"It is a book for manufacturing companies that are fighting desperately for survival and that will go to any length to improve their factories and overcome the obstacles to success. One could even call this book a ?bible? for corporate survival."

?Hiroyuki Hirano

Known as the JIT bible in Japan, JIT Implementation Manual ? The Complete Guide to Just-in-Time Manufacturing presents the genius of Hiroyuki Hirano, a top international consultant with vast experience throughout Asia and the West. Encyclopedic in scope, this six-volume practical reference provides unparalleled information on every aspect of JIT? the waste-eliminating, market-oriented production system. This historic, yet timeless classic is just as crucial in today's fast-changing global marketplace as when it was first published in Japan 20 years ago.

Volume 4: Leveling ? Changeover and Quality Assurance provides essential background on the core concept of level production in a JIT, or lean, manufacturing system and the implementation techniques. It also discusses changeover and the rules and procedures for changeover improvement and covers quality assurance in the context of level production, including how to recognize structures that create defects, plan for achieving zero defects, and make use of mistake-proofing devices.

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors all leaders in the statistics community introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

• Four new chapters on nonparametric modeling
• Coverage of weakly informative priors and boundary-avoiding priors
• Updated discussion of cross-validation and predictive information criteria
• Improved convergence monitoring and effective sample size calculations for iterative simulation
• Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
• New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book s web page."

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers in statistics, epidemiology, medicine, economics, education, sociology, political science, and anyone else doing empirical research to evaluate the causal effects of interventions.

Contrasts are statistical procedures for asking focused questions of data. Researchers, teachers of research methods and graduate students will be familiar with the principles and procedures of contrast analysis included here. But they, for the first time, will also be presented with a series of newly developed concepts, measures, and indices that permit a wider and more useful application of contrast analysis. This volume takes on this new approach by introducing a family of correlational effect size estimates. By returning to these correlations throughout the book, the authors demonstrate special adaptations in a variety of contexts from two group comparison to one way analysis of variance contexts, to factorial designs, to repeated measures designs and to the case of multiple contrasts.

Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Contrasts are statistical procedures for asking focused questions of data. Researchers, teachers of research methods and graduate students will be familiar with the principles and procedures of contrast analysis included here. But they, for the first time, will also be presented with a series of newly developed concepts, measures, and indices that permit a wider and more useful application of contrast analysis. This volume takes on this new approach by introducing a family of correlational effect size estimates. By returning to these correlations throughout the book, the authors demonstrate special adaptations in a variety of contexts from two group comparison to one way analysis of variance contexts, to factorial designs, to repeated measures designs and to the case of multiple contrasts.