Exploring Regression Models: Applications and Purposes of Linear, Logistic, and Polynomial Approaches in Engineering and Technologies

Regression analysis is a vital data-driven technique for examining variable relationships and predicting outcomes. It allows researchers to identify how independent variables influence a dependent variable, enabling predictions, trend analysis, and decision-making across various fields. This paper provides a comprehensive overview of regression techniques, which includes simple and multiple linear regression as well as logistic and polynomial regression models. Each technique is explained with its assumption, mathematical formulation, and use cases. The study also highlights the strengths and limitations of each model. This work aims to gain a deeper understanding of regression analysis and its practical application in real-world datasets. This also examines their applications in the field of modern engineering and machine learning.