Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 237-243 |
| Number of pages | 7 |
| Journal | American Statistician |
| Volume | 43 |
| Issue number | 4 |
| DOIs | |
| State | Published - Nov 1989 |
Keywords
- Iteratively reweighted least squares
- Nonlinear model
- Normal scores
- Order statistic
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- General Mathematics