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I am an acupuncturist who has never had Traeger work, but I recognize key principles from Chinese medicine in what Traeger does and teaches. It is also similar to Chinese medicine in that most of the 'learning' cannot come from theories, it comes through the teacher. It's no wonder that his efforts to teach what he knew in his hands took him to such far away places.
I enjoyed this book so much that I deliberately read only a chapter at a time to savor it.
I recommend it to anyone, health care practitioner or not. I think it should be on the 'must read' list at massage schools. I wish my doctor would read it too. I hope you buy it.
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History, population, urbanization, and economy are transforming forces that molded North Carolina into what it is today. Each of these sections are clearly laid out so that the reader can make a critical analysis of the change and form an assessment of the coming changes that the future may bring.
Especially interesting are the sections that deal with quality of life in North Carolina. Crime, education, health care, water and air quality, cultural arts and outdoor recreation are profiled and supported by scores of maps, charts and diagrams. This is a book I would especially want in my possession if I was considering moving my family and business to this State. Highly Recommended.
Subjects matter includes the natural environment, history, population, urbanization, economy, agriculture, manufacturing, transportation, trade, politics, air quality, water resources, crime, health, education, arts, and recreation. I found these topics to be presented in an effective manner and certainly more enlightening than the statistical record one might imagine.
I also discovered, before I placed my order, that I was able to preview some of the book's illustrations at the UNC Charlotte Cartography Lab web site.
I would recommend this text not only to students, researchers and teachers, but anyone interested in a comprehensive and knowledgeable summary of the diverse state of North Carolina.
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Ingram Olkin was one of my professor's when I was a graduate student at Stanford. I met Milton Sobel through my colleague the late Ram Uppuluri, who I worked with at Oak Ridge.
Sobel was one of the originators of the ranking and selection approach to comparison of three or more populations. His work with Jack Kiefer and Robert Bechhofer was published in a text in the late 1960s. The basic idea was that many problems come up where several populations are being compared. An example would be the comparison of a placebo with two or more competitive drugs. The traditional analysis of variance tests equality of means versus a general alternative that at least one of the means is different from the rest. When the null hypothesis is rejected, the answer is that at least one mean is probably different, but it does not answer the basic questions. Which one or group of means is different and how large is the difference? Contrasts are then used and the method of multiple comparisons is used to identify populations with statistically significantly different means from rest.
The ranking and selection approach is different. It asks given the data on the distributions of these K populations, what is the probability that we can correctly rank them from worst to best? What is the probability that we can choose the best population (perhaps the one with the largest population mean)or at least the best M out of the K populations?
The idea is simple, the methodology is well developed but the approach has to this day not caught on as a basic component of statistical training and is not being applied in practice. This was the state of affairs that motivated Sobel to initiate the project of writing this book. The idea was that if the theory could be presented in an elementary way it might be better appreciated and more often used. Greater exposure of the methods could also stimulate further research.
The book provides the clear exposition. The fact that the techniques have not caught on remains a mystery. Milton Sobel discusses this issue in an interview that was just published in the May 2000 issue of the journal "Statistical Science".
The three authors each provide special talents that make this an excellent book. Olkin is thorough in his research and this is reflected in the completeness of the references (for that time). Gibbons is an excellent writer who must have had a strong influence on the clarity of exposition. Sobel is one of the founding fathers of the methodology who provides the knowledge of the theory and applications.
I will not duplicate what is in Vickie Kearn's review. She gives an accurate description of the book and its value. In my view the authors have successfully demonstrated the value of ranking and selection yet it has not caught on. Partially this is because everyone knows the standard ANOVA approach. This is what they are trained and it has consequently become their natural approach to such problems. It is unfortunate that many well trained statisticians do not even know of the existence of this large body of literature on ranking and selection. Sobel has noted in his experience that many younger statisticians rediscover the ranking and selection ideas. Until it becomes a part of the standard courses this will continue.
Another factor is software. These days procedures get used in practice only if they are included in some standard commercial package. The statisticians that invented ranking and selection did not see to it that it was incorporated in SAS or some other important package.
Another factor may be that the methodology might provide an answer like "the probability of correctly selecting the best population is 0.15" and this may not seem too spectacular an answer to the investigator.
