The purpose of regression analysis is to analyze relationships among variables. The analysis is carried out through the estimation of a relationship
y = f(x1, x2,..., xk)
and the results serve the following two purposes:
A model with only one independent variable (x).
A model with more than one independent variable. (The model above represents a multiple regression model with k independent variables.)
A regression model is called linear if the equation y = f(x1, x2,...,xk) can be written as:
y = b0 + b1 x1 + b2 x2 +...+ bk xk + e
where b0, b1,...,bk are parameters to be estimated, and e is an error term.
Note. For more details on the simple and multiple linear regression model, go to their respective maps.