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The genes found to be important by unsupervised forward selection multiple regression match well with those thought to be important from previous studies in cell lines and multiple tumor types.
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Univariate and forward selection multiple logistic regressions were used to identify factors associated with lack of awareness of chronic infection and HCV clearance.
The FEAR method is compared with Multiple Linear Regression (MLR) methods, as forward selection and stepwise regression, and with the alternative ridge regression method.
A forward, stepwise, multiple regression analysis was used to find predictors for outcome.
Forward selection logistic regression verified the final model.
The tuberculosis testing and positivity models were implemented using stepwise forward selection logistic regression.
Stepwise forward (SW) multiple regression (MR) method was applied for development of 3D-QSAR model.
Stepwise forward multiple regression method was utilized using the advanced variable selection and model building wizard for development of the 3D-QSAR model with default values.
After the variable selection, multiple linear regression (MLR) was utilized to construct linear QSPR models.
Significant risk factors were entered into a forward selection multivariate logistic regression analysis.
A stepwise forward selection (Wald) logistic regression procedure was used to derive the model.
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