About the Book
The Tools You Need To Be A Successful Engineer As you read through this new text, you'll discover the importance of Statistical Quality Control (SQC) tools in engineering process monitoring and improvement. You'll learn what SQC methods can and cannot do, and why these are valuable additions to your engineering tool kit. And instead of overwhelming you with unnecessary details, the authors make the implementation of statistical tools "user-friendly." The rich set of examples and problems integrated throughout this book will help you gain a better understanding of where and how to apply SQC tools. Real projects, cases and data sets show you clearly how SQC tools are used in practice. Topics are covered in the right amount of detail to give you insight into their relative importance in modern quality assurance and the ability to immediately use them. This approach provides the mix of tools you'll need to succeed in your engineering career. Key Features of the Text Provides a coherent presentation of the role of statistics in quality assurance. Places special attention on making sure that while the technical details are absolutely correct, they do not overwhelm the reader.
Presents the material in realistic contexts, with examples and problems that are based on real-world projects, cases and data sets. The implementation of statistical tools is user-friendly. The statistical treatment emphasizes graphics and estimation (and de-emphasizes hypothesis testing).
Table of Contents:
Simple Quality Assurance Tools. Process Monitoring Part 1: Basics. Process Monitoring Part 2: Additional Methods. Process Characterization and Capability Analysis. Experimental Design and Analysis for Process Improvement Part 1: Basics. Experimental Design and Analysis for Process Improvement Part 2: Advanced Topics. Sampling Inspection. The TQM Environment.
About the Author :
Stephen B. Vardeman, Iowa State University
J. Marcus Jobe, Miami University
Review :
Reprinted with permission from "The American Statistician," Copyright 2001 by the American Statistical Association.
Statistical Quality Assurance Methods for Engineers. Stephen B. VARDEMAN and J. Marcus JOBE. New York: Wiley, 1999, xiii As a young instructor preparing to teach a course in quality control for the first time, I was excited by my first big decision about the course, selecting a textbook. Starting from scratch and leveraging the Internet, I did my own informal survey of textbooks being used in courses across the country. I made requests from publishers for review copies of textbooks, some of which were not received in time for the choice. In the face of this daunting and important task, I felt the direction of my career would be affected by my ability to select an effective textbook.
I selected Statistical Quality Assurance Methods for Engineers by Stephen B. Vardeman and J. Marcus Jobe. The involvement by the authors in the active research and teaching of this field was evident throughout the book. Chapters cover basic quality assurance methods, process monitoring, process characterization, experimental design, and sampling inspection. There is also some brief material about defining qaulity with respect to statistics as well as Total Quality Management. Providing the statistics for a course in quality is challenging, as the main tools are a mix of basic and advanced concepts from statistics specialized to a particular context. Vardeman and Jobe offer a good selection of the fundamental statistical tools used in quality assurance. Notations in the margin highlighting key terms and efficient chapter summaries are features that students appreciate. Support for the instructor beyond the text was helpful as well, as the supplementary material material available on CD includes full solutions to all problems in the text as well as Minitab datasets for the exercises in the text. In addition, the online resources available at http: //www.wiley.com/college/vard/students.html are definitely worth exploring when considering this book.
In my view, the chief advantage of the Vardeman and Jobe text was the clear decision by the authors to make choices concerning the content of the book. The authors do not try to be all things to all people, in contrast to the overabundance of material available in some textbooks. In addition, the text is written very effificently, requiring that instructors be ready to guide students through some of the finer lessons in the text.
The text is rich with real-world problems. It is clear that the authors have drawn upon many real life examples in developing the problem sets. I found the exercises to be very useful and grounded in the real world. Students sometimes found that an extremely close reading of the chapter was necessry to get the point of some exercises and then correctly answer them. I found the focused and in-depth case study approach in the exercises to be very useful. However, I suffered a public relations setback when I assigned few problems but all of their many subparts, sometimes as many as eight or nine. (It is interesting how much more grueling a lettered problem is than a numbered problem to a student.) In addition, an instructor may find it necessary to present some of the exercises given at the end of the chapters in lecture or section to augment the amount of worked out examples given in the text.
As the book is titled Statistical Quality Assurance Methods for Engineers, the reader should not expect much coverage of "softer" subjects relevant to quality assurance. There are a few pages about TQM but for the most part, you will need to go outisde of the text to cover the buzzwords and managerial trends in quality assurance. As this portion of the literature is the least stable, it is not a bad idea for an instructor to refresh this material independently anyway. Many of these topics were well explored by students in course projects.
