Box-Cox Transformation

Box-Cox Transformation
Description	The Box-Cox transformation is a mathematical technique that transforms a non-normal or skewed dataset into a more normal distribution.
Why to use	Normality Homoscedasticity Linearity
When to use	Non-Normal data	When not to use	Negative or zero values
Prerequisites	Continuous variable Positive values Handling Outliers Choice of lambda
Input	Any dataset containing numerical variables.	Output	A transformed dataset that follows a more normal distribution.
Statistical Methods Used	Maximum Likelihood Estimation (MLE) Method of moments	Limitations	Limited applicability Sensitivity to outliers Subjectivity in lambda selection Interpretability Data assumptions

The Box-Cox transformation is a statistical technique that transforms non-normal or skewed data into a more normal distribution. It involves applying a power transformation to the data using a lambda parameter. The specific formula for the change is:

where,

is the transformed variable,

is the original variable, and

is the transformation parameter.