Multiple linear regression should be used when multiple independent variables determine the outcome of a single dependent variable. This is often the case when forecasting more complex relationships.

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

What is linear regression? Linear regression is a basic machine learning algorithm that is used for predicting a variable based on its linear relationship between other independent variables.

The statistical literature and folklore contain many methods for handling missing explanatory variable data in multiple linear regression. One such approach is to incorporate into the regression model ...

Linear forecasting models can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables.

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.