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# Probability and Statistics - 3rd edition

ISBN13: 978-0201524888
ISBN10: 0201524880

Summary: Probability & Statistics was written for a one or two semester probability and statistics course offered primarily at four-year institutions and taken mostly by sophomore and junior level students, majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. The revision of this well-respected text presents a balanced approach of the classical and Bayesian metho
ds and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.

Features:
• NEW! A new chapter on simulation has been added. This includes methods for simulating specific distributions, importance sampling, Markov chain Monte Carlo, and the bootstrap.
• NEW! Expanded coverage of residual analysis in linear models.
• NEW! More examples now use real data.
• NEW! New sections or subsections on conditionally independent events and random variables, the log normal distribution, quantiles, prediction and prediction intervals, improper priors, Bayes tests, power functions, M-estimators, residual plots in linear models, and Bayesian analysis of simple linear regression are now included.
• NEW! Brief introductions and summaries have been added to each technical section. The introductory paragraphs give readers a hint about what they are going to encounter. The summaries list the most important ideas.
• NEW! The author has added special notes where it is useful to briefly summarize or make a connection to a point made elsewhere in the text.
• NEW! Some material has been reorganized. Independence is now introduced after conditional probability. The first five chapters of the text are devoted to probability and can serve as the text for a one-semester course on probability.
• In addition to examples using current data, some elementary concepts of probability are illustrated by famous examples such as the birthday problem, the tennis tournament problem, the matching problem, and the collector's problem.
• Included as a special feature are sections on Markov chains, the Gambler's Ruin problem, and utility and preferences among gambles. These topics are treated in a completely elementary fashion, and can be omitted without loss of continuity if time is limited.
• Chapters 6 through 10 are devoted to statistical inference. Both classical and Bayesian statistical methods are developed in an integrated presentation which will be useful to students when applying the concepts to the real world.
Summary: Probability & Statistics was written for a one or two semester probability and statistics course offered primarily at four-year institutions and taken mostly by sophomore and junior level students, majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.

Features:
• NEW! A new chapter on simulation has been added. This includes methods for simulating specific distributions, importance sampling, Markov chain Monte Carlo, and the bootstrap.
• NEW! Expanded coverage of residual analysis in linear models.
• NEW! More examples now use real data.
• NEW! New sections or subsections on conditionally independent events and random variables, the log normal distribution, quantiles, prediction and prediction intervals, improper priors, Bayes tests, power functions, M-estimators, residual plots in linear models, and Bayesian analysis of simple linear regression are now included.
• NEW! Brief introductions and summaries have been added to each technical section. The introductory paragraphs give readers a hint about what they are going to encounter. The summaries list the most important ideas.
• NEW! The author has added special notes where it is useful to briefly summarize or make a connection to a point made elsewhere in the text.
• NEW! Some material has been reorganized. Independence is now introduced after conditional probability. The first five chapters of the text are devoted to probability and can serve as the text for a one-semester course on probability.
• In addition to examples using current data, some elementary concepts of probability are illustrated by famous examples such as the birthday problem, the tennis tournament problem, the matching problem, and the collector's problem.
• Included as a special feature are sections on Markov chains, the Gambler's Ruin problem, and utility and preferences among gambles. These topics are treated in a completely elementary fashion, and can be omitted without loss of continuity if time is limited.
• Chapters 6 through 10 are devoted to statistical inference. Both classical and Bayesian statistical methods are developed in an integrated presentation which will be useful to students when applying the concepts to the real world.
...show less

Cover: Paperback
Published: 10/10/2001
International: No

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