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

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Features

  • Emphasizes risk and reliability for the engineering statistics market from a practical point of view
  • Includes various applications in engineering
  • Provides additional material on simulation, the mathematics related to uncertainty, the rand function, sample variability, dependence, the Poisson process, and more
  • Contains additional illustrations on histogram samples and hypothesis testing along with Venn diagrams for conditional probabilities

Solutions manual and PowerPoint slides available with qualifying course adoption

Summary

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.

Table of Contents

Introduction
Introduction
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.

Author Bio(s)

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.