Detection of collinearity- influential observations

Collinearity is defined as the existence of near linear dependencies among the columns of the matrix. It has been found, though, that a strategically located point (i.e. a row of ) can greatly modify the collinearity structure of the data. The whole approach to regression analysis depends, largely, on the measured level of collinearity. It is, thus, crucial for the analyst to be able to detect points that modify this level significantly. The main objective of this paper is to develop a diagnostic tool for the detection of collinearity-influential points. This diagnostic is easy to compute and capable of detecting collinearity influential sets of points, Two examples are given to illustrate its use.