Machine Learning for Business Analytics, 2nd Edition

6 MULTIPLE LINEAR REGRESSION

In this chapter, we introduce linear regression models for the purpose of prediction. We discuss the differences between fitting and using regression models for the purpose of inference (as in classical statistics) and for prediction. A predictive goal calls for evaluating model performance on a holdout (validation) set and for using predictive metrics. We then raise the challenges of using many predictors and describe variable selection algorithms that are often implemented in linear regression procedures. Finally, we introduce regularized regression techniques, which are particularly useful for datasets with a large number of highly‐correlated predictors.

Multiple Linear Regression in JMP: Multiple linear regression models can be fit using the standard version of JMP. However, to compute validation statistics using a validation column or to fit regularized regression models, JMP Pro is required.

6.1 INTRODUCTION

The most popular model for making predictions is the multiple linear regression model encountered in most introductory statistics courses and textbooks. This model is used to fit a relationship between a numerical outcome variable Y (also called the response, target, or dependent variable) and a set of predictors (also referred to as independent variables, input variables, regressors, effects, or covariates). The assumption is that ...