On the Accuracy of Bonferroni Significance Levels for Detecting Outliers in Linear Models

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Abstract
At present, the first-order Bonferroni upper bound is the only practically useful tool for determining approximate critical values or p values for the maximum absolute studentized residual as a criterion for detecting a single outlier in a linear model. Available methods for assessing the accuracy of this bound require numerical integration and are difficult to apply routinely. We present a relatively simple alternative method that can be applied to any linear model and is suitable for routine use. The application to analyses of 2 m factorial experiments and regression models is illustrated with several examples.

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