See how highly correlated predictor variables act when used together to model an outcome






New York University

Multicollinearity = Very  strong correlation between two or more predictor variables. 

- As a result the posterior distribution will appear to suggest that none of the variables are reliably  associated with the outcome - when in fact they might be strongly associated with the outcome.
- The model will still have accurate predictions, but interpreting it will lead to confusion.  You wont be able to determine the actual effect of the variables on the outcome. 

- See next node for an example 

Solutions:
- Identifying highly correlated variables, eliminating all but one
- Combining them into one variable
- However, it is best to take a causal approach -  to determine conditional associations, and not just correlations. 

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