What should constitute suitably the cost-optimal values of alpha and beta in tests of hypotheses regarding the producer¿s and consumer¿s risks in a business setting? This is not a question to answer unequivocally for all situations. When establishing a test procedure to investigate statistically the credibility of a stated hypothesis, several factors must be considered one of which is the size of the sample. However, the most significant of all these factors is unquestionably to optimize Type I and II errors. Statisticians have by rule of thumb selected, such as ¿=0.05, none for ¿ depending on the alternative hypothesis at hand. Although, common logic usually played a major role such as in the case of testing null hypothesis of the patient being sick needs a fairly significant size of type I error lest we lose the patient if we reject that she is sick while she truly is sick and probably dying. But all these previous up-to-date arguments are not somewhat connected with cost or utility of producer's and consumer's risks in the sense of quality control or life sciences or in the cyber-risk domain or other manufacturing industries while testing a hypothesis of a good product vs. bad.
| Autorius: | Mehmet Sahinoglu |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 68 |
| ISBN-10: | 6135830317 |
| ISBN-13: | 9786135830316 |
| Formatas: | 220 x 150 x 5 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Best Business Practices for Optimizing Producers and Consumers Risks: Innovating Type-I and Type-II Error Probabilities with Game-Theory for Realistic OC Curves in Acceptance Sampling Plans“
