How does linear regression combine independent variables?

Question

Accepted Answer

Linear regression combines independent variables to create a predictive model.

Linear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables.
It combines the independent variables by estimating the coefficients that best fit the data and create a linear equation.
The equation represents the relationship between the independent variables and the dependent variable.
The coefficients determine the slope and intercept of the regression line.
The model can then be used to make predictions or understand the impact of the independent variables on the dependent variable.
For example, in a simple linear regression with one independent variable, the equation would be of the form: y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b is the intercept.