Regression Equation Finder Calculator

Turn scatter points into clear equations in minutes. Switch models and see fit statistics instantly. Download tables, plots, and coefficients for your work now.

Enter points, choose a model, then calculate to get an equation and fit metrics.

Calculator

Pick a model that matches your trend shape.
Higher degrees can overfit small datasets.
Affects display and exports only.
Model-specific rules apply.
Expected columns: x,y (header optional).
Keep at least 2 points for a fit.

Enter (x, y) points

# x y
1
2
3
4
5
6
7
8

Example Data Table

Use this sample to see how the calculator behaves. Click “Load example” to copy it into the input table.

# x y
112.2
224.1
336.3
448.0
5510.2
6612.1

Formula Used

Linear regression (least squares)
b = (nΣxy − ΣxΣy) / (nΣx² − (Σx)²)
a = ȳ − b x̄
ŷ = a + b·x
Polynomial regression
Coefficients solve the normal equations for degree d: A·c = B, where Aᵢⱼ = Σx^(i+j), Bᵢ = Σy·x^i.
Other models
  • Exponential: y = a·e^(b·x) via ln(y).
  • Power: y = a·x^b via ln(x), ln(y).
  • Logarithmic: y = a + b·ln(x).
  • Reciprocal: y = a + b/x.
Fit metrics
SSE = Σ(y − ŷ)²
RMSE = √(SSE / n)
MAE = Σ|y − ŷ| / n
R² = 1 − SSE / SST, where SST = Σ(y − ȳ)²

For transformed models, metrics are computed on the original y values using the back-transformed predictions.

How to Use This Calculator

  1. Enter your (x, y) points in the table, or import a CSV.
  2. Select a regression model that matches your expected trend.
  3. If using Polynomial, choose the degree and avoid overfitting.
  4. Click Submit to compute the best-fit equation.
  5. Review R², RMSE, and the prediction table for model quality.
  6. Download CSV or PDF to store results in your workflow.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.