Interpret My Regression Output
Read coefficients, intervals, and diagnostics.
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Interpret my regression results for a non-statistician. Model description and variables: [MODEL_DESCRIPTION]. Output (coefficients, standard errors, p-values, R squared, sample size): [PASTE_OUTPUT]. The question the model should answer: [QUESTION]. Write: 1. A table: Term | Coefficient | Plain-language meaning with units (for example each extra year of tenure is associated with 1,250 higher salary, holding the other variables constant) | 95% interval (estimate plus or minus 1.96 x SE as a rough guide) | Practical size. 2. What R squared does and does not tell me, with my number. 3. A caution list: association is not causation; omitted variables; multicollinearity (what to check: variance inflation factor); extrapolation beyond the data range. 4. Diagnostics to run, each with the plot or test and what a bad result looks like: residuals versus fitted, normal Q-Q plot, leverage and influence, heteroscedasticity. 5. Whether the sample size supports this many predictors (a rough guide of 10 to 20 observations per predictor). 6. A paragraph for a report in plain language, and what I should not claim. Mark anything you cannot judge from the output as unknown.
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