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We may learn from our mistakes, but Deborah Mayo argues that, where experimental knowledge is concerned, we haven't begun to learn enough. Error and the Growth of Experimental Knowledge launches a vigorous critique of the subjective Bayesian view of statistical inference, and proposes Mayo's own error-statistical approach as a more robust framework for the epistemology of experiment. Mayo genuinely addresses the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality.

Mayo has long argued for an account of learning from error that goes far beyond detecting logical inconsistencies. In this book, she presents her complete program for how we learn about the world by being "shrewd inquisitors of error, white gloves off." Her tough, practical approach will be important to philosophers, historians, and sociologists of science, and will be welcomed by researchers in the physical, biological, and social sciences whose work depends upon statistical analysis.

Long before the age of "Big Data" or the rise of today's "self-quantifiers," American capitalism embraced "risk"--and proceeded to number our days. Life insurers led the way, developing numerical practices for measuring individuals and groups, predicting their fates, and intervening in their futures. Emanating from the gilded boardrooms of Lower Manhattan and making their way into drawing rooms and tenement apartments across the nation, these practices soon came to change the futures they purported to divine.

How Our Days Became Numbered tells a story of corporate culture remaking American culture--a story of intellectuals and professionals in and around insurance companies who reimagined Americans' lives through numbers and taught ordinary Americans to do the same. Making individuals statistical did not happen easily. Legislative battles raged over the propriety of discriminating by race or of smoothing away the effects of capitalism's fluctuations on individuals. Meanwhile, debates within companies set doctors against actuaries and agents, resulting in elaborate, secretive systems of surveillance and calculation.

Dan Bouk reveals how, in a little over half a century, insurers laid the groundwork for the much-quantified, risk-infused world that we live in today. To understand how the financial world shapes modern bodies, how risk assessments can perpetuate inequalities of race or sex, and how the quantification and claims of risk on each of us continue to grow, we must take seriously the history of those who view our lives as a series of probabilities to be managed.

In this volume, Ekeland and Turnbull are mainly concerned with existence theory. They seek to determine whether, when given an optimization problem consisting of minimizing a functional over some feasible set, an optimal solution—a minimizer—may be found.

This thoroughly revised third edition of Harry H. Harman's authoritative text incorporates the many new advances made in computer science and technology over the last ten years. The author gives full coverage to both theoretical and applied aspects of factor analysis from its foundations through the most advanced techniques. This highly readable text will be welcomed by researchers and students working in psychology, statistics, economics, and related disciplines.

Prediction and Regulation by Linear Least-Square Methods was first published in 1963. This revised second edition was issued in 1983. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions.During the past two decades, statistical theories of prediction and control have assumed an increasing importance in all fields of scientific research. To understand a phenomenon is to be able to predict it and to influence it in predictable ways. First published in 1963 and long out of print, Prediction and Regulation by Linear Least-Square Methods offers important tools for constructing models of dynamic phenomena. This elegantly written book has been a basic reference for researchers in many applied sciences who seek practical information about the representation and manipulation of stationary stochastic processes. Peter Whittle’s text has a devoted group of readers and users, especially among economists. This edition contains the unchanged text of the original and adds new works by the author and a foreword by economist Thomas J. Sargent.

Ivar Ekeland extends his consideration of the catastrophe theory of the universe begun in his widely acclaimed Mathematics and the Unexpected, by drawing on rich literary sources, particularly the Norse saga of Saint Olaf, and such current topics as chaos theory, information theory, and particle physics.

"Ivar Ekeland gained a large and enthusiastic following with Mathematics and the Unexpected, a brilliant and charming exposition of fundamental new discoveries in the theory of dynamical systems. The Broken Dice continues the same theme, and in the same elegant, seemingly effortless style, but focuses more closely on the implications of those discoveries for the rest of human culture. What are chance and probability? How has our thinking about them been changed by the discovery of chaos? What are all of these concepts good for? . . . Ah, but, I mustn't give the game away, any more than I should if I were reviewing a detective novel. And this is just as gripping a tale. . . . Beg, borrow, or preferably buy a copy. . . . I guarantee you won't be disappointed."—Ian Stewart, Science

Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions.  Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill.

The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results.

For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses.

This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.

“McCloskey and Ziliak have been pushing this very elementary, very correct, very important argument through several articles over several years and for reasons I cannot fathom it is still resisted. If it takes a book to get it across, I hope this book will do it. It ought to.”

—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics

“With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.”

—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health

The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.

Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).

