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This book presents matrix algebra in a way that is well-suited for those with an interest in statistics or a related discipline. It provides thorough and unified coverage of the fundamental concepts along with the specialized topics encountered in areas of statistics such as linear statistical models and multivariate analysis. It includes a number of very useful results that have only been available from relatively obscure sources. Detailed proofs are provided for all results. The style and level of presentation are designed to make the contents accessible to a broad audience. The book is essentially self-contained, though it is best-suited for a reader who has had some previous exposure to matrices (of the kind that might be acquired in a beginning course on linear or matrix algebra). It includes exercises, it can serve as the primary text for a course on matrices or as a supplementary text in courses on such topics as linear statistical models or multivariate analysis, and it will be a valuable reference..
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SPC for Right-Brain Thinkers is not simply another made-easy book on the subject of statistical process control (SPC). The guiding principle in writing this book was to make SPC accessible to that large group of individuals who would readily characterize themselves as right-brain thinkers. The challenge that right-brained thinkers face in understanding and applying SPC goes beyond the math; it is also a matter of approaching the subject from a different perspective altogether—--through the side door, if you will, where the inner workings of SPC may be seen in action. The book is also intended to serve the information needs of those who either own or work within the job processes wherein SPC is applied. Since right-brain thinkers are often inclined to gravitate to service-oriented jobs, the examples used in this book demonstrate the use of SPC in a service organization: a pseudo law firm called Advocate General. These examples demonstrate the basic principles of SPC in way that can be adapted to any situation. Benefits: This is a book for those who: are inclined to label themselves as right-brain thinkers; are intimidated by math, possibly even the mere mention of something as ominous-sounding as statistical process control; and/or need only a basic understanding of SPC, perhaps from a systems perspective or as a potential user of an SPC tracking system. Contents: Chapter 1 SPC in Perspective Chapter 2 Framework for Applying SPC Chapter 3 SPC in Action Chapter 4 Solving Problems Using SPC Chapter 5 Analyzing the Process Aspects of Your Job Chapter 6 Deciding Where to Focus Your Effort Chapter 7 Bare Bones Statistics Chapter 8 Planning Your Charting & Measurement Strategy Chapter 9 Process Capability and Control Limits Chapter 10 Collecting and Plotting Your Data Chapter 11 Continuous Improvement Concluding Remarks Appendix A Constructing a Pareto Analysis Chart Appendix B Charting Individual Values Glossary References Index.
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This book presents topics in numerical analysis for statisticians It would be suitable as a text for a graduate course in statistical computing. The focus is on principles of numerical analysis intended to equip students to craft their own software and to understand the advantages and disadvantages of different numerical methods..
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This exposition of the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students is unique in presenting its subject with a practical flavor and an emphasis on mainstream statistics. It shows how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many new statistical methods, not available elsewhere. It will be essential reading for all statisticians, statistics students, and related interdisciplinary researchers..
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This book develops methods for two key problems in the analysis of large-scale surveys: dealing with incomplete data and making inferences about sparsely represented subdomains. The presentation is committed to two particular methods, multiple imputation for missing data and multivariate composition for small-area estimation. The methods are presented as developments of established approaches by attending to their deficiencies. Thus the change to more efficient methods can be gradual, sensitive to the management priorities in large research organisations and multidisciplinary teams and to other reasons for inertia. The typical setting of each problem is addressed first, and then the constituency of the applications is widened to reinforce the view that the general method is essential for modern survey analysis. The general tone of the book is not "from theory to practice," but "from current practice to better practice." The third part of the book, a single chapter, presents a method for efficient estimation under model uncertainty. It is inspired by the solution for small-area estimation and is an example of "from good practice to better theory."

A strength of the presentation is chapters of case studies, one for each problem. Whenever possible, turning to examples and illustrations is preferred to the theoretical argument. The book is suitable for graduate students and researchers who are acquainted with the fundamentals of sampling theory and have a good grounding in statistical computing, or in conjunction with an intensive period of learning and establishing one's own a modern computing and graphical environment that would serve the reader for most of the analytical work in the future.

While some analysts might regard data imperfections and deficiencies, such as nonresponse and limited sample size, as someone else's failure that bars effective and valid analysis, this book presents them as respectable analytical and inferential challenges, opportunities to harness the computing power into service of high-quality socially relevant statistics.

Overriding in this approach is the general principle—to do the best, for the consumer of statistical information, that can be done with what is available. The reputation that government statistics is a rigid procedure-based and operation-centred activity, distant from the mainstream of statistical theory and practice, is refuted most resolutely.

After leaving De Montfort University in 2004 where he was a Senior Research Fellow in Statistics, Nick Longford founded the statistical research and consulting company SNTL in Leicester, England. He was awarded the first Campion Fellowship (2000–02) for methodological research in United Kingdom government statistics. He has served as Associate Editor of the Journal of the Royal Statistical Society, Series A, and the Journal of Educational and Behavioral Statistics and as an Editor of the Journal of Multivariate Analysis. He is a member of the Editorial Board of the British Journal of Mathematical and Statistical Psychology. He is the author of two other monographs, Random Coefficient Models (Oxford University Press, 1993) and Models for Uncertainty in Educational Testing (Springer-Verlag, 1995).

From the reviews:

"Ultimately, this book serves as an excellent reference source to guide and improve statistical practice in survey settings exhibiting these problems." Psychometrika

"I am convinced this book will be useful to practitioners...[and a] valuable resource for future research in this field." Jan Kordos in Statistics in Transition, Vol. 7, No. 5, June 2006

"To sum up, I think this is an excellent book and it thoroughly covers methods to deal with incomplete data problems and small-area estimation. It is a useful and suitable book for survey statisticians, as well as for researchers and graduate students interested on sampling designs." Ramon Cleries Soler in Statistics and Operations Research Transactions, Vol. 30, No. 1, January-June 2006

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Students of statistics, operations research, and engineering will be informed of simulation methodology for problems in both mathematical statistics and systems simulation. This discussion presents many of the necessary statistical and graphical techniques. A discussion of statistical methods based on graphical techniques and exploratory data is among the highlights of Simulation Methodology for Statisticians, Operations Analysts, and Engineers. For students who only have a minimal background in statistics and probability theory, the first five chapters provide an introduction to simulation..
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This relatively nontechnical book is the first account of the history of statistics from the Fisher revolution to the computer revolution. It sketches the careers, and highlights of some of the work, of 65 people, most of them statisticians. What gives the book its special character is its emphasis on the author's interaction with these people and the inclusion of many personal anecdotes.

Combined, these portraits provide a panoramic view of statistics during the period in question. Included are discussions of such topics as nonparametrics, Bayesian approaches, and data analysis. The stress is on ideas and technical material is held to a minimum. Thus the book is accessible to anyone with at least an elementary background in statistics.

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