Distribution-free resampling methods—permutation tests decision trees and the bootstrap—are used today in virtually every research area. A Practitioner’s Guide to Resampling for Data Analysis Data Mining and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size data permutations to test hypotheses and the readily-interpreted decision tree to replace arcane regression methods. Highlights Each chapter contains dozens of thought provoking questions along with applicable R and Stata codeMethods are illustrated with examples from agriculture audits bird migration clinical trials epidemiology image processing immunology medicine microarrays and gene selectionLists of commercially available software for the bootstrap decision trees and permutation tests are incorporated in the textAccess to APL MATLAB and SC code for many of the routines is provided on the author’s websiteThe text covers estimation two-sample and k-sample univariate and multivariate comparisons of means and variances sample size determination categorical data multiple hypotheses and model buildingStatistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting Agriculture Anthropology Aquatic science Archaeology Astronomy and Atmospheric science to V for Virology and Vocational Guidance and Z for Zoology. Practitioners and research workers and in the biomedical engineering and social sciences as well as advanced students in biology business dentistry medicine psychology public health sociology and statistics will find an easily-grasped guide to estimation testing hypotheses and model building.