What is the difference/relation between variables that are multicollinear, confounding, interacting What is the exact difference between two variables that are multicollinear and two variables that are interacting and two variables that are confounding?
Are they multiple meanings for the same thing?
Or is any one a subset, superset of the other?
 A: A simple way of thinking about multicolinearity is that some independent/explanatory/right-hand-side/x-variables are (strongly) correlated.
Confounding means that some variable causes both an explanatory variable of interest and the outcome variable. A classic example would be the observation that regions with more storks also have a higher birth-rate, but this is due to a third (confounding) variable: more rural regions have more storks and have a higher birthrate.
So a correlation between two explanatory variables is not enough to conclude that there is confounding (although it can support collinearity): first it needs to be causal, second, the direction of causality must be from the confounding to the explanatory variable of interest, third the confounding variable also needs to cause the outcome variable.
If the explanatory variable of interest causes the third variable, which in turn causes the outcome varialbe, then the third variable is an intervening variable; It represents a mechanism through which the explanatory variable of interest influences the outcome variable. For example, the socioeconomic status of the parents influence the education of the child that in turn influences the socioeconomic status of the child.
An interaction is completely different: it means the effect of the first variable differs depending on the second variable, e.g. is the effect of education on wage the same for men and women? In a linear model the interaction for continuous values will involve the product of the two values with a parameter for the contribution wiki link
