DEFINITION of '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.
INVESTOPEDIA EXPLAINS 'Multicollinearity'
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.

Nonlinearity
A relationship which cannot be explained as a linear combination ... 
Variance Inflation Factor
A measure of the amount of multicollinearity in a set of multiple ... 
Statistical Significance
A result that is not likely to occur randomly, but rather is ... 
Statistics
A type of mathematical analysis involving the use of quantified ... 
Regression
A statistical measure that attempts to determine the strength ... 
Autoregressive
A stochastic process used in statistical calculations in which ...

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