## The Efficient Use of Quality Control DataAccurate 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 inevitably leads to right and wrong conclusions. This book describesmethods 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. The monograph begins by describing simple statistical decision ruleswhich 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. Thereafter it proceeds to illustrate improvements in the property of decision rules which can beachieved with appropriate choices of control rule parameters, test statistics and methods of control which selectively utilise information contained in the test data which is indicating that a change in quality level has occurred. |

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### Contents

Some aspects of statistical quality control | 3 |

Small samples Decisions and consequences | 28 |

Distributions relevant to process and test control | 53 |

Effective use of sampled data | 92 |

Principles and criteria of statistical quality control | 131 |

Better control rules | 158 |

Really efficient use of test data | 183 |

Bibliography | 254 |

### Common terms and phrases

achieved action lines assumption batch binomial variate calculations central limit theorem changes Chapter clinical testing close continuous variate control charts control rules control schemes control statistic critical region cusum charts cusum schemes decision boundaries decision interval decision rules defective items defined denote distribution function envelope equation example expected value expressions formulate frequency function given gives hypothesis illustrate increases indicate L(aa L(ma L(mr manufacturing nomogram normal approximation normal variate normally distributed number of defective obtain out-of-control decision particular plotted Poisson distribution practical probability production proportion of defective quality control quality level range of values rejecting result ringiness run length profiles sample mean sample values Shewhart chart simple random sampling specified standard deviation statistically independent statistician Suppose Table target Type I error unbiased estimator V-mask values of L(0 variance Wald test warning and action whilst yarn