Random Phenomena: Fundamentals of Probability and Statistics for Engineers, 1st Edition (Hardback) book cover

Random Phenomena

Fundamentals of Probability and Statistics for Engineers, 1st Edition

By Babatunde A. Ogunnaike

CRC Press

1,056 pages | 193 B/W Illus.

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pub: 2009-09-21
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Description

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.

    Reviews

    The author does an excellent job presenting the material in an interesting way, making connections between theoretical and experimental statistics and between deterministic and probabilistic models. … a good book for engineers. … an excellent introductory mathematical statistics textbook for engineers. I like the fact that the theory is well developed throughout the chapters and that the transition between chapters is smooth. Compared with another introductory statistics resource for engineering (Probability and Statistics for Engineering and the Sciences by Jay Devore), I would choose this text … . I recommend this textbook with full confidence for engineering students who have the strong mathematical background, specifically differential and integral calculus. This book is distinguished from the crowded field by the well-explained theory of statistics and how it provides interesting applications. The big plus about this text is the variety and large amount of review questions, exercises, and application problems that the author provides, which in my opinion is crucial to the understanding of the theoretical concepts.

    —Walid K. Sharabati, The American Statistician, August 2011

    This book offers many unique features in a crowded field of statistics books for engineers. … The core concepts are written in an easy-to-understand format and students from various engineering disciplines can easily follow the theoretical concepts presented. The examples and application problems are selected from a wide range that spans catalysts in a chemical reactor, in-vitro fertilization, molecular biology, reliability of parallel computer systems, population demographics, polymers, and finance. … I highly recommend this book for engineering students, professional engineers and applied statisticians dealing with systems involving random phenomena. Instructors who are looking for an alternative textbook should give a serious consideration to adopting this book.

    —Ali Cinar, Technometrics, August 2011

    The theory is built up from real-life examples from which the first principles are deduced. This works well as it becomes immediately clear that these principles are relevant and useful in practical situations. … The book clearly explains both probabilistic and statistical concepts in minute detail and none of the essentials seem to be missing. All this is done without ever resorting to abstract mathematics, which is quite an achievement. … As an engineer involved in statistical data analysis, I would have loved to be taught from a book like this and I heartily recommend this book as a classroom textbook for both the clarity of the explanations and the amount of material covered. The book further accommodates such use with a large amount of review questions, exercises, application problems, and project assignments. However, the book is also suitable for self-study … . It will allow engineers who have to deal with statistics, but lack sufficient statistical background, to easily gain fundamental insights that are readily applicable in their working environment.

    —Pieter Bastiaan Ober, Journal of Applied Statistics, 2011

    Introduces the theory by starting from well described engineering examples such that the resulting probability equations appear as the natural outcomes from engineering first principles and not as esoteric mathematics. Engineering significance is then reinforced with discussion of how the results apply to other problems… provide[s] an understanding of why and when statistical methods apply, and equally importantly, when pitfalls lurk. The continual relating of probability and statistics throughout the book is one of its strongest features. … Concepts are clearly explained. A good balance is struck between the providing critical theoretical underpinnings without overwhelming mathematical detail….

    Examples from many engineering and science fields illustrate ideas and methods throughout the book, especially in the statistics material. …[presented] examples allow the reader to obtain a sense of the limitations of theory and methods and of the practical judgments required in applications to move to a problem resolution. A useful pedagogical feature is the repeated use of some data sets [on an accompanying CD], allowing students to see how new material provides new understanding.

    Although aimed at the textbook market (several syllabus suggestions for 1 and 2-semester undergraduate and graduate courses are given in the Preface), Random Phenomena has much to offer the industrial practitioner. As a chemical engineer who came to statistics out of industrial necessity and not from formal training or a career plan, I found new insights despite more than 20 years of practice, which includes providing internal statistics consulting and training

    …all the fundamentals needed for further study in any of its topics are certainly provided. In summary, Random Phenomena is an excellent choice for anyone, educator or practitioner, wishing to impart or gain a fundamental understanding of probability and statistics from an engineering perspective.

    —Dennis C. Williams, LyondellBasell Industries, The American Institute of Chemical Engineers Journal (AIChE Journal)

    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

    About the Originator

    Subject Categories

    BISAC Subject Codes/Headings:
    MAT029000
    MATHEMATICS / Probability & Statistics / General
    TEC009000
    TECHNOLOGY & ENGINEERING / Engineering (General)