ABSTRACT
In Chapter 7, we briefly introduced the concepts of linear regression and showed how cross-validation can be used to determine a model that provides a good fit to the data. We return to linear regression in this section to introduce nonparametric regression and smoothing. We first revisit classical linear regression and provide more information on how to analyze and visualize the results of the model. We also examine more of the capabilities available in MATLAB for this type of analysis. In Section 10.2, we present a method for scatterplot smoothing called loess. Kernel methods for nonparametric regression are discussed in Section 10.3, and regression trees are presented in Section 10.4.
