ABSTRACT
This chapter presents an alternative data mining method for moderating outliers instead of discarding them. It illustrates the data mining feature of the GenIQ Model as a method for moderating outliers with a simple, compelling presentation. There are numerous statistical methods for identifying outliers. The most popular methods are the univariate tests. The statistical community has not addressed uniting the outlier detection methodology and the reason for the existence of the outlier. Statistical regression models are quite sensitive to outliers, which render an estimated regression model with questionable predictions. Comparing relationships between pairs of variables, in scatterplots, is a way of drawing closer to outliers. The scatterplot is an effective nonparametric, assumption-free technique. The GenIQ Model as illustrated to moderate a single outlier can handily serve as a multivariate method for moderating virtually all outliers in the data. Multivariate moderation is possible due to GenIQ's fitness function.
