Introduction to Statistics Through Resampling Methods and R, 2nd Edition

Preface

Tell me and I forget. Teach me and I remember. Involve me and I learn.

Benjamin Franklin

Intended for class use or self-study, the second edition of this text aspires as the first to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand.

The methodology proceeds from chapter to chapter from the simple to the complex. The stress is always on concepts rather than computations. Similarly, the R code introduced in the opening chapters is simple and straightforward; R’s complexities, necessary only if one is programming one’s own R functions, are deferred to Chapter 7 and Chapter 8.

The resampling methods—the bootstrap, decision trees, and permutation tests—are easy to learn and easy to apply. They do not require mathematics beyond introductory high school algebra, yet are applicable to an exceptionally broad range of subject areas.

Although introduced in the 1930s, the numerous, albeit straightforward calculations that resampling methods require were beyond the capabilities of the primitive calculators then in use. They were soon displaced by less powerful, less accurate approximations that made use of tables. Today, with a powerful computer on every desktop, resampling methods have resumed their dominant role and table lookup is an anachronism.

Physicians and physicians in training, nurses and nursing students, business persons, business majors, research workers, and students in the biological and social sciences ...