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Quadratic regression calculator

Enter matching predictor and response lists to fit the model and predict at a chosen x value.

Calculator

Predicted response
9.000000
R squared
1.000000
Residual sum of squares
0.000000
Residual degrees of freedom
1.000000
Residual standard deviation
0.000000
Coefficients in original x units
CoefficientEstimate
b00.000000
b10.000000
b21.000000
Fitted observations
xObserved yPredicted yResidual
-1.0000001.0000001.0000000.000000
0.0000000.0000000.000000-0.000000
1.0000001.0000001.0000000.000000
2.0000004.0000004.0000000.000000

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How this calculator works

Fit a parabola to paired observations. The least-squares solution minimises squared vertical residuals and includes an intercept. At least three distinct x values identify the coefficients; more than three pairs are needed to estimate residual variance. A centred and scaled QR fit reduces round-off from large predictor offsets.

Formula

ŷ = b0 + b1x + b2x²; choose coefficients to minimise Σ(y - ŷ)².

Worked example

Points (-1,1), (0,0), (1,1), (2,4) give b0 = 0, b1 = 0, b2 = 1. At x = 3 the prediction is 9.

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Frequently asked questions

What controls the curvature?

The coefficient b2 is positive for an upward-opening parabola and negative for a downward-opening one.

Do three points prove a quadratic relationship?

Three distinct x values determine a quadratic exactly. More observations and a scientific explanation are needed to assess the model.

What if b2 is nearly zero?

The fitted quadratic is close to a straight line over the data range.

Sources

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