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Jul 19, 2011 at 22:30 comment added Ram Ahluwalia Correct - the standard error is minimized at the mean and symmetrically on both sides. I suggest sorting on the absolute value of the t-statistic to address that symmetry. You need a ruler to compare which residuals are more "outlier-y". The magnitude of the residual is not a good measure because it depends on its proximity to the mean. So I suggest normalizing the absolute residual with respect to the prediction interval so you can compare across residuals. If the residual is large but farther from the mean then this approach will proportionally give that residual more tolerance for error.
Jul 19, 2011 at 19:22 comment added raegtin Great suggestion! However, isn't the standard error of the prediction interval minimized at the mean of the x-range, and then monotonically and symmetrically increasing on the other sides? (That is, I do like this t-statistic approach, but if I remember the standard error calculation correctly, this particular t-stat seems a bit "artificial" in a sense -- why should I consider residuals at the mean of my x-range to be more outlier-y than residuals at the extremes?)
Jul 18, 2011 at 21:33 history answered Ram Ahluwalia CC BY-SA 3.0