Modern Engineering Statistics

ISBN-13: 9780470081877 / Angielski / Twarda / 2007 / 608 str.

Thomas P. Ryan

Opis

The objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of tools rather than endless streams of seemingly unrelated methods and formulae. The book has many novel features, including (among others) the connection that is made (but rarely illustrated) between hypothesis testing and confidence intervals and the discussions of topics such as mechanistic models and prediction intervals. Though computing equations are kept to a minimum, MINITAB and JMP from SAS are discretely employed as the statistical software packages of choice due to their simplicity and popularity among Six Sigma enthusiasts. There is a generous selection of homework problems.

Recenzje

"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)

Spis treści

Preface.

1. Methods of Collecting and Presenting Data.

2. Measures of Location and Dispersion.

3. Probability and Common Probability Distributions.

4. Point Estimation.

5. Confidence Intervals and Hypothesis Tests–One Sample.

6. Confidence Intervals and Hypothesis Tests–Two Samples.

7. Tolerance Intervals and Prediction Intervals.

8. Simple Linear Regression, Correlation, and Calibration.

9. Multiple regression.

10. Mechanistic Models.

11. Control Charts and Quality Improvement.

12. Design and Analysis of Experiments.

13. Measurement System Appraisal.

14. Reliability Analysis and Life Testing.

15. Analysis of Categorical Data.

16. Distribution–Free Procedures.

17. Tying It All together.

Answers to Selected Exercises.

Appendix. Statistical Tables.

Author Index.

Subject Index.

O autorze

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.

Blurb

An introductory perspective on statistical applications in the field of engineering

Modern 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 engineers

• A large number of chapter exercises that provide the opportunity for readers to solve engineering–related problems, often using real data sets

• Clear illustrations of the relationship between hypothesis tests and confidence intervals

• Extensive use of Minitab and JMP to illustrate statistical analyses

The 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.