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E-grāmata: Probability, Statistics, and Reliability for Engineers and Scientists

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(University of Maryland, College Park, USA), (University of Maryland, College Park, USA)
  • Formāts: 663 pages
  • Izdošanas datums: 19-Apr-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781439895337
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Probability, Statistics, and Reliability for Engineers and ScientistsPalielināt
  • Formāts: 663 pages
  • Izdošanas datums: 19-Apr-2016
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781439895337
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In a technological society, 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.

Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making.

The third edition of this bestselling text presents 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. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision making processes. This edition 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 not only 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.

Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.
Preface xvii
Acknowledgments xxi
Authors xxiii
Chapter 1 Introduction
1(26)
1.1 Introduction
1(3)
1.1.1 Decision Making in Engineering and Science
2(1)
1.1.2 Expected Educational Outcomes
3(1)
1.2 Knowledge, Information, and Opinions
4(2)
1.3 Ignorance and Uncertainty
6(2)
1.4 Aleatory and Epistemic Uncertainties in System Abstraction
8(2)
1.5 Characterizing and Modeling Uncertainty
10(3)
1.6 Simulation for Uncertainty Analysis and Propagation
13(7)
1.6.1 Simulation by Coin Flipping
14(1)
1.6.2 Generation of Random Numbers
14(2)
1.6.3 Computer Generation of Random Numbers
16(1)
1.6.3.1 Midsquare Method
17(1)
1.6.3.2 The rand Function
17(1)
1.6.4 Transformation of Random Variables
18(2)
1.7 Simulation Projects
20(4)
1.7.1 Structural Beam Study
20(1)
1.7.2 Stream Erosion Study
21(2)
1.7.3 Traffic Estimation Study
23(1)
1.7.4 Water Evaporation Study
24(1)
1.8 Problems
24(3)
Chapter 2 Data Description and Treatment
27(34)
2.1 Introduction
28(1)
2.2 Classification of Data
28(2)
2.2.1 Nominal Scale
28(1)
2.2.2 Ordinal Scale
29(1)
2.2.3 Interval Scale
29(1)
2.2.4 Ratio Scale
29(1)
2.2.5 Dimensionality of Data
29(1)
2.3 Graphical Description of Data
30(7)
2.3.1 Area Charts
30(1)
2.3.2 Pie Charts
31(1)
2.3.3 Bar Charts
31(1)
2.3.4 Column Charts
32(1)
2.3.5 Scatter Diagrams
32(2)
2.3.6 Line Graphs
34(1)
2.3.7 Combination Charts
34(2)
2.3.8 Three-Dimensional Charts
36(1)
2.4 Histograms and Frequency Diagrams
37(5)
2.5 Descriptive Measures
42(5)
2.5.1 Central Tendency Measures
42(1)
2.5.2 Dispersion Measures
43(2)
2.5.3 Percentiles
45(1)
2.5.4 Box-and-Whisker Plots
45(2)
2.6 Applications
47(3)
2.6.1 Two Random Samples
47(1)
2.6.2 Stage and Discharge of a River
48(2)
2.7 Analysis of Simulated Data
50(4)
2.8 Simulation Projects
54(2)
2.8.1 Structural Beam Study
54(1)
2.8.2 Stream Erosion Study
54(1)
2.8.3 Traffic Estimation Study
55(1)
2.8.4 Water Evaporation Study
55(1)
2.8.5 Pile Strength Study
55(1)
2.8.6 Bridge Scour Study
