Cubic Regression Calculator

Your details

Enter pairs as "x,y" separated by semicolons or new lines. Example: 0,1; 2,0; 3,3. At least 4 pairs required.
Number of decimal places shown for the coefficients and statistics.
Cubic equationExcellent fit
y = 0.9973 - 5.0755x + 3.0687x² - 0.3868x³

The fitted polynomial y = a + bx + cx² + dx³

Intercept (a)0.9973
Linear (b)-5.0755
Quadratic (c)3.0687
Cubic (d)-0.3868
R² (coefficient of determination)0.9997
RMSE0.0328
Data points5
0.9997 R²
Poor fit<0.6Moderate fit0.6-0.8Good fit0.8-0.95Excellent fit0.95+

Cubic fit from 5 points: an excellent fit (R² = 0.9997).

  • The model is: y = 0.9973 - 5.0755x + 3.0687x² - 0.3868x³.
  • R² = 0.9997 means the cubic polynomial explains 100.0% of the variance in y.
  • The RMSE is 0.0328, the average absolute prediction error in the same units as your y values.
  • With very few points a near-perfect R² can be misleading because the model has little room to differ from the data.

Next stepUse the fitted equation to predict y values by substituting x. Check the chart: if the curve misses clusters of points, a different polynomial degree may fit better.

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