Akaike Information Criterion (AIC) is a statistical measure used to compare different models in terms of their goodness of fit while also penalizing for model complexity. It helps in identifying the most suitable model among a set by balancing the trade-off between the accuracy of the model and its simplicity. AIC is particularly useful in multiple linear regression as it assists in selecting the best predictors by evaluating how well each model explains the data without overfitting.
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