55(1)
2.8.7 Highway Accident Study
55(1)
2.8.8 Academic Grade Study
55(1)
2.8.9 River Discharge Study
56(1)
2.9 Problems
56(5)
Chapter 3 Fundamentals of Probability
61(56)
3.1 Introduction
62(1)
3.2 Sets, Sample Spaces, and Events
62(10)
3.2.1 Sets
62(1)
3.2.2 Sample Spaces and Events
63(1)
3.2.3 Venn-Euler Diagrams
64(3)
3.2.4 Basic Event Operations
67(5)
3.2.5 Cartesian Product
72(1)
3.3 Mathematics of Probability
72(16)
3.3.1 Definition of Probability
72(4)
3.3.2 Axioms of Probability
76(2)
3.3.3 Counting
78(4)
3.3.4 Conditional Probability
82(5)
3.3.5 Partitions, Total Probability, and Bayes' Theorem
87(1)
3.4 Random Variables and Their Probability Distributions
88(8)
3.4.1 Probability of Discrete Random Variables
89(4)
3.4.2 Probability for Continuous Random Variables
93(3)
3.5 Moments
96(8)
3.5.1 Mean
97(2)
3.5.2 Variance
99(2)
3.5.3 Standard Deviation and Coefficient of Variation
101(1)
3.5.4 Skew
102(2)
3.6 Application: Water Supply and Quality
104(1)
3.7 Simulation and Probability Distributions
104(2)
3.8 Simulation Projects
106(1)
3.8.1 Structural Beam Study
106(1)
3.8.2 Stream Erosion Study
106(1)
3.8.3 Traffic Estimation Study
107(1)
3.8.4 Water Evaporation Study
107(1)
3.9 Problems
107(10)
Chapter 4 Probability Distributions for Discrete Random Variables
117(26)
4.1 Introduction
117(1)
4.2 Bernoulli Distribution
118(1)
4.3 Binomial Distribution
119(4)
4.4 Geometric Distribution
123(2)
4.5 Poisson Distribution
125(3)
4.6 Negative Binomial and Pascal Probability Distributions
128(1)
4.7 Hypergeometric Probability Distribution
129(1)
4.8 Applications
130(3)
4.8.1 Earthquakes and Structures
130(1)
4.8.2 Floods and Coffer Dams
131(2)
4.9 Simulation of Discrete Random Variables
133(3)
4.10 A Summary of Distributions
136(1)
4.11 Simulation Projects
136(3)
4.11.1 Structural Beam Study
136(1)
4.11.2 Stream Erosion Study
137(2)
4.11.3 Traffic Estimation Study
139(1)
4.11.4 Water Evaporation Study
139(1)
4.12 Problems
139(4)
Chapter 5 Probability Distributions for Continuous Random Variables
143(36)
5.1 Introduction
144(1)
5.2 Uniform Distribution
144(2)
5.3 Normal Distribution
146(3)
5.4 Lognormal Distribution
149(4)
5.5 Exponential Distribution
153(1)
5.6 Triangular Distribution
154(2)
5.7 Gamma Distribution
156(1)
5.8 Rayleigh Distribution
157(1)
5.9 Beta Distribution
158(1)
5.10 Statistical Probability Distributions
158(4)
5.10.1 Student's t Distribution
159(1)
5.10.2 Chi-Square Distribution
160(1)
5.10.3 F Distribution
161(1)
5.11 Extreme Value Distributions
162(7)
5.11.1 Introduction to Extreme Value Estimation
162(2)
5.11.2 Type I Extreme Value (Gumbel) Distributions
164(2)
5.11.3 Type II Extreme Value (Frechet) Distributions
166(2)
5.11.4 Type III Extreme Value (Weibull) Distributions
168(1)
5.12 Applications
169(2)
5.12.1 Earthquakes and Structures
169(1)
5.12.2 Foundation of a Structure
169(1)
5.12.3 Failure of Highway Drainage
170(1)
5.13 Simulation and Probability Distributions
171(1)
5.14 A Summary of Distributions
171(2)
5.15 Simulation Projects
173(2)
5.15.1 Structural Beam Study
173(1)
5.15.2 Stream Erosion Study
173(1)
5.15.3 Traffic Estimation Study
173(2)
5.15.4 Water Evaporation Study
