 The application for Bootstrap is wide. I think this book does a good job touching as many areas as possible to give a sufficient starting place for more specialized fields. My favorite specialized topic was a section on complex dependencies. In this section, they cover how to use Bootstrap in time series situations. This book is CDF-centric, relative to PDF-centric. Readers who make it through, the first two chapters will have a good working knowledge of the basic Bootstrap. Readers are tested on comprehension at the end of each section with two types of problems. The first type are analytical problems and the second type are practicals. The practicals give readers a chance to get hands-on experience with the Bootstrap. Chapters following Chapter 2 cover graduate level statistical material. Topics include censoring and semi-parametric likelihood. You may wonder if a specialized book on Bootstrap is right for you. The Bootstrap builds a foundation for thinking correctly about how probability and statistics interact. In some cases, it may be hard to find an analytical solution to problems you may come across in practice. The Bootstrap is a general tool that can solve many problems. Thanks for watching.