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You might just make the prediction intervals wider, or you might yet again design it, particularly when the subgroups are identifiable.
one $begingroup$ It's unusual that one diagnostic plot tells everything you would possibly find out about the in shape of a design. In interpreting it you must convey to bear issue-issue abilities and thought of what else is within your model. Mentions of age and gender suggest social science and/or medical science as the world.
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The absolute price transforms all the residuals right into a magnitude scale (eliminating course) along with the sq.-root helps you see distinctions in variability far more properly. The Visible evaluation is analogous in The 2 plots – you'd like to contemplate no matter if it seems that the teams have rather identical or significantly unique quantities of variability. If you see a clear funnel condition (slim (a lot less variability) over the remaining or suitable and large (extra variability) at the best or left) within the Residuals vs Equipped and/or a rise or lower in the height of your upper edge of details in the size-Place plot that may suggest a violation on the continual variance assumption. Do not forget that some variation across the teams is predicted, does not advise a violation of the validity situations, and means you could commence with trusting your inferences, but big dissimilarities while in the distribute are problematic for every one of the procedures that entail linear models. When talking about these success, you wish to explore how clearly the variances in variation are and whether or not that displays a transparent
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As @COOLserdash pointed out, I wouldn't worry about this for uses of statistical inference, Whilst if you can determine a heterogeneous subgroup, you may model your knowledge employing weighted minimum squares. For purposes of prediction, signify