An approach to preliminary detection and confirmation of potential outliers using r: a case of false suspicion of outliers in data

To verify, regulate and monitor processes, it is critical to use visual as well as analytical tools to identify outliers. The root cause analysis of a possible outlier should be done using analytical approaches to establish, if a suspect point is indeed an outlier and whether it should be deleted from the data set. While outliers can be spotted visually, they can also be identified analytically, hence this study aims to draw a line between the two methods to prevent misinterpretation and "legitimately" classify a dubious observation as an outlier.