Probability, Statistics, and Reliability for Engineers and Scientists

Virtually every engineer and scientist must 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. The goal of this popular and proven book is to introduce the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purpose of data and uncertainty analysis and modeling in support of decision-making. The primary objectives to the author’s approach include: (1) introducing probability, statistics, reliability, and risk methods to students and practicing professionals in engineering and the sciences; (2) emphasizing the practical use of these methods; and (3) establishing the limitations, advantages, and disadvantages of the methods. The book was developed with an emphasis on solving real-world technological problems that engineers and scientists are asked to solve as part of their professional responsibilities. Upon graduation, engineers and scientists must have a solid academic foundation in methods of data analysis and synthesis, as the analysis and synthesis of complex systems are common tasks that confront even entry-level professionals. The underlying theory, especially the assumptions central to the methods, is presented, but then the proper application of the theory is presented through realistic examples, often using actual data. Every attempt is made to show that methods of data analysis are not independent of each other. Instead, we show that real-world problem-solving often involves applying many of the methods presented in different chapters. Probability, Statistics, and Reliability for Engineers and Scientists, here in its fourth edition, is a very popular textbook. Ultimately, readers will find its content of great value in problem-solving and decision-making, particularly in practical applications.

Table of Contents:
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 Treatment Introduction Classification of Data Graphical Description of Data Histograms and Frequency Diagrams Descriptive Measures Applications Analysis of Simulated Data Simulation Projects Fundamentals of Probability Introduction Sets, Sample Spaces, and Events Mathematics of Probability Random Variables and Their Probability Distributions Moments Application: Water Supply and Quality Simulation and Probability Distributions Simulation Projects Probability Distributions for Discrete Random Variables Introduction 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 Variables Introduction Uniform Distribution Normal Distribution Lognormal Distribution Exponential Distribution Triangular Distribution Gamma Distribution Rayleigh Distribution Beta Distribution Statistical Probability Distributions Extreme Value Distributions Applications Simulation and Probability Distributions A Summary of Distributions Simulation Projects Multiple Random Variables Introduction Joint Random Variables and Their Probability Distributions Functions of Random Variables Modeling Aleatory and Epistemic Uncertainty Applications Multivariable Simulation Simulation Projects Simulation Introduction Monte Carlo Simulation Random Numbers Generation of Random Variables Generation of Selected Discrete Random Variables Generation of Selected Continuous Random Variables Applications Simulation Projects Fundamentals of Statistical Analysis Introduction Properties of Estimators Method-of-Moments Estimation Maximum Likelihood Estimation Sampling Distributions Univariate Frequency Analysis Applications Simulation Projects Hypothesis Testing Introduction General Procedure Hypothesis Tests of Means Hypothesis Tests of Variances Tests of Distributions Applications Simulation of Hypothesis Test Assumptions Simulation Projects Analysis of Variance Introduction 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 Determination Introduction General Procedure Confidence Intervals on Sample Statistics Sample Size Determination Relationship between Decision Parameters and Types I and II Errors Quality Control Applications Simulation Projects Regression Analysis Introduction 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 Introduction Correlation Analysis Multiple Regression Analysis Polynomial Regression Analysis Regression Analysis of Power Models Applications Simulation in Curvilinear Modeling Simulation Projects Reliability Analysis of Components Introduction 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 Introduction Reliability of Systems Risk Analysis Risk-Based Decision Analysis Application: System Reliability of a Post-Tensioned Truss Simulation Projects Bayesian Methods Introduction Bayesian Probabilities Bayesian Estimation of Parameters Bayesian Statistics Applications Appendix A: Probability and Statistics Tables Appendix B: Taylor Series Expansion Appendix C: Data for Simulation Projects Appendix D: Semester Simulation Project Index Problems appear at the end of each chapter.

About the Author :
Bilal M. Ayyub, PhD, PE, DistMASCE, HonMASME, is an A. James Clark School of Engineering Professor and Director of the Center for Technology and Systems Management at the University of Maryland, College Park and was a visiting fellow at the National Security Analysis Department of the Applied Physics Laboratory from 2015–2016. He was a chair professor at Tongji University, Shanghai, China (2016–2018) and is currently the Co-Director of its International Joint Research Center for Resilient Infrastructure. He completed his PhD and MSCE degrees from the Georgia Institute of Technology in 1983 and 1981, and BSCE from Kuwait University in 1980. Dr. Ayyub’s main research interests and work are in risk, resilience, sustainability, uncertainty, and decision analysis, applied to civil, infrastructure, and energy. Professor Ayyub is also a fellow of the Society of Naval Architects and Marine Engineers (SNAME), the Structural Engineering Institute (SEI), and the Society for Risk Analysis (2017–018 Treasurer), and a senior member of the Institute of Electrical and Electronics Engineers (IEEE). Dr. Ayyub completed research and development projects for governmental and private entities worldwide. He is the recipient of several awards, most recently the 2024 ASCE OPAL Award for Education; the 2018 ASCE Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure; the 2019 ASCE President Medal for efforts to bring adaptive design to the profession to help address a changing climate; the 2019 ASCE Le Val Lund Award for contributions to resilience enhancement and risk reduction of lifeline-networked systems through measurement science and associated economics of informing policy and decision-making practices; the 2018 ENR Newsmaker Award for passionate efforts in giving engineers their first formal guidance to be more resilient to weather extremes when designing infrastructure; and the 2016 ASNE Solberg Award for significant engineering research and development accomplishments in the field of ship survivability. He is the author and co-author of more than 650 publications in journals, conference proceedings, and reports, and the founding editor-in-chief of the ASCEASME Journal on Risk and Uncertainty in Engineering Systems in its two parts on civil and mechanical engineering. In addition to 15 edited books, his eight textbooks include the following titles: Uncertainty Modeling and Analysis for Engineers and Scientists (Chapman & Hall/CRC 2006 with G. Klir), Risk Analysis in Engineering and Economics (Chapman & Hall/CRC 2003, 2014), Elicitation of Expert Opinions for Uncertainty and Risks (CRC Press 2002), and Numerical Methods for Engineers (Prentice Hall 1996 with McCuen, 2nd ed. Chapman & Hall/CRC 2016). Dr. Ayyub is an academician of the Georgian National Academy of Science, Tbilisi, Georgia, and serves on the National Oceanic and Atmospheric Administration (NOAA) Science Advisory Board, the National Academies Board of Environmental Change and Society, and the Roundtable on Macroeconomics and Climate Related Risks and Opportunities. Richard H. McCuen, PhD, is an emeritus professor of civil and environmental engineering at the University of Maryland. He retired as the Ben Dyer Professor of Civil & Environmental Engineering (1998–2020). Dr. McCuen received a BSCE degree from Carnegie-Mellon University (1967) and MSCE and PhD (1971) degrees from the Georgia Institute of Technology. He was a faculty member at the University of Maryland for 49 years and served as Director of the Engineering Honors Program for more than 35 years. He is the author of 29 textbooks including Hydrologic Analysis and Design (4th ed., 2017), Modeling Hydrologic Change (2002), and Critical Thinking, Idea Innovation, and Creativity (2023). He received the 2015 Ven Te Chow Award for Research, Education, and Service from ASCE, the 1991 James M. Robbins Award for Excellence in Teaching from Chi Epsilon, the 2017 President’s Outstanding Service Award from the AWRA, and the 1988 Icko Iben Award from the AWRA.