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.