The Importance and Application of Regression Analysis in Advanced Health Sciences Research

Abstract

Regression Analysis is one of a well-known statistical technique used to examine and determine the relationship between two or more variables and to describe the variables in a mathematical equation that improves a crucial dimension needed to make investment decisions and predictions. Researchers may forecast or explain changes (variation) in one variable based on another using regression. Researchers across various disciplines, including health sciences, frequently use regression analysis as a statistical approach to describe the nature of the relationship between variables, which can be linear or non-linear, positive or negative. The study aimed to evaluate and assess the importance and application of regression analysis in advanced health sciences research. This review article systematically examines and synthesizes the existing literature on the importance and application of regression analysis in advanced health sciences research. This statistical technique is globally used in engineering, business, finance, health sciences, and other areas with the goal of determining the relationship between one dependent variable and a number of other independent variables, and is also widely used in the literature for scientific purposes, while its common methods include linear regression, multiple regression, logistic regression, and cox regression. Regression analysis is essential to health sciences research because it allows researchers to look at correlations between variables, pinpoint risk factors, and forecast health outcomes using data. In complicated biological and social systems, it makes it possible to control confounding variables, which enhances the validity and accuracy of results. It improves the capacity to convert data into useful insights, facilitating the formulation of healthcare policies, treatment plans, and disease preventive initiatives.