Probability, Statistics, and Reliability for Engineers and Scientists

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.

IntroductionIntroduction 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 TreatmentIntroduction Classification of Data Graphical Description of Data Histograms and Frequency Diagrams Descriptive Measures Applications Analysis of Simulated Data Simulation Projects Fundamentals of ProbabilityIntroduction Sets, Sample Spaces, and EventsMathematics of Probability Random Variables and Their Probability Distributions Moments Application: Water Supply and Quality Simulation and Probability DistributionsSimulation Projects Probability Distributions for Discrete Random VariablesIntroduction Bernoulli Distribution Binomial Distribution Geometric Distribution Poisson Distribution Negative Binomial and Pascal Probability Distributions Hypergeometric Probability Distribution Applications Simulation of Discrete Random Variables A Summary of Distributions Simulation Projects Probability Distributions for Continuous Random VariablesIntroduction Uniform DistributionNormal Distribution Lognormal Distribution Exponential Distribution Triangular Distribution Gamma Distribution Rayleigh Distribution Beta Distribution Statistical Probability Distributions Extreme Value Distributions Applications Simulation and Probability DistributionsA Summary of Distributions Simulation Projects Multiple Random VariablesIntroduction Joint Random Variables and Their Probability DistributionsFunctions of Random Variables Modeling Aleatory and Epistemic UncertaintyApplications Multivariable Simulation Simulation Projects SimulationIntroduction Monte Carlo Simulation Random Numbers Generation of Random Variables Generation of Selected Discrete Random Variables Generation of Selected Continuous Random VariablesApplicationsSimulation Projects Fundamentals of Statistical AnalysisIntroductionProperties of Estimators Method-of-Moments Estimation Maximum Likelihood Estimation Sampling Distributions Univariate Frequency Analysis Applications Simulation Projects Hypothesis TestingIntroduction General Procedure Hypothesis Tests of MeansHypothesis Tests of VariancesTests of Distributions Applications Simulation of Hypothesis Test Assumptions Simulation Projects Analysis of VarianceIntroduction Test of Population Means Multiple Comparisons in the ANOVA Test Test of Population Variances Randomized Block Design Two-Way ANOVA Experimental Design Applications Simulation Projects Confidence Intervals and Sample-Size DeterminationIntroduction General Procedure Confidence Intervals on Sample Statistics Sample Size Determination Relationship between Decision Parameters and Types I and II Errors Quality ControlApplicationsSimulation Projects Regression AnalysisIntroduction Correlation AnalysisIntroduction to RegressionPrinciple of Least SquaresReliability 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 AnalysisIntroductionCorrelation AnalysisMultiple Regression AnalysisPolynomial Regression Analysis Regression Analysis of Power Models Applications Simulation in Curvilinear Modeling Simulation Projects Reliability Analysis of ComponentsIntroduction Time to Failure Reliability of Components First-Order Reliability Method Advanced Second-Moment Method Simulation Methods Reliability-Based DesignApplication: Structural reliability of a Pressure Vessel Simulation Projects Reliability and Risk Analysis of SystemsIntroduction Reliability of Systems Risk Analysis Risk-Based Decision Analysis Application: System Reliability of a Post-Tensioned Truss Simulation Projects Bayesian MethodsIntroduction Bayesian Probabilities Bayesian Estimation of Parameters Bayesian StatisticsApplications Appendix A: Probability and Statistics TablesAppendix B: Taylor Series ExpansionAppendix C: Data for Simulation ProjectsAppendix D: Semester Simulation Project Index 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.