Interactive Spline Regression Calculator
Unleash the power of spline regression to model complex relationships in your data. Enter your data points, adjust the knots and degree, and visualize the fitted curve instantly.
Input Data
Enter your independent (X) and dependent (Y) variable data points. You can input comma-separated values or a JSON array.
Parameters
Adjust the spline parameters to control the flexibility of the fitted curve.
Number of internal points controlling spline flexibility.
Degree of polynomial segments (1 for linear, 2 for quadratic, etc.). Max 5.
Spline Coefficients
Predicted Values
Goodness of Fit Metrics
Spline Regression Visualization
About Spline Regression
Spline regression is a non-parametric regression technique that models the relationship between independent and dependent variables using piecewise polynomial functions called splines. Instead of fitting a single polynomial over the entire data range, spline regression divides the data into segments and fits separate polynomials to each segment. The points where segments connect are called knots. Splines are particularly useful for fitting complex, non-linear relationships that cannot be adequately captured by simple linear or polynomial models. They offer flexibility and smoothness, making them a powerful tool in data analysis and curve fitting.
- Useful for modeling non-linear relationships.
- Provides a smooth and flexible curve fit.
- Controlled by parameters like knots and degree.
- Widely used in statistics, data science, and engineering.