Multicolinearity: Why you should care and what to do about it Multicolinearity is a problem for statistical analyses. This large, unwieldy word essentially refers to situations where your predictor variables so highly correlated with one another, they become redundant. Generally speaking, this is a problem because it will increase your Type II error rate (i.e., false negatives). In the most severe cases, multicolinearity can produce really bizarre results that defy logic. For example, the direction of relationships can sometimes reverse (e.g., a positive relationship...

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