Regression Diagnostics Checklist
Check a regression model's assumptions.
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Check my regression model. Model description and variables: [MODEL]. Output summary and plots described: [OUTPUT_PLOTS]. Purpose (prediction or explanation): [PURPOSE]. Walk through diagnostics: linearity, residual patterns, constant variance, normality of residuals, influential points, multicollinearity (variance inflation factors), and omitted variables. For each give the plot or test to run in Python, how to read it, what a concerning result looks like, and the usual remedies. Prioritise the checks by purpose. Provide a report template for documenting checks and decisions and a caution about data dredging. Note that diagnostics do not establish causation. Add a short example reading of four residual plots described in words, with what each indicates, and a decision table for remedies: transform, add a term, use robust errors, remove an unjustified outlier, or change the model. Include a note on documenting every change made after diagnostics. Also add a short list of what to include in the appendix of a report so that readers can repeat the diagnostics.
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