Good P.I. A Practical Guide to Data Analysis: Resampling Methods

Boston: Birkhäuser, 2005. — 266 p. — ISBN: 978-0-8176-4386-7.Intended for class use or self-study, this text aspires to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand.
The resampling methods — permutations, cross-validation, and the bootstrap — are easy to learn and easy to apply. They require no mathematics beyond introductory high-school algebra, yet are applicable in an exceptionally broad range of subject areas.
Introduced in the 1930s, the numerous, albeit straightforward, calculations resampling methods require were beyond the capabilities of the primitive calculators then in use. And 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 will find here a practical and easily-grasped guide to descriptive statistics, estimation, and testing hypotheses.
For advanced students in biology, dentistry, medicine, psychology, sociology, and public health, this text can provide a first course in statistics and quantitative reasoning.
For industrial statisticians, statistical consultants, and research workers, this text provides an introduction and day-to-day guide to the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests.
For mathematics majors, this text will form the first course in statistics to be followed by a second course devoted to distribution theory.
Hopefully, all readers will find my objectives are the same as theirs: To use quantitative methods to characterize, review, report on, test, estimate, and classify findings.Software for Resampling
Estimating Population Parameters
Comparing Two Populations
Choosing the Best Procedure
Experimental Design and Analysis
Categorical Data
Multiple Variables and Multiple Hypotheses
Model Building
Decision Trees