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Ajouter au panierEtat : New. pp. 340 Illus.
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Ajouter au panierPaperback. Etat : Good. Paperback. Third edition. Several scores on covers. Spine foot and lower edge of rear cover are a little bumped, affecting closing pages. Light wear on spine head and leading corners. Front upper leading corner is slightly bent, affecting early pages. Minor film peeling on leading edge of rear cover. Binding is intact, contents are clean and clear. AM. Used.
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Ajouter au panierGebunden. Etat : New. Contains valuable techniques for reducing computation time, practical advice on experimental design, comparisons with bootstrap, parametric, and nonparametric techniques and a three-part bibliography featuring more than 1,000 articlesContains .
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Ajouter au panierEtat : New.
Edité par Springer New York, Springer US, 2010
ISBN 10 : 1441919074 ISBN 13 : 9781441919076
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e ect, 'Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try ' But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse-acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student's t continue to play an essential role in statistical practice.
Edité par Springer New York, Springer US Dez 2010, 2010
ISBN 10 : 1441919074 ISBN 13 : 9781441919076
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 213,99
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e ect, ¿Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try ¿ But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse¿acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student¿s t continue to play an essential role in statistical practice.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Ajouter au panierEtat : New.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Ajouter au panierEtat : New.
Edité par Springer New York Dez 2004, 2004
ISBN 10 : 038720279X ISBN 13 : 9780387202792
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 252,05
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Ajouter au panierBuch. Etat : Neu. Neuware - This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e ect, 'Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try ' But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse-acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student's t continue to play an essential role in statistical practice.
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. pp. 340 3rd Edition.
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Ajouter au panierPaperback. Etat : Like New. Like New. book.
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Ajouter au panierPaperback. Etat : Brand New. 3rd ed. edition. 376 pages. 9.00x6.00x0.77 inches. In Stock.
Edité par Springer-Verlag New York Inc., 2010
ISBN 10 : 1441919074 ISBN 13 : 9781441919076
Langue: anglais
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 573.
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EUR 180,07
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Contains valuable techniques for reducing computation time, practical advice on experimental design, comparisons with bootstrap, parametric, and nonparametric techniques and a three-part bibliography featuring more than 1,000 articlesContains .
Edité par Springer New York, Chapman And Hall/CRC Dez 2010, 2010
ISBN 10 : 1441919074 ISBN 13 : 9781441919076
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 213,99
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e ect, 'Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try ' But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse-acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi cance levels, not approximations. 3. A growing recognition that most real-world data are drawn from mixtures of populations. 4. A growing recognition that missing data is inevitable, balanced designs the exception. Thus, it seems natural that the theory of testing hypothesis and the more general decision theory in which it is embedded should be introduced via the permutation tests. On the other hand, certain relatively robust param- ric tests such as Student's t continue to play an essential role in statistical practice. 340 pp. Englisch.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 298,42
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Ajouter au panierEtat : New. Print on Demand pp. 340 14 Illus.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 340.