Does this graphics support the assumption of homoscedasticity?
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kjetil b halvorsen
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Gabriel
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1The same question was asked and answered on this forum two years ago. – Xi'an Nov 27 '20 at 08:09
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1@Xi'an the plot shown in the question to which you link has many other features though and so, even though reading it might help the OP, I do not think this is a duplicate. – mdewey Nov 27 '20 at 13:44
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@mdewey: it is impossible to tell the OP's intents given the current question is just made of a graph. – Xi'an Nov 27 '20 at 15:03
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These are two different graphs from the previous question, that is, another different analysis. Could you help me, please? – Gabriel Nov 27 '20 at 15:52
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my question is that of the other one you sent the link to, but the graph is different, and his analysis is also – Gabriel Nov 27 '20 at 15:56
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Those two graphs are not the same so which one is your question about? – mdewey Nov 27 '20 at 16:58
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Both graphics !! – Gabriel Nov 28 '20 at 12:50
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The first graph (residuals versus predicted) could be interpreted to show a variance systematically increasing with predicted value. If that is important or not depends on your goals, but it might be. I would maybe try a model where residual variance is modeled as a function of expectation $\mu$, in R such models can for instance be fitted with the package gamlss.
For an example see Are there better approaches than the weighted mean?.
kjetil b halvorsen
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