Investigating the Least Sample Size for Convergence to Normality from Quantile Measure of Continuous Distributions with R
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
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Research Paper | Statistics | India | Volume 6 Issue 7, July 2017

Investigating the Least Sample Size for Convergence to Normality from Quantile Measure of Continuous Distributions with R

Soumyadip Das, Arjun Dutta

For testing of hypothesis, the knowledge of the distribution of the test statistic is necessary to find the cut-off point or the p-value. But in most of the cases, the distribution of quantile measures of samples is not known or not standard or complicated. Thus, for testing the value of quantile measures the common practice is to use the asymptotic distribution of the statistic which is normal in general. But for this asymptotic distribution to be accurate the sample size must be large. Now the question is how large the sample size should be to ensure the convergence of the statistic to a normal distribution. This paper proposes a procedure to find the least Sample size required for some selected continuous distributions to converge to normality using the Shapiro-Wilks Test of Normality. Simulation and Visualisation are done in the R programming language.

Keywords: Continuous Distributions, Central Limit Theorem, Convergence, Least Sample Size, Shapiro Wilks test, R

Edition: Volume 6 Issue 7, July 2017

Pages: 1038 - 1042

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How to Cite this Article?

Soumyadip Das, Arjun Dutta, "Investigating the Least Sample Size for Convergence to Normality from Quantile Measure of Continuous Distributions with R", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20175447, Volume 6 Issue 7, July 2017, 1038 - 1042

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