An Empirical Nonlinear Data-Fitting Approach for Transforming Data to Normality
Lawrence I‐Kuei Lin, Edward F. Vonesh · The American Statistician · 1989
A general method is proposed by which nonnormally distributed data can be transformed to achieve approximate normality. The method uses an empirical nonlinear data-fitting approach and can be applied to a broad class of transformations including the Box-Cox, arcsine, generalized logit, and Weibull-type transformations. It is easy to implement using standard statistical software packages. Several examples are provided.