现有的不确定度事件的Z检验没有给出关于与该检验相关的不确定度/不确定度的度量的信息。
本文介绍了中性统计学下不确定性事件的Z检验。在嗜中性粒细胞的哲学下,对现有测试的测试统计量进行了修改。介绍了测试过程,并将其应用于Covid-19数据。
根据这些信息,拟议的检验被解释为Covid-19不确定度没有降低的概率被接受,概率为0.95,I型错误为0.05,不确定度为0.10。根据分析,结论是,所提出的测试比现有的测试有一定的参考价值。对于模糊逻辑下的不确定性,拟议的测试也比Z测试更好,因为使用模糊逻辑的测试给出了2.20至2.42的统计值,而没有任何关于不确定性度量的信息。区间统计下的测试仅考虑区间内的值,而不是清晰的值。
根据Covid-19数据分析,发现在中性统计学下提出的不确定性事件的Z检验比经典统计学下的现有检验有效。模糊方法,和信息方面的间隔统计,灵活性,测试的力量,和充分性。
The existing Z-test for uncertainty events does not give information about the measure of indeterminacy/uncertainty associated with the test.
This paper introduces the Z-test for uncertainty events under neutrosophic statistics. The test statistic of the existing test is modified under the philosophy of the Neutrosophy. The testing process is introduced and applied to the Covid-19 data.
Based on the information, the proposed test is interpreted as the probability that there is no reduction in uncertainty of Covid-19 is accepted with a probability of 0.95, committing a type-I error is 0.05 with the measure of an indeterminacy 0.10. Based on the analysis, it is concluded that the proposed test is informative than the existing test. The proposed test is also better than the Z-test for uncertainty under fuzzy-logic as the test using fuzz-logic gives the value of the statistic from 2.20 to 2.42 without any information about the measure of indeterminacy. The test under interval statistic only considers the values within the interval rather than the crisp value.
From the Covid-19 data analysis, it is found that the proposed Z-test for uncertainty events under the neutrosophic statistics is efficient than the existing tests under classical statistics, fuzzy approach, and interval statistics in terms of information, flexibility, power of the test, and adequacy.