Regression is a statistical method used to model the relationship between one or more independent variables (often referred to as “predictors” or “features”) and a dependent variable (often referred to as the “target” or “outcome”). The goal of regression analysis is to understand and quantify how changes in the independent variables are associated with changes in the dependent variable.

There are several types of regression analysis, including:

Linear Regression: Linear regression models the relationship between the independent variables and the dependent variable using a linear equation.

Multiple Regression: Multiple regression extends linear regression to include more than one independent variable. It models the relationship between the dependent variable and multiple predictors simultaneously.

Logistic Regression: Logistic regression is used when the dependent variable is binary (e.g., yes/no, 0/1). It models the probability of a binary outcome as a function of the independent variables using the logistic function.

Polynomial Regression: Polynomial regression allows for non-linear relationships between the independent and dependent variables by including polynomial terms (e.g., quadratic, cubic) in the regression equation.

Regression analysis is used for prediction, hypothesis testing, and understanding the relationships between variables for machine learning. It also finds applications in other fields, including economics, finance, social sciences, and more.