Polynomial Regression Calculator

Uncover the relationships in your data by fitting a polynomial curve. Visualize your data and understand the trend.

Independent Variable (X)

Enter comma-separated values for the independent variable.

Dependent Variable (Y)

Enter comma-separated values for the dependent variable.

Polynomial Degree

Choose the degree of the polynomial to fit (e.g., 1 for linear, 2 for quadratic).

Regression Results

Coefficients:

Predicted Values:

R-squared Value:

Regression Visualization

About Polynomial Regression

Polynomial Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables as an nth-degree polynomial. It's used when the relationship between variables is curvilinear. For example, in a quadratic regression (degree 2), the model equation is \( y = b_0 + b_1x + b_2x^2 \), where \( y \) is the dependent variable, \( x \) is the independent variable, and \( b_0, b_1, b_2 \) are the regression coefficients. This tool helps you find these coefficients, predict values, and understand how well the polynomial fits your data using the R-squared value, which ranges from 0 to 1, with higher values indicating a better fit. Use this calculator to analyze trends, fit curves to data points, and explore non-linear relationships in your datasets.

Enter your X and Y values as comma-separated lists.
Choose the degree of the polynomial. Degree 1 is linear, 2 is quadratic, and so on.
Click 'Calculate Regression' to see the coefficients, predicted values, R-squared, and interactive chart.
Use 'Reset' to clear inputs and start a new calculation.
'Copy Results' to clipboard for easy sharing or documentation.