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Synopsis
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers
Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.
Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression.
Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
James H. Stapleton, PhD, has recently retired after forty-nine years as professor in the Department of Statistics and Probability at Michigan State University, including eight years as chairperson and almost twenty years as graduate director. Dr. Stapleton is the author of Linear Statistical Models (Wiley), and he received his PhD in mathematical statistics from Purdue University.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurWiley-Interscience
- Date d'édition2008
- ISBN 10 0470073721
- ISBN 13 9780470073728
- ReliureRelié
- Langueanglais
- Nombre de pages464
- Coordonnées du fabricantnon disponible
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Models for Probability and Statistical Inference
Edité par
John Wiley & Sons, 2008
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Neuf
Couverture rigide
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Etat : New. James H. Stapleton, PhD, has recently retired after forty-nine years as professor in the Department of Statistics and Probability at Michigan State University, including eight years as chairperson and almost twenty years as graduate director. Dr. Stapleton . N° de réf. du vendeur 446911540
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Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics)
Edité par
Wiley-Interscience, 2007
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Neuf
Couverture rigide
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Models for Probability and Statistical Inference - Theory and Applications
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Models for Probability and Statistical Inference - Theory and Applications
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Models for Probability and Statistical Inference : Theory and Applications
Edité par
Wiley-Interscience, 2007
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Neuf
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Models for Probability and Statistical Inference : Theory and Applications
Edité par
Wiley-Interscience, 2007
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Neuf
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Models for Probability and Statistical Inference : Theory and Applications
Edité par
Wiley-Interscience, 2007
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Models for Probability and Statistical Inference : Theory and Applications
Edité par
Wiley-Interscience, 2007
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 4224673
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Models for Probability and Statistical Inference : Theory and Applications
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Neuware - This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression.Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers. N° de réf. du vendeur 9780470073728
Quantité disponible : 2 disponible(s)
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Models for Probability and Statistical Inference (Hardcover)
Edité par
John Wiley & Sons Inc, New York, 2008
ISBN 10 : 0470073721
ISBN 13 : 9780470073728
Neuf
Couverture rigide
Edition originale
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression. Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers. Serving as a text for a two semester sequence on probability and statistical inference complex Models for Probability and Statistical Inference: Theory and Applications features exercises throughout the book and selected answers (not solutions). Each section is followed by a selection of problems, from simple to more complex. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9780470073728
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