Simple regression residual audit
Fit an intercept and slope and inspect residual variation and leverage.
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Use at least three pairs with varying x. Outputs refer to y predicted from x with an intercept; use numeric units consistently.
The starting values are hypothetical. Results update as you edit. Use aggregate or synthetic inputs only.
Calculation results
- Intercept
- 1.3
- Slope
- 0.9
- Residual SD
- 0.79582243
- Slope SE under equal independent errors
- 0.25166115
- R squared
- 0.81
- Maximum leverage
- 0.6
- Residuals in input order
- -0.200000, -0.100000, 1.00000, -0.900000, 0.200000
How to interpret the output
Ordinary least squares minimizes squared vertical residuals. Reported R² describes this fitted sample, not external predictive performance. Maximum leverage identifies unusual x positions, not necessarily erroneous observations. Causal inference, autocorrelation, heteroscedasticity and nonlinear patterns require further evaluation.
Method referenceSource checked 2026-11-06. Read the reproducible calculator validation report.