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In den WarenkorbHardcover. Zustand: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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In den WarenkorbZustand: New. PP. 516 Index.
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 498 pages. 10.75x7.50x1.25 inches. In Stock.
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Verlag: Taylor & Francis Inc, Bosa Roca, 2010
ISBN 10: 1439803544 ISBN 13: 9781439803547
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: new. Hardcover. Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book's website.The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website. Emphasizing the use of WinBUGS and R to analyze real data, thsi book presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Verlag: Taylor & Francis Inc, Bosa Roca, 2010
ISBN 10: 1439803544 ISBN 13: 9781439803547
Sprache: Englisch
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EUR 205,32
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In den WarenkorbHardcover. Zustand: new. Hardcover. Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book's website.The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website. Emphasizing the use of WinBUGS and R to analyze real data, thsi book presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Anbieter: moluna, Greven, Deutschland
EUR 91,74
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In den WarenkorbGebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ronald Christensen is a Professor in the Department of Mathematics and Statistics at the University of New Mexico, Albuquerque. He is also a Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistic.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 109,12
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In den WarenkorbZustand: New. PP. 516 Illus. This item is printed on demand.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 113,46
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 498 pages. 10.75x7.50x1.25 inches. In Stock. This item is printed on demand.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 145,46
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In den WarenkorbBuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Introducing Bayesian methods, this book emphasizes that the primary role of statistics is to provide appropriate tools for addressing scientific questions. As such, the authors present a collaborative approach to data analysis involving both scientists and statisticians that can be used to model unknown parameters using scientific information, called prior information or apriori. Keeping the mathematics to a minimum, the text focuses on applications, statistical ideas, models, and interpretations. It features WinBUGS throughout the computational problems and uses Monte Carlo methods for all simulations.