✨
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
🎯
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.
📊
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.
💡
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
🔖
Cite This Article
GOKPINAR, E., GOKPINAR, F. (2022). A computational approach for testing equality of coefficients of variation in k normal populations. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 5, 1197–1213. https://ajindex.com/articles/530255