In regression analysis, multicollinearity refers to a situation of collinearity of independent variables, often involving more than two independent variables, or more than one pair of collinear variables.
Multicollinearity means redundancy in the set of variables. This can render ineffective the numerical methods used to solve regression regression equations, typically resulting in a “multicollinearity” error when regression software is used. A practical solution to this problem is to remove some variables from the model.
The extreme case of multicollinearity, where the variables are perfectly correlated, is called singularity.