# Multicollinearity

*Math & Statistics — Finicade finance glossary*

Multicollinearity is when predictors in a regression are highly correlated with each other, so the model cannot tell their effects apart. Coefficients become unstable and standard errors balloon, producing the tell-tale symptom of a strong overall fit with no individually significant variable. Prediction still works; interpretation does not, which matters because interpretation is usually the point.

**Also known as:** collinearity, variance inflation factor, VIF

**Related terms:** [Ordinary Least Squares](https://finicade.com/glossary/ordinary-least-squares), [Regression](https://finicade.com/glossary/regression), [Standard Error](https://finicade.com/glossary/standard-error), [Principal Component Analysis](https://finicade.com/glossary/principal-component-analysis), [R-Squared](https://finicade.com/glossary/r-squared)

**Taught in:** Regression Range — Standard Errors & Gauss–Markov

Source: https://finicade.com/glossary/multicollinearity