Titlebook: Cumulative Sum Charts and Charting for Quality Improvement; Douglas M. Hawkins,David H. Olwell Book 1998 Springer Science+Business Media N

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https://doi.org/10.1007/978-1-4419-5876-1 SPC data type is “attribute” data, originally counts of the number of good and of defective (or in current terminology “conforming” and “nonconforming ” items) in a sample. We draw the distinction a little differently: into the standard statistical distinction between continuous and discrete measur

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https://doi.org/10.1007/978-1-4419-5876-1hem adequately. To get some feeling for this, we try to quantify the impact of uncertainty in the parameter estimates in the case of a normal CUSUM. Suppose that the process stream is . (μ, σ.). To calibrate the CUSUM, we take a sample ofsize m and compute its mean . and standard deviation ., and su

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https://doi.org/10.1007/978-1-4419-5876-1 SPC data type is “attribute” data, originally counts of the number of good and of defective (or in current terminology “conforming” and “nonconforming ” items) in a sample. We draw the distinction a little differently: into the standard statistical distinction between continuous and discrete measurements.

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