An integrated coverage of probability, statistics, Monte Carlo simulation, inferential statistics, design of experiments, systems reliability, fitting random data to models, analysis of variance, stochastic processes, and stochastic differential equations for engineers and scientists. The author for first time presents an introduction to the broad field of applied engineering uncertainty analysis in one comprehensive, friendly, coverage.
Each concept is illustrated with several examples of relevance in engineering applications (no cards, colored balls, or dice):
478 pages; 177 solved examples; 147 proposed problems; 174 illustrations, 69 short computer programs; and 51 data and statistical tables.Clear presentation of concepts. Practical engineering examples. No cards, nodice, no colored balls.Prepares the reader for today’s problems in engineering analysis, modeling,and design under uncertainty.This edition includes new research advances in nonlinear stochastic equations; simple methods to solve and graph boundary-value problems in several dimensions. Integrated treatment of probability, statistics, and stochastic modeling.Includes numerical (Monte Carlo) simulations and analytical modeling.Intuitive and graphical introduction to stochastic processes.Practical introduction to applied stochastic differential equations.INDEPENDENT REVISIONS
". . . offers the engineering community and integrated, balanced, and clear presentation to probability, statistics, stochastic models, and stochastic differential equations. The aim is to demonstrate to the reader that the fundamental principles are inherently simple and that the methods are practical and extremely useful in everyday engineering analysis or design. The book succeeds admirably in these aims." Aerospace (from the Royal Aeronautical Society).
". . .discusses uncertainty in engineering, contrasting it with uncertainty as usually encountered in pure science. The essential differences are beautifully explained, providing a philosophical and practical basis for the rest of the book. This essential introduction is lacking in most books on statistics applications in engineering ...Overall, the book presents clear and interesting descriptions and explanations. The level of mathematics is appropriate to reasonably numerate engineers, and the use of spreadsheets and Maple enhance the practical value to engineers. I strongly recommend this book to design and systems engineers. . ." Journal of Quality and Reliability Engineering International.
". . . written in a clear and easy-to-understand manner. It requires no prior background in statistics. The best part of the book is that it has numerous solved practical examples, end-of-the-chapter problems, and a significant amount of new material. The emphasis is on concepts and their illustration, and the author has made a concerted effort in avoiding lengthy derivations and this is an attractive feature from a student's perspective. I believe the book will be useful not only for teaching a course on uncertainty, risk and reliability but also for applying these concepts to solving practical engineering problems." Stochastic Environmental Research and Risk Assessment.
Dr. Sergio E. Serrano received his Ph.D. degree at the University of Waterloo (Canada). He is a full professor of engineering science and applied mathematics at a Research I university in the U. S. For the past thirty years, he has taught in several universities in the United States, Canada, Colombia, Spain, and China. He has over one hundred research publications in international science, engineering, and mathematics journals. He is also the author of nine books in environmental engineering, statistics, philosophy, and psychology. He has been an associate editor of various technical journals; the author of over one hundred publications in scientific journals and books in the field of probability, statistics and stochastic analysis; and a pioneer of several new solutions of nonlinear stochastic differential equations in complex systems subject to hysteresis, transient variability, random media heterogeneity, and scale dependency. Dr. Serrano has been awarded four times with nationally-competitive research grants by the National Science Foundation, Washington, DC.
