Statistical modeling using linear regression in the presence of un usual observations, or outliers, is discussed and illustrated with an example from entomological field research. The effect of outliers on estimates of coefficients using least squares regression is discussed and compared with estimates using weighted least squares robust regression. The ability of the latter technique to detect outliers and estimate regression coefficients in their presence is demonstrated. Several other diagnostic displays for the objective detection of outliers and the evaluation of the fitted model are illustrated.