3rd Edition

Probability, Statistics, and Reliability for Engineers and Scientists

By Bilal M. Ayyub, Richard H. McCuen Copyright 2011
    664 Pages 226 B/W Illustrations
    by CRC Press

    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.

    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
    Two-Way ANOVA
    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.


    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.