Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

This paper considers issues related to multiple structural changes, occurring at unknown dates, in the linear regression model estimated by least squares. The main aspects are the properties of the ...

First, multiple linear regression models are considered and the design matrices are allowed to be different. Second, the predictor variables are either unconstrained or constrained to finite intervals ...