I am still working on fitting a binary Logistic Regression model in which the DV is land 'optimal vs. non-optimal quality', and the IVs are both continuous (e.g., elevation, slope, distance from the coast, etc) and categorical (soil types [with 5 levels], geology [4 levels]).

In the last months I had good time reading a lot on LR. So far so good. What I am wondering now is if the following situation can be considered a case of 'collinearity' among two predictors.

I have plotted notched boxplots of

*elevation*by

*soil types*, and it seem that there is a significant tendency for some soil types to have larger elevation values (here, I am basing this statement on a broad definition of the Wruskal-Wallis test). What I am concerned about is to use soil types AND elevation as predictors. Can the described situation represent a 'collinearity' issue?

If it can, what would be the more sound approach: to retain just one of the two? Further, shall I have to repeat the same 'screening' for all the other continuous IVs?

Best

Gm