It is worth noting that, while most multivariate data analysis methods - such as ordinary least
squares regressions - are oriented towards central tendency estimates, DEA is directed towards optimal
estimates for each individual observation represented in a dataset. More precisely, the performance of
these observations is evaluated relative to the frontiers formed by the performance that data shows is
possible to attain (Cooper et al., 2007). By contrast, DEA is individually, rather than averages,
oriented and deals with frontiers rather than central tendencies.