An empirical nonlinear data-fitting approach for transforming data to normality

Edward F. Vonesh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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 languageEnglish (US)
Pages (from-to)237-243
Number of pages7
JournalAmerican Statistician
Volume43
Issue number4
DOIs
StatePublished - Jan 1 1989

Keywords

  • Iteratively reweighted least squares
  • Nonlinear model
  • Normal scores
  • Order statistic

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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