175(1)
5.16 Problems
175(4)
Chapter 6 Multiple Random Variables
179(48)
6.1 Introduction
180(1)
6.2 Joint Random Variables and Their Probability Distributions
180(14)
6.2.1 Probability for Discrete Random Vectors
180(4)
6.2.2 Probability for Continuous Random Vectors
184(3)
6.2.3 Conditional Moments, Covariance, and Correlation Coefficient
187(4)
6.2.4 Common Joint Probability Distributions
191(3)
6.3 Functions of Random Variables
194(10)
6.3.1 Probability Distributions for Dependent Random Variables
195(3)
6.3.2 Mathematical Expectation
198(2)
6.3.3 Approximate Methods
200(1)
6.3.3.1 Single Random Variable X
200(1)
6.3.3.2 Random Vector X
201(3)
6.4 Modeling Aleatory and Epistemic Uncertainty
204(2)
6.5 Applications
206(5)
6.5.1 Reactions Due to a Random Load
206(1)
6.5.2 Buckling of Columns
206(1)
6.5.3 Reactions Due to Random Loads
207(1)
6.5.4 Open Channel Flow
208(2)
6.5.5 Warehouse Construction
210(1)
6.6 Multivariable Simulation
211(9)
6.6.1 Simulation of Expected Values
212(4)
6.6.2 Simulation and Correlation
216(4)
6.7 Simulation Projects
220(1)
6.7.1 Structural Beam Study
220(1)
6.7.2 Stream Erosion Study
221(1)
6.7.3 Traffic Estimation Study
221(1)
6.7.4 Water Evaporation Study
221(1)
6.8 Problems
221(6)
Chapter 7 Simulation
227(38)
7.1 Introduction
228(4)
7.1.1 Engineering Decision Making
228(1)
7.1.2 Sampling Variation
228(1)
7.1.3 Coin-Flipping Simulation
229(2)
7.1.4 Definitions
231(1)
7.1.5 Benefits of Simulation
232(1)
7.2 Monte Carlo Simulation
232(1)
7.3 Random Numbers
233(2)
7.3.1 Arithmetic Generators
234(1)
7.3.2 Testing of Generators
235(1)
7.4 Generation of Random Variables
235(6)
7.4.1 Inverse Transformation Method
236(3)
7.4.2 Composition Method
239(1)
7.4.3 Function-Based Generation
240(1)
7.4.4 Acceptance-Rejection Method
240(1)
7.4.5 Generation Based on Special Properties
241(1)
7.5 Generation of Selected Discrete Random Variables
241(5)
7.5.1 Bernoulli Distribution
241(1)
7.5.2 Binomial Distribution
241(2)
7.5.3 Geometric Distribution
243(1)
7.5.4 Poisson Distribution
244(2)
7.6 Generation of Selected Continuous Random Variables
246(4)
7.6.1 Uniform Distribution
246(1)
7.6.2 Normal Distribution
246(2)
7.6.3 Lognormal Distribution
248(1)
7.6.4 Exponential Distribution
249(1)
7.7 Applications
250(8)
7.7.1 Simulation of a Queuing System
250(5)
7.7.2 Warehouse Construction
255(3)
7.8 Simulation Projects
258(1)
7.8.1 Structural Beam Study
258(1)
7.8.2 Stream Erosion Study
258(1)
7.8.3 Traffic Estimation Study
259(1)
7.8.4 Water Evaporation Study
259(1)
7.9 Problems
259(6)
Chapter 8 Fundamentals of Statistical Analysis
265(26)
8.1 Introduction
265(2)
8.1.1 Samples and Populations
266(1)
8.2 Properties of Estimators
267(3)
8.2.1 Bias
267(1)
8.2.2 Precision
268(1)
8.2.3 Accuracy
269(1)
8.2.4 Comparison of Bias, Precision, and Accuracy
269(1)
8.2.5 Mean Square Error
269(1)
8.2.6 Consistency, Sufficiency, and Efficiency
269(1)
8.3 Method of Moments Estimation
270(3)
8.4 Maximum Likelihood Estimation
273(3)
8.5 Sampling Distributions
276(4)
8.5.1 Sampling Distribution of the Mean
276(1)
8.5.2 Sampling Distribution of the Variance
277(3)
8.5.3 Sampling Distributions for Other Parameters
