6 Regression Diagnostic Checks Every Data Scientist Should Run
mdancho84 · x · 2026-09-20
Data scientist mdancho84 breaks down six diagnostic checks to run after building a regression model:
- Posterior predictive check: simulated data from your model should resemble the observed data; wild divergence means the model is missing something.
- Linearity: plot residuals vs. fitted values — a curve or pattern means a linear model is fitting nonlinear data.
- Homogeneity of variance: residual spread should stay constant across fitted values; a funnel shape (heteroscedasticity) means your standard errors are wrong.
- Influential observations: points outside Cook's distance contours with high leverage and large residuals can silently distort coefficients — investigate before trusting results.
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