Step 1: Understanding the concept.
In regression, when independent variables are highly correlated, it becomes difficult to separate their individual effects. This condition is called multicollinearity.
Step 2: Eliminate distractors.
(a) Hypercollinearity — informal term, not standard in statistics.
(b) Partial collinearity — refers to correlation among some variables, but not the general phenomenon.
(d) Variable collinearity — descriptive but not the official term.
\[
\boxed{(c)}
\]