Transformation of Data for Statistical Processing

Abstract

The use of many statistical tools depends on normality of processed data. There are different methods for transformation of non-normally distributed data sets toward to normally distributed ones. The goal of the work has been to investigate usability of four types of transformations (Box-Cox, exponential, power and logarithmic) for transformation of data sets with four non-normal distributions (logarithmic-normal, exponential, gamma, and Weibull) toward to normally distributed data. The usability and efficiency of individual transformation functions for transformation of data sets with different types of distributions have been found.