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.
- Iteratively reweighted least squares
- Nonlinear model
- Normal scores
- Order statistic
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty