Data analysis what learned von huber peter (5 Ergebnisse)
- Hardcover
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- Hardcover
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- Hardcover
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EUR 183,34
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Hardcover. Zustand: Brand New. 1st edition. 234 pages. 10.00x6.75x0.50 inches. In Stock.
- Hardcover
Anbieter: Kennys Bookstore, Olney, MD, USAKennys Bookstore
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EUR 201,12
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Zustand: New. This book explores the historical and philosophical implications inherent in any study of statistical data analysis. It addresses the needs of researchers who are working with larger, complicated data sets by offering an understanding of the significance of robust data sets, the implementation of software languages…, and the use of models. Series: Wiley Series in Probability and Statistics. Num Pages: 234 pages, Illustrations. BIC Classification: PB. Category: (P) Professional & Vocational. Dimension: 235 x 163 x 17. Weight in Grams: 464. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: Neu. Neuware - This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis How should it be taught What techniques work best, and for whom How valid are the re…sults How much data should be tested Which machine languages should be used, if used at all Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy - when to use which technique - are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.
