Probability Concepts in Engineering Emphasis on Applications to Civil and Environmental Engineering

ISBN-10: 047172064X

ISBN-13: 9780471720645

Edition: 2nd 2007 (Revised)

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The material in the book is intended for a first course on applied probability and statistics for engineering students at the sophomore or junior level, or for self study, stressing probabilistic modeling and the fundamentals of statistical inferences. The primary aim is to provide an in-depth understanding of the fundamentals for the proper application in engineering problems.The second edition of this well-known book (previously titled Probability Concepts in Engineering Planning and Design) by Alfredo Ang and Wilson Tang, two world-renowned educators, has been revised to simplify understanding the fundamentals of probability and statistics for engineering students. The second edition includes many new and expanded topics, including hypothesis testing and confidence intervals in regression analysis. Students using this text will develop the ability to formulate and solve real-world problems in engineering. The authors accomplish this by explaining all the concepts and methods through a variety of relevant engineering and physical problems.Each basic principle is presented and illustrated through different examples relevant to engineering and the physical sciences, particularly civil and environmental engineering. The exercise problems in each chapter further enhance understanding of basic concepts and reinforce a working knowledge of concepts and methods. The authors firmly believe that the easiest and most effective way for engineers to learn and master a new set of abstract principles is to apply them to a variety of applications.
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Book details

List price: $231.95
Edition: 2nd
Copyright year: 2007
Publisher: John Wiley & Sons, Incorporated
Publication date: 3/3/2006
Binding: Hardcover
Pages: 420
Size: 8.37" wide x 10.25" long x 0.87" tall
Weight: 2.728
Language: English

Roles of Probability and Statistics in Engineering
Uncertainty in Engineering
Uncertainty Associated with Randomness-The Aleatory Uncertainty
Uncertainty Associated with Imperfect Knowledge-The Epistemic Uncertainty
Design and Decision Making under Uncertainty
Planning and Design of Transportation Infrastructures
Design of Structures and Machines
Planning and Design of Hydrosystems
Design of Geotechnical Systems
Construction Planning and Management
Photogrammetric, Geodetic, and Surveying Measurements
Applications in Quality Control and Assurance
Concluding Summary
Fundamentals of Probability Models
Events and Probability
Characteristics of Problems Involving Probabilities
Estimating Probabilities
Elements of Set Theory-Tools for Defining Events
Important Definitions
Mathematical Operations of Sets
Mathematics of Probability
The Addition Rule
Conditional Probability
The Multiplication Rule
The Theorem of Total Probability
The Bayes' Theorem
Concluding Summary
Analytical Models of Random Phenomena
Random Variables and Probability Distribution
Random Events and Random Variables
Probability Distribution of a Random Variable
Main Descriptors of a Random Variable
Useful Probability Distributions
The Gaussian (or Normal) Distribution
The Lognormal Distribution
The Bernoulli Sequence and the Binomial Distribution
The Geometric Distribution
The Negative Binomial Distribution
The Poisson Process and the Poisson Distribution
The Exponential Distribution
The Gamma Distribution
The Hypergeometric Distribution
The Beta Distribution
Other Useful Distributions
Multiple Random Variables
Joint and Conditional Probability Distributions
Covariance and Correlation
Concluding Summary
Functions of Random Variables
Derived Probability Distributions
Function of a Single Random Variable
Function of Multiple Random Variables
Extreme Value Distributions
Moments of Functions of Random Variables
Mathematical Expectations of a Function
Mean and Variance of a General Function
Concluding Summary
Computer-Based Numerical and Simulation Methods in Probability
Numerical and Simulations Methods
Essentials of Monte Carlo Simulation
Numerical Examples
Problems Involving Aleatory and Epistemic Uncertainties
MCS Involving Correlated Random Variables
Concluding Summary
References and Softwares
Statistical Inferences from Observational Data
Role of Statistical Inference in Engineering
Statistical Estimation of Parameters
Random Sampling and Point Estimation
Sampling Distributions
Testing of Hypotheses
Hypothesis Test Procedure
Confidence Intervals
Confidence Interval of the Mean
Confidence Interval of the Proportion
Confidence Interval of the Variance
Measurement Theory
Concluding Summary
Determination of Probability Distribution Models
Probability Papers
Utility and Plotting Position
The Normal Probability Paper
The Lognormal Probability Paper
Construction of General Probability Papers
Testing Goodness-of-Fit of Distribution Models
The Chi-Square Test for Goodness-of-Fit
The Kolmogorov-Smirnov (K-S) Test for Goodness-of-Fit
The Anderson-Darling Test for Goodness-of-Fit
Invariance in the Asymptotic Forms of Extremal Distributions
Concluding Summary
Regression and Correlation Analyses
Fundamentals of Linear Regression Analysis
Regression with Constant Variance
Variance in Regression Analysis
Confidence Intervals in Regression
Correlation Analysis
Estimation of the Correlation Coefficient
Regression of Normal Variates
Linear Regression with Nonconstant Variance
Multiple Linear Regression
Nonlinear Regression
Applications of Regression Analysis in Engineering
Concluding Summary
The Bayesian Approach
Estimation of Parameters
Basic Concepts-The Discrete Case
The Continuous Case
General Formulation
A Special Application of the Bayesian Updating Process
Bayesian Concept in Sampling Theory
General Formulation
Sampling from Normal Populations
Error in Estimation
The Utility of Conjugate Distributions
Estimation of Two Parameters
Bayesian Regression and Correlation Analyses
Linear Regression
Updating the Regression Parameters
Correlation Analysis
Concluding Summary
Elements of Quality Assurance and Acceptance Sampling
Probability Tables
Standard Normal Probabilities
CDF of the Binomial Distribution
Critical Values of t-Distribution at Confidence Level (1-[alpha]) = p
Critical Values of the x[superscript 2] Distribution at probability Level [alpha]
Critical Values of D[superscript alpha subscript n] at Significance Level [alpha] in the K-S Test
Critical Values of the Anderson-Darling Goodness-of-Fit Test
Combinatorial Formulas
The Basic Relation
The Binomial Coefficient
The Multinomial Coefficient
Stirling's Formula
Derivation of the Poisson Distribution
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