The Robust Statistical Normality Transformation method with Outlier Consideration in Bitcoin Exchange Rate Analysis

Abstract

Bitcoin is the first decentralized peer-to-peer payment network that is powered by its users with no central authority or middlemen. The objective of this study is to evaluate the normality of data distribution for exchange rate of Bitcoin. The method implemented in this study is Shapiro-Wilk normality test including graphical approach namely box plot .Results show the data distribution of exchange rate for Bitcoin follows non-normal distribution. Therefore, the normality transformation is important to make sure the distribution of data follows normal distribution. The normal distribution is very crucial as one of the requirement for validity of statistical test.Normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.This study implemented two-stages of outliers detection and deletion process.The final results shows the distribution of Bitcoin exchange rate with first difference is follow normal distribution with probability of 0.722.Result concluded the distribution of data after second stages of outlies deletion treatment shows high normal distribution characteristics. This finding concludes that Bitcoin data is highly volatile with existence of many outliers. The transformation process is highly important to make sure the Bitcoin data follows normal distribution that underlying critical assumption for statistical tests.