# smoothing

## Regression splines tutorial

This tutorial describes the spline basis and smoothing techniques which are based on splines. using Plots pyplot() Here is a simple function and noisy data. The object of this tutorial is to estimate the sinuosoidal curve given the noisy observations. x = linspace(0, 2*pi, 100)[1:end-1] y = sin(4*x) yn = y + randn(size(y)) *.2 plot(x,y) scatter!(x,yn) To do this we will use cubic splines. The spline basis with knots at $\xi_i$ is given by $(x-\xi_i)^3_i$ .