The Kernel Smoother command produces a curve formed by repeatedly finding a locally weighted fit of a simple curve (a line or a quadratic) at sampled points in the domain. The many local fits (128 in total) are combined to produce the smooth curve over the entire domain. This method is also called Loess or Lowess, which was originally an acronym for Locally Weighted Scatterplot Smoother. See Cleveland (1979).
The Local Smoother report contains the RSquare for the kernel smoother fit and the Sum of Squares Error. You can use these values to compare the kernel smoother fit to other fits, or to compare different kernel smoother fits to each other.
Sum of squared distances from each point to the fitted kernel smoother. It is the unexplained error (residual) after fitting the kernel smoother model.


Specify how to weight the data in the neighborhood of each local fit. Loess uses tricube. The weight function determines the influence that each xi and yi has on the fitting of the line. The influence decreases as xi increases in distance from x and finally becomes zero.

