The New Statistical Analysis of Data
The New Statistical Analysis of Data
复制标题
新的数据统计分析
DOI：
10.1080/00401706.2000.10486001
复制
发表时间：
2000
期刊：
影响因子：
2.5
通讯作者：
Mark Carpenter
中科院分区：
文献类型：
--
作者：
Mark Carpenter
统计分析简易版是H.詹姆斯哈灵顿性能改进系列由麦格劳-希尔出版。这三位作者在商业和质量方面有着近世纪的经验。他们对统计思维和七种传统质量控制工具进行了高度可读性的解释。目标受众是很少或根本没有质量改进经验的商业人士，他们害怕或不习惯使用数据和数字。这种配置文件不符合典型的从业者在technomerics观众，所以它不太可能，这本书将呼吁他们中的许多人。性能改进系列的其他卷包括统计过程控制、过程工程和可靠性分析。统计分析Simphjied令人钦佩地消除了行话，并将理论保持在绝对最低限度，同时仍然为使用数字进行改进的好处建立了强有力的案例。为了吸引尽可能广泛的读者，并保持讨论简单，作者使用了许多日常经验来支持他们的概念-保龄球得分控制图，烤馅饼设计的实验，并移动一个宽卡车在一个狭窄的桥梁来解释过程能力指数。除了在书的开头出现的一个来自他们与保险公司合作的故事外，作者没有包括他们咨询工作中的商业例子。这是一个非常不幸的遗漏，错过了一个与商业读者建立信誉的理想机会。书中的章节是（I）“测量，一种用于交流的客观语言”;（2）“模式，洞察事物的方式”;（3）“表征，用一个数字代表一组数字”;（4）“运动，看随着时间的推移的趋势”;（5）“判断，何时采取行动，何时离开事物”;（6）“灵活性，您的流程如何满足客户的需求”;（7）“实验，寻找新的见解”;（8）“分层，将数据划分为子集”;（9）“关系，确定因果关系”;（10）“实施，应用您学到的东西”。虽然这本书努力避免依赖统计学家来解释定量方法，但它并没有消除所有的依赖性。在阅读这本书之后，人们不太可能诊断出他/她的组织改进需求并挑选出最好的项目。这本书给人的印象是作为一门课程的教科书，而不是一个独立的操作指南。书中的插图很充足，很容易阅读，它们很好地强化了概念。参考书目不多，只有20个条目。其中7个条目是作者的作品或安永会计师事务所的作品，哈灵顿是该公司的主要负责人。最后一次是1995年。更全面的参考书目会使这本书更好。每一章都以一个数学脑筋急转弯结束，似乎是为了插入一个有趣的元素。不幸的是，这些玩笑并没有与所提出的概念联系在一起，所以它们并没有加强本书的目标。作者成功地提供了正确的，以及简单的，统计过程的改进解释。第117页的底部有一个小问题。作者解释了散点图并写了说明，以便他们可以被解释为允许读者通过手绘添加回归线。最好是明确推荐计算和绘制最小二乘法直线。作者将具体技术的讨论与实践者在追求改进时应该做的一些大局思考相结合，值得称赞。一些例子是成功所需的三件事--对预期结果的清晰描述，对实现目标的最佳方法的理解，以及健康的常识。另一个例子是统计分析应该回答的三个简单问题：我得到了我想要的结果吗？我得到的结果有太多的变化吗？我得到的结果随着时间的推移是否稳定？由于其非正式和非威胁性的方法，简化的数字分析是一个理想的第一步，考虑数字和统计数据，而不是神秘，复杂和恐惧诱导。不幸的是，它没有为读者提供新的技术或现有技术的新应用。在努力做到简单的过程中，它忘记了指出一些质量问题自然是复杂的，需要复杂的策略才能成功。全科医生和读者的技术应该记住这本书的同事谁是新的质量。在阅读完之后，这些同事应该被引导到杂志文章或案例研究书籍，这些文章或案例研究书籍可以提供更深入的关于如何选择和应用统计技术的讨论。
Statistical Analysis Simplijed is the introductory volume in the H. James Harrington Performance Improvement Series published by McGraw-Hill. The three authors have almost a century of business and quality experience between them. They have produced a highly readable explanation of statistical thinking and the seven traditional quality-control tools. The target audience is business people with little or no qualityimprovement experience who are afraid or unaccustomed to using data and numbers. This profile does not match the typical practitioner in the Technomerrics audience, so it is not likely that this book will appeal to many of them. Other volumes in the Performance Improvement Series cover statistical process control, process engineering, and reliability analysis. Statistical Analysis Simphjied admirably eliminates jargon and keeps the theory to the absolute minimum while still building a strong case for the benefits of using numbers for improvement. To appeal to as broad an audience as possible and to keep the discussion simple, the authors use many everyday experiences to support their concepts-bowling scores for control charts, baking pies for designed experiments, and moving a wide truck over a narrow bridge to explain process capability indexes. Except for one story from their work with an insurance company, which appears at the beginning of the book, the authors do not include business examples from their consulting work. This is a most unfortunate omission and passes up an ideal opportunity to build credibility with the business reader. The chapters in the book are (I) “Measure, an Objective Language for