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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Furthermore, applications of these tests are given on a real dataset.
📌 3 Key Takeaways
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Objective & Hypothesis:In this article, a testing procedure based on computational approach testis proposed for the equality of coefficients of variation in k normal populations.
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Key Findings & Data:We compare this procedure to some of the existing tests; the likelihood ratio, modified Bennett’s, score, generalized p -value tests in terms of the estimated type I error rates and powers by using Monte Carlo simulation.
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Conclusion & Impact:Furthermore, applications of these tests are given on a real dataset.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Computational approach test · Generalized p-value test · Likelihood ratio test
Abstract
In this article, a testing procedure based on computational approach testis proposed for the equality of coefficients of variation in k normal populations. We compare this procedure to some of the existing tests; the likelihood ratio, modified Bennett’s, score, generalized p -value tests in terms of the estimated type I error rates and powers by using Monte Carlo simulation. Furthermore, applications of these tests are given on a real dataset.
Keywords
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Cite This Article
Gokpınar, F., Gokpınar, E. (2015). A computational approach for testing equality of coefficients of variation in k normal populations. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 5. https://ajindex.com/articles/1403248