I have a quick question about the correct way to describe variance functions when seeking to cope with heteroscedasticity. As I understand it the statistical error of a model represents the departure between a sample and the true function value. In practice we don't usually know the true function value. In contrast, residuals are the difference between a sample and the estimated function value, which we do know. Now, assume that I have a model with heteroscedasticity and I want to model it as

$$\varepsilon\_i \sim N(0, \sigma^2 \times X\_i).$$

Would it be correct to say that the variance in the statistical errors were modeled as a function of $X$? Or, would it better to state that the variance in the residuals was modeled as a function of $X$?