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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