Forward selection is a stepwise regression technique used for variable selection, where variables are added to a model one at a time based on their statistical significance. This method starts with no variables in the model and adds the most significant variable at each step until no additional variables meet a predetermined criterion for inclusion. Forward selection helps in simplifying models and improving prediction accuracy by focusing on the most relevant predictors.
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