ABSTRACT
Chapter 8 briefly introduced the concepts of linear regression and showed how cross-validation can be used to find a model that provides a good fit to the data. We return to linear regression in this section to help introduce other parametric models for estimating relationships between variables. We first revisit classical linear regression, providing more information on how to analyze and visualize the results of the model. We will also examine more of the capabilities available in MATLAB® for this type of analysis. In Section 12.2, we present spline regression models. Logistic regression is discussed in Section 12.3. Logistic regression is a special case of generalized linear models, which are presented in Section 12.4. We discuss model selection and regularization methods in Section 12.5 and conclude the chapter with a discussion of partial least squares regression in Section 12.6.
