Linear Regression Also called simple regression, linear regression establishes the relationship between two variables. Linear regression is graphically depicted using a straight line; the slope ...

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 ...

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 ...

Many of Pew Research Center’s survey analyses show relationships between two variables. For example, our reports may explore how attitudes about one thing — such as views of the economy — are ...

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 ...

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 ...

The components of progression as explanatory variables for overall survival in the RECIST database. Authors: Saskia Litière, Elisabeth De Vries, Lesley Seymour, Daniel J. Sargent, Lalitha Shankar, and ...

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 ...