I have carried out a linear regression. This is the form of the model:

bounded (0-1) response variable ~ factor1 (2 levels) + factor2 (5 levels) + interaction between factor1:factor2 + factor3 (2 levels)

Sample size is about 350.

I have plotted the residuals against the predicted values from that model:

As there is a distinct pattern in the residuals, it appears linear regression is not a suitable model. I have carried out several other linear regressions lately with different data, and I have repeatedly seen this pattern in the residuals.

Does this pattern provide information on what model would be best to use? Should an interaction term be added? Is there a predictor missing? Should a non-linear model be used? Or instead, does this particular pattern in the residuals actually not provide any indicators as to what model to use?

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#### Best Answer

Luciano: 1) at first glance, your residual plot seemingly shows heteroskedasticity of residuals. This violates one of the requirements of OLS. This often happens when factor variables are the only predictors. 2) But the most substantive issue rests on the consideration that OLS is unfit to deal with a binary dependent variable (if this is your case).

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