Johnson Transformation

Johnson Transformation
Description	The Johnson transformation is a statistical technique that transforms non-normal data into a normal distribution. It extends the Box-Cox transformation and can handle positively and negatively skewed data.
Why to use	To normalize the distribution of a continuous variable.
When to use	Non-normality of data	When not to use	Data with extreme outliers Categorical or ordinal data Non-linear relationships
Prerequisites	A data with non-normal distribution.
Input	Any dataset containing numerical variables.	Output	A transformed version of the original dataset.
Statistical Methods Used	Maximum Likelihood Estimation (MLE) Moment Estimation	Limitation	Data range Parameter estimation Sensitivity to outliers.

Johnson Transformation or Yeo-Johnson Transformation is a statistical method to convert a numerical variable such that its distribution is more closer to normal distribution. Unlike Box-Cox transformation which is not applicable on negative values, Johnson transformation can handle negative values.
The following formula describes the Johnson transformation

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