Communication”; (2) “Patterns, Insights Into the Way Things Are”: (3) “Characterization, Using One Number to Represent a Group of Numbers”; (4) “Movement, Looking at Trends Over Time”; (5) “Judgment, When to Take Action and When to Leave Things Alone”; (6) “Usefulness, How Well Your Process Meets Your Customer’s Needs”; (7) “Experimentation, Finding New Insights”; (8) “Stratification, Dividing Data Into Subsets”; (9) “Relationships, Identifying Cause and Effect”; and (10) “Implementation, Applying What You Have Learned.” Although the book strives to avoid dependence on a statistician to explain quantitative approaches, it does not remove all dependencies. It is unlikely after reading this book that one could diagnose his/her organizational improvement needs and pick the best projects. The book leaves the impression of being a textbook for a course rather than a stand-alone how-to guide. The illustrations in the book are ample and easily read, and they reinforce the concepts very well. The bibliography is skimpy, consisting of 20 entries. Seven of the entries are the authors’ works or the works of Ernst & Young, the company of which Harrington is a principal. The latest entry is from 1995. A more comprehensive bibliography would improve the book. Each chapter concludes with a mathematical brain teaser that seems intended to interject an element of fun. Unfortunately, the teasers are not tied to the concepts presented, so they do not reinforce the goals of the book. The authors have succeeded in providing correct, as well as simple, explanations of statistical process improvement. One minor point relates to the bottom of page 117. The authors explain scatterplots and write the instructions so that they could be interpreted as permitting the reader to add a regression line by hand drawing. It would be better to explicitly recommend calculating and plotting the least squares line. The authors deserve credit for mixing the discussion of specific techniques with some of the big-picture thinking the practitioner should do when pursuing improvement. Some examples are the three things needed for success-a clear picture of the desired results, an understanding of the best way to achieve them, and a healthy dose of common sense. Another example is the three simple questions statistical analysis should answer: Am I getting the results I want? Is there too much variation in the results I get’? Are the results I get stable over time? As a result of its informal and nonthreatening approach, Sraristical Analysis Simplified is an ideal first step for considering numbers and statistics as something other than mysterious, complex, and fear inducing. Unfortunately, it offers the reader no new techniques or novel applications of existing ones. In striving so hard to be simple, it forgets to point out that some quality problems are naturally complex and require a complex strategy for success. The general practitioner and reader of Technornetrics should keep this book in mind for colleagues who are new to quality. After reading it, these colleagues should be steered to magazine articles or case-study books that can provide more in-depth treatment of how statistical techniques are selected and applied.