Statistical methods in the theory of economic analysis of entrepreneurial activity

Abstract

In today's conditions of globalization, digitalization, and high uncertainty in the market environment, statistical methods are a key tool for objective analysis, forecasting, and making effective management decisions in business. Objective: The purpose of this study is to examine the role and application of statistical methods in the economic analysis of entrepreneurial activity and to propose approaches for enhancing their effectiveness in managerial decision-making. The study also aims to provide recommendations for improving statistical tools for strategic planning, risk assessment, forecasting, and optimization of enterprise performance. Methods: In the process of writing the article, general scientific methods of analysis, synthesis, induction, deduction, generalization and a systematic approach were used. The basis of the study was special statistical methods: descriptive statistics, statistical groupings, index analysis, analysis of dynamic series, correlation-regression and dispersion analysis, econometric modeling and statistical forecasting methods. Results: The study demonstrates that statistical methods are essential for analyzing complex economic systems characterized by multifactorial interactions, stochastic processes, and structural heterogeneity. Descriptive statistics and index analysis allow for summarizing and comparing heterogeneous data and tracking changes over time. Grouping methods enable classification of production processes, clients, suppliers, and costs, supporting optimization in marketing, logistics, and production planning. Time series and trend analysis facilitate the identification of cyclical, seasonal, and random patterns, crucial for forecasting and resource planning. The research identifies challenges that limit the effectiveness of statistical applications, including low-quality or incomplete data, insufficient analytical training, complexity of statistical models, and inadequate integration with digital systems. Directions for improvement include enhancing data quality and standardization, developing personnel competencies in statistical and econometric analysis, incorporating artificial intelligence and machine learning, expanding predictive and econometric modeling, and optimizing statistical applications for finance, production, marketing, and investment management. Conclusions: Statistical methods constitute a fundamental instrument in economic analysis and enterprise management. Their proper use enables comprehensive assessment of economic processes, identification of causal relationships, accurate forecasting, and informed managerial decision-making. Integrating statistical methods with digital technologies and modern analytical approaches enhances flexibility, reduces risks, improves operational efficiency, and strengthens the competitive position of enterprises. These results highlight the significance of continued development and adaptation of statistical methodologies in the context of dynamic and uncertain market environments.