9786135830316 - Best Business Practices for Optimizing Producer's and Consumer's Risks: Innovating Type-I and Type-II Error Probabilities with Game-Theory for Realistic OC Curves i... (5 resultados)

Paperback. Condición: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock.

Taschenbuch. Condición: Neu. Best Business Practices for Optimizing Producer's and Consumer's Risks | Innovating Type-I and Type-II Error Probabilities with Game-Theory for Realistic OC Curves in Acceptance Sampling Plans | Mehmet Sahinoglu | Taschenbuch | 68 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786135830316 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 beta 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. 68 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 ß 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 beta 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.