Probability Statistics & Reliability for Engineers
1st Edition
Published on 10. June 1997
Book
Hardback
XII, 514 pages
978-0-8493-2690-5 (ISBN)
Description
Engineers commonly encounter problems that require them to make decisions under conditions of uncertainty. The uncertainty can be in the definition of the problem, the available information, the alternative solutions and their results, or the random nature of the solution outcomes. As engineers are required to solve increasingly complex design problems with limited resources, they must rely more and more on the proper treatment of uncertainty to make the best decisions. Probability, Statistics, and Reliability for Engineers will assist both engineering students and practicing engineers in understanding the fundamentals of probability, statistics, and reliability methods, especially their applications, limitations, and potentials.
Full of examples, this practical guide allows engineers to model very complex situations and predict an array of possible outcomes. It will also show readers how to write computational algorithms to solve probability and statistical problems.
Among the many examples cited are:
Time to Failure of Cranes
Discharge and Flow of Rivers
Hydraulic Pump Reliability
Predicting Defects in Manufacturing
Nuclear Reactor Reliability
Traffic Flow Patterns
For each chapter in the book, computational examples are given in individual sections, and more detailed engineering applications are presented in a concluding section. Each chapter also includes exercise problems covering the material presented, which will assist readers in practicing the fundamental concepts. Probability, Statistics, and Reliability for Engineers provides a well-rounded introduction to these methods for students in engineering, mathematics, and statistics; practicing engineers in all disciplines; and mathematicians and scientists.
Full of examples, this practical guide allows engineers to model very complex situations and predict an array of possible outcomes. It will also show readers how to write computational algorithms to solve probability and statistical problems.
Among the many examples cited are:
Time to Failure of Cranes
Discharge and Flow of Rivers
Hydraulic Pump Reliability
Predicting Defects in Manufacturing
Nuclear Reactor Reliability
Traffic Flow Patterns
For each chapter in the book, computational examples are given in individual sections, and more detailed engineering applications are presented in a concluding section. Each chapter also includes exercise problems covering the material presented, which will assist readers in practicing the fundamental concepts. Probability, Statistics, and Reliability for Engineers provides a well-rounded introduction to these methods for students in engineering, mathematics, and statistics; practicing engineers in all disciplines; and mathematicians and scientists.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Illustrations
104 s/w Tabellen
104 tabs.
Dimensions
Height: 235 mm
Width: 156 mm
Weight
895 gr
ISBN-13
978-0-8493-2690-5 (9780849326905)
Schweitzer Classification
Other editions
New editions
Bilal M. Ayyub | Richard H. McCuen
Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition
Book
06/2002
2nd Edition
CRC Press
€112.84
Article exhausted; check for reprint
Content
INTRODUCTION
TYPES OF UNCERTAINTY
TAYLOR SERIES EXPANSION
DATA DESCRIPTION AND TREATMENT
Introduction
Classification of Data
Graphical Description of Data
Histograms and Frequency Diagrams
Descriptive Measures
Applications
Problems
FUNDAMENTALS OF PROBABILITY
Introduction
Sample Spaces, Sets, and Events
Mathematics of Probability
Random Variables and Their Probability Distributions
Moment
Common Discrete Probability Distributions
Common Continuous Probability Distributions
Applications
Problems
MULTIPLE RANDOM VARIABLES
Introduction
Joint Random Variables and Their
Functions of Random Variables
Applications
Problems
FUNDAMENTALS OF STATISTICAL ANALYSIS
Introduction
Estimation of Parameters
Sampling Distributions
Hypothesis Testing: Procedure
Hypothesis Tests of Means
Hypothesis Tests of Variances
Confidence Intervals
Sample-Size Determination
Selection of Model Probability Distributions
Applications
Problems
CURVE FITTING AND 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
Multiple Regression Analysis
Regression Analysis of Nonlinear Models
Applications
Problems
SIMULATION
Introduction
Monte Carlo Simulation
Random Numbers
Generation of Random Variables
Generation of Selected Discrete Random Variables
Generation of Selected Continuous Random Variables
Applications
Problems
RELIABILITY AND RISK ANALYSIS
Introduction
Time to Failure
Reliability of Components
Reliability of Systems
Risk-Based Decision Analysis
Applications
Problems
BAYESIAN METHODS
Introduction
Bayesian Probabilities
Bayesian Estimation of Parameters
Bayesian Statistics
Applications
Problems
APPENDIX A: Probability and Statistics Tables
APPENDIX B: Values of the Gamma Function
SUBJECT INDEX
TYPES OF UNCERTAINTY
TAYLOR SERIES EXPANSION
DATA DESCRIPTION AND TREATMENT
Introduction
Classification of Data
Graphical Description of Data
Histograms and Frequency Diagrams
Descriptive Measures
Applications
Problems
FUNDAMENTALS OF PROBABILITY
Introduction
Sample Spaces, Sets, and Events
Mathematics of Probability
Random Variables and Their Probability Distributions
Moment
Common Discrete Probability Distributions
Common Continuous Probability Distributions
Applications
Problems
MULTIPLE RANDOM VARIABLES
Introduction
Joint Random Variables and Their
Functions of Random Variables
Applications
Problems
FUNDAMENTALS OF STATISTICAL ANALYSIS
Introduction
Estimation of Parameters
Sampling Distributions
Hypothesis Testing: Procedure
Hypothesis Tests of Means
Hypothesis Tests of Variances
Confidence Intervals
Sample-Size Determination
Selection of Model Probability Distributions
Applications
Problems
CURVE FITTING AND 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
Multiple Regression Analysis
Regression Analysis of Nonlinear Models
Applications
Problems
SIMULATION
Introduction
Monte Carlo Simulation
Random Numbers
Generation of Random Variables
Generation of Selected Discrete Random Variables
Generation of Selected Continuous Random Variables
Applications
Problems
RELIABILITY AND RISK ANALYSIS
Introduction
Time to Failure
Reliability of Components
Reliability of Systems
Risk-Based Decision Analysis
Applications
Problems
BAYESIAN METHODS
Introduction
Bayesian Probabilities
Bayesian Estimation of Parameters
Bayesian Statistics
Applications
Problems
APPENDIX A: Probability and Statistics Tables
APPENDIX B: Values of the Gamma Function
SUBJECT INDEX