The Efficient Use of Quality Control Data

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Accurate measurements in clinical and industrial testing are often not possible. Each measurement contains what can be regarded as containing an uncontrollable component of error. Their use to control quality therefore inevitable leads to right and wring conclusions. This book describes methods which can be used to control the frequency with which these occur. It describes recent developments which can be employed when very few control measurements can be taken due to limitations of cost or technical difficulty....
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Overview


Accurate measurements in clinical and industrial testing are often not possible. Each measurement contains what can be regarded as containing an uncontrollable component of error. Their use to control quality therefore inevitable leads to right and wring conclusions. This book describes methods which can be used to control the frequency with which these occur. It describes recent developments which can be employed when very few control measurements can be taken due to limitations of cost or technical difficulty. It begins by describing simple statistical decision rules which were initially used to control the quality of industrial processes. These then form a basis on which to describe the concepts and practical consequences of the use of statistical quality control. It then illustrates improvements in the property of decision rules which can be achieved with appropriate choices of control rule parameters, test statistics and methods of control which selectively utilize information contained in the test data which is indicating that a change in quality level had occurred.
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Editorial Reviews

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"Accurate measurements in clinical and industrial testing are often not possible; when used to control quality, the measurements lead to some right and some wrong conclusions. Kemp (U. of Wales) discusses the inevitability of wrong decisions and control of the frequency with which they occur. He presents concepts of statistical theory and their application in simply designed control charts, along with the concepts of acceptable and rejectable levels, to form a simple model to establish principles and criteria for the design and operation of statistical control procedures. Written for clinical technicians and quality control engineers with a reasonable knowledge of mathematics."--SciTech Book News

"An accessible but very intelligent monograph on the statistics of quality control. Chapters 1 to 4 can nearly stand on their own as a first course in mathematical statistics."--Publication of the International Statistical Institute

Booknews
Accurate measurements in clinical and industrial testing are often not possible; when used to control quality, the measurements lead to some right and some wrong conclusions. Kemp (U. of Wales) discusses the inevitability of wrong decisions and control of the frequency with which they occur. He presents concepts of statistical theory and their application in simply designed control charts, along with the concepts of acceptable and rejectable levels, to form a simple model to establish principles and criteria for the design and operation of statistical control procedures. Written for clinical technicians and quality control engineers with a reasonable knowledge of mathematics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Meet the Author

University of Wales (Emeritus)
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Table of Contents

1 Some aspects of statistical quality control 3
1.1 Historical note 3
1.2 Control 4
1.3 Industrial example 8
1.4 Control and removal of ringiness 10
1.5 Clinical testing 13
1.6 Efficient use of data 14
1.7 Average run length 15
1.8 Control techniques 16
1.9 Control statistics 22
1.10 Sampling interval 23
1.11 Randomness 24
1.12 Statistical method 25
1.13 Right and wrong decisions 26
2 Small samples. Decisions and consequences 28
2.1 Small samples 28
2.2 Measurements 32
2.3 Events 32
2.4 Probability 33
2.5 Definitions 37
2.6 Statistical decision rules 44
2.7 Summarizing parameters 47
3 Distributions relevant to process and test control 53
3.1 Binomial distribution 53
3.2 The moments of a distribution 55
3.3 Skewness and kurtosis 60
3.4 The Poisson distribution 61
3.5 Useful relationships 65
3.6 The normal distribution 69
3.7 Gamma variates 74
3.8 Sum of variates 78
3.9 Probability generating function 81
3.10 The p.g.f. of a variate sum 83
3.11 Moment generating function (m.g.f.) 86
3.12 An important m.g.f. property 88
3.13 Characteristic function 90
4 Effective use of sampled data 92
4.1 Efficient estimators 99
4.2 Hypothesis testing 102
4.3 Simple hypotheses 103
4.4 Composite hypotheses 105
4.5 Significance 107
4.6 Central limit theorem 123
5 Principles and criteria of statistical quality control 131
5.1 Control of proportion of defective items 133
5.2 Proportion defective control charts 141
5.3 Variate transformation 147
5.4 Randomness 152
6 Better control rules 158
6.1 Average run length of a Shewhart chart 159
6.2 Double- and single-sided control schemes 160
6.3 Choice between different schemes 161
6.4 ARL profile 162
6.5 More effective control charts 163
6.6 Charts with warning and action lines 165
7 Really efficient use of test data 183
7.1 The cusum technique 186
7.2 Visual aid 187
7.3 Significant changes in the slope of a cusum chart 190
7.4 An alternative to plotting full cusum charts 190
7.5 The ARI of a V Mask 193
7.6 Wald sequential tests 195
7.7 Some assumptions 196
7.8 Wald's identity 196
7.9 Some useful relationships 200
7.10 The run length distribution 203
7.11 An expression for P(O) 205
7.12 Average sample number E(N:O) 206
7.13 Determination of y[subscript o], y[subscript h], Y[subscript O] and Y[subscript h] 207
7.14 Iteration for values of P(y) and E(N:y) 209
7.15 Upper and lower bounds for P(y) and E(N:y) 210
7.16 The case of the normal distribution 213
7.17 Simplified expressions for E(R:O) 216
7.18 Simplified design of cusum schemes 220
7.19 Run length distribution 223
7.20 Control of process standard deviation 228
7.21 Combined decision interval schemes 241
Appendix Tables 249
Bibliography 254
Index 259
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