Linear regression is a statistical method used to model the relationship between a dependent variable (Y) and one or more independent variables (X) in a linear manner. It is particularly useful for predicting continuous outcomes, such as sales or prices, rather than classifying data into categories. The simplest form is simple linear regression, which can be expressed with the equation (y = mx + b), where (m) is the slope and (b) is the y-intercept ².

In more complex scenarios, multivariable regression is employed, allowing for multiple independent variables to predict the dependent variable. The general form of the equation in this case is (y = \beta_0 + \beta_1x_1 + \ldots + \beta_px_p + \epsilon), where (\beta) represents the coefficients for each feature, and (\epsilon) is the error term ⁵.

For linear regression to be effective, certain assumptions must be met, including the continuity of data and the existence of correlation and causation between the independent and dependent variables ⁴.