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The Economic Design of Multivariate<i>MSE</i>Control Chart

2011· article· en· W2334275879 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality Technology & Quantitative Management · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultivariate statisticsControl chartStatistics\bar x and R chartChartTaguchi methodsControl limitsMean squared errorSample size determinationMathematicsComputer scienceEconometricsProcess (computing)

Abstract

fetched live from OpenAlex

In this paper, the economic design of MSE control chart is extended to the multivariate case. The important feature of this control chart is that it uses the target value instead of the process mean. According to Taguchi’s viewpoint, any deviation from the target value represents a kind of loss. Therefore, we construct the model of economic design by considering not only the control costs occurred in the production process but also the loss resulted to the customer because the quality characteristics shifted from the target value. The expected loss of multivariate squared error is presented and used in the formulated cost model. A True Basic program is used to find the optimum parameters of the sample size, n; the sample interval, h and the width, E, of the control limits of the multivariate MSE chart. Finally, an example is used to illustrate the application of the proposed economic design of the multivariate MSE control chart.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.299
GPT teacher head0.469
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it