The Role of Regression Analysis in Data-Driven Research: Foundation and Real-Life Applications

Abstract

Regression analysis is one of the most fundamental and widely applied statistical techniques in engineering and data science. This literature review synthesizes findings from ten research papers spanning theoretical foundations, methodological developments, and practical applications across diverse domains, including healthcare, education, real estate, transportation safety, and environmental science. The review demonstrates that regression methods—ranging from simple linear regression to advanced logistic and multivariate techniques—serve as essential tools for prediction, optimisation, and decision-making in engineering practice. Key findings reveal that while linear regression remains highly effective for continuous outcome prediction, logistic regression extends these capabilities to classification problems, and advanced techniques like regularisation and ensemble methods address challenges such as overfitting and multicollinearity. This review is structured to provide first-year engineering students with a clear understanding of regression fundamentals, awareness of common pitfalls, and appreciation for the breadth of real-world applications. The evidence base demonstrates that mastery of regression analysis is critical for modern engineering practice, enabling data-driven solutions to complex problems across multiple disciplines.