An Approached of Box-Cox Data Transformation to Biostatistics Experiment

Abstract

The Box-Cox family of transformation is a well-known approach to make data behave accordingly to
assumption of linear regression and ANOVA. The regression coefficients, as well as the
parameter defining the transformation are generally estimated by maximum likelihood, assuming
homoscedastic normal error. In application of ANOVA for hypothesis testing in biostatistics science
experiments, the assumption of homogeneity of errors often is violating because of scale effects and
the nature of the measurements. We demonstrate a method of transformation data so that the
assumptions of ANOVA are met (or violated to a lesser degree) and apply it in analysis of data from
biostatistics experiments. We will illustrate the use of the Box-Cox method by using MINITAB
software.