Models for Transformation: A Global Optimization Transformation method with Some Extension from Box-Cox Transformation
Abstract
The choice of a transformation has often been made in an ad hoc trial-and-error fashion but in this
research paper we deals with the new solution of transformation which is covering all the real numbers.
The assumption of normality that gained from the optimization method is much better improved
compared to Box-Cox transformation through the statistical P value. The biggest values of the
statistical P value (> 0.05) reflect the goodness of the normality achievement. In order to obtain the
efficiency status, we will illustrate the application of transformation method with the data that are
getting from Hospital University Science Malaysia (HUSM).
research paper we deals with the new solution of transformation which is covering all the real numbers.
The assumption of normality that gained from the optimization method is much better improved
compared to Box-Cox transformation through the statistical P value. The biggest values of the
statistical P value (> 0.05) reflect the goodness of the normality achievement. In order to obtain the
efficiency status, we will illustrate the application of transformation method with the data that are
getting from Hospital University Science Malaysia (HUSM).
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PDF (Bahasa Indonesia)DOI: https://doi.org/10.29313/jstat.v9i1.987
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