ISBN-10:
1584882867
ISBN-13:
9781584882862
Pub. Date:
06/28/2002
Publisher:
Taylor & Francis
Probability,Statistics,and Reliability for Engineers and Scientists / Edition 2

Probability,Statistics,and Reliability for Engineers and Scientists / Edition 2

by Bilal M. Ayyub, Richard H. McCuen

Hardcover

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Overview

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.

Product Details

ISBN-13: 9781584882862
Publisher: Taylor & Francis
Publication date: 06/28/2002
Edition description: REV
Pages: 656
Product dimensions: 6.20(w) x 9.40(h) x 1.50(d)

Table of Contents

Chapter 1Introduction1
1.1.Introduction1
1.2.Types of Uncertainty5
1.3.Introduction to Simulation9
1.4.Problems17
1.5.Simulation Projects19
Chapter 2Data Description and Treatment25
2.1.Introduction26
2.2.Classification of Data26
2.3.Graphical Description of Data28
2.4.Histograms and Frequency Diagrams36
2.5.Descriptive Measures39
2.6.Applications46
2.7.Analysis of Simulated Data49
2.8.Problems54
2.9.Simulation Projects60
Chapter 3Fundamentals of Probability63
3.1.Introduction64
3.2.Sample Spaces, Sets, and Events64
3.3.Mathematics of Probability69
3.4.Random Variables and their Probability Distributions83
3.5.Moments91
3.6.Application: Water Supply and Quality101
3.7.Simulation and Probability Distributions102
3.8.Problems104
3.9.Simulation Projects109
Chapter 4Probability Distributions for Discrete Random Variables111
4.1.Introduction111
4.2.Bernoulli Distribution112
4.3.Binomial Distribution113
4.4.Geometric Distribution115
4.5.Poisson Distribution116
4.6.Negative Binomial and Pascal Probability Distributions118
4.7.Hypergeometric Probability Distribution118
4.8.Applications119
4.9.Simulation of Discrete Random Variables121
4.10.Problems127
4.11.Simulation Projects129
Chapter 5Probability Distributions for Continuous Random Variables131
5.1.Introduction132
5.2.Uniform Distribution132
5.3.Normal Distribution134
5.4.Lognormal Distribution138
5.5.Exponential Distribution141
5.6.Triangular Distribution143
5.7.Gamma Distribution144
5.8.Rayleigh Distribution145
5.9.Statistical Probability Distributions146
5.10.Extreme Value Distributions149
5.11.Applications155
5.12.Simulation and Probability Distributions157
5.13.Problems160
5.14.Simulation Projects161
Chapter 6Multiple Random Variables165
6.1.Introduction165
6.2.Joint Random Variables and their Probability Distributions166
6.3.Functions of Random Variables182
6.4.Applications192
6.5.Multivariable Simulation199
6.6.Problems209
6.7.Simulation Projects213
Chapter 7Simulation215
7.1.Introduction216
7.2.Monte Carlo Simulation221
7.3.Random Numbers222
7.4.Generation of Random Variables225
7.5.Generation of Selected Discrete Random Variables232
7.6.Generation of Selected Continuous Random Variables238
7.7.Applications242
7.8.Problems252
7.9.Simulation Projects257
Chapter 8Fundamentals of Statistical Analysis259
8.1.Introduction259
8.2.Estimation of Parameters261
8.3.Sampling Distributions276
8.4.Applications280
8.5.Problems285
8.6.Simulation Project287
Chapter 9Hypothesis Testing289
9.1.Introduction290
9.2.General Procedure290
9.3.Hypothesis Tests of Means295
9.4.Hypothesis Tests of Variances302
9.5.Tests of Distributions308
9.6.Applications319
9.7.Simulation of Hypothesis Test Assumptions326
9.8.Problems328
9.9.Simulation Projects333
Chapter 10Analysis of Variance335
10.1.Introduction335
10.2.Test of Population Means336
10.3.Multiple Comparisons in the ANOVA Test345
