Application of Regression Analysis in Advance Research

A Literature Review

Abstract

This comprehensive study delves into the multifaceted applications and challenges of regression analysis across a wide array of academic disciplines, including economics, finance, healthcare, medicine, social sciences, environmental science, and engineering. Employing a rigorous systematic literature review methodology, the research scrutinizes peer-reviewed articles from the last 20 years to assess how different types of regression models—such as linear, logistic, ridge, lasso, and elastic net—are utilized to address complex research questions. The literature review further explores the adaptability and robustness of regression analysis in facilitating interdisciplinary investigations. It also critically examines the inherent limitations and challenges associated with the use of regression models, including issues like overfitting, multicollinearity, and ethical concerns. Special attention is given to the trade-offs between predictive power and interpretability, as well as the importance of domain-specific expertise for effective model application. The study concludes by highlighting the indispensable role of regression analysis in advancing human understanding through data-driven research, while also cautioning researchers about the challenges and limitations that must be taken into account for future rigorous and responsible scholarship.