A graphic for Random intercepts via distributions

? ? Can you clarify your concern?

I can see things to quibble about here (the scales of the level-2 and 
level-1 diagrams are different; I don't know why they're using e_{ij} 
for the residual error of individual i in school j but U_{0j} for the 
deviation of school j around the grand mean; it's a little confusing to 
have "level 1" above "level 2" in the text but level 2 above level 1 in 
the picture; it's potentially confusing for the arrow showing the 
deviation from the baseline to intersect with the population density 
curve [technically, the deviation doesn't have a "level", so could be 
drawn instead as an arrow between two vertical lines rather than from a 
line to a particular point ...

... but nothing that seems actively misleading.

 ? Others may have other opinions or see something I'm missing.

Good afternoon,

I came across a picture (https://github.com/hkil/m/blob/master/mlm.PNG)
that tries to show the concept of random-intercept models using
distributions.

I think, however, the picture erroneously mixes regression concepts (e.g.,
error terms) with distributional properties of those regression concepts.

I appreciate confirmation from the expert members?

Thanks,
Simon

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