An Application of Box-Cox Transformation to Biostatistics Experiment Data

Data screening is the most important technique to check the nature of the data. One of the methods to screen the data is the Box-Cox Transformation. The Box-Cox family of transformation is a well-known approach to make data behave accordingly to assumption of linear regression and ANOVA. After screening the data method, the parametric method can be applied. The regression coefficients, as well as the parameter λ defining the transformation, are generally estimated by maximum likelihood, assuming homoscedastic normal error (Bartlett 1947). In application of ANOVA for hypothesis testing in biostatistics science experiments, the assumption of homogeneity of errors is often 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. In this paper, we will illustrate the use of the Box-Cox method by using MINITAB software.