MATHEMATICAL MODELS AND STATISTICAL METHODS FOR ANALYZING EXPERIMENTAL DATA

The analysis of experimental data occupies an important place in modern scientific research. The reliability, accuracy, and practical significance of scientific conclusions directly depend on the correct application of mathematical models and statistical methods. Mathematical modeling allows researchers to describe natural, technical, economic, and social processes quantitatively, while statistical methods help evaluate the reliability and significance of obtained results. This article examines the theoretical foundations and practical importance of mathematical models and statistical methods used in the analysis of experimental data. Particular attention is paid to regression analysis, correlation analysis, dispersion analysis, probability theory, hypothesis testing, and optimization models. The article also discusses the stages of data processing, methods of assessing statistical reliability, and approaches to eliminating errors in experimental studies. The presented information is based on internationally recognized scientific literature and methodological sources.