docs need to explain "aliases" better #254

https://juliastats.org/StatsModels.jl/latest/contrasts/#Further-details says

A categorical variable in a term aliases the term that remains when that variable is dropped.

I can't figure out what this means, even after looking at the examples following that quote. Are there some words missing?

For example:

1. In ~a+b+c referring to the term that remains after dropping a is meaningless since there are 2.
2. So does a alias both b and c in that case?
3. Which seems to mean everything aliases everything, a not particularly helpful concept.
4. The first example says the sole variable a aliases the intercept 1. But if there is no explicit 1 then what? (A bit further down it says if y~0+a then a aliases nothing. But what if the intercept is implicit?)
5. "Linear Dependence" or "not full rank" at least mean something to me, and seem in the same ballpark. But the discussion clearly intends aliasing to occur even absent linear dependence.
6. My immediate association with alias in the context of computers is 2 variables referring to overlapping memory. That's clearly not the intended meaning here.
7. In ~a&b + a&b&c the first expression a&b is completely redundant. It is unclear how that's handled by StatsModels or how it relates to the discussion of aliases.
8. Handling of ~a&b + a&c also unclear.
9. But since I couldn't even understand a simple main effects model in 1., it's unsurprising I don't understand interactions.