LLOYD S. NELSON博士 Journal of Quality Technology書評

2003

去 Journal of Quality Technology看看 即使只能看些大眾的東西 一定有點收穫

我非會員 所以只能讀

Journal of Quality Technology

Volume 35 • Issue 3 • July 2003

這本季刊是美國品質學會和統計學會共同創設的 從1969年以來 編輯作風一致(其創始編輯(FOUNDING EDITOR 1969-70) 是LLOYD S. NELSON博士 熟悉戴明博士著作的人 對於他可能不會陌生 因為多次感謝他

事實上 台灣品管界的人 很多人更早”利用他”而不自知 因為在80年代初期 劉振老師就將LLOYD S. NELSON博士發表在JQT的SHEWHART管制圖的判定準則等 作成墊版 由台灣的品管學會販售推廣

LLOYD S. NELSON博士一直掛Statistical Consultant (這也是Deming博士唯一的頭銜) JQT除了每期有一主題論文集(時而有專家的評論討論) 另外有三專欄(departments) 分別探討電腦工具如應用程式集(computer programs) 品質技術輔助小道具和知識(technical aids) 書評 (books review) Nelson博士一直是後兩專欄的重要作家和貢獻者

現在仍然是JQT的

1969-70

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Joint Optimization of Mean and Standard Deviation Using Response Surface Methods

ONUR KÖKSOY and NECIP DOGANAKSOY

～～～～～～～～～～～～～～～～～～～～～～～～～～～～～～～～

1969-70

是 應用創的

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Editorial Board

DEPARTMENT EDITORS

JAMES R. SIMPSON

Florida State University

Florida A&M University LLOYD S. NELSON

Computer Programs

Technical Aids

Book Reviews

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Lloyd S. Nelson, Statistical Consultant, Londonderry, NH 03053-3647.

Teaching Statistics, Resources for Undergraduate Instructors edited by Thomas L. Moore. The Mathematical Association of America and The American Statistical Association. ix + 222 pp. $31.95.

THE general problem addressed by this book is how best to teach statistics to beginning students. The authors make the case that the answer is simply data, data, data. That is to say, let the concepts come in through the doorway of data. The book is divided into six sections as follows.

Section 1. Hortatory Imperatives; Section 2. Teaching with Data; Section 3. Established Projects in Active Learning; Section 4. Textbooks; Section 5. Technology; and Section 6. Assessment.

In the first section, the authors make the case for "more data, less lecturing." This sets the tone for the entire book. I believe that anyone who has taught this subject can not have failed to observe that students’ interest is highest when it is focused on data that they themselves have gathered. The purpose of this book is to emphasize this point and to illustrate how to take advantage of it.

There are numerous examples of student projects that are described in detail together with the analyses. This could very well turn out to be the most useful part of the book. My experience is that students very frequently have great difficulty in coming up with good projects, i.e., projects that will have a significant learning component. This book can be a tremendous help in this regard.

A good analogy for this type of teaching and learning can be found in a sport, say tennis. No matter how perfectly the actions of using the racquet are described (including which muscles do what), no description can take the place of a few practice sessions. For those who would like to enjoy optimum popularity with their statistics course, this is the text for them. Oral presentations of projects by the students will give them useful experience. Students will both enjoy the course and learn important working principles that they should and will take with them: and all this from such a modestly priced book!

Lloyd S. Nelson, Statistical Consultant, Londonderry, NH 03053-3647.

Statistical Process Control, The Deming Paradigm and Beyond 2nd edition by James R. Thompson and Jacek Koronacki. Chapman & Hall/ CRC, Boca Raton, FL. 2002. xxii + 431 pp. $89.95.

USING Deming’s ideas, the authors concentrate on illustrating and explaining his philosophy by means of numerical examples taken from real case histories. There are long sections that deal with various historical aspects of quality history that should be of great interest to those in management. Contrariwise, there are sections that are tutorials involving some quite advanced mathematical ideas (for example, "Appendix A: A Brief Introduction to Linear Algebra" and "Appendix B: A Brief Introduction to Stochastics"). I doubt if high-level managers would have any interest in these subjects.

