Regression modeling is a statistical method used to investigate the relationship between one dependent variable and one or more independent variables. It aims to understand how the independent variables impact or predict the behavior of the dependent variable. The process involves fitting a regression equation to the data, allowing us to estimate the strength and direction of relationships between variables.
There are various types of regression models, such as linear regression (which assumes a linear relationship between variables), logistic regression (used for binary outcomes), polynomial regression (captures non-linear relationships), and multiple regression (includes multiple independent variables), among others. These models serve different purposes, offering insights into patterns, predictions, and relationships within datasets.