Probability, statistics, & reliability for engineers

著者

書誌事項

Probability, statistics, & reliability for engineers

Bilal M. Ayyub, Richard H. McCuen

CRC Press, c1997

大学図書館所蔵 件 / 全21件

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注記

Includes index

内容説明・目次

内容説明

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.

目次

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

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