Design and analysis of experiments in the health sciences
Material type: TextPublication details: Hoboken, N.J. Wiley, c2012Description: xv, 229 p. illISBN: 9780470127278 (hardback)Subject(s): Medical informatics | Medical sciences | Experimental design | Mathematics, Probability & Statistics ,GeneralDDC classification: 610.727 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"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library Stack | Stack | 610.727 VAN/D (Browse shelf (Opens below)) | Available | 37207 |
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610.727 HAN Handbook of survival analysis | 610.727 HOS/A Applied survival analysis : regression modeling of time to event data | 610.727 MAC/M Medical ststistics: a textbook for the health sciences | 610.727 VAN/D Design and analysis of experiments in the health sciences | 610.730 76 POT/B Basic nursing | 610.73 DEW/F Fundamental concepts and skills for nursing | 610.73 GAS/I Introduction to patient care : a cmprehensive approach to nursing |
"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"--
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