Box-Cox transformation

The Box-Cox transformation is a mathematical technique used in data analysis to make data more normal-like, which helps improve modeling and statistical tests. It involves applying a specific power transformation to the data, adjusting the data’s shape to reduce skewness and heteroscedasticity (unequal spread). Essentially, it finds the best way to stretch or compress data values so they fit better with standard statistical assumptions, making analysis more reliable. This method is especially useful when data is skewed or has varying spread, allowing for more accurate and meaningful insights.