Long before Moneyball became a sensation or Nate Silver turned the knowledge he’d honed on baseball into electoral gold, John Thorn and Pete Palmer were using statistics to shake the foundations of the game. First published in 1984, The Hidden Game of Baseball ushered in the sabermetric revolution by demonstrating that we were thinking about baseball stats—and thus the game itself—all wrong. Instead of praising sluggers for gaudy RBI totals or pitchers for wins, Thorn and Palmer argued in favor of more subtle measurements that correlated much more closely to the ultimate goal: winning baseball games.
            The new gospel promulgated by Thorn and Palmer opened the door for a flood of new questions, such as how a ballpark’s layout helps or hinders offense or whether a strikeout really is worse than another kind of out. Taking questions like these seriously—and backing up the answers with data—launched a new era, showing fans, journalists, scouts, executives, and even players themselves a new, better way to look at the game.
            This brand-new edition retains the body of the original, with its rich, accessible analysis rooted in a deep love of baseball, while adding a new introduction by the authors tracing the book’s influence over the years. A foreword by ESPN’s lead baseball analyst, Keith Law, details The Hidden Game’s central role in the transformation of baseball coverage and team management and shows how teams continue to reap the benefits of Thorn and Palmer’s insights today. Thirty years after its original publication, The Hidden Game is still bringing the high heat—a true classic of baseball literature.

"[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, Journal of Philosophy

An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified statistically. Mark Taper, Subhash Lele, and an esteemed group of contributors explore the relationships among hypotheses, models, data, and inference on which scientific progress rests in an attempt to develop a new quantitative framework for evidence. Informed by interdisciplinary discussions among scientists, philosophers, and statisticians, they propose a new "evidential" approach, which may be more in keeping with the scientific method. The Nature of Scientific Evidence persuasively argues that all scientists should care more about the fine points of statistical philosophy because therein lies the connection between theory and data.

Though the book uses ecology as an exemplary science, the interdisciplinary evaluation of the use of statistics in empirical research will be of interest to any reader engaged in the quantification and evaluation of data.

In 1939, George Gallup's American Institute of Public Opinion published a pamphlet optimistically titled The New Science of Public Opinion Measurement. At the time, though, survey research was in its infancy, and only now, six decades later, can public opinion measurement be appropriately called a science, based in part on the development of the total survey error approach.

Herbert F. Weisberg's handbook presents a unified method for conducting good survey research centered on the various types of errors that can occur in surveys—from measurement and nonresponse error to coverage and sampling error. Each chapter is built on theoretical elements drawn from specific disciplines, such as social psychology and statistics, and follows through with detailed treatments of the specific types of error and their potential solutions. Throughout, Weisberg is attentive to survey constraints, including time and ethical considerations, as well as controversies within the field and the effects of new technology on the survey process—from Internet surveys to those completed by phone, by mail, and in person. Practitioners and students will find this comprehensive guide particularly useful now that survey research has assumed a primary place in both public and academic circles.

Simply put, Thinking Through Statistics is a primer on how to maintain rigorous data standards in social science work, and one that makes a strong case for revising the way that we try to use statistics to support our theories. But don’t let that daunt you. With clever examples and witty takeaways, John Levi Martin proves himself to be a most affable tour guide through these scholarly waters.

Martin argues that the task of social statistics isn't to estimate parameters, but to reject false theory. He illustrates common pitfalls that can keep researchers from doing just that using a combination of visualizations, re-analyses, and simulations. Thinking Through Statistics gives social science practitioners accessible insight into troves of wisdom that would normally have to be earned through arduous trial and error, and it does so with a lighthearted approach that ensures this field guide is anything but stodgy.
 

The subject of torsion-free modules over an arbitrary integral domain arises naturally as a generalization of torsion-free abelian groups. In this volume, Eben Matlis brings together his research on torsion-free modules that has appeared in a number of mathematical journals. Professor Matlis has reworked many of the proofs so that only an elementary knowledge of homological algebra and commutative ring theory is necessary for an understanding of the theory.

The first eight chapters of the book are a general introduction to the theory of torsion-free modules. This part of the book is suitable for a self-contained basic course on the subject. More specialized problems of finding all integrally closed D-rings are examined in the last seven chapters, where material covered in the first eight chapters is applied.

An integral domain is said to be a D-ring if every torsion-free module of finite rank decomposes into a direct sum of modules of rank 1. After much investigation, Professor Matlis found that an integrally closed domain is a D-ring if, and only if, it is the intersection of at most two maximal valuation rings.

One of the hallmarks of the development of political science as a discipline has been the creation of new methodologies by scholars within the discipline--methodologies that are well-suited to the analysis of political data. Gary King has been a leader in the development of these new approaches to the analysis of political data. In his book, Unifying Political Methodology, King shows how the likelihood theory of inference offers a unified approach to statistical modeling for political research and thus enables us to better analyze the enormous amount of data political scientists have collected over the years. Newly reissued, this book is a landmark in the development of political methodology and continues to challenge scholars and spark controversy.

"Gary King's Unifying Political Methodology is at once an introduction to the likelihood theory of statistical inference and an evangelist's call for us to change our ways of doing political methodology. One need not accept the altar call to benefit enormously from the book, but the intellectual debate over the call for reformation is likely to be the enduring contribution of the work."

--Charles Franklin, American Political Science Review

"King's book is one of the only existing books which deal with political methodology in a clear and consistent framework. The material in it is now and will continue to be essential reading for all serious students and researchers in political methodology." --R. Michael Alvarez, California Institute of Tech-nology

Gary King is Professor of Government, Harvard University. One of the leading thinkers in political methodology, he is the author of A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data and other books and articles.