Okay, I think I've got this. Based on the question, the Fit - Mean and Residual Plot wouldn't be the right choice here. I'm leaning towards Cook's D by Observation to identify influential observations.
The key here is to focus on the diagnostic plots. I'd go with option A, Cook's D by Observation. That's the best way to spot any observations that are having an outsized impact on the model.
I'm a bit confused on this one. I'm not sure if I should be looking at the Residual by Quantile or the Residual by Predicted plot. They both seem like they could be relevant.
Hmm, I think I'd use Cook's D by Observation to identify influential observations. That plot shows how much each observation is influencing the overall model fit.
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