Linear Regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. The model estimates the coefficients of the linear equation, which best predicts the dependent variable from the independent variables. In simple linear regression, the model consists of a single independent variable, while in multiple linear regression, multiple independent variables are used. The goal is to find the line (or hyperplane in higher dimensions) that minimizes the sum of the squared differences between the observed and predicted values. Linear regression is widely used for predictive modeling, trend analysis, and inferential statistics. It provides insights into the strength and direction of relationships between variables and is foundational for many other statistical and machine learning techniques.