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Author's response to letter to the editor 'Regression to the mean, apparent data errors, and biologically extraordinary results'.

07:00 EST 19th December 2017 | BioPortfolio

Summary of "Author's response to letter to the editor 'Regression to the mean, apparent data errors, and biologically extraordinary results'."

International Journal of Obesity accepted article preview online, 19 December 2017. doi:10.1038/ijo.2017.300.

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This article was published in the following journal.

Name: International journal of obesity (2005)
ISSN: 1476-5497
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Medical and Biotech [MESH] Definitions

Work consisting of an acknowledgment of an error, issued by a publisher, editor, or author. It customarily cites the source where the error occurred, giving complete bibliographic data for retrieval. In the case of books and monographs, author, title, imprint, paging, and other helpful references will be given; in the case of journal articles, the author, title, paging, and journal reference will be shown. An erratum notice is variously cited as Errata or Corrigenda.

Work consisting of written or printed communication between individuals or between persons and representatives of corporate bodies. The correspondence may be personal or professional. In medical and other scientific publications the letter is usually from one or more authors to the editor of the journal or book publishing the item being commented upon or discussed. LETTER is often accompanied by COMMENT.

Work consisting of a statement issued by one or more authors of an article or a book, withdrawing or disavowing acknowledgment of their participation in performing research or writing the results of their study. In indexing, the retraction is sent to the editor of the publication in which the article appeared and is published under the rubric "retraction" or in the form of a letter. This publication type designates the author's statement of retraction: it should be differentiated from RETRACTED PUBLICATION which labels the retracted publication.

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The statistical manipulation of hierarchically and non-hierarchically nested data. It includes clustered data, such as a sample of subjects within a group of schools. Prevalent in the social, behavioral sciences, and biomedical sciences, both linear and nonlinear regression models are applied.

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