Verlag: Oxford University Press, Incorporated, 2013
ISBN 10: 0199676755 ISBN 13: 9780199676750
Sprache: Englisch
Anbieter: Better World Books, Mishawaka, IN, USA
EUR 24,32
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In den WarenkorbZustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
Verlag: Oxford. Oxford Univ. Press. 2002., 2002
Anbieter: Antiquariat am Flughafen, Berlin, Deutschland
EUR 34,95
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In den WarenkorbHardcover. Zustand: sehr gut. Hardcover gr. 8°. xv, 379 pages. second edition. with fig. and tables within the chapters. little rubbed. good condition. Series Oxford Statistical Science", 25. in englischer Sprache (in english).
EUR 92,27
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In den WarenkorbZustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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In den WarenkorbGebundene Ausgabe. Zustand: Neu. Neu Neuware, Importqualität, auf Lager, Sofortversand - The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values.
EUR 112,97
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In den WarenkorbPaperback. Zustand: Brand New. 2nd edition. 400 pages. 9.25x6.25x1.00 inches. In Stock.
EUR 119,03
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In den WarenkorbZustand: New. In.
EUR 118,21
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In den WarenkorbZustand: New. pp. xv + 379 Illus.
EUR 136,76
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In den WarenkorbZustand: New. A reference text in the area of biostatistics (medical statistics). It discusses fully parametric models for discrete repeated measures data. It explores statistical models for time-dependent predictors where there may be feedback between the predictor and .