280(1)
8.6 Univariate Frequency Analysis
280(5)
8.7 Applications
285(2)
8.7.1 Tollbooth Rates of Service
285(1)
8.7.2 Frictional Resistance to Shear
286(1)
8.8 Simulation Projects
287(1)
8.9 Problems
287(4)
Chapter 9 Hypothesis Testing
291(40)
9.1 Introduction
292(1)
9.2 General Procedure
292(5)
9.2.1 Step: 1Formulation of Hypotheses
292(1)
9.2.2 Step 2: Test Statistic and Its Sampling Distribution
293(1)
9.2.3 Step 3: Level of Significance
293(1)
9.2.4 Step 4: Data Analysis
294(1)
9.2.5 Step 5: Region of Rejection
295(1)
9.2.6 Step 6: Select Appropriate Hypothesis
296(1)
9.3 Hypothesis Tests of Means
297(6)
9.3.1 Test of Mean with Known Population Variance
297(2)
9.3.2 Test of Mean with Unknown Population Variance
299(1)
9.3.3 Hypothesis Test of Two Means
300(3)
9.3.4 Summary
303(1)
9.4 Hypothesis Tests of Variances
303(4)
9.4.1 One-Sample Chi-Square Test
304(2)
9.4.2 Two-Sample F Test
306(1)
9.4.3 Summary
307(1)
9.5 Tests of Distributions
307(11)
9.5.1 Chi-Square Test for Goodness of Fit
308(3)
9.5.2 Application of Chi-Square Test: Normal Distribution
311(4)
9.5.3 Kolmogorov-Smirnov One-Sample Test
315(3)
9.6 Applications
318(6)
9.6.1 Test of Mean Strength of Steel Prestressing Cables
318(1)
9.6.2 Test of Mean Water Use of Hotels
319(1)
9.6.3 Test of Two Sample Means for Car Speed
319(1)
9.6.4 Variation of Effective Cohesion
320(1)
9.6.5 Uniformity of Illumination
321(1)
9.6.6 Variation of Infiltration Capacity
321(1)
9.6.7 Test of Two Variances for Car Speed
322(1)
9.6.8 Test on Variances of Soil Friction Angle
323(1)
9.6.9 Test on Variances of Nitrogen Levels
323(1)
9.7 Simulation of Hypothesis Test Assumptions
324(1)
9.8 Simulation Projects
325(1)
9.9 Problems
326(5)
Chapter 10 Analysis of Variance
331(42)
10.1 Introduction
332(1)
10.2 Test of Population Means
332(8)
10.2.1 Steps in ANOVA
333(1)
10.2.2 Rationale of ANOVA Test
334(4)
10.2.3 Linear Separation of Total Variation
338(1)
10.2.4 Computational Equations
339(1)
10.3 Multiple Comparisons in ANOVA Test
340(3)
10.3.1 Duncan Multiple Range Test
340(1)
10.3.1.1 Multiple Group Comparisons
341(1)
10.3.2 Scheffe Test
342(1)
10.4 Test of Population Variances
343(2)
10.5 Randomized Block Design
345(5)
10.5.1 Randomized Block Design Model
346(4)
10.6 Two-Way ANOVA
350(10)
10.6.1 ANOVA2 Model
351(4)
10.6.2 Computational Examples
355(4)
10.6.3 Discussion
359(1)
10.7 Experimental Design
360(2)
10.8 Applications
362(3)
10.8.1 Steel Corrosion
362(2)
10.8.2 Sheet Erosion
364(1)
10.9 Simulation Projects
365(1)
10.10 Problems
366(7)
Chapter 11 Confidence Intervals and Sample Size Determination
373(20)
11.1 Introduction
373(1)
11.2 General Procedure
374(1)
11.3 Confidence Intervals on Sample Statistics
374(5)
11.3.1 Confidence Interval for the Mean
374(2)
11.3.2 Factors Affecting a Confidence Interval and Sampling Variation
376(3)
11.3.3 Confidence Interval for Variance
379(1)
11.4 Sample Size Determination
379(2)
11.5 Relationship between Decision Parameters and Type I and II Errors
381(5)
11.6 Quality Control
386(2)
11.7 Applications
388(2)
11.7.1 Accuracy of Principal Stress
388(1)
11.7.2 Compression of Steel
389(1)
11.7.3 Sample Size of Organic Carbon
389(1)
