As we know, plotting residuals against fitted values may indicate whether the model is misspecified or the variance is not constant, or both. Let's focus on model misspecification only and set aside heteroskedasticity for the moment.
For example, example (b) shows that residuals increase with fitted values. What kind of model misspecification does example (b) imply? Might it indicate that some important predictor is not included at the right-hand side of the equation? Or might it indicate that some of the predictors need to be transformed to a higher order, for example, from its original scale to square?
Also what might example (c) say about model misspecification?
These examples seem to be pretty common in textbooks or online tutorial but it appears to me that few have tried to figure out what the underlying model misspecification contributes to these different types of residuals-fitted values plots.