Comparative analysis of software for the study of statistical methods of control of product quality
Bibliographic record
Abstract
The article discusses the comparative analysis of software (MS Excel, Mathcad and Matlab) for the study of statistical methods of product quality control necessary for the training of future specialists in the field of product quality. The choice is determined by the presence of this software in the educational institution. Statistical methods of product quality control are mandatory elements of modern quality management systems implemented at Russian enterprises, the competitiveness of which largely depends on the ability of the company's personnel to apply these methods in practice at all stages of the product life cycle. The analysis of product quality begins with the construction of a histogram to identify avoidable and irremediable defects and compare it with the normal distribution curve. The process of forming the shape of the normal distribution curve can be traced in the construction of control maps based on a scatter plot of the sample. The next step is the construction of dot diagrams, on the basis of which control maps are built (in our case - the control map of Shewhart). The final step in training is the construction of Pareto diagrams to identify the causes of defects with ABC analysis. The authors conducted a comparative analysis of software products MS Excel, Mathcad and Matlab for the implementation of basic statistical methods of product quality control. For training it is proposed to choose MS Excel because of the availability of the Data Analysis package, free analogues that are widely used in enterprises, the ability to store the original data and use them in other programs (data import).
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".