Regression analysis is an important statistical tool that is routinely applied
in most sciences. Out of many possible regression techniques, the least
squares (LS) method has been generally adopted because of tradition and
ease of computation. However, there is presently a widespread awareness
of the dangers posed by the occurrence of outliers, which may be a result
of keypunch errors, misplaced decimal points, recording or transmission
errors, exceptional phenomena such as earthquakes or strikes, or members
of a different population slipping into the sample. Outliers occur
very frequently in real data, and they often go unnoticed because nowdays
much data is processed by computers, without careful inspection or
screening. Not only the response variable can be outlying, but also the
explanatory part, leading to so-called leverage points. Both types of
outliers may totally spoil an ordinary LS analysis. Often, such influential
points remain hidden to the user, because they do not always show up in
the usual LS residual plots.