated using error indicators (MAPE, MAE, RMSE), which makes it possible to determine the effectiveness of models and select the most reliable ones for practical use. It is generalized that the application of mathematical statistics in enterprise revenue analysis contributes to improving the accuracy of financial planning, substantiating managerial decisions, identifying hidden trends, optimizing resources, and reducing financial risks. Prospects for further research are related to the improvement of econometric forecasting models and the integration of classical statistical methods with modern digital data analysis technologies.