An Algorithm for Fitting Johnson Transformations to Non-Normal Data

Abstract

A FORTRAN program which selects the estimated best-fit Johnson transformation for a set of non-normal data is presented. The parameter estimates are computed using sample percentiles, and the quality of the fit is assessed using the Shapiro-Wilk test statistic on the transformed data. The estimated transformation with the largest p-value is provided along with the transformed data.

• Johnson System
• Percentiles
• Quantiles
• Shapiro-Wilk Test

Additional information

Notes on contributors

Alan M. Polansky

Dr. Polansky is an Assistant Professor in the Division of Statistics. He is a Member of ASQ. His email address is [email protected].

Youn-Min Chou

Dr. Chou is a Professor in the Division of Mathematics and Statistics. She is a Senior Member of ASQ.

Robert L. Mason

Dr. Mason is Manager of the Statistical Analysis Section. He is a Senior Member of ASQ.