Regression variable selection

Stepwise regression

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a forward, backward, or combined sequence of F-tests or t-tests. The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogether or to at least make sure model uncertainty is correctly reflected.Alternatives include other model selection techniques, such as adjusted R2, Akaike information criterion, Bayesian information criterion, Mallows's Cp, PRESS, or false discovery rate. (Wikipedia).

Stepwise regression
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Related pages

Logistic regression | Regression analysis | Least-angle regression | Regression validation | Statistics | Mallows's Cp | Residual sum of squares | Efficiency (statistics) | Design of experiments | False discovery rate | Parameter | Experiment | Model selection | Occam's razor | Akaike information criterion | Approximation | Box–Behnken design | F-test | Variable (mathematics) | Data dredging | Freedman's paradox | Bayesian information criterion | PRESS statistic | Overfitting | Ensemble learning | Lasso (statistics) | Mean absolute percentage error | Degrees of freedom (statistics) | Validation set | Cross-validation (statistics) | Data mining