Multicollinearity
Multicollinearity occurs when two or more predictor variables are highly correlated in a regression model
Multicollinearity is the phenomenon where two or more predictor variables entered into a multivariate model are highly correlated. In essence, multicollinearity is like measuring the same thing twice. When predictor variables are highly correlated, it is impossible to assess the variables independently within the model. The F-values and change in r-squared will be artificially inflated or deflated when multicollinearity occurs.
In order to account for multicollinearity in regression modeling, researchers should conduct bivariate correlations between the predictor variables. If any of the variables are significantly correlated, then one of the variables should be dropped from the model. Researchers can make this decision based on the relative empirical or clinical context in which the model is being built.
In applied regression modeling, multicollinearity is assessed using the Variance Inflation Factor (VIF) and tolerance.
In order to account for multicollinearity in regression modeling, researchers should conduct bivariate correlations between the predictor variables. If any of the variables are significantly correlated, then one of the variables should be dropped from the model. Researchers can make this decision based on the relative empirical or clinical context in which the model is being built.
In applied regression modeling, multicollinearity is assessed using the Variance Inflation Factor (VIF) and tolerance.
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