Modern Engineering Statistics 1st Edition by Thomas P. Ryan (PDF)

Description

An introductory perspective on statistical applications in the field of engineeringModern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering.With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features:Examples demonstrating the use of statistical thinking and methodology for practicing engineersA large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data setsClear illustrations of the relationship between hypothesis tests and confidence intervalsExtensive use of Minitab and JMP to illustrate statistical analysesThe book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.

User’s Reviews

Editorial Reviews: Review “Overall this is an excellent book, which defines a broader mandate than many of its competing texts. By providing, clear, understandable discussion of the basics of statistics through to more advanced methods commonly used by engineers, this book is an essential reference for practitioners, and an ideal text for a two semester course introducing engineers to the power and utility of statistics.” (The American Statistician, August 2008)”In this book on modern engineering statistics, Ryan does an excellent job of providing the appropriate statistical concepts and tools using engineering resources…. Highly recommended. Lower- and upper-division undergraduates” (CHOICE, April 2008)”This self-contained volume motivates an appreciation of statistical techniques within the context of engineering; many datasets that are used in the chapters and exercises are from engineering sources. This book is ideal for either a one- or two-semester course in engineering statistics.” (Computing Reviews, April 2008) From the Inside Flap An introductory perspective on statistical applications in the field of engineeringModern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features: Examples demonstrating the use of statistical thinking and methodology for practicing engineersA large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data setsClear illustrations of the relationship between hypothesis tests and confidence intervalsExtensive use of Minitab® and JMP® to illustrate statistical analysesThe book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics. From the Back Cover An introductory perspective on statistical applications in the field of engineeringModern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features: Examples demonstrating the use of statistical thinking and methodology for practicing engineersA large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data setsClear illustrations of the relationship between hypothesis tests and confidence intervalsExtensive use of Minitab® and JMP® to illustrate statistical analysesThe book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics. About the Author THOMAS P. RYAN, PHD, served on the Editorial Review Board of the Journal of Quality Technology from 1990 to 2006, including three years as the book review editor. He is the author of four books published by Wiley and is an elected Fellow of the American Statistical Association, the American Society for Quality, and the Royal Statistical Society. He currently teaches advanced courses on design of experiments and engineering statistics at statistics.com and serves as a consultant to Cytel Software Corporation. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I am writing this review from perspective of a practicing engineer, who’s had some basic background in probability theory but needed additional exposure into statistical inference and experiments design. This book’s approach is to look at statistical inference from a practical applications standpoint and as such don’t expect much mathematical exposure here. The book covers a lot of relevant content, all seemingly relevant to general engineering applications. From that perspective, if one is looking for a broad overview, the coverage is superb. What I found limiting is the writing tends to bounce around topics, feeling like it’s trying to compress all other relevant topics into discussion at the same time. Yet pther times writing feels sparse, with equations presented without much context. Unless you had prior to exposure it may feel difficult to follow at times as an introductory to the subjects. For these reasons, I would rate it at about 4.5

⭐My viewpoint expressed is that of an EE needing to do a bit of reliability and lifetime testing. I think the book is fine at providing a sweeping overview of statistics, and I believe the author chose important and relevant and non-standard topics (bootstrapping, analysis of means as opposed to ANOVA, life testing, etc.) but he does not bother to go into much detail about most. He presents a lot of Minitab or JMP results instead of presenting the basic formulae. This may be wise, in the sense that more mistakes are likely to be made by a non-statistician implementing techniques from scatch rather than relying on the software to do it, but I find it somewhat unnerving to use techniques without getting a bit of a look at the mechanics (i.e. equations and formula) involved. The author refers a lot to online free e-handbooks for basic details, and I could not help think, if I wanted to go online to study this, why did I buy this book? Meeker and Escobar have written what (in my somewhat stats-uneducated opinion) looks like a fine book on reliability and lifetime testing but seems a bit too advanced for me in spots. I was hoping to use Ryan’s book as a bridge, but it seems not very useful for this (not enough detail behind the methods whether life testing, bootstrapping, ANOM etc.). I think this is a fine lower level stats book, but for something like boostrapping I have learned a lot more from the 3rd edition of deGroot/Schervish (a general undergrad stat book) e.g. how to make a confidence interval for the IQR, or for ANOM from Ott’s original process quality control book.

⭐The book is i good condition with no scratch or writing on its page. But the only thing was it is price.

