Fundamentals of Probability and Statistics for Engineers
E-Book, Englisch, 1056 Seiten
ISBN: 978-1-4398-9426-2
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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.
Zielgruppe
Engineering students
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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 ß, 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
