Standard error of the coefficient of variation

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SUMMARY

The standard error of the coefficient of variation (SE(CV)) in an exponential distribution is defined by the formula SE(CV) = CV / √(2n) * √(1 + 2 * (CV/100)²). This formula incorporates the coefficient of variation (CV) and the number of observations (n). The discussion raises questions about the applicability of this formula specifically for the exponential distribution, indicating a need for further validation of its use in this context.

PREREQUISITES
  • Understanding of coefficient of variation (CV)
  • Knowledge of standard error (SE) concepts
  • Familiarity with exponential distribution properties
  • Basic statistical analysis skills
NEXT STEPS
  • Research the derivation of the standard error of the coefficient of variation in various distributions
  • Explore statistical software tools for calculating SE(CV) in R or Python
  • Investigate the implications of using SE(CV) in hypothesis testing
  • Learn about the limitations of the coefficient of variation in statistical analysis
USEFUL FOR

Statisticians, data analysts, and researchers working with exponential distributions and those interested in understanding the implications of the coefficient of variation in their analyses.

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TL;DR
What is the standard error of the coefficient of variation in an exponential distribution?
What is the standard error of the coefficient of variation in an exponential distribution?
 
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What do you think it is?
 
hutchphd said:
What do you think it is?
On

https://influentialpoints.com/Training/standard_error_of_coefficient_of_variation.htm

I read:
$$SE(CV) \, = \, \frac{CV}{\sqrt{2 \, n}} \, \sqrt{1+2 \, \left(\frac{CV}{100}\right) ^2}$$
where ##SE## is the standard error, ##CV## is the coefficient of variation and ##n## is the number of observations.
However, I don't know if this formula holds for the exponential distribution.
 

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