For the engineer deeply involved in statistical process control, this book should be of great interest and considerable help. Many examples of control charts are used and discussed. The authors appear to have a favorite expression for designating an out-ofcontrol point; they repeatedly refer to it as a "Poisson glitch."

It was disappointing to see Evolutionary Operations (EVOP) devalued! The authors state that "Production sta.s are entitled to expect that most production time will be spent in an ‘in control’ situation. To expect production personnel to function almost continuously ‘on the edge’ is not particularly reasonable or desirable." The implication that EVOP causes a process to go out-of-control is misleading. This excellent procedure does not deserve such criticism. It is true that it is not used as much as it should be. But I believe that this is caused by a lack of understanding rather than a desire not to do something that would seem to reduce productivity. On the other hand, the Nelder-Mead Simplex algorithm is exemplified very nicely and with considerable attention to detail.

Various probability distributions, both continuous and discrete, are discussed in some detail. Other subjects treated are: bootstrapping, laws of large numbers, moment-generating functions, central limit theorem, conditional density functions, random vectors, quadratic forms of normal vectors, Poisson process, and Bayesian statistics. Tables of the common statistical distributions are provided. Following the Indian philosophy of never producing a perfect piece of work (to avoid angering the gods), the authors have misspelled Student’s name; it is Gosset, not Gossett.

Reviewer: Lloyd S. Nelson, Statistical Consultant, Londonderry, NH 03053-3647.

Statistical Process Control for Health Care by M. K. Hart and R. F. Hart. Duxbury, Pacific Grove, CA. 2002, ix + 343 pp. $77.95.

THE stated objectives for the reader of this book are: (1) to understand the theory of statistical process control; (2) to analyze the data on the computer; and (3) to apply these techniques to real data. The inside back cover states that data sets are available on the Internet at www.duxbury.com/datasets. htm. There are numerous small data sets given in the text to illustrate procedures. However, readers are enjoined to carry out their computations on a computer. Examples are given on how to carry out analyses using two computer packages (Statit and Minitab).

This is a very nicely designed book that would be suitable as a text for a beginning class on control charts. It is also su.ciently self-contained to be well suited for self-teaching. I was pleased to notice that the authors resisted the temptation to explain why the denominator of the expression for the standard deviation is n-1 rather than n. When discussing the arithmetic mean, the old-fashioned descriptive term central tendency is used. It would have been helpful if the modern term (meaning the same thing) location had been introduced.

Excellent examples of real-life data are used. The authors do not just describe their examples; they take the reader through them. Although the examples deal with hospital problems, anyone with the desire to learn about quality control could appreciate the basics that are introduced.

I have one criticism that I think is very important. The authors, in their discussion of control charts, continually refer to the significance (the Type I error probability) associated with points being out of control. Furthermore, they introduce a parallelism between a point being "out of control" on a Shewhart control chart and a "significant" result arising from the application of the analysis of means. The difficulty is that the analysis of means produces a significance test. The control chart is not a significance test in strict statistical parlance. This has been discussed in some detail by Nelson (1999) and Woodall (2000).

A second, much less important, criticism concerns the statement about halfway down page 3; namely, that common-cause variation is due only to random chance. That this is not so is discussed by Tukey and quoted in Nelson (1999). The point is that what is left after removing the special causes is not random but consists of causes of variation that are too small to be of economic interest. Despite these criticisms, however, I would be happy to recommend this book to any beginner with an urge to learn the wonders of control charting.

Reference

Nelson, L. S. (1999). "Notes on the Shewhart Control Chart". Journal of Quality Technology 31, pp. 124–126.

Woodall, W. H. (2000). "Controversies and Contradictions in Statistical Process Control". Journal of Quality Technology 32, pp. 344–378.

Lloyd S. Nelson, Statistical Consultant, Londonderry, NH 03053-3647.