MCPCalc

Regression Calculator

Fit linear, polynomial, exponential, or logarithmic regression models to x-y data with prediction and residual output.

Scratchpad (not saved)

What This Calculator Does

This calculator fits several common regression models to pasted x-y data pairs and reports the resulting coefficients, fit quality, residual samples, and a forward prediction.

It combines x,y Data Pairs, Model Type, Polynomial Degree, Predict at x to estimate Equation, Coefficients, R Squared.

Formula & Method

Common models include linear y=a+bx, polynomial y=sum_{i=0}^n a_i x^i, exponential y=a e^{bx}, and logarithmic y=a+bln(x). Fit quality is summarized with R^2=1- rac{SS_{res}}{SS_{tot}}.

Notation used in the formulas: R = Equation; x_{1} = x,y Data Pairs; x_{2} = Model Type; x_{3} = Polynomial Degree; x_{4} = Predict at x.

Method summary: inputs are normalized to consistent units, core equations are evaluated, then secondary values are derived and rounded for display.

Use it when you need a quick best-fit model for a small dataset and want the coefficients and residuals without opening a spreadsheet or notebook.

Inputs Used

  • x,y Data Pairs: Used directly in the calculation.
  • Model Type: Used directly in the calculation.
  • Polynomial Degree: Used directly in the calculation.
  • Predict at x: Used directly in the calculation.

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