11.7.4 Resampling for Greater Accuracy
390(1)
11.8 Simulation Projects
390(1)
11.9 Problems
390(3)
Chapter 12 Regression Analysis
393(46)
12.1 Introduction
394(1)
12.2 Correlation Analysis
394(9)
12.2.1 Graphical Analysis
395(2)
12.2.2 Bivariate Correlation
397(1)
12.2.3 Separation of Variation
397(2)
12.2.4 Correlation: Fraction of Explained Variation
399(1)
12.2.5 Computational Form for Correlation Coefficient
399(1)
12.2.6 Distribution of Correlation Coefficient
399(1)
12.2.6.1 Effect of p (n Constant)
400(1)
12.2.6.2 Effect of n (p Constant)
400(1)
12.2.7 Hypothesis Testing
400(3)
12.3 Introduction to Regression
403(3)
12.3.1 Elements of Statistical Optimization
403(1)
12.3.2 Zero-Intercept Model
404(1)
12.3.3 Regression Definitions
405(1)
12.4 Principle of Least Squares
406(3)
12.4.1 Definitions
406(1)
12.4.2 Solution Procedure
407(2)
12.5 Reliability of Regression Equation
409(6)
12.5.1 Correlation Coefficient
409(1)
12.5.2 Standard Error of Estimate
409(1)
12.5.3 Analysis of Variance
410(2)
12.5.4 Standardized Partial Regression Coefficients
412(1)
12.5.5 Assumptions Underlying Regression Model
413(2)
12.6 Reliability of Point Estimates of Regression Coefficients
415(3)
12.6.1 Sampling Distributions of Regression Coefficients
415(2)
12.6.2 Hypothesis Test on Slope Coefficient
417(1)
12.6.3 Hypothesis Test on Intercept Coefficient
418(1)
12.7 Confidence Intervals of Regression Equation
418(4)
12.7.1 Confidence Interval for Line as a Whole
418(1)
12.7.2 Confidence Interval Estimate for a Single Point on Line
419(1)
12.7.3 Confidence Interval Estimate for a Future Value
419(1)
12.7.4 Summary of Confidence Intervals
420(2)
12.8 Correlation versus Regression
422(1)
12.9 Applications of Bivariate Regression Analysis
423(7)
12.9.1 Estimating Trip Rate
423(1)
12.9.2 Breakwater Cost
424(1)
12.9.3 Stress-Strain Analysis
425(1)
12.9.4 Project Cost versus Time
426(2)
12.9.5 Effect of Extreme Event
428(1)
12.9.6 Variable Transformation
429(1)
12.10 Simulation and Prediction Models
430(2)
12.11 Simulation Projects
432(1)
12.11.1 Calibration of a Bivariate Linear Model
432(1)
12.11.2 Simulating Distributions of Correlation Coefficient
432(1)
12.12 Problems
433(6)
Chapter 13 Multiple and Nonlinear Regression Analysis
439(38)
13.1 Introduction
440(1)
13.2 Correlation Analysis
440(2)
13.3 Multiple Regression Analysis
442(8)
13.3.1 Calibration of Multiple Linear Model
442(1)
13.3.2 Standardized Model
443(2)
13.3.3 Intercorrelation
445(1)
13.3.4 Criteria for Evaluating a Multiple Regression Model
446(1)
13.3.5 Analysis of Residuals
447(1)
13.3.6 Computational Example
448(2)
13.4 Polynomial Regression Analysis
450(4)
13.4.1 Structure of Polynomial Models
450(1)
13.4.2 Calibration of Polynomial Models
451(1)
13.4.3 Analysis of Variance for Polynomial Models
452(2)
13.5 Regression Analysis of Power Models
454(2)
13.5.1 Fitting a Power Model
454(1)
13.5.2 Goodness of Fit
455(1)
13.5.3 Additional Model Forms
455(1)
13.6 Applications
456(9)
13.6.1 One-Predictor Polynomial of Evaporation versus Temperature
456(1)
13.6.2 Single-Predictor Power Model
457(1)
13.6.3 Multivariate Power Model
458(2)
13.6.4 Estimating Breakwater Costs
