Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition

Bilal M. Ayyub, Richard H. McCuen

April 26, 2011 by CRC Press
Textbook - 663 Pages - 226 B/W Illustrations
ISBN 9781439809518 - CAT# K10476


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  • Emphasizes risk and reliability for the engineering statistics market from a practical point of view
  • Includes various applications in engineering
  • Provides additional material on simulation, the mathematics related to uncertainty, the rand function, sample variability, dependence, the Poisson process, and more
  • Contains additional illustrations on histogram samples and hypothesis testing along with Venn diagrams for conditional probabilities

Solutions manual and PowerPoint slides available with qualifying course adoption


In a technological society, virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential.

Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making.

The third edition of this bestselling text presents probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision making processes. This edition also features expanded discussions of the analysis of variance, including single- and two-factor analyses, and a thorough treatment of Monte Carlo simulation. The authors not only clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.

Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.


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