Regression diagnostics
Keep X and Y in the same observation order.
Residuals versus fitted values
Look for structure, changing spread and isolated points.
Observation diagnostics
| No. | X | Y | Predicted | Residual | Std. residual | Leverage | Cook D | Flag |
|---|---|---|---|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | ||||||||
How do I use this page?
- Paste paired observations.
- Inspect the residual plot and flagged rows.
What this page does not do: The tool fits one predictor and does not replace subject-matter validation, cross-validation or a full time-series model.
Further reading
Theory — what is linear regression diagnostics for?
Linear regression diagnostics highlight observations and residual patterns worth reviewing after fitting a simple linear model to X and Y data. The calculator requires X and Y to stay in the same observation order, since the fit relies on matched pairs of points. Flagging a point as unusual does not automatically mean it should be removed from the analysis. It is worth checking the quality of the data itself first, and whether the chosen model form actually fits the relationship between the variables.
Frequently asked questions
Does a flagged point have to be removed?
See also
A/B test calculator
Compare two proportions in an A/B test. Calculate conversion rates, difference, uplift, z score, p value and c
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.