Although nonlinear, tree-based machine learning (ML) models have been developed and applied to the survival analysis, no methodology exists for computing HRs associated with explanatory variables from ...

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 ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Apart from problems caused by quadratic terms and product interaction terms, the centring of explanatory variables, in general, does not solve the problem of collinearity or multicollinearity ...

MANOVA is a statistical test that extends the scope of the more commonly used ANOVA, that allows differences between three or more independent groups of explanatory (independent or predictor) ...

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 main tool of econometrics is the linear multiple regression model, which provides a formal approach to estimating how a change in one economic variable, the explanatory variable, affects the ...

The main tool of econometrics is the linear multiple regression model, which provides a formal approach to estimating how a change in one economic variable, the explanatory variable, affects the ...

5,9 Race is an explanatory variable in the current eGFRcr and eGFRcr-cys equations but not in the current eGFRcys equation. The first set of new equations uses the same coefficients for the ...