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Product defect rate confidence bound with attribute and variable data

机译：产品缺陷率置信度受属性和变量数据约束

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Purpose: To provide a bootstrap methodology to construct a (1-alpha)100 percent upper confidence bound (UCB) for the overall defect rate of a product whose quality assessment involves multiple pass/fail binary data and multiple continuous data. Summary: The UCB in question is an important index for evaluating production process in industry. When only the pass/fail data are included, the proposed bootstrap method is consistent with the Clopper-Pearson one-sided confidence interval (Ref. 1). With only continuous data, the BC_a bootstrap method is recommended. These two methods are combined to provide an UCB for the overall defect rate when multiple pass/fail data and multiple continuous data are present. All methods are stated in algorithmic form, investigated through simulation and demonstrated using example data sets. The proposed algorithms show clear advantages in both coverage probability and computational efficiency. (12 refs.)
机译：目的：提供一种引导程序方法，以针对质量评估涉及多个通过/失败二进制数据和多个连续数据的产品的总缺陷率构建一个（1-alpha）100％的置信度上限（UCB）。简介：所讨论的UCB是评估行业生产过程的重要指标。当仅包含通过/失败数据时，建议的自举方法与Clopper-Pearson的单侧置信区间（参考文献1）一致。对于仅连续数据，建议使用BC_a引导方法。当存在多个通过/失败数据和多个连续数据时，将这两种方法结合起来可为整体缺陷率提供UCB。所有方法均以算法形式陈述，通过仿真进行调查并使用示例数据集进行了演示。所提出的算法在覆盖概率和计算效率上均显示出明显的优势。 （12个参考）

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