This article provides an in-depth analysis of the theoretical foundations of regression analysis in econometrics. Regression analysis is considered an important methodological tool for identifying, evaluating, and forecasting functional and statistical relationships between economic processes. The study theoretically examines simple and multiple regression models, their mathematical expressions, the main assumptions of the classical linear regression model, and parameter estimation methods, particularly the Ordinary Least Squares (OLS) method.

In addition, the article discusses criteria used to evaluate the reliability of regression models, including the coefficient of determination (R²), t-statistics, the F-test, and the economic interpretation of errors. Problems such as multicollinearity, heteroskedasticity, and autoregression, as well as methods for addressing them, are also analyzed from a scientific perspective.

This research reveals the role and importance of regression analysis in econometrics and substantiates the possibilities of its effective application in economic modeling and decision-making processes.

regression analysis, econometrics, linear regression model, multiple regression, Ordinary Least Squares (OLS), coefficient of determination (R²), t-statistics, F-test, multicollinearity, heteroskedasticity, autoregression, statistical estimation, econometric modeling, forecasting.

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