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Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details.
Additional features of this Second Edition include:
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A new chapter on random samples
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Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site
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Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating
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Real-world examples that illuminate presented concepts
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Exercises at the end of each section
Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.
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The late Robert Bartoszynski, PhD, was Professor in the Department of Statistics at The Ohio State University. His scientific contributions included research in the theory of stochastic processes and modeling biological phenomena. Throughout his career, Dr. Bartoszynski published over 80 journal articles, books, and book chapters. He was a Fellow of the Institute of Mathematical Statistics as well as a member of the American Statistical Association, the International Statistical Institute, and the Bernoulli Society.
Magdalena Niewiadomska-Bugaj, PhD, is Professor in the Department of Statistics at Western Michigan University. An active member of numerous societies including the American Statistical Association, the Institute of Mathematical Statistics, and the Classification Society of North America, Dr. Niewiadomska-Bugaj's areas of interest include general statistical methodology, nonparametric statistics, classification, and categorical data analysis. She has published over 50 papers, books, and book chapters in theoretical and applied statistics.
