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

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

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.

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

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 ...

Multiple linear regression. Multiple linear regression models are much more complicated and can work with a greater number of lines and shapes on charts.

Multiple linear regression is widely used in the real estate industry to estimate housing prices. Instead of relying on a single factor, it considers several variables simultaneously.