{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:19:16Z","timestamp":1700525956942},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The existing Fisher\u2019s exact test has been widely applied for investigating whether the difference between the observed frequencies is significant or not. The existing Fisher\u2019s exact test can be applied only when the observed frequencies are in determinate form and has no vogues information. In practice, due to the complicity in the production process, it is not always possible to have observed frequencies in determinate form. Therefore, the use of the existing Fisher\u2019s exact test may mislead the industrial engineers. The paper presents the modification of Fisher\u2019s exact test using neutrosophic statistics. The operational process, simulation study, and application using the production data will be given in the paper. From the analysis of industrial data, it can be concluded that the proposed Fisher\u2019s exact test performs well than the existing Fisher\u2019s exact test.<\/jats:p>","DOI":"10.1186\/s40537-023-00812-6","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T16:03:20Z","timestamp":1692806600000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data analysis for vague contingency data"],"prefix":"10.1186","volume":"10","author":[{"given":"Muhammad","family":"Aslam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faten S.","family":"Alamri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"812_CR1","doi-asserted-by":"publisher","DOI":"10.4135\/9781849208499","volume-title":"100 Statistical tests","author":"GK Kanji","year":"2006","unstructured":"Kanji GK. 100 Statistical tests. 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Ann Arbor: Neutrosophic probability, set, and logic, proquest information & learning; 1998."},{"key":"812_CR10","unstructured":"Smarandache F. Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability. Infinite Study; 2013"},{"key":"812_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-06103-7","author":"SH Basha","year":"2021","unstructured":"Basha SH, et al. Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic. Soft Comput. 2021. https:\/\/doi.org\/10.1007\/s00500-021-06103-7.","journal-title":"Soft Comput"},{"key":"812_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-021-00363-8","author":"R Das","year":"2021","unstructured":"Das R, Mukherjee A, Tripathy BC. Application of neutrosophic similarity measures in Covid-19. 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