We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

Stepwise Regression So, here you are, with a plethora of potential independent variables arrayed before you. There is no substitute for the use of good judgment in choosing which to include in your ...

Subsequently, the team constructed metabolite content conversion models for 101 key metabolites using stepwise multiple regression, achieving high accuracy (R² > 0.9).

Information for evaluating the statistical significance of stepwise regression models developed with a forward selection procedure is presented. Cumulative distributions of the adjusted coefficient of ...

The stepwise selection process consists of a series of alternating step-up and step-down phases. The former adds variables to the model, while the latter removes variables from the model. Stepwise ...

When the investigators applied a conventional stepwise regression model to these data, they were able to predict AD with 66% accuracy using between 12 and 27 variables from the baseline scans.