Random Phenomena: Fundamentals of Probability and Statistics for Engineers / Edition 1

Random Phenomena: Fundamentals of Probability and Statistics for Engineers / Edition 1

by Babatunde A. Ogunnaike
     
 

Many of the problems that engineers face involve randomly varying phenomena of one sort or another. However, if characterized properly, even such randomness and the resulting uncertainty are subject to rigorous mathematical analysis.

Taking into account the uniquely multidisciplinary demands of 21st-century science and engineering, Random Phenomena:

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Overview

Many of the problems that engineers face involve randomly varying phenomena of one sort or another. However, if characterized properly, even such randomness and the resulting uncertainty are subject to rigorous mathematical analysis.

Taking into account the uniquely multidisciplinary demands of 21st-century science and engineering, Random Phenomena: Fundamentals of Probability and Statistics for Engineers provides students with a working knowledge of how to solve engineering problems that involve randomly varying phenomena. Basing his approach on the principle of theoretical foundations before application, Dr. Ogunnaike presents a classroom-tested course of study that explains how to master and use probability and statistics appropriately to deal with uncertainty in standard problems and those that are new and unfamiliar.

Giving students the tools and confidence to formulate practical solutions to problems, this book offers many useful features, including:

  • Unique case studies to illustrate the fundamentals and applications of probability and foster understanding of the random variable and its distribution
  • Examples of development, selection, and analysis of probability models for specific random variables
  • Presentation of core concepts and ideas behind statistics and design of experiments
  • Selected "special topics," including reliability and life testing, quality assurance and control, and multivariate analysis
  • As classic scientific boundaries continue to be restructured, the use of engineering is spilling over into more non-traditional areas, ranging from molecular biology to finance. This book emphasizes fundamentals and a "first principles" approach to deal with this evolution. It illustrates theory with practical examples and case studies, equipping readers to deal with a wide range of problems beyond those in the book.

    About the Author:
    Professor Ogunnaike is Interim Dean of Engineering at the University of Delaware. He is the recipient of the 2008 American Automatic Control Council's Control Engineering Practice Award, the ISA's Donald P. Eckman Education Award, the Slocomb Excellence in Teaching Award, and was elected into the US National Academy of Engineering in 2012.

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    Product Details

    ISBN-13:
    9781420044973
    Publisher:
    Taylor & Francis
    Publication date:
    09/21/2009
    Edition description:
    New Edition
    Pages:
    1064
    Sales rank:
    1,276,504
    Product dimensions:
    6.40(w) x 9.30(h) x 2.10(d)

    Table of Contents

    Prelude
    Approach Philosophy
    Four Basic Principles
    I Foundations
    Two Motivating Examples
    Yield Improvement in a Chemical Process
    Quality Assurance in a Glass Sheet Manufacturing Process
    Outline of a Systematic Approach
    Random Phenomena, Variability, and Uncertainty
    Two Extreme Idealizations of Natural Phenomena
    Random Mass Phenomena
    Introducing Probability
    The Probabilistic Framework
    II Probability
    Fundamentals of Probability Theory
    Building Blocks
    Operations
    Probability
    Conditional Probability
    Independence
    Random Variables and Distributions
    Distributions
    Mathematical Expectation
    Characterizing Distributions
    Special Derived Probability Functions
    Multidimensional Random Variables
    Distributions of Several Random Variables
    Distributional Characteristics of Jointly Distributed Random Variables
    Random Variable Transformations
    Single Variable Transformations
    Bivariate Transformations
    General Multivariate Transformations
    Application Case Studies I: Probability
    Mendel and Heredity
    World War II Warship Tactical Response Under Attack
    III Distributions
    Ideal Models of Discrete Random Variables
    The Discrete Uniform Random Variable
    The Bernoulli Random Variable
    The Hypergeometric Random Variable
    The Binomial Random Variable
    Extensions and Special Cases of the Binomial Random Variable
    The Poisson Random Variable
    Ideal Models of Continuous Random Variables
    Gamma Family Random Variables
    Gaussian Family Random Variables
    Ratio Family Random Variables
    Information, Entropy, and Probability Models
    Uncertainty and Information
    Entropy
    Maximum Entropy Principles for Probability Modeling
    Some Maximum Entropy Models
    Maximum Entropy Models from General Expectations
    Application Case Studies II: In-Vitro Fertilization
    In-Vitro Fertilization and Multiple Births
    Probability Modeling and Analysis
    Binomial Model Validation
    Problem Solution: Model-Based IVF Optimization and Analysis
    Sensitivity Analysis
    IV Statistics
    Introduction to Statistics
    From Probability to Statistics
    Variable and Data Types
    Graphical Methods of Descriptive Statistics
    Numerical Descriptions
    Sampling
    The Distribution of Functions of Random Variables
    Sampling Distribution of the Mean
    Sampling Distribution of the Variance
    Estimation
    Criteria for Selecting Estimators
    Point Estimation Methods
    Precision of Point Estimates
    Interval Estimates
    Bayesian Estimation
    Hypothesis Testing
    Basic Concepts
    Concerning Single Mean of a Normal Population
    Concerning Two Normal Population Means
    Determining β, Power, and Sample Size
    Concerning Variances of Normal Populations
    Concerning Proportions
    Concerning Non-Gaussian Populations
    Likelihood Ratio Tests
    Discussion
    Regression Analysis
    Simple Linear Regression
    "Intrinsically" Linear Regression
    Multiple Linear Regression
    Polynomial Regression
    Probability Model Validation
    Probability Plots
    Chi-Squared Goodness-of-Fit Test
    Nonparametric Methods
    Single Population
    Two Populations
    Probability Model Validation
    A Comprehensive Illustrative Example
    Design of Experiments
    Analysis of Variance
    Single Factor Experiments
    Two-Factor Experiments
    General Multi-factor Experiments
    2k Factorial Experiments and Design
    Screening Designs: Fractional Factorial
    Screening Designs: Plackett-Burman
    1Response Surface Methodology
    Introduction to Optimal Designs
    Application Case Studies III: Statistics
    Prussian Army Death-by-Horse Kicks
    WW II Aerial Bombardment of London
    US Population Dynamics: 1790–2000
    Process Optimization
    V Applications
    Reliability and Life Testing
    System Reliability
    System Lifetime and Failure-Time Distributions
    The Exponential Reliability Model
    The Weibull Reliability Model
    Life Testing
    Quality Assurance and Control
    Acceptance Sampling
    Process and Quality Control
    Chemical Process Control
    Process and Parameter Design
    Introduction to Multivariate Analysis
    Multivariate Probability Models
    Multivariate Data Analysis
    Principal Components Analysis
    Appendix
    Index

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