The Box-Cox transformation is a family of power transformations designed to stabilize variance and make data more normally distributed. It provides a systematic way to identify the best transformation for a given dataset by considering various power transformations based on a parameter, lambda (λ). This method is particularly useful when dealing with non-normal data, as it helps improve the validity of statistical analyses and modeling.
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