Abstract
In a regression analysis there may be certain data points (which may or may not be outliers) that are influential in the sense that their presence or absence significantly influences the obtained values of the estimated regression coefficients. The nature of these effects needs to be analyzed in order to determine which, if any, data points should be removed from the data set in order to improve coefficient estimates. A relatively new technique for identifying influential data points is called regression diagnostics. In this presentation, the technique is discussed and its potential usefulness demonstrated by an application on a data set previously analyzed by Tufte (1974).
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