Multiple Linear Regression Variable Selection - Latest Information & Updates 2026

Web Reference: Variable selection in regression is arguably the hardest part of model building. The purpose of variable selection in regression is to identify the best subset of predictors among many variables to include in a model. The task of identifying the best subset of predictors to include in a multiple regression model, among all possible subsets of predictors, is referred to as variable selection. Forward selection: Starting from a null model, include variables one at a time, minimizing the RSS at each step. Backward selection: Starting from the full model, eliminate variables one at a time, choosing the one with the largest p-value at each step.

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Last Updated: April 5, 2026

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