To address this issue, studentized residuals offer an alternative criterion for identifying outliers. When trying to identify outliers, one problem that can arise is when there is a potential outlier that influences the regression model to such an extent that the estimated regression function is "pulled" towards the potential outlier, so that it isn't flagged as an outlier using the standardized residual criterion. So far, we have learned various measures for identifying extreme x values (high leverage observations) and unusual y values (outliers).
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