An unsparing, incisive, yet ultimately hopeful look at how we can shed the American obsession with self-reliance that has made us less healthy, less secure, and less fulfilled The promise that you can “pull yourself up by your bootstraps” is central to the story of the American Dream. It’s the belief that if you work hard and rely on your own resources, you will eventually succeed. However, time and again we have seen how this foundational myth, with its emphasis on individual determination, brittle self-sufficiency, and personal accomplishment, does not help us. Instead, as income inequality rises around us, we are left with shame and self-blame for our condition. Acclaimed journalist Alissa Quart argues that at the heart of our suffering is a do-it-yourself ethos, the misplaced belief in our own independence and the conviction that we must rely on ourselves alone. Looking at a range of delusions and half solutions—from “grit” to the false Horatio Alger story to the rise of GoFundMe—Quart reveals how we have been steered away from robust social programs that would address the root causes of our problems. Meanwhile, the responsibility for survival has been shifted onto the backs of ordinary people, burdening generations with debt instead of providing the social safety net we so desperately need. Insightful, sharply argued, and characterized by Quart’s lively writing and deep reporting, and for fans of Evicted and Nickel and Dimed, Bootstrapped is a powerful examination of what ails us at a societal level and a plan for how we can free ourselves from these self-defeating narratives.

This tells the story of Douglas Engelbart's revolutionary vision, reaching beyond conventional histories of Silicon Valley to probe the ideology that shaped some of the basic ingredients of contemporary life.

Disk contains the library functions and documentation for use with Splus for Windows.

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

This monograph addresses two quite different topics, each being able to shed light on the other. Firstly, it lays the foundation for a particular view of the bootstrap. Secondly, it gives an account of Edgeworth expansion. The first two chapters deal with the bootstrap and Edgeworth expansion respectively, while chapters 3 and 4 bring these two themes together, using Edgeworth expansion to explore and develop the properties of the bootstrap. The book is aimed at graduate level for those with some exposure to the methods of theoretical statistics. However, technical details are delayed until the last chapter such that mathematically able readers without knowledge of the rigorous theory of probability will have no trouble understanding most of the book.

"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.