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Variable selection refers to the process of choosing which independent variables to include in a multiple regression model to ensure the model is both parsimonious and predictive. This step is crucial as it affects the model's accuracy, interpretability, and generalizability by eliminating irrelevant or redundant variables, while retaining those that significantly contribute to explaining the dependent variable.
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