The common and almost default approach is to fix

`age`

to a constant. This is really what our model does in the first place: the coefficient of`height`

represents the expected change in`weight`

while`age`

is fixed and not allowed to vary. What constant? A natural candidate (and indeed`emmeans`

’ default) is the mean. In our case, the mean age is 14.9 years. So the expected values produced above are for three 14.9 year olds with different heights. But is this data plausible? If I told you I saw a person who was 120cm tall, would you also assume they were 14.9 years old?No, you would not. And that is exactly what covariance and multicollinearity mean – that some combinations of predictors are more likely than others.

I liked the explanation Mattan provides us. Also be sure to read the warnings near the end of the post around other things to try. H/T R-bloggers