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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:
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.
"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)
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.