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CAIRO BOOKS's Description
Apply the principles of probability and statistics to realistic engineering
The easiest and most effective way to learn the principles of probabilistic
modeling and statistical inference is to apply those principles to a variety of
applications. That's why Ang and Tang's Second Edition of Probability Concepts
in Engineering (previously titled Probability Concepts in Engineering Planning
and Design) explains concepts and methods using a wide range of problems
related to engineering and the physical sciences, particularly civil and
Now extensively revised with new illustrative problems and new and expanded
topics, this Second Edition will help you develop a thorough understanding of
probability and statistics and the ability to formulate and solve real-world
problems in engineering. The authors present each basic principle using
different examples, and give you the opportunity to enhance your understanding
with practice problems. The text is ideally suited for students, as well as
those wishing to learn and apply the principles and tools of statistics and
probability through self-study.
Key Features in this 2nd Edition:
* A new chapter (Chapter 5) covers Computer-Based Numerical and Simulation
Methods in Probability, to extend and expand the analytical methods to more
complex engineering problems.
* New and expanded coverage includes distribution of extreme values (Chapter
3), the Anderson-Darling method for goodness-of-fit test (Chapter 6),
hypothesis testing (Chapter 6), the determination of confidence intervals in
linear regression (Chapter 8), and Bayesian regression and correlation analyses
* Many new exercise problems in each chapter help you develop a working
knowledge of concepts and methods.
* Provides a wide variety of examples, including many new to this edition, to
help you learn and understand specific concepts.
* Illustrates the formulation and solution of engineering-type probabilistic
problems through computer-based methods, including developing computer codes
using commercial software such as MATLAB and MATHCAD.
* Introduces and develops analytical probabilistic models and shows how to
formulate engineering problems under uncertainty, and provides the fundamentals
for quantitative risk assessment.