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Nyi Nyi Naing
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Models for Transformation: A Global Optimization Transformation method with Some Extension from Box-Cox Transformation Wan Muhamad Amir Bin W Ahmad; Nyi Nyi Naing; Tengku Mohd Ariff Raja Hussein
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 9, No 1 (2009)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v9i1.987

Abstract

The choice of a transformation has often been made in an ad hoc trial-and-error fashion but in thisresearch 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 improvedcompared to Box-Cox transformation through the statistical P value. The biggest values of thestatistical P value (> 0.05) reflect the goodness of the normality achievement. In order to obtain theefficiency status, we will illustrate the application of transformation method with the data that aregetting from Hospital University Science Malaysia (HUSM).
An Approached of Box-Cox Data Transformation to Biostatistics Experiment Wan Muhamad Amir Bin W Ahmad; Nyi Nyi Naing; Norhayati Rosli
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 2 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i2.937

Abstract

The Box-Cox family of transformation is a well-known approach to make data behave accordingly toassumption of linear regression and ANOVA. The regression coefficients, as well as theparameter defining the transformation are generally estimated by maximum likelihood, assuminghomoscedastic normal error. In application of ANOVA for hypothesis testing in biostatistics scienceexperiments, the assumption of homogeneity of errors often is violating because of scale effects andthe nature of the measurements. We demonstrate a method of transformation data so that theassumptions of ANOVA are met (or violated to a lesser degree) and apply it in analysis of data frombiostatistics experiments. We will illustrate the use of the Box-Cox method by using MINITABsoftware.
Selection for Parameter  by Using Newton-Raphson Method Wan Muhamad Amir Bin W Ahmad; Nyi Nyi Naing; Mohd Tengku Ariff Raja Hussein
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 2 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i2.960

Abstract

Many problems in mathematics or statistics involve, at some point or another, solving an equation foran unknown quantity. To solve the problem many of methods are applied as such MaximumLikelihood Method, Least Square Method and Newton-Raphson Method and much more. One of thefamous methods is Newton Method. The Newton-Raphson method or Newton–Fourier method is anefficient algorithm for finding approximations to the zeros (or roots) of a real-valued function. As such,it is an example of a root-finding algorithm. It can also be used to find a minimum or maximum valueof such a function, by finding a zero in the function's first derivative. In this paper, we will work withNewton-Raphson method to estimate the value of for the use of transformation. The value of ,determine the kind of transformation should be done according to the data. These papers emphasizethe Newton Method in finding the value of by using C++ software.
A Modification of Box-Cox Transformation Wan Muhamad Amir Bin W Ahmad; Nyi Nyi Naing; Mohd Tengku Ariff Raja Hussein
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 1 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i1.952

Abstract

Data screening is the most important technique to check the nature of the data. One of the methodsto screen the data is the transformation method. There are a lot of transformations but the famousone is the Box-Cox Transformation. The purpose of this study is to find the alternative way oftransformation by making some modification of the original Box-Cox formula. As we know, the Box-Cox transformation method only transform the data if and only if the data in a positive values. If thevalues in the analysis are negative, the formula of the Box-Cox cannot be used. This problem alwaysoccur when the researcher want to transform the data which the values is negative. In thismethodology section, we will build an alternative method to solve the problem if the data in the studyare negative.
An Application of Alternative Method of Transformation Wan Muhamad Amir Bin W Ahmad; Nyi Nyi Naing; Mohd Tengku Ariff Raja Hussein
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 1 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i1.953

Abstract

Data transformation plays a major role when behave of the data do not meet the assumption of linearregression and ANOVA. In this paper we will demonstrate an application of the alternative method oftransformation, so that the assumptions of ANOVA are met (or violated to a lesser degree). These datawere collected from biostatistics experiments. Some of calculation is done by manually and the restby the MINITAB software. The results will be discussed at the end of the paper.
Co-Authors Mohd Tengku Ariff Raja Hussein Norhayati Rosli Tengku Mohd Ariff Raja Hussein Wan Muhamad Amir Bin W Ahmad