Linear regression is a statistical method used for modeling the relationship between a dependent variable (often denoted as yy) and one or more independent variables (often denoted as xx). It assumes that there is a linear relationship between the independent variable(s) and the dependent variable.

The simplest form of linear regression, called simple linear regression, involves only one independent variable. The relationship between the independent variable xx and the dependent variable yy is represented by the equation of a straight line:

y=mx+by=mx+b

Where:

- y: is the dependent variable (the variable we are trying to predict)
- x: is the independent variable (the variable used to make predictions)
- m: is the slope of the line (the change in yy for a unit change in xx)
- b: is the y-intercept (the value of yy when xx is zero)

The goal of linear regression is to find the best-fitting line through the data, where "best-fitting" typically means minimizing the difference between the observed values of yy and the values predicted by the line.