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 to 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
A residual is the difference between an observed value and the value fitted by the model. The plot helps reveal curvature, unequal spread and individual observations that deserve review.
Residual/RMSE makes error sizes easier to compare, but it is not a fully studentised residual because it does not adjust for leverage.
Frequently asked questions
What should a good residual plot look like?
Points should scatter on both sides of zero without clear curvature or a systematically widening spread.
Does a large residual mean the observation is wrong?
Not necessarily. It may be a data error, an unusual case or evidence that the model misses important structure.
See also
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.
Adjusted R-squared calculator
Convert R² to adjusted R² using the number of observations and predictors in a regression model.
Beta distribution calculator
Calculate beta density, CDF, upper tail and quantile together with its mean and variance.