@article{Aslam2024, 
author = {Muhammad Aslam and Osama H. Arif},
title = {Simulating chi-square data through algorithms in the presence of uncertainty},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {5},
pages = {12043-12056},
keywords = {chi-square distribution, random numbers, simulation, classical statistics, neutrosophic statistics},
url = {https://www.sciopen.com/article/10.3934/math.2024588},
doi = {10.3934/math.2024588},
abstract = {This paper presents a novel methodology aimed at generating chi-square variates within the framework of neutrosophic statistics. It introduces algorithms designed for the generation of neutrosophic random chi-square variates and illustrates the distribution of these variates across a spectrum of indeterminacy levels. The investigation delves into the influence of indeterminacy on random numbers, revealing a significant impact across various degrees of freedom. Notably, the analysis of random variate tables demonstrates a consistent decrease in neutrosophic random variates as the degree of indeterminacy escalates across all degrees of freedom values. These findings underscore the pronounced effect of uncertainty on chi-square data generation. The proposed algorithm offers a valuable tool for generating data under conditions of uncertainty, particularly in scenarios where capturing real data proves challenging. Furthermore, the data generated through this approach holds utility in goodness-of-fit tests and assessments of variance homogeneity.}
}