Probability and Statistics for Engineers and Scientists by Anthony J. Hayter: A Book Review
Probability and Statistics for Engineers and Scientists is a textbook that covers the fundamental concepts and methods of probability and statistics for students in engineering and science. The book is written by Anthony J. Hayter, a professor of applied mathematics at the Colorado School of Mines. The fourth edition of the book was published by Cengage Learning in 2012.
The book aims to provide a student-oriented approach that combines clear and readable writing style, relevant examples and data sets, and flexible use of computer tools. The book covers topics such as probability theory, random variables, discrete and continuous probability distributions, descriptive statistics, statistical estimation and sampling distributions, hypothesis testing, regression and correlation analysis, experimental design and analysis, nonparametric statistical analysis, quality control methods, and reliability analysis and life testing. The book also includes tables, answers to odd-numbered problems, and tips for using various software packages such as MINITAB.
The book is suitable for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as for students in physics, chemistry, computing, biology, management, and mathematics. The book assumes that the students have a background in calculus and linear algebra. The book can be used as a textbook for a one-semester or two-semester course in probability and statistics for engineers and scientists.
The book has received positive reviews from instructors and students who have used it. Some of the strengths of the book are its clarity of exposition, its relevance to engineering and science applications, its balance between theory and practice, its integration of computer tools, and its variety of exercises and examples. Some of the weaknesses of the book are its occasional errors and typos, its lack of depth in some topics, its inconsistency in notation and terminology, and its high price.
Overall, Probability and Statistics for Engineers and Scientists by Anthony J. Hayter is a comprehensive and accessible textbook that covers the essential topics of probability and statistics for engineering and science students. The book is well-written, well-organized, well-illustrated, and well-supported by computer tools. The book is recommended for anyone who wants to learn or teach probability and statistics for engineers and scientists.In this section, we will provide a brief overview of some of the main topics covered in the book. We will also highlight some of the key features and examples that illustrate the application of probability and statistics to engineering and science problems.
The first chapter of the book introduces the basic concepts and rules of probability theory, such as probabilities, events, combinations of events, conditional probability, and Bayes' theorem. The chapter also discusses some common probability models, such as the binomial, geometric, hypergeometric, and Poisson distributions. The chapter provides several examples of how to use probability theory to model and analyze engineering and science phenomena, such as reliability of components, quality control of products, genetics of traits, and radioactive decay.
The second chapter of the book defines the concept of a random variable and its probability distribution. The chapter also introduces some important characteristics of a random variable, such as its expected value, variance, standard deviation, moment generating function, and transformation. The chapter explains how to use these characteristics to compute probabilities and moments of random variables. The chapter also provides some examples of how to use random variables to model and analyze engineering and science phenomena, such as lifetime of devices, weight of materials, speed of vehicles, and temperature of fluids.
Discrete Probability Distributions
The third chapter of the book focuses on discrete probability distributions, which are probability distributions that have a finite or countable number of possible values. The chapter reviews some common discrete probability distributions, such as the binomial, geometric, hypergeometric, Poisson, negative binomial, and multinomial distributions. The chapter also discusses some properties and applications of these distributions. The chapter provides several examples of how to use discrete probability distributions to model and analyze engineering and science phenomena, such as number of defects in products, number of arrivals in a queueing system, number of successes in trials, and number of votes in an election.
Continuous Probability Distributions
The fourth chapter of the book focuses on continuous probability distributions, which are probability distributions that have an infinite or uncountable number of possible values. The chapter reviews some common continuous probability distributions, such as the uniform, exponential, gamma, normal, lognormal, Weibull, beta, and chi-square distributions. The chapter also discusses some properties and applications of these distributions. The chapter provides several examples of how to use continuous probability distributions to model and analyze engineering and science phenomena, such as length of service time in a queueing system, 248dff8e21