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Multicollinearity


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Drugs are not always necessary, but belief in recovery always is.

— Norman Cousins

Multicollinearity is the occurrence of high intercorrelations among independent variables in a multiple regression model. Multicollinearity can lead to skewed or misleading results when a researcher or analyst attempts to determine how well each independent variable can be used most effectively to predict or understand the dependent variable in a statistical model. In general, multicollinearity can lead to wider confidence intervals and less reliable probability values for the independent variables. That is, the statistical inferences from a model with multicollinearity may not be dependable.


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