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Confidence interval for the mean of non‐normal data
Author(s) -
Wang F. K.
Publication year - 2001
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.400
Subject(s) - confidence interval , robust confidence intervals , cdf based nonparametric confidence interval , statistics , confidence distribution , transformation (genetics) , power transform , tolerance interval , construct (python library) , mathematics , coverage probability , confidence region , interval (graph theory) , credible interval , normal distribution , computer science , artificial intelligence , biochemistry , chemistry , consistency (knowledge bases) , combinatorics , gene , programming language
The problem of constructing a confidence interval for the mean of non‐normal data is considered. The Bootstrap method and the Box–Cox transformation method of constructing the confidence interval are compared with the normal theory method. Simulation studies are used to evaluate the performance of these different methods of constructing confidence intervals. The result is not surprising; the Bootstrap method is more effective and efficient than the Box–Cox transformation method and the normal theory method in this simulation study. A real example demonstrates the ability of these methods to construct a confidence interval for the mean of audit accounting data. Copyright © 2001 John Wiley & Sons, Ltd.