How to do Statistics Without the Math | Bootstrap Book Review

Resampling method and the Bootstrap make it easy to do much of statistics without the analytical training many of us go through. It is a powerful tool for any statistician, data scientists, or machine learning engineer.

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The applications 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 the section on Complex Dependences. In this section they cover how to used bootstrap in time series situations.

The book is CDF centric which I have become a little more aligned with lately relative to PDF centric descriptions.

Readers who read just 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. Analytical Problems and Practicals. The practicals give the reader a chance to get hands on experience with the bootstrap.

Chapters following chapter two cover graduate level statistical material. Topic include censoring and semi parametric likelihood.

You may wonder if a specialized book on the 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 analytical solution to problems you may come across in practice. The bootstrap is a general tool that can solve many problems.

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