Abstract
Influential data points can affect the results of a regression analysis; for example, the usual sum mary statistics and tests of significance may be misleading. The importance of regression diagnostics in detecting influential points is discussed, and five statistics are recommended for the applied researcher. The suggested diagnostics were used on a small dataset to detect an influen tial data point, and the effects were analyzed. Colinearity-based diagnostics also are discussed and illustrated on the same dataset. The non- robustness of the least squares estimates in the presence of influential points is emphasized. Diagnostics for multiple influential points, multi variate regression, multicolinearity, nonlinear regression, and other multivariate procedures also are discussed.
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