Residual plot generator
Paste paired X and Y values to fit a line and inspect errors against fitted values.
Residuals versus fitted values
Random scatter around zero supports a linear model; curvature or widening spread indicates structure that the line misses.
Fit and residuals
| Row | X | Y | Fitted ŷ | Residual | Residual / RMSE |
|---|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||||
How do I use this page?
- Paste matching X and Y values.
- Inspect the zero line and the shape of the residual spread.
- Review rows with large errors in the table.
What this page does not do: The chart diagnoses the fitted line in the supplied sample. It does not by itself establish normality, independence or constant residual variance.
Further reading
Theory — what is a residual plot for?
A residual is the difference between an observed value and the value fitted by a linear model built from paired X and Y data. The residual plot helps spot curvature in the relationship, uneven spread of the errors, and individual outliers worth checking. A good residual plot shows points scattered randomly on both sides of zero, without a visible curve or a systematically growing spread. The residual-to-RMSE ratio makes it easier to compare the size of errors between observations.
Frequently asked questions
What should a good residual plot look like?
Does a large residual mean the observation is wrong?
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.