460(1)
13.6.5 Trip Generation Model
461(2)
13.6.6 Estimation of the Reaeration Coefficient
463(1)
13.6.7 Estimating Slope Stability
464(1)
13.7 Simulation in Curvilinear Modeling
465(3)
13.8 Simulation Projects
468(1)
13.9 Problems
468(9)
Chapter 14 Reliability Analysis of Components
477(42)
14.1 Introduction
477(1)
14.2 Time to Failure
478(2)
14.3 Reliability of Components
480(2)
14.4 First-Order Reliability Method
482(4)
14.5 Advanced Second-Moment Method
486(11)
14.5.1 Uncorrelated, Normally Distributed Random Variables
486(5)
14.5.2 Uncorrelated, Nonnormally Distributed Random Variables
491(3)
14.5.3 Correlated Random Variables
494(3)
14.6 Simulation Methods
497(8)
14.6.1 Direct Monte Carlo Simulation
497(2)
14.6.2 Importance Sampling Method
499(1)
14.6.3 Conditional Expectation Method
500(1)
14.6.4 Generalized Conditional Expectation Method
501(1)
14.6.5 Antithetic Variates Method
502(3)
14.7 Reliability-Based Design
505(5)
14.7.1 Direct Reliability-Based Design
506(1)
14.7.2 Load and Resistance Factor Design
506(4)
14.8 Application: Structural Reliability of a Pressure Vessel
510(4)
14.9 Simulation Projects
514(1)
14.9.1 Structural Beam Study
514(1)
14.9.2 Stream Erosion Study
514(1)
14.9.3 Traffic Estimation Study
514(1)
14.9.4 Water Evaporation Study
514(1)
14.10 Problems
514(5)
Chapter 15 Reliability and Risk Analysis of Systems
519(32)
15.1 Introduction
519(1)
15.2 Reliability of Systems
520(12)
15.2.1 Systems in Series
520(3)
15.2.2 Systems in Parallel
523(2)
15.2.3 Mixed Systems in Series and Parallel
525(1)
15.2.4 Fault Tree Analysis
526(4)
15.2.5 Event Tree Analysis
530(2)
15.3 Risk Analysis
532(7)
15.4 Risk-Based Decision Analysis
539(5)
15.4.1 Objectives of Decision Analysis
540(1)
15.4.2 Decision Variables
540(1)
15.4.3 Decision Outcomes
540(1)
15.4.4 Associated Probabilities and Consequences
540(1)
15.4.5 Decision Trees
540(4)
15.5 Application: System Reliability of a Post-Tensioned Truss
544(2)
15.6 Simulation Projects
546(1)
15.7 Problems
547(4)
Chapter 16 Bayesian Methods
551(22)
16.1 Introduction
551(1)
16.2 Bayesian Probabilities
552(5)
16.3 Bayesian Estimation of Parameters
557(7)
16.3.1 Discrete Parameters
557(3)
16.3.2 Continuous Parameters
560(4)
16.4 Bayesian Statistics
564(3)
16.4.1 Mean Value with Known Variance
564(2)
16.4.2 Mean Value with Unknown Variance
566(1)
16.5 Applications
567(3)
16.5.1 Sampling from an Assembly Line
567(2)
16.5.2 Scour around Bridge Piers
569(1)
16.6 Problems
570(3)
Appendix A Probability and Statistics Tables
573(20)
A.1 Cumulative Distribution Function of Standard Normal (Φ(z))
574(3)
A.2 Critical Values for Student's t Distribution (ta, k)
577(2)
A.3 Critical Values for Chi-Square Distribution (ca, k = x2a, k)
579(2)
A.4 Critical Values for F Distribution (fa, k, u = fa, v1, v2)
581(5)
A.5 Critical Values for Pearson Correlation Coefficient for Null Hypothesis H0: p = 0 and Both One-Tailed Alternative HA: |p| > 0 and Two-Tailed Alternative HA: p ≠ 0
586(1)
A.6 Uniformly Distributed Random Numbers
587(2)
A.7 Critical Values for Kolmogorov-Smirnov One-Sample Test
589(1)
A.8 Values of Gamma Function
590(1)
