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Multicollinearity & VIF: Why Correlated Predictors Break Regression

Drag two predictor variables from independent to nearly collinear on a 2D scatter and watch the OLS coefficient estimates and their bootstrap standard-error bars blow up in real time, alongside a live Variance Inflation Factor.

Statistics2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

Regression textbooks warn that a Variance Inflation Factor above 10 signals trouble, but the number alone hides what's actually happening to the fit. This 2D simulator draws two predictor variables with an adjustable correlation, fits a real ordinary-least-squares model to a noisy response by inverting the normal-equations matrix from scratch, and refits it on 200 bootstrap resamples so you can watch the coefficient estimates' standard-error bars widen as the predictors become collinear — exactly when VIF explodes and R² barely moves.

⚙ Under the hood

Drag two predictor variables from independent to nearly collinear and watch an OLS regression plane become unstable in real time, with a live Variance Inflation Factor and an overlaid Ridge-regularized plane that stays steady.

regressionstatisticsmulticollinearityVIFridge-regressionOLS

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install