1. A method for determining one or more causes for variability in data, comprising: monitoring process data of process equipment, wherein at least some of the process data comprises data derived from a physical measurement of a process variable by a sensor; applying the process data to a multivariate model to generate multivariate model output data; displaying the multivariate model output data on a user interface; selecting a first range of the multivariate model output data on the user interface; employing a computing system, operatively coupled to the user interface, to: determine one or more of the process data causing a variability of the multivariate model output data in the first range when compared to a second range of the multivariate model output data, wherein determining the process data causing the variability of the multivariate model output data comprises executing a multivariate analysis on the first range and the second range to generate a ranking of one or more process data contributing most to a difference between the two ranges; and using the ranking and the multivariate model output data to adjust an existing multivariate fault or add a new multivariate fault to be monitored by the computing system, wherein the multivariate fault that is adjusted or added is configured to detect a fault related to the process equipment.