The book also emphasizes its service to engineers, and more generally to those that aren't statisticians. This can be frustrating for those with a very strong statistics background, as some students found the material to be a repackaging of material with which they were already comfortable, althought this is a challenge for any text on quality control. For these students, Vardeman and Jobe are careful to point out that some of the techniques they present are not statistically superior but for reasons of shop floor practicality or professional tradition they are more dominant. Of course, students who were chiefly interested in the "softer" side of quality assurance found any instance of statistics too technical. For students with less background, the wealth of viewpoints offered by the authors on some topics came across as less authoritative and more confusing.
In my course, the role of textbook was to aid in providing students with the statistical toolbox needed for quality assurance. The text filled that role effectively and efficiently. With the experience gained from teaching this course, I would encourage colleagues who choose this tgext to be more proactive in highlighting the many subtle points made by Vardeman and Jobe.
Paul HYDEN Cornell University
Reprinted with permission from The American Statistician. (c) Copyright 2001 by the American Statistical Association.
Statistical Quality Assurance Methods for Engineers. Stephen B. VARDEMAN and J. Marcus JOBE. New York: Wiley, 1999, xiii As a young instructor preparing to teach a course in quality control for the first time, I was excited by my first big decision about the course, selecting a textbook. Starting from scratch and leveraging the Internet, I did my own informal survey of textbooks being used in courses across the country. I made requests from publishers for review copies of textbooks, some of which were not received in time for the choice. In the face of this daunting and important task, I felt the direction of my career would be affected by my ability to select an effective textbook.
I selected "Statistical Quality Assurance Methods for Engineers" by Stephen B. Vardeman and J. Marcus Jobe. The involvement by the authors in the active research and teaching of this field was evident throughout the book. Chapters cover basic quality assurance methods, process monitoring, process characterization, experimental design, and sampling inspection. There is also some brief material about defining qaulity with respect to statistics as well as Total Quality Management. Providing the statistics for a course in quality is challenging, as the main tools are a mix of basic and advanced concepts from statistics specialized to a particular context. Vardeman and Jobe offer a good selection of the fundamental statistical tools used in quality assurance. Notations in the margin highlighting key terms and efficient chapter summaries are features that students appreciate. Support for the instructor beyond the text was helpful as well, as the supplementary material material available on CD includes full solutions to all problems in the text as well as Minitab datasets for the exercises in the text. In addition, the online resources available at http: //www.wiley.com/college/vard/students.html are definitely worth exploring when considering this book.
In my view, the chief advantage of the Vardeman and Jobe text was the clear decision by the authors to make choices concerning the content of the book. The authors do not try to be all things to all people, in contrast to the overabundance of material available in some textbooks. In addition, the text is written very effificently, requiring that instructors be ready to guide students through some of the finer lessons in the text.
The text is rich with real-world problems. It is clear that the authors have drawn upon many real life examples in developing the problem sets. I found the exercises to be very useful and grounded in the real world. Students sometimes found that an extremely close reading of the chapter was necessry to get the point of some exercises and then correctly answer them. I found the focused and in-depth case study approach in the exercises to be very useful. However, I suffered a public relations setback when I assigned few problems but all of their many subparts, sometimes as many as eight or nine. (It is interesting how much more grueling a lettered problem is than a numbered problem to a student.) In addition, an instructor may find it necessary to present some of the exercises given at the end of the chapters in lecture or section to augment the amount of worked out examples given in the text.
As the book is titled "Statistical Quality Assurance Methods for Engineers," the reader should not expect much coverage of "softer" subjects relevant to quality assurance. There are a few pages about TQM but for the most part, you will need to go outisde of the text to cover the buzzwords and managerial trends in quality assurance. As this portion of the literature is the least stable, it is not a bad idea for an instructor to refresh this material independently anyway. Many of these topics were well explored by students in course projects.
The book also emphasizes its service to engineers, and more generally to those that aren't statisticians. This can be frustrating for those with a very strong statistics background, as some students found the material to be a repackaging of material with which they were already comfortable, althought this is a challenge for any text on quality control. For these students, Vardeman and Jobe are careful to point out that some of the techniques they present are not statistically superior but for reasons of shop floor practicality or professional tradition they are more dominant. Of course, students who were chiefly interested in the "softer" side of quality assurance found any instance of statistics too technical. For students with less background, the wealth of viewpoints offered by the authors on some topics came across as less authoritative and more confusing.
In my course, the role of textbook was to aid in providing students with the statistical toolbox needed for quality assurance. The text filled that role effectively and efficiently. With the experience gained from teaching this course, I would encourage colleagues who choose this tgext to be more proactive in highlighting the many subtle points made by Vardeman and Jobe.
Paul HYDEN "Cornell University" Reprinted with permission from "The American Statistician." (c) Copyright 2001 by the American Statistical Association.
