Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent variable depends on the value of another explanatory variable. In this case, the ...

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 ...

Another diagnostic tool available in the fit window is partial leverage plots. When there is more than one explanatory variable in a model, the relationship of the residuals to one explanatory ...

Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 47, No. 2 (1998), pp. 377-383 (7 pages) In medical and social surveys a large number of multiply correlated explanatory ...

Linear models with linear equality constraints on the coefficients are translated into models with a singular design matrix and a nonsingular disturbances covariance matrix in order to deduce general ...

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 ...