Assessing the relationship between a predictor variable and a target variable is an essential task in the model building process. If the relationship is identified and tractable, then the predictor variable is re-expressed to reflect the uncovered relationship, and consequently tested for inclusion into the model. Most methods of variable assessment are based on the well-known correlation coefficient, which is often misused because its linearity assumption is not tested. The purpose of this chapter is twofold: to present both the
as an easy and effective data mining method as well as another simple data mining method. The purpose of the latter is to assess a general association between two variables, while the purpose of the former is to embolden the data analyst to test the assumption to assure the proper use of the correlation coefficient.