Multicollinearity
Two or more independent variables in a regression being highly correlated with one another. Coefficients stay unbiased but their standard errors inflate, so a well-fitting model can show no significant variable.
Where you will meet it
Multicollinearity appears in 2 learning outcomes on the CFA syllabus. A few of them:
- Explain how a wrongly specified model distorts regression output and how the usual specification errors can be prevented
“Inappropriate scaling can create heteroskedasticity or multicollinearity.”
- Say what multicollinearity is and what it does to a regression analysis
“Multicollinearity arises when two or more independent variables are highly correlated with each other.”
See also
Independent variable · Standard error
Practise it
Practise multicollinearity until it is automatic: questions that explain every wrong option.
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