9780470073728-0470073721-Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics)

Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics)

ISBN-13: 9780470073728
ISBN-10: 0470073721
Edition: 1
Author: James H. Stapleton
Publication date: 2007
Publisher: Wiley-Interscience
Format: Hardcover 464 pages
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Book details

ISBN-13: 9780470073728
ISBN-10: 0470073721
Edition: 1
Author: James H. Stapleton
Publication date: 2007
Publisher: Wiley-Interscience
Format: Hardcover 464 pages

Summary

Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics) (ISBN-13: 9780470073728 and ISBN-10: 0470073721), written by authors James H. Stapleton, was published by Wiley-Interscience in 2007. With an overall rating of 4.0 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Models for Probability and Statistical Inference: Theory and Applications (Wiley Series in Probability and Statistics) (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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.

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