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Informationen zum Autor 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. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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 an Hypothesis test - One Sample. 6. Confidence intervals and Hypothesis Tests-Two Samples. 7. Tolerance Intervals an 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. ...
