Regression is a powerful statistical method used to examine the relationships between one or more independent variables and a dependent variable. It allows researchers to identify which factors significantly impact the outcome of interest and how these factors interact with each other. The primary goal of regression analysis is to model the relationship between variables, enabling predictions and insights into the data.

There are different types of regression, including simple linear regression, which involves one independent variable, and multiple linear regression, which includes two or more independent variables. The linear regression model is typically expressed in the form of an equation, such as ( y = \beta_0 + \beta_1 x ), where ( y ) is the dependent variable, ( x ) is the independent variable, and ( \beta_0 ) and ( \beta_1 ) are coefficients that represent the relationship between the variables ²³.

In practice, regression analysis is widely used in various fields, including economics, social sciences, and machine learning, to derive meaningful insights from data and make informed decisions.