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

Regression analysis is a quantitative tool that is easy to use and can provide valuable information on financial analysis and forecasting.

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.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

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

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9.

One common problem in the use of multiple linear or logistic regression when analysing clinical data is the occurrence of explanatory variables (covariates) which are not independent, ie ...

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 ...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods ...

To help answer these types of questions, economists use a statistical tool known as regression analysis. Regressions are used to quantify the relationship between one variable and the other variables ...