Statistics for Engineers

"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)

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.

About the Author vii Foreword ix Preface xi Acknowledgements xiii 1 Nature of Variability 1 2 Basic Statistical Methods 9 2.1 Variance 9 2.2 Divisor n or n-1? 11 2.3 Covariance and Correlation 13 2.4 Normal Distribution 14 2.5 Cumulative Frequency Distributions 18 2.6 Binomial Distribution 20 2.7 Poisson Distribution 25 2.8 Chi-squared Distribution 26 Bibliography 31 3 Production 33 3.1 Sampling Inspection 34 3.2 Control Charts 37 3.3 Cusum Charts 40 3.4 Significance Tests 43 3.5 Analysis of Variance 49 3.6 Linear Regression 52 Bibliography 57 4 Engineering Design 61 4.1 Variance Synthesis 61 4.2 Factors of Safety 68 4.3 Tolerances 69 4.4 The Future 71 Bibliography 72 5 Research and Development 75 5.1 Design of Experiments 76 5.2 Evolutionary Operation 95 5.3 Multiple Regression 96 5.4 More Statistical Methods 111 Bibliography 115 6 Background 119 6.1 Measurement 119 6.2 Statistical Computing 121 Bibliography 127 7 Quality Management 129 7.1 Quality Planning 129 7.2 Quality Organisation 135 7.3 Directing the Quality Function 137 7.4 Controlling the Quality Function 140 7.5 Statistical Engineering 141 Bibliography 142 8 Conclusion 145 Appendix A: Guidelines 147 Appendix B: Recommended Books 151 Appendix C: Periodicals 161 Appendix D: Supplementary Bibliography 165 Appendix E: Statistical Tables 171 Index 173