ABSTRACT
This chapter focuses on the importance of straight data for the simplicity and desirability it brings for good model-building practice. It explores the content by giving details of what to do when an observed relationship between two variables depicted in a scatterplot is masking an acute underlying relationship. Data mining is employed to unmask and straighten the obtuse relationship. The correlation coefficient is used to quantify the strength of the exposed relationship, which possesses straight-line simplicity. Data mining is a high concept of three key elements: fast action in its development, glamor in its imagination for the unexpected, and mystique that feeds the curiosity of human thought. The correlation coefficient requires that the underlying relationship between two variables is linear. The correlation coefficient is used correctly because the exposed character of the exampled relationship has straight-line simplicity.
