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

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. The second edition of this bestselling text introduces 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. It 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 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. Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition, was designed as both a reference and as a textbook, and it serves each purpose well. Ultimately, readers will find its content of great value in problem solving and decision making, particularly in practical applications.

INTRODUCTION
Types of Uncertainty
Introduction to Simulation
DATA DESCRIPTION AND TREATMENT
Classification of Data
Graphical Description of Data
Histograms and Frequency Diagrams
Descriptive Measures
Applications
Analysis of Simulated Data
FUNDAMENTALS OF PROBABILITY
Sample Spaces, Sets, and Events
Mathematics of Probability
Random Variables and their Probability Distributions
Moments
Application: Water Supply and Quality
Simulation and Probability Distributions
PROBABILITY DISTRIBUTIONS FOR DISCRETE RANDOM VARIABLES
Bernoulli Distribution
Binomial Distribution
Geometric Distribution
Poisson Distribution
Negative Binomial and Pascal Probability Distributions
Hypergeometric Probability Distribution
Applications.
Simulation of Discrete Random Variables
PROBABILITY DISTRIBUTIONS FOR CONTINUOUS RANDOM VARIABLES
Uniform Distribution
Normal Distribution
Lognormal Distribution
Exponential Distribution
Triangular Distribution
Gamma Distribution
Rayleigh Distribution
Statistical Probability Distributions
Extreme Value Distributions
Applications
Simulation and Probability Distributions
MULTIPLE RANDOM VARIABLES
Joint Random Variables and their Probability Distributions
Functions of Random Variables
Applications
Multivariable Simulation
SIMULATION
Monte Carlo Simulation
Random Numbers
Generation of Random Variables
Generation of Selected Discrete Random Variables
Generation of Selected Continuous Random Variables
Applications
FUNDAMENTALS OF STATISTICAL ANALYSIS
Estimation of Parameters
Sampling Distributions
Applications
HYPOTHESIS TESTING
General Procedure
Hypothesis Tests of Means
Hypothesis Tests of Variances
Tests of Distributions
Applications
Simulation of Hypothesis Test Assumptions
ANALYSIS OF VARIANCE
Test of Population Means
Multiple Comparisons in the ANOVA Test
Test of Population Variances
Randomized Block Design
Two-Way Analysis of Variance
Applications
CONFIDENCE INTERVALS AND SAMPLE SIZE DETERMINATION
General Procedure
Confidence Intervals on Sample Statistics
Sample-Size Determination
Applications
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 vs. Regression
Applications of Bivariate Regression Analysis
Simulation and Prediction Models
MULTIPLE AND NONLINEAR REGRESSION ANALYSIS
Correlation Analysis
Multiple Regression Analysis
Polynomial Regression Analysis
Regression Analysis of Power Models
Applications.
Simulation in Curvilinear Modeling
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
RELIABILITY AND RISK ANALYSIS OF SYSTEMS
Reliability of Systems
Risk Analysis
Risk-Based Decision Analysis
Application: System Reliability of a Post-Tensioned Truss
BAYESIAN METHODS
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.
Index

Each chapter also contains an Introduction, Problems, and Simulation Projects.