The W/S Test for Data Having Neutrosophic Numbers: An Application to USA Village Population

Mohammed Albassam,Nasrullah Khan,Muhammad Aslam

doi:10.1155/2020/3690879

Abstract

The W/S test under neutrosophic statistics is proposed in this paper. The Monte Carlo simulation under the neutrosophic statistical interval method is proposed and applied to study the sensitivity of various neutrosophic statistical distributions. The power of test curves for neutrosophic distributions is presented. The efficiency of the proposed W/S test under neutrosophic statistics is compared with that of the W/S test under classical statistics. The proposed test is explained with the aid of an example.

Highlights

E W/S test for testing the normality assumption of the data was proposed by Shapiro and Wilk and later on modified by Shapiro and Francia, see [5,6,7,8,9] for more details
Sometimes the exact value of a variable is hidden deliberately for some confidentiality reasons.” e various problems related to interval data in regression are given by [12], in time series by [13], in principle components by [14], in classification by [15], in the analysis of variance by [16], and testing of the hypothesis by [17]. e fuzzy logic is an alternative to analyze the data given in interval. e authors of [18,19,20] applied the fuzzy sets for modeling the interval data
We presented the designing and application of the W/S test under neutrosophic statistics

Summary

Data Analysis

For the neutrosophic sample nNε[nL, nU] > [5, 5], the neutrosophic Cauchy distribution has a smaller error rate among other distributions. It can be concluded that the W/S test under neutrosophic statistics will be applied to the neutrosophic Weibull distribution when nNε[5, 5] and on the neutrosophic Cauchy distribution when nNε[nL, nU] > [5, 5]. We can note the increasing trend in power as the neutrosophic sample size increases for neutrosophic tdistribution and neutrosophic Cauchy distribution. The neutrosophic Cauchy distribution is the best power curve

Comparative Study

Conclusion

Example

Concluding Remarks