**4.2 The modified winsorization with graphical diagnostic (MW-GD) method**

In this section, the modified winsorization with graphical diagnostic (MW-GD) method originally proposed by Iduseri and Osemwenkhae [6] is presented. In addition, a proposed alternative statistical interpretation of the informative graphical diagnostic associated with MW-GD method when confronted with the challenge of differentiating between bar shapes of the 2-D area plot is also presented. The MW-GD method, which involves a three-step procedure, will effectively identify and eliminate legitimate contaminants from predictor variables so that their variances between the groups are similar. The aim is to ensure that the training sample, *Dt <sup>N</sup>* satisfies the basic assumptions (particularly the assumption of homogeneity of variances) of the PDA. The three steps procedure produced an optimal training sample that was used to construct a PDF whose percentage of correct classification was not only statistically optimal for the training sample that produced it, but also for other training samples from the same population.
