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A Detection of Outliers in Random Sample from Normally Distributed Population Using Coefficient of Skewness | Jareankam | งดใช้ระบบ 3-31 กค 66 Burapha Science Journal

A Detection of Outliers in Random Sample from Normally Distributed Population Using Coefficient of Skewness

Woraphan Jareankam

Abstract


The objective of this research is to the propose a detection of outliers in a test statistic. Such a test statistic is developed from the concept of GESD by Rosner, where the assumption of this test statistic is a normally distributed population. In this paper, we shall show a comparison of an ability of control type I error and power of the test under a simulation of normal distributions induced by the sample sizes 10, 20, 30, 50, 100 and 200. The outliers in this simulation are defined in many different situations, which the situations are replicated 1,000 times. The significance level is 0.05. The results show that the proposed test statistic can control the probability of type I error. The percentage of correct decision are greater than 95 when the alternative hypothesis is true.

Keywords : outliers, normal distribution, coefficient of skewness


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References


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