10.4.Test of Population Variances349
10.5.Randomized Block Design351
10.6.Two-Way Analysis of Variance357
10.7.Applications370
10.8.Problems372
10.9.Simulation Projects377
Chapter 11Confidence Intervals and Sample Size Determination379
11.1.Introduction379
11.2.General Procedure380
11.3.Confidence Intervals on Sample Statistics381
11.4.Sample-Size Determination384
11.5.Applications387
11.6.Problems389
11.7.Simulation Projects391
Chapter 12Regression Analysis393
12.1.Introduction394
12.2.Correlation Analysis394
12.3.Introduction to Regression404
12.4.Principle of Least Squares409
12.5.Reliability of the Regression Equation412
12.6.Reliability of Point Estimates of the Regression Coefficients420
12.7.Confidence Intervals of the Regression Equation423
12.8.Correlation vs. Regression428
12.9.Applications of Bivariate Regression Analysis429
12.10.Simulation and Prediction Models437
12.11.Problems439
12.12.Simulation Projects445
Chapter 13Multiple and Nonlinear Regression Analysis447
13.1.Introduction448
13.2.Correlation Analysis448
13.3.Multiple Regression Analysis450
13.4.Polynomial Regression Analysis459
13.5.Regression Analysis of Power Models464
13.6.Applications466
13.7.Simulation in Curvilinear Modeling478
13.8.Problems481
13.9.Simulation Projects484
Chapter 14Reliability Analysis of Components485
14.1.Introduction485
14.2.Time to Failure486
14.3.Reliability of Components489
14.4.First-Order Reliability Method491
14.5.Advanced Second-Moment Method496
14.6.Simulation Methods509
14.7.Reliability-Based Design519
14.8.Application: Structural Reliability of a Pressure Vessel525
14.9.Problems530
14.10.Simulation Projects534
Chapter 15Reliability and Risk Analysis of Systems535
15.1.Introduction535
15.2.Reliability of Systems537
15.3.Risk Analysis551
15.4.Risk-Based Decision Analysis557
15.5.Application: System Reliability of a Post-Tensioned Truss562
15.6.Problems565
15.7.Simulation Projects568
Chapter 16Bayesian Methods569
16.1.Introduction569
16.2.Bayesian Probabilities570
16.3.Bayesian Estimation of Parameters575
16.4.Bayesian Statistics584
16.5.Applications588
16.6.Problems591
Appendix AProbability and Statistics Tables595
A-1.Cumulative Distribution Function of Standard Normal ([Phi](z))596
A-2.Critical Values for the student's t Distribution (t[subscript alpha,k])599
A-3.Critical Values for the Chi-Square Distribution (c[subscript alpha,k] = X[superscript 2 subscript alpha,k])601
A-4.Critical Values for the F Distribution (f[subscript alpha,k,u] = f[subscript alpha, v subscript 1, v subscript 2])603
A-5.Critical Values for the Pearson Correlation Coefficient for the Null Hypothesis H[subscript 0]: [rho] = 0 and Both the One-Tailed Alternative H[subscript A]: ##[rho]## > 0 and the Two-Tailed Alternative H[subscript A] : [rho] [not equal] 0608
A-6.Uniformly Distributed Random Numbers609
A-7.Critical Values for the Kolmogorov-Smirnov One-Sample Test610
A-8.Values of the Gamma Function611
A-9.Critical Values for the Duncan Multiple Range Test for a 5% Level of Significance and Selected Degrees of Freedom (df) and p Groups612
Appendix BTaylor Series Expansion613
B-1.Taylor Series613
B-2.Common Taylor Series616
B-3.Applications: Taylor Series Expansion of the Square Root617
B-4.Problems618
Appendix CData for Simulation Projects621
C-1.Stream Erosion Study621
C-2.Traffic Estimation Study622
C-3.Water Evaporation Study623
Index625

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