Simulating Chi Square Data Through Algorithms In The Presence Of Uncertainty
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Simulating chi-square data through algorithms in the presence of uncertainty
Author | : Muhammad Aslam |
Publisher | : Infinite Study |
Total Pages | : 14 |
Release | : 2024-01-01 |
Genre | : Mathematics |
ISBN | : |
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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.
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