Multicollinearity
Also called: collinearity, variance inflation factor, VIF
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.
Where this is taught
Definitions are the trailer. These free levels turn Multicollinearity into something you play — one bite-size lesson, with worked examples, a quiz and XP.