Polynomial regression calculator
Fit a second- or third-degree curve to paired X and Y observations.
Fit and residuals
| Row | X | Y | Ŷ | Residual |
|---|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | ||||
How do I use this page?
- Paste X and Y pairs in matching order.
- Choose degree 2 or 3.
- Review coefficients, R², RMSE and residuals.
What this page does not do: Extrapolation outside the X range can quickly become unrealistic. The calculator supports one predictor and degree 2 or 3; it does not establish causality.
Further reading
Theory — what is polynomial regression?
Polynomial regression extends the linear model with additional powers of X. A quadratic model has the form ŷ = b₀ + b₁x + b₂x², while a cubic version adds a b₃x³ term. Coefficients are found using least squares, and the fit quality is summarised by R², which shows how much of Y's variability the model explains. Choosing a higher degree increases the risk of overfitting, so the decision should rely on validation data rather than training R² alone.
Frequently asked questions
When should I choose degree 3?
Does a high R² guarantee useful predictions?
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.