Control Charts for Skewed Distributions: Johnson’s Distributions
Why this work is in the frame
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Bibliographic record
Abstract
In this study, some important issues regarding process capability and performance have been highlighted, particularly in case when the distribution of a process characteristic is non-normal. The process capability and performance analysis has become an inevitable step in quality management of modern industrial processes. Determination of the performance capability of a stable process using the standard process capability indices (Cp, Cpk) requires that the quality characteristics of the underlying process data should follow a normal distribution. Statistical Process Control charts widely used in industry and services by quality professionals require that the quality characteristic being monitored is normally distributed. If, in contrast, the distribution of this characteristic is not normal, any conclusion drawn from control charts on the stability of the process may be misleading and erroneous. In this paper, an alternative approach has been suggested that is based on the identification of the best distribution that would fit the data. Specifically, the Johnson distribution was used as a model to normalize real field data that showed departure from normality. Real field data from the construction industry was used as a case study to illustrate the proposed analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.423 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it