A.9 Critical Values for Duncan Multiple Range Test for a 5% Level of Significance and Selected Degrees of Freedom (df) and p Groups
591(2)
Appendix B Taylor Series Expansion
593(6)
B.1 Taylor Series
593(3)
B.2 Common Series
596(1)
B.3 Applications: Taylor Series Expansion of Square Root
597(1)
B.4 Problems
597(2)
Appendix C Data for Simulation Projects
599(4)
C.1 Stream Erosion Study
599(1)
C.2 Traffic Estimation Study
600(1)
C.3 Water Evaporation Study
601(2)
Appendix D Semester Simulation Project
603(24)
D.1 Project Problem Statement
603(1)
D.2 Progress Reports and Due Dates
604(1)
D.3 A Sample Solution
605(22)
Index 627
Bilal M. Ayyub is a professor of civil and environmental engineering and the director of the Center for Technology and Systems Management in the A. James Clark School of Engineering at the University of Maryland, where he has been since 1983. He is a leading authority in risk analysis, uncertainty modeling, decision analysis, and systems engineering. Dr. Ayyub earned degrees from Kuwait University and the Georgia Institute of Technology. He is a fellow of the ASCE, the ASME, and the SNAME, and a senior member of the IEEE. Dr. Ayyub has served on many national committees and investigation boards and completed numerous research and development projects for governmental and private entities, including the National Science Foundation; the U.S. Air Force, Coast Guard, Army Corps of Engineers, Navy, and Department of Homeland Security; and insurance and engineering firms. He has received multiple ASNE Jimmie Hamilton Awards for best papers in the Naval Engineers Journal, the ASCE Outstanding Research-Oriented Paper in the Journal of Water Resources Planning and Management, the ASCE Edmund Friedman Award, the ASCE Walter Huber Research Prize, the K.S. Fu Award of NAFIPS, and the Department of the Army Public Service Award. Dr. Ayyub is the author/co-author of more than 550 publications in journals, conference proceedings, and reports, as well as 20 books, including Uncertainty Modeling and Analysis for Engineers and Scientists; Risk Analysis in Engineering and Economics; Elicitation of Expert Opinions for Uncertainty and Risks; Probability, Statistics and Reliability for Engineers and Scientists, Second Edition; and Numerical Methods for Engineers.

Richard H. McCuen is the Ben Dyer Professor of civil and environmental engineering at the University of Maryland. Dr. McCuen earned degrees from Carnegie Mellon University and the Georgia Institute of Technology. His primary research interests are statistical hydrology and stormwater management. He has received the Icko Iben Award from the American Water Resource Association and was co-recipient of the Outstanding Research Award from the ASCE Water Resources, Planning and Management Division. He is the author/co-author of over 250 professional papers and 21 books, including Fundamentals of Civil Engineering: An Introduction to the ASCE Body of Knowledge; Modeling Hydrologic Change; Hydrologic Analysis and Design, Third Edition; The Elements of Academic Research; Estimating Debris Volumes for Flood Control; and Dynamic Communication for Engineers.
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