Skimming the sections on linear models, I was surprised not to see a discussion of model fit, beyond just plotting predicted values against observed ones. Basic predictive models like linear and logistic regression are simple to construct mechanically. It's a substantially more involved task to quantify their degree of fit and, better yet, prove that the methods are optimal and unbiased (in a statistical sense).