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Taylor & Francis
Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition / Edition 1

Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition / Edition 1

by Bilal M. Ayyub, Richard H. McCuen


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Engineers commonly encounter problems that require them to makedecisions under conditions of uncertainty. The uncertainty can be in the definition of the problem, the available information, the alternative solutions and their results, or the random nature of the solution outcomes. As engineers are required to solve increasingly complex design problems with limited resources, they must rely more and more on the proper treatment of uncertainty to make the best decisions. Probability, Statistics, and Reliability for Engineers will assist both engineering students and practicing engineers in understanding the fundamentals of probability, statistics, and reliability methods, especially their applications, limitations, and potentials.

Using examples, this practical guide allows engineers to model very complex situations and predict an array of possible outcomes. It will also show readers how to write computational algorithms to solve probability and statistical problems.

Among the many examples cited are:

Product Details

ISBN-13: 9780849326905
Publisher: Taylor & Francis
Publication date: 06/10/1997
Edition description: Older Edition
Pages: 528
Product dimensions: 6.40(w) x 9.50(h) x 1.28(d)

About the Author

Bilal M. Ayyub is a professor of civil and environmental engineering and the director of the Center for Technology and Systems Management in the A. James Clark School of Engineering at the University of Maryland, where he has been since 1983. He is a leading authority in risk analysis, uncertainty modeling, decision analysis, and systems engineering. Dr. Ayyub earned degrees from Kuwait University and the Georgia Institute of Technology. He is a fellow of the ASCE, the ASME, and the SNAME, and a senior member of the IEEE. Dr. Ayyub has served on many national committees and investigation boards and completed numerous research and development projects for governmental and private entities, including the National Science Foundation; the U.S. Air Force, Coast Guard, Army Corps of Engineers, Navy, and Department of Homeland Security; and insurance and engineering firms. He has received multiple ASNE Jimmie Hamilton Awards for best papers in the Naval Engineers Journal, the ASCE Outstanding Research-Oriented Paper in the Journal of Water Resources Planning and Management, the ASCE Edmund Friedman Award, the ASCE Walter Huber Research Prize, the K.S. Fu Award of NAFIPS, and the Department of the Army Public Service Award. Dr. Ayyub is the author/co-author of more than 550 publications in journals, conference proceedings, and reports, as well as 20 books, including Uncertainty Modeling and Analysis for Engineers and Scientists; Risk Analysis in Engineering and Economics; Elicitation of Expert Opinions for Uncertainty and Risks; Probability, Statistics and Reliability for Engineers and Scientists, Second Edition; and Numerical Methods for Engineers.

Richard H. McCuen is the Ben Dyer Professor of civil and environmental engineering at the University of Maryland. Dr. McCuen earned degrees from Carnegie Mellon University and the Georgia Institute of Technology. His primary research interests are statistical hydrology and stormwater management. He has received the Icko Iben Award from the American Water Resource Association and was co-recipient of the Outstanding Research Award from the ASCE Water Resources, Planning and Management Division. He is the author/co-author of over 250 professional papers and 21 books, including Fundamentals of Civil Engineering: An Introduction to the ASCE Body of Knowledge; Modeling Hydrologic Change; Hydrologic Analysis and Design, Third Edition; The Elements of Academic Research; Estimating Debris Volumes for Flood Control; and Dynamic Communication for Engineers.

Table of Contents

Knowledge, Information, and Opinions
Ignorance and Uncertainty
Aleatory and Epistemic Uncertainties in System Abstraction
Characterizing and Modeling Uncertainty
Simulation for Uncertainty Analysis and Propagation
Simulation Projects

Data Description and Treatment
Classification of Data
Graphical Description of Data
Histograms and Frequency Diagrams
Descriptive Measures
Analysis of Simulated Data
Simulation Projects

Fundamentals of Probability
Sets, Sample Spaces, and Events
Mathematics of Probability
Random Variables and Their Probability Distributions
Application: Water Supply and Quality
Simulation and Probability Distributions
Simulation Projects

Probability Distributions for Discrete Random Variables
Bernoulli Distribution
Binomial Distribution
Geometric Distribution
Poisson Distribution
Negative Binomial and Pascal Probability Distributions
Hypergeometric Probability Distribution
Simulation of Discrete Random Variables
A Summary of Distributions
Simulation Projects

Probability Distributions for Continuous Random Variables
Uniform Distribution
Normal Distribution
Lognormal Distribution
Exponential Distribution
Triangular Distribution
Gamma Distribution
Rayleigh Distribution
Beta Distribution
Statistical Probability Distributions
Extreme Value Distributions
Simulation and Probability Distributions
A Summary of Distributions
Simulation Projects

Multiple Random Variables
Joint Random Variables and Their Probability Distributions
Functions of Random Variables
Modeling Aleatory and Epistemic Uncertainty
Multivariable Simulation
Simulation Projects

Monte Carlo Simulation
Random Numbers
Generation of Random Variables
Generation of Selected Discrete Random Variables
Generation of Selected Continuous Random Variables
Simulation Projects

Fundamentals of Statistical Analysis
Properties of Estimators
Method-of-Moments Estimation
Maximum Likelihood Estimation
Sampling Distributions
Univariate Frequency Analysis
Simulation Projects

Hypothesis Testing
General Procedure
Hypothesis Tests of Means
Hypothesis Tests of Variances
Tests of Distributions
Simulation of Hypothesis Test Assumptions
Simulation Projects

Analysis of Variance
Test of Population Means
Multiple Comparisons in the ANOVA Test
Test of Population Variances
Randomized Block Design
Experimental Design
Simulation Projects

Confidence Intervals and Sample-Size Determination
General Procedure
Confidence Intervals on Sample Statistics
Sample Size Determination
Relationship between Decision Parameters and Types I and II Errors
Quality Control
Simulation Projects

Regression Analysis
Correlation Analysis
Introduction to Regression
Principle of Least Squares
Reliability of the Regression Equation
Reliability of Point Estimates of the Regression Coefficients
Confidence Intervals of the Regression Equation
Correlation versus Regression
Applications of Bivariate Regression Analysis
Simulation and Prediction Models
Simulation Projects

Multiple and Nonlinear Regression Analysis
Correlation Analysis
Multiple Regression Analysis
Polynomial Regression Analysis
Regression Analysis of Power Models
Simulation in Curvilinear Modeling
Simulation Projects

Reliability Analysis of Components
Time to Failure
Reliability of Components
First-Order Reliability Method
Advanced Second-Moment Method
Simulation Methods
Reliability-Based Design
Application: Structural reliability of a Pressure Vessel
Simulation Projects

Reliability and Risk Analysis of Systems
Reliability of Systems
Risk Analysis
Risk-Based Decision Analysis
Application: System Reliability of a Post-Tensioned Truss
Simulation Projects

Bayesian Methods
Bayesian Probabilities
Bayesian Estimation of Parameters
Bayesian Statistics

Appendix A: Probability and Statistics Tables
Appendix B: Taylor Series Expansion
Appendix C: Data for Simulation Projects
Appendix D: Semester Simulation Project


Problems appear at the end of each chapter.

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