Includes bibliographical references (p. 68-72).

Traditional Parametric Statistical Inference -- Bootstrap Statistical Inference -- Bootstrapping a Regression Model -- Theoretical Justification -- The Jackknife -- Monte Carlo Evaluation of the Bootstrap -- Statistical Inference Using the Bootstrap -- Bias Estimation -- Bootstrap Confidence Intervals -- Applications of Bootstrap Confidence Intervals -- Confidence Intervals for Statistics With Unknown Sampling Distributions -- The Sample Mean From a Small Sample -- The Difference Between Two Sample Medians -- Inference When Traditional Distributional Assumptions Are Violated -- OLS Regression With a Nonnormal Error Structure -- Future Work -- Limitations of the Bootstrap -- Bootstrapping With Statistical Software Packages.

"This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.