With the exception of three chapters, several sections, and a few shorter passages, the prerequisite for reading the book is a course in basic applied statistics that covers the elements of statistical data analysis and inference. This approach begins closer to the data than the traditional starting point of linear least-squares regression, and should make readers sceptical about glib assumptions of normality, constant variance, and so on. More difficult chapters and sections are marked with asterisks. These parts of the text can be omitted without loss of continuity, but they provide greater understanding and depth, along with coverage of some topics that depend upon more extensive mathematical or statistical background. Appendices to the text provide additional background material.
All chapters end with summaries, and most include recommendations for additional reading.
Summary points are also presented in boxes interspersed with the text. This perspective provides the foundation for the data-analytic focus of the text. Chapter 2 introduces the notion of regression analysis as tracing the conditional distribution of a 'dependent' variable as a function of one or several 'independent' variables. This idea is initially explored 'nonparametrically,' in the absence of a restrictive statistical model for the data. Chapter 3 describes a variety of graphical tools for examining data.