Probability and Statistical InferenceUnderstanding the "why" of statistics and probabilityâ??a unique and useful emphasis on theory This outstanding textbook emphasizes theoretical comprehension rather than the narrow acquisition of concepts or skills. Probability and Statistical Inference focuses on the development of intuition and understanding through diversity of experience. This thought-provoking text reintroduces mathematics, abstractions, and theory into the study of statistics and probability, and demonstrates that greater abstraction leads to a wider applicability of the methods under discussion. Its unique approach to exercises integrates the knowledge gained here and promotes a more complete understanding of the material. Probability and Statistical Inference features: A wealth of examples illustrating concepts, theorems, and methodsâ??from numerical data and details of calculations, to ideas behind some of the methods, and more Accessible, user-friendly treatments that clearly explain concepts and motivations while pointing out pitfalls and difficulties of arguments A selection of advanced topics for students who would benefit from more thorough explanations An instructor's manual with solutions available from the publisher Suitable for upper-level undergraduate and graduate courses in statistics, and as a professional reference, this unparalleled volume offers useful insights to anyone who uses statistical tools, whatever the discipline. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. |
Contents
Experiments Sample Spaces and Events | 1 |
Probability | 31 |
Combinatorial Probability | 57 |
Copyright | |
18 other sections not shown
Other editions - View all
Probability and Statistical Inference Robert Bartoszynski,Magdalena Niewiadomska-Bugaj Limited preview - 2007 |
Probability and Statistical Inference Robert Bartoszynski,Magdalena Niewiadomska-Bugaj Limited preview - 2020 |
Probability and Statistical Inference Robert Bartoszynski,Magdalena Niewiadomska-Bugaj Limited preview - 2020 |
Common terms and phrases
A₁ alternative H₁ approximation assume assumption average balls Bernoulli trials binomial distribution Chapter chi-square distribution conditional confidence interval Consequently consider continuous random variable converges critical region defined Definition degrees of freedom denote density f(x depend determine elements equal error Example exists expected exponential distribution fact finite following theorem formula function gamma distribution given hence independent inequality integral joint density joint distribution Let X1 linear marginal mean methods N₁ normal distribution null hypothesis observations obtain occurs outcomes p-value pair partition Poisson distribution Poisson process possible problem procedure proof quantile random sample regression reject sample space satisfies sequence situation solution specific standard normal subsets sufficient statistic Suppose T₁ theory tosses total number trials unbiased estimator Var(X variance X₁ Y₁ zero σ²