Kernel Regression Calculator
Perform Non-Parametric Regression to estimate regression functions without assuming a specific form. Visualize your data and density estimates.
Enter the independent variable values separated by commas.
Enter the dependent variable values corresponding to X, separated by commas.
Choose the kernel function for regression.
Adjust the bandwidth to control the smoothness of the regression curve.
Predicted Values:
Density Estimate:
Regression Visualization
About Kernel Regression
Kernel Regression is a non-parametric technique used in statistics to estimate the conditional expectation of a random variable. Unlike parametric regression, it does not assume a specific functional form for the relationship between the independent and dependent variables. Instead, it estimates the regression function by averaging the dependent variable values of the 'nearest neighbors' of each point, weighted by a kernel function. The bandwidth parameter controls the range of these neighbors; a smaller bandwidth makes the estimate more sensitive to local variations, while a larger bandwidth smooths out the estimate. Kernel regression is useful when the underlying relationship between variables is complex or unknown, making it a flexible tool for data analysis and prediction. Common kernel functions include Gaussian, Epanechnikov, and Uniform.
- Kernel Function: Determines the shape of the weight function.
- Bandwidth: Controls the smoothness of the fitted curve.
- Non-Parametric: No assumption about the functional form of the regression.
- Applications: Widely used in various fields like economics, environmental science, and machine learning for flexible regression analysis.