Definition and assessment of heteroskedasticity in data Why is heteroskedasticity assessed by the variance of the error terms in a regression model? Doing so requires one to always first fit a statistical model in order to have access to the error terms. Couldn't we just determine whether the data is heteroskedastic by 'looking' at the 'raw data', e.g. in the form of a scatter plot?
I wonder whether the reason for assessing heteroskedasticity with error terms is due to the fact, that it might be difficult to assess heteroskedasticity from 'raw data' via a scatter plot (or any other visualisation method) when the data is high-dimensional?

Heteroskedasticity in regression refers to the conditional distributions having unequal variances. If you just look at the marginal/pooled/unconditional distributions, you have no idea how the conditional distributions behave and cannot learn anything about heteroskedasticity.
If you do a scatterplot, you are implicitly regressing on something (and that something need not even be the entire set of features used in your full regression). Thus, you might not be calculating coefficients explicitly, but there’s still something regression-like happening.
