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I'd want to take a look at other plots for example residual as opposed to fitted. By way of example, It truly is doable that you've got some type of heterogeneity. Do the factors in either tail have something else in common? Or do you merely have mistake distributions heavier than the conventional? $endgroup$

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happens when Every group is calculated the identical range of occasions. The resistance decreases as the info established becomes less well balanced, as the sample measurements from the teams tend to be more different, so possessing near to stability is most well-liked to a more imbalanced condition when there is a decision available. There's some intuition obtainable listed here – it would make some feeling you would've superior ends in evaluating teams if the knowledge obtainable is similar in every one of the teams and none are somewhat underneath-represented.

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Here is an example evaluating authentic-earth knowledge with a standard distribution. In this instance, it's the city populace figures for each point out in The us.

There are various distributions which can be symmetrical and possess fatter tails than the traditional. I'd generally start by checking out $t$-distributions, because they are well understood, and you may change the tail 'fatness' by modulating the levels of liberty parameter. Your case in point is noteworthy in that the center is extremely straight, along with the finishes also are pretty straight and roughly parallel to each other, with pretty sharp corners in between.

gung - Reinstate Monicagung - Reinstate Monica 145k8888 gold badges400400 silver badges708708 bronze badges $endgroup$ 3 $begingroup$ Extremely nicely defined! Anything at all to analyze from residual vs equipped plot In such a case? I'd additional that also in my issue over. $endgroup$