# Multicollinearity

Multicollinearity
In statistics, the occurrence of several independent variables in a multiple regression model are closely correlated to one another. Multicollinearity can cause strange results when attempting to study how well individual independent variables contribute to an understanding of the dependent variable. In general, multicollinearity can cause wide confidence intervals and strange P values for independent variables.

Multicollinearity suggests that several of the independent variables are closely linked in some way. Once the collinear variables are identified, it may be helpful to study whether there is a causal link between the variables. The simplest way to resolve multicollinearity problems is to reduce the number of collinear variables until there is only one remaining out of the set. Sometimes, after some study it may be possible to identify one of the variables as being extraneous. Alternatively, it may be possible to combine two or more closely related variables into a single input.

Investment dictionary. . 2012.

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