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

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. I will start with a ...

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 ...

Of course, we need more information about the regression to make any reliable conclusions. Does the model satisfy the assumptions of linear regression? Does the model fit the data (high R 2)? The the ...

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

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our ...