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Statistical quality control : a modern introduction
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Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Chapter 3: Statistical Methods Useful in Quality Control and Improvement Chapter 4: Inferences about Process QualityAbstract:
Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Part II: Statistical Methods Useful in Quality Control and Improvement Chapter 3: Modeling Process Quality Chapter 4: Inferences about Process Quality Part III: Basic Methods of Statistical Process Control and Capability Analysis Chapter 5: Methods and Philosophy of Statistical Process Control Chapter 6: Control Charts for Variables Chapter 7: Control Charts for Attributes Chapter 8: Process and Measurement System Capability Analysis Part IV: Other Statistical Process-Monitoring and Control Techniques Chapter 9: Cumulative Sum and Exponentially Weighted Moving Average Control Charts Chapter 10: Other Univariate Statistical Process Monitoring and Control Techniques Chapter 11: Multivariate Process Monitoring and Control Chapter 12: Engineering Process Control and SPC Part V: Process Design and Improvement with Designed Experiments Chapter 13: Factorial and Fractional Experiments for Process Design and Improvements Chapter 14: Process Optimization and Designed Experiments Part VI: Acceptance Sampling Chapter 15: Lot-by-Lot Acceptance Sampling for Attributes Chapter 16: Other Acceptance Sampling Techniques Appendixread more
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Evaluating measurement and process capabilities by GR&R with four quality measures
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Distribution-free exponentially weighted moving average control charts for monitoring unknown location
TL;DR: A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics, is proposed in this paper for detecting a shift in the location parameter of a continuous distribution.
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Performance of t control charts in short runs with unknown shift sizes
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The p-control chart: a tool for care improvement
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TL;DR: Key elements on how to develop and interpret a p-chart for clinical practice, how to successfully integrate this tool within a comprehensive approach, and how to report a study based on p- chart utilization are provided.
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A tolerance interval based criterion for optimizing discrete point sampling strategies
TL;DR: In this article, the authors proposed a new approach for selecting the optimal locations of the points for any given sample size, which is based on estimating the manufacturing signature, the systematic pattern left by the manufacturing process on the machined items, and then selecting the measurement point locations by minimizing the distance between the maximum and the minimum points of the regression-based tolerance interval.