Backward elimination is a statistical method used in multiple linear regression to systematically remove predictor variables from a model to improve its performance. This process starts with a full model containing all potential predictors and iteratively eliminates the least significant variables based on their p-values, aiming for a more parsimonious model that maintains predictive accuracy while minimizing complexity.
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