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I wish to examine assumptions for single regression using the built in diagnostics within R. The residuals vs fitted plot below seemly violated the assumption. There are 2 dummy coded variables within my model and 1 categorical variable (testing probability). The model only explains 8% of the variance in selection (DV). What improvements/Advice can be tell from the plot? Thanks.

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  • $\begingroup$ The definition residual $=$ observed $-$ fitted implies that distinct observed values define parallel lines on this plot, in this case residual $= 0\ -$ fitted and residual $= 1\ -$ fitted. Nothing is really violated here; it is just that your data aren't well suited to the model you fit. $\endgroup$
    – Nick Cox
    May 18, 2021 at 15:23
  • $\begingroup$ For diagnosing a logistic regression, consider using DHARMa, see hints on logistic regression in cran.r-project.org/web/packages/DHARMa/vignettes/… $\endgroup$ Jun 24, 2021 at 11:41

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Without wanting this to sound snotty, my advice would be not to use linear regression with a binary outcome variable. Use regression instead. (In R, that's: glm(<formula>, <data>, family=binomial). When you do use logistic regression, ignore these plots (see my answer to: Interpretation of plot (glm.model)).

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