An assumption for linear regression confidence intervals is that the variance is the same for the dependent variable for whatever of the independent variable.
If in practice the variance is different, what does this imply for the ability of the model to function as a prediction mechanism?
I have a few questions that come immediately to mind:
Does this mean that the confidence intervals will be wider in some places than they need to be?
Does it mean the model will be inaccurate for some inputs?
Are linear regression models nearly-always inaccurate, and so the last point might be a minor problem compared to the general inaccuracy?