Statistical Control: By Monitoring and Feedback Adjustment
A detailed, practical, accessible guide to efficient statistical control.
Efficient control is a key element in the improvement and maintenance of quality and productivity. This book shows the advantages of bringing together the more commonly used methods of statistical quality control with appropriate techniques of feedback adjustment. It uses recent research and practical experience to provide feedback methods of immediate use in the workplace.
Statistical Control by Monitoring and Feedback Adjustment introduces a new coordinated approach to quality control. The authors' clear and cogent presentation uses extensive graphical explanation supplemented by numerous examples and computational tables. A helpful selection of problems and solutions further facilitates understanding.
Topics covered include:
* A fresh look at process monitoring
* Using feedback adjustment charts
* Minimizing the size of adjustments
* Feedback techniques that minimize costs of adjustment and sampling
* Detection of special causes with Cuscore Statistics
* Efficient monitoring of operating feedback systems
* The roles of models, optimization, and robustness
* A brief review of time series analysis.
Statistical Control by Monitoring and Feedback Adjustment is important reading for quality control engineers and statisticians as well as graduate students in quality control, industrial engineering, and applied statistics.
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Control Charts for Frequencies and Proportions
Control Charts for Measurement Data
Time Series Models for Process Disturbances
9 other sections not shown
action adjustment Analysis applied appropriate approximately assumption autocorrelation average calculated called causes Chapter chart consider constant continually corresponding cost Cuscore Cusum depends detect discrepancy discussed distribution disturbance Edition effect equal Equation error estimate EWMA example exponential factor feedback Figure follows forecast frequency function given gives illustration IMA model important increase interval kind limits linear lines look mean measurements method monitoring moving normal distribution Note observations obtained occur output parameter particular period plotted possible practice probability problem procedure produce proportion random range reasonably reference represented residuals sample scheme Second series model Shewhart shown shows signal smoothing specific square standard deviation Statistical step Suppose Table target value temperature thickness tion true unit variable variance variation week weights white noise zero