In order to estimate the “pure” effect of some explanatory variable on the dependent variable, we want to control for as many other effects as possible. That is, we’d like to see how our prediction ...

At the same time, it is likely that the beta-weight of the hot-tub variable would be relatively small when compared with the beta-weights of other explanatory variables in the Winnetka study, and ...

Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

Special Considerations A simple regression model, or equation, consists of four terms. On the left side is the dependent variable. It represents the phenomenon the model seeks to "explain." On the ...

The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models ...

Walter Torous, Rossen Valkanov, Shu Yan, On Predicting Stock Returns with Nearly Integrated Explanatory Variables, The Journal of Business, Vol. 77, No. 4 (October 2004), pp. 937-966 ...

Terry D. Warfield, John J. Wild, Accounting Recognition and the Relevance of Earnings as an Explanatory Variable for Returns, The Accounting Review, Vol. 67, No. 4 ...