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 ...

Common regression techniques include multiple linear regression, tree-based regression (decision tree, AdaBoost, random forest, bagging), neural network regression, and k-nearest neighbors (k-NN) ...

Research on multiple comparison during the past 50 years or so has focused mainly on the comparison of several population means. Several years ago, Spurrier considered the multiple comparison of ...

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been ...