Written by renowned experts in the field, this textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing the goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of the conditional probability that will prepare them for more advanced courses in stochastic processes.
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This popular text promotes cross-disciplinary research into the modeling of the ever-increasingly complex data involved in scientific and technological research. It also provides a unique emphasis on where and how to apply probability models to real phenomena and an unusually strong preparation in the tools necessary for such applications. In this latest edition, the material on the distributions of sums of independent random variables is now presented before the discussion of individual distributions.
This title features, as a unifying theme, the role probability models can play in scientific and practical applications. It offers numerous examples, with actual data, of real-world uses of probability models. In addition, the book features detailed coverage of the properties and uses of probability models, other than the normal distribution, that have been successful in modeling real phenomena.
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