Regression Calculator

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

Scratchpad (not saved)

Save to create a persistent, shareable workspace.

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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+b\ln(x)

. Fit quality is summarized with

R^2=1-\frac{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.

Reference Book

Introduction to Mathematical Statistics

Global Edition

Robert V. Hogg, Joseph McKean, and Allen T. Craig · Pearson

A standard reference for probability distributions, estimation, hypothesis testing, regression, and ANOVA.

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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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