Design and analysis of experiments in the health sciences / Gerald van Belle, Kathleen F. Kerr.
Material type:
TextPublication details: Hoboken, N.J. : Wiley, c2012. Description: xv, 229 p. : ill. ; 25 cmISBN: 9780470127278 (hardback)Subject(s): Medical informatics | Medical sciences -- Statistical methods | Experimental design | MATHEMATICS / Probability & Statistics / GeneralDDC classification: 610.72/7 LOC classification: R858 | .van 2012Other classification: MAT029000 Summary: "This volume provides technical professionals and students with three uniquely integrative enhancements to the study of predictive modeling not typically found in data-mining books: an applied approach, immediate practice using Microsoft Excel, and easy-to-use access to multiple online model-building tools. Since actual datasets are employed, users deal with real-life modeling issues and situations such as handling missing values, applying variable transformations, and addressing outliers, among others. An easy-to-learn Microsoft Excel add-in (Predictive MinerXL) and all applicable datasets are available for free on an associated Web site"-- Provided by publisher.
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| R856 sah2021 Instrumentation Handbook for Biomedical engineering: | R857.M3 .bio 2008 Biomaterials fabrication and processing handbook / | R857.M3 .bla 2006 Biological performance of materials : fundamentals of biocompatibility / | R858 .van 2012 Design and analysis of experiments in the health sciences / | R865 .web 2020 Medical instrumentation: application and design | R865 .web 2020 Medical instrumentation: application and design | R895 .hob 2015 Intermediate Physics for Medicine and Biology / |
Includes bibliographical references (p. 207-216) and indexes.
"This volume provides technical professionals and students with three uniquely integrative enhancements to the study of predictive modeling not typically found in data-mining books: an applied approach, immediate practice using Microsoft Excel, and easy-to-use access to multiple online model-building tools. Since actual datasets are employed, users deal with real-life modeling issues and situations such as handling missing values, applying variable transformations, and addressing outliers, among others. An easy-to-learn Microsoft Excel add-in (Predictive MinerXL) and all applicable datasets are available for free on an associated Web site"-- Provided by publisher.
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