This SIAM Classics edition is an unabridged, corrected republication of the work first published in 1977. It provides a compendium of applied aspects of ordering and selection procedures and includes tables that permit the practitioner to carry out the experiment and draw statistically justified conclusions. These tables are not readily available in other texts. Although more than 1000 papers and several books on the general theory of ranking and selection have been published since this book first appeared, the methodology is presented in a more elementary fashion with numerous examples to help the reader apply it to a specific problem.
There is a dichotomy in modern statistics that distinguishes between analyses done before an experiment is completed and those done afterward. Ranking and selection methods are useful in both of these categories. The authors provide an alternative to the overused "testing the null hypothesis" when what the practitioner really needs is a method of ranking k given populations, selecting the t best populations, or some similar goal. That need and purpose is as important today as when the subject was first developed nearly 50 years ago.
Applied statisticians as well as researchers who use the basic methods of statistical analysis (psychologists, engineers, biologists, management scientists, etc.) will find this book a valuable reference. Readers should be familiar with standard first-year statistics; no knowledge of calculus is necessary.
Jean Dickinson Gibbons is the Thomas D. Russell Professor Emerita of Applied Statistics at the University of Alabama. She has published numerous articles and books on nonparametric statistics, both theoretical and applied. She has been a Fellow of the American Statistical Association (ASA) since 1972 and was elected to their Board of Directors for four different terms. Ingram Olkin is Professor of Statistics and Education at Stanford University in California and is a member of the SIAM Classics in Applied Mathematics editorial board. He has been a Fellow of the ASA since 1962. He was awarded the Samuel L. Wilks Memorial Medal by the ASA in 1991 and the Elizabeth L. Scott Award by the Committee of Presidents of Statistical Societies (COPSS) in 1998. Milton Sobel is Professor Emeritus of Statistics and Applied Probability at the University of California, Santa Barbara. He is a Fellow of both the ASA and the Institute of Mathematical Statistics (IMS) and was elected a member of the International Statistical Institute (ISI). He has authored several books, published well over 100 journal articles and reports, collaborated with many researchers, and served as advisor to numerous Ph.D. students in statistics.
Contents Chapter 1: The Philosophy of Selecting and Ordering Populations; Chapter 2: Selecting the One Best Population for Normal Distributions with Common Known Variance; Chapter 3: Selecting the One Best Population for Other Normal Distribution Models; Chapter 4: Selecting the One Best Population Bionomial (or Bernoulli) Distributions; Chapter 5: Selecting the One Normal Population with the Smallest Variance; Chapter 6: Selecting the One Best Category for the Multinomial Distribution; Chapter 7: Nonparametric Selection Procedures; Chapter 8: Selection Procedures for a Design with Paired Comparisons; Chapter 9: Selecting the Normal Population with the Best Regression Value; Chapter 10: Selecting Normal Populations Better than a Control; Chapter 11: Selecting the t Best Out of k Populations; Chapter 12: Complete Ordering of k Populations; Chapter 13: Subset Selection (or Elimination) Procedures; Chapter 14: Selecting the Best Gamma Population; Chapter 15: Selection Procedures for Multivariate Normal Distributions; Appendix A: Tables for Normal Means Selection Problems; Appendix B: Figures for Normal Means Selection Problems; Appendix C: Table of the Cumulative Standard Normal Distribution F(z); Appendix D: Table of Critical Values for the Chi-Square Distribution; Appendix E: Tables for Binomial Selection Problems; Appendix F: Figures for Binomial Selection Problems; Appendix G: Tables for Normal Variances Selection Problems; Appendix H: Tables for Multinomial Selection Problems; Appendix I: Curtailment Tables for the Multinomial Selection Problem; Appendix J: Tables of the Incomplete Beta Function; Appendix K: Tables for Nonparametric Selection Problems; Appendix L: Tables for Paired-Comparison Selection Problems; Appendix M: Tables for Selecting from k Normal Populations Those Better Than a Control ; Appendix N: Tables for Selecting the t Best Normal Populations; Appendix O: Table of Critical Values of Fisher's F Distribution; Appendix P: Tables for Complete Ordering Problems; Appendix Q: Tables for Subset Selection Problems; Appendix R: Tables for Gamma Distribution Problems; Appendix S: Tables for Multivariate Selection Problems; Appendix T: Excerpt of Table of Random Numbers; Appendix U: Table of Squares and Square Roots; Bibliography; References for Applications; Index for Data and Examples; Name Index; Subject Index.
June, 1999 / xxvi + 569 pages / Softcover / ISBN 0-89871-439-7