⭐I know Tom Ryan well. He and I both teach on the faculty at statistics.com. I have reviewed other books that he has written. He is an excellent teacher and always write very clearly and is thorough in his coverage of a topic. He is especially good at introductory statistics and he uses Minitab to work out examples in his texts as well a the SAS product JMP. This book is in line with the high quality of his experimental design book, his regression text and is text on quality control.Statistics is a diverse science with applications in every discipline that has to deal with uncertainty. But terms like biostatistics, engineering statistics, and environmental statistics have come about because they each have special statistical tools that they use that other disciplines may not.In biostatistics, survival curves, longitudinal data analysis, missing data analysis and categorical data analysis are especially important. In quality control, control charts, process control indices and tolerance intervals are important. In epidemiology case control and other observation studies are important. In economic statistics (econometrics) time series analysis is important. These are the reasons why instead of just a general introductory book in fields like biostatistics and engineering statistics specialized texts are needed.There are several good texts for engineers but there are not many that are as current or encompassing as Ryan’s text. The first 6 chapters are standard to any introductory course. He covers descriptive statistics, basic probability and the common important probability distributions, point estimation and confidence intervals and hypothesis testing. Then in chapter 7 he covers tolerance intervals and prediction intervals. These are important concepts for engineering students to know but are usually not covered in a general introductory statistics course.Basic regression and correlation are covered in chapter 8 and are topics that can be found in any introductory text, but for the engineers, he also looks at inverse regression or calibration which rarely appears in introductory texts. Chapter 9 covers the standard multiple regression problems and includes logistic regression and nonlinear regression as well. Chapter 10 covers mechanistic models which is a specialize topic for the physical sciences and engineering. Chapter 11 deals with issues of quality control, a topic very important to reliability engineers.Chapter 12 is a standard introduction to experimental design but with a very practical flavor that is also seen in his design of experiments book as well as in the text by Box, Hunter and Hunter. Chapter 13 is also specialized and covers how to assess measurement systems and includes techniques such as R and R Gage analysis. Chapter 14 is on reliability and life testing (including accelerated testing). This is a topic important to reliability engineers and biostatisticians as well.Chapters 15 and 16 are common standard topics, categorical data analysis and nonparametric methods. As with all chapters in the book, engineering applications are always emphasized even when the topic has general interest.Chapter 17 is a summary chapter which ties all the topics together. It allows the reader to step back and see the forest from the trees. It shows where engineering statistics is today and where it is headed in the future. Most importantly by reviewinfg the key material and tying it together Ryan helps the student learn how to solve real statistical problems rather than just the canned problems in the other chapters.I really like the fact that he introduces, in the simplest way, modern concepts such as the bootstrap and includes other topics not mentioned in most introductory books such as ANOM. However, I think he characterizes the state of knowledge about the bootstrap in much too conservative terms.I can understand the hesitancy because in the early 1980s after the Scientific American article by Efron and Diaconis was published, many engineers jumped on the bandwagon and misused the method. The Scientific American article was easily misunderstood and many people believed that bootstrap simulation was like adding data and increasing the information beyond what is seen in the sample. This is patently false.On the other hand the bootstrap theory is well-established and although it is asymptotic, simulation studies have been used to show its wide applicability and even identify cases where it works well in small samples. Also, the various situations where it has been shown to fail have led to modifications that do work. The m out of n bootstrap is a prime example.On page 132, Ryan provides a very nice basic discription of the bootstrap, but I take issue with the following sentences. He writes “Although bootstrapping is a popular procedure, we should keep in mind that in using the procedure one is trying to generate new data from the original data, something that, strictly speaking is impossible. For this reason bootstrapping is somewhat controversial.” Actually the bootstrap is not trying to create new data. That is a common misconception. The bootstrap is merely trying to approximate the sampling distribution of the statistic computed from the sample.The Monte Carlo aspect of its implementation is just an approximation to this bootstrap distribution. In some cases the bootstrap distribution for the parameter estimate can be computed exactly without Monte Carlo. Bootstrapping does not use any information that is not in the original sample and the only assumption is, in the simplest situations, that the observations are independent and identically distributed. Now the asymptotic theory works because the mimicking of the sampling distribution works properly as the sample size gets large as long as certain smoothness conditions are satisfied to allow the Edgeworth expansions to exist. Since there are cases where the smoothness conditions are not satisfied, there have been examples that show inconsistency. On the other hand, Dr. Ryan is absolutely correct in saying that in very small samples the bootstrap cannot be trusted. This is because small samples are often not representative of the distribution from which they come and the success of the mimicking does depend on that sample.In one of the chapters on confidence intervals Dr. Ryan points out the inadequacy of the percentile method bootstrap in a particular example. Skewness can be a problem as he mentions but there are higher order bootstrap intervals, including bootstrap t, double bootstrap, bias corrected and bias corrected and accelerated bootstraps that handle the skewed cases.I would not expect Dr. Ryan to discuss these fine points in detail in an introductory book, but I would expect a brief mention of these methods so that the state of the art of bootstrapping is properly characterized. I feel that his characterization of it really only applied up to the early 1980s.The bootstrap is not a major part of the book ,so I do not find much fault with the book because of this. The few technical misstatements are far outweighed by the excellent intuitive discussion and examples throughout the text. Also in most of the chapters there are between 18 and 100 homework problems to pick from! For this reason a separate solution manual was published. Unfortunately I do not have that to review but there are enough solutions to selected problems that the subject could be understood without needing the solutions manual.

⭐An exceptionally well written book with many useful examples, case studies, and end of chapter exercises. Balances the theoretical aspect of statistics with real world examples. One of the few books that specifically covers the application of statistics for a manufacturing / engineering environment.A very useful resource for anyone involved in Six Sigma, quality, or engineering in a manufacturing setting.Although not touted as a Minitab resource / reference book there are plenty of worked examples that use Minitab, which I found to be an added bonus.

⭐Delivered in perfect condition

Keywords

Free Download Modern Engineering Statistics 1st Edition in PDF format
Modern Engineering Statistics 1st Edition PDF Free Download
Download Modern Engineering Statistics 1st Edition 2007 PDF Free
Modern Engineering Statistics 1st Edition 2007 PDF Free Download
Download Modern Engineering Statistics 1st Edition PDF
Free Download Ebook Modern Engineering Statistics 1st Edition