Polynomial Regression Calculator

Polynomial Regression

Polynomial regression fits a polynomial equation to your data points. It's useful when the relationship between variables is non-linear and can't be adequately modeled by a straight line.

How It Works

Polynomial regression extends linear regression by including polynomial terms of the independent variable. The general form of a polynomial regression equation is:

y = β₀ + β₁x + β₂x² + β₃x³ + ... + βₙxⁿ

Where:

• y is the dependent variable
• x is the independent variable
• β₀, β₁, β₂, ..., βₙ are the coefficients
• n is the degree of the polynomial

When to Use Polynomial Regression

Use polynomial regression when:

• The relationship between variables appears curved or non-linear
• A linear model provides a poor fit to your data
• You need to model peaks, valleys, or inflection points in your data

Choosing the Right Degree

Higher degree polynomials can fit the data more closely, but may lead to overfitting. Use the degree slider to find the best balance between:

• Underfitting (too low degree): model doesn't capture important patterns
• Overfitting (too high degree): model captures noise rather than true patterns

How to Use This Calculator

Enter your data below as comma-separated x,y pairs (one pair per line) or upload a CSV file. Adjust the polynomial degree using the slider to find the best fit for your data.