Stepwise Regression
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High Quality Content by WIKIPEDIA articles! In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the form of a sequence of F-tests, but other techniques are possible, such as t-tests, adjusted R-square, Akaike information criterion, Bayesian information criterion, Mallows' Cp, or false discovery rate. The main approaches are: Forward selection, which involves starting with no variables in the model, trying out the variables one by one and including them if they are 'statistically…mehr

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High Quality Content by WIKIPEDIA articles! In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the form of a sequence of F-tests, but other techniques are possible, such as t-tests, adjusted R-square, Akaike information criterion, Bayesian information criterion, Mallows' Cp, or false discovery rate. The main approaches are: Forward selection, which involves starting with no variables in the model, trying out the variables one by one and including them if they are 'statistically significant'. Backward elimination, which involves starting with all candidate variables and testing them one by one for statistical significance, deleting any that are not significant. Methods that are a combination of the above, testing at each stage for variables to be included or excluded.