Probability, Statistics and Reliability for Engineers and Scientists

Probability, Statistics and Reliability for Engineers and Scientists

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Description

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.
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Product details

  • Hardback | 656 pages
  • 158.5 x 244.9 x 39.6mm | 1,088.63g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • Revised
  • 2nd Revised edition
  • 173 black & white illustrations, 144 black & white tables
  • 1584882867
  • 9781584882862
  • 2,520,744

Table of contents

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.
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