Data Assumption: Multicollinearity

May 13, 2016

Very brief description Multicollinearity is a condition in which the independent variables are highly correlated (r=0.8 or greater) such that the effects of the independents on the outcome variable cannot be separated. In other words, one of the predictor variables can be nearly perfectly predicted by one of the other predictor variables.  Singularity is when the independent variables are (almost) perfectly correlated (r=1) so any one of the independent variables could be regarded as a combination of one or more of the other independent variables. In practice, you should not [READ MORE]