This practical text is an essential source of information for those wanting to know how to deal with the variability that exists in every engineering situation. Using typical engineering data, it presents the basic statistical methods that are relevant, in simple numerical terms. In addition, statistical terminology is translated into basic English. In the past, a lack of communication between engineers and statisticians, coupled with poor practical skills in quality management and statistical engineering, was damaging to products and to the economy. The disastrous consequence of setting tight tolerances without regard to the statistical aspect of process data is demonstrated.
This book offers a solution, bridging the gap between statistical science and engineering technology to ensure that the engineers of today are better equipped to serve the manufacturing industry.
Inside, you will find coverage on:
- the nature of variability, describing the use of formulae to pin down sources of variation;
- engineering design, research and development, demonstrating the methods that help prevent costly mistakes in the early stages of a new product;
- production, discussing the use of control charts, and;
- management and training, including directing and controlling the quality function.
The Engineering section of the index identifies the role of engineering technology in the service of industrial quality management. The Statistics section identifies points in the text where statistical terminology is used in an explanatory context.
Engineers working on the design and manufacturing of new products find this book invaluable as it develops a statistical method by which they can anticipate and resolve quality problems before launching into production. This book appeals to students in all areas of engineering and also managers concerned with the quality of manufactured products.
Academic engineers can use this text to teach their students basic practical skills in quality management and statistical engineering, without getting involved in the complex mathematical theory of probability on which statistical science is dependent.
Table of Contents:
About the Author. Foreword.
Preface.
Acknowledgements.
1 Nature of Variability.
2 Basic Statistical Methods.
2.1 Variance.
2.2 Divisor ‘n’ or ‘n_1’?
2.3 Covariance and Correlation.
2.4 Normal Distribution.
2.5 Cumulative Frequency Distributions.
2.6 Binomial Distribution.
2.7 Poisson Distribution.
2.8 Chi-squared Distribution.
Bibliography.
3 Production.
3.1 Sampling Inspection.
3.2 Control Charts.
3.3 Cusum Charts.
3.4 Significance Tests.
3.5 Analysis of Variance.
3.6 Linear Regression.
Bibliography.
4 Engineering Design.
4.1 Variance Synthesis.
4.2 Factors of Safety.
4.3 Tolerances.
4.4 The Future.
Bibliography.
5 Research and Development.
5.1 Design of Experiments.
5.2 Evolutionary Operation.
5.3 Multiple Regression.
5.4 More Statistical Methods.
Bibliography.
6 Background.
6.1 Measurement.
6.2 Statistical Computing.
Bibliography.
7 Quality Management.
7.1 Quality Planning.
7.2 Quality Organisation.
7.3 Directing the Quality Function.
7.4 Controlling the Quality Function.
7.5 Statistical Engineering.
Bibliography.
8 Conclusion.
Appendix A: Guidelines.
Appendix B: Recommended Books.
Appendix C: Periodicals.
Appendix D: Supplementary Bibliography.
Appendix E: Statistical Tables.
Index.
About the Author :
S.J. Morrison, retired, Department of Operational Research, University of Hull
Mr. Samuel Jim Morrision has been retired for 20 years now, his last post being as Head of the Department of Operational Research at the University of Hull. He contributed a great deal to the pioneering of statistics in industry and operational research, and set up the first postgraduate management course at Hull. He is a Fellow of the Institution of Mechanical Engineers, the Royal Statistical Institute and the Chartered Mangagement Institute. He is also a senior member of the American Society for Quality.
Jim has been a speaker at conferences and meetings (including the IMS meeting of 1999 'Statistics in the Education of Engineers') where he has encouraged the use of simple statistics within the engineering industry. He has written many articals and papers on statistical engineering as the key to quality, writing often for the IET.
Review :
"This book appeals to students in all areas of engineering and also managers concerned with the quality of manufactured products. Academic engineers can use this text to teach their students basic practical skills in quality management and statistical engineering, without getting involved in the complex mathematical theory of probability on which statistical science is dependent." (Zentralblatt MATH, 1 August 2013)
"This is a timely text that helps to support the development of these important skills. Its no-nonsense and useful approach gives a flavour of the main statistical tools and techniques in basic language." (Quality World, December 2009) "It deserves to become a standard text to encourage the best in industrial practice." (Engineering & Technology, November 2009)
