Does this graphics support the assumption of homoscedasticity?
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1$\begingroup$ The same question was asked and answered on this forum two years ago. $\endgroup$– Xi'anNov 27, 2020 at 8:09
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1$\begingroup$ @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. $\endgroup$– mdeweyNov 27, 2020 at 13:44
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$\begingroup$ @mdewey: it is impossible to tell the OP's intents given the current question is just made of a graph. $\endgroup$– Xi'anNov 27, 2020 at 15:03
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$\begingroup$ These are two different graphs from the previous question, that is, another different analysis. Could you help me, please? $\endgroup$– GabrielNov 27, 2020 at 15:52
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$\begingroup$ my question is that of the other one you sent the link to, but the graph is different, and his analysis is also $\endgroup$– GabrielNov 27, 2020 at 15:56
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1 Answer
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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?.
answered Nov 28, 2020 at 20:10