Modern Business Statistics

作者:Anderson, David R./ Sweeney, Dennis J./ Williams, Thomas A.

页数:940

出版时间:2008-6

价格:$ 238.37

isbn号码:9780324598278

具体描述

Gain a strong conceptual understanding of statistics with the third edition of MODERN BUSINESS STATISTICS?s balance of real-world applications and focus on the integrated strengths of Microsoft? Excel? 2007. To ensure your understanding, this best-selling, comprehensive text carefully discusses and clearly develops each statistical technique in a solid application setting. Immediately after each easy-to-follow presentation of a statistical procedure, a subsection discusses how to use Excel? to perform the procedure. This integrated approach emphasizes the applications of Excel? while maintaining a focus on the statistical methodology. Step-by-step instructions and screen captures further clarify the presentation to ensure your understanding. A wealth of timely business examples, proven methods, and application exercises clearly demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. The book?s class-tested problem-scenario approach emphasizes how you can apply statistical methods to today?s practical business situations. New case problems and self-tests throughout this edition allow you to check your personal understanding. Additional learning resources, including CengageNOW? for online homework assistance and a complete support Website, provide everything you need for the Excel? 2007 skills and understanding of business statistics that is simply EXCEL?lent!

作者简介

David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the College's first Executive Program. In addition to teaching introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Professor Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has coauthored ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods.

Dennis J. Sweeney is Professor of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned BS and BA degrees from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served five years as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. He has published more than 30 articles in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded his research, which has been published in MANAGEMENT SCIENCE, OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING, DECISION SCIENCES, and other journals. Professor Sweeney has coauthored ten textbooks in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate program in Information Systems. At RIT he was the first chair of the Decision Sciences Department. Professor Williams is the coauthor of 11 textbooks in the areas of management science, statistics, production and operations management, and mathematics. He has been a consultant for numerous Fortune 500 companies in areas ranging from the use of elementary data analysis to the development of large-scale regression models.

用户评价

这本书的排版和印刷质量简直无可挑剔，阅读体验非常舒适。那种纸张的质感，拿在手里沉甸甸的，一看就知道是精心制作的。我尤其喜欢它在每个章节末尾设置的“批判性思考环节”。这部分内容不是让你去死记硬背知识点，而是抛出一个充满争议性的商业决策，要求读者运用刚刚学到的统计工具去论证或反驳。比如，在讲解回归分析时，它没有停留在模型拟合度上，而是深入探讨了“相关性不等于因果性”在实际商业谈判中的陷阱。我记得有一次我正在为一个项目做数据报告，遇到了数据异方差的问题，正当我头疼该如何处理时，随手翻到了书中的那一节，里面的案例和解决方案简直是教科书级别的演示。它不仅提供了数学上的修正方法，还从商业风险管理的角度解释了为什么必须修正。这种深度融合了数学严谨性和商业直觉的写作风格，是这本书最宝贵的地方。很多统计书读完后，你只能在理论上侃侃而谈，但这本书读完后，你敢于在董事会上用数据支撑你的观点，因为你知道自己的论证是无懈可击的。

这本书的封面设计实在太抓人眼球了，那种简约又不失专业感的配色，让人一看就知道里面装的是干货。我是在一个学术论坛上偶然看到有人推荐的，说它在处理实际商业案例时特别接地气。当我翻开第一章时，那种流畅的叙事方式立刻抓住了我。作者并没有急于抛出复杂的公式，而是先用几个与我们日常工作息息相关的商业场景来引导，比如市场占有率的波动分析，或者新产品定价策略的优化选择。这种“问题先行，方法随后”的编排逻辑，极大地降低了初学者的入门门槛。特别是关于抽样调查那一章，它详细剖析了如何识别和避免常见的采样偏差，并且提供了大量的软件操作指南，清晰到连我这个对统计软件不太熟练的人都能很快上手。读完这部分，我感觉自己仿佛完成了一次实地调研，而不是枯燥地学习理论。它不像很多教科书那样，把统计学束之高阁，而是真正将它变成了一种解决商业难题的强大工具。我个人非常欣赏作者在理论阐述中穿插的那些“专家笔记”，它们往往是一些点到为止的经验之谈，但对理解统计假设背后的商业含义帮助极大。

这本书的配套资源和在线支持系统简直是业界良心。我购买的是平装版，但光盘里附带的案例数据包和软件宏文件已经足够我进行深入练习了。尤其值得称赞的是，作者建立了一个专门的社区论坛，用于解答读者在实践中遇到的具体问题。我记得我刚开始尝试运行书中复杂的蒙特卡洛模拟练习时，遇到了一个关于随机数生成器的环境配置问题，发帖不到半天，就有来自不同国家读者的热心回复，其中甚至有一位看起来像是作者的助教亲自下场指导。这种强烈的学术共同体的感觉，让学习过程充满了活力。它不是一本“写完就扔”的书，而是像一个活的、不断进化的学习平台。在处理那些涉及到大数据的章节时，作者非常体贴地提供了几种不同计算复杂度的解决方案，允许读者根据自己的硬件条件选择最合适的路径。这种对读者体验的细致入微的考量，体现了作者深厚的教学经验和对现代学习环境的深刻理解。

我是一个对数据可视化有极高要求的读者，而这本书在这方面简直是超乎预期的惊喜。它不仅仅是简单地展示了柱状图和饼图，而是深入探讨了如何利用视觉化手段来揭示隐藏在复杂数据背后的商业故事。作者非常擅长用图形语言来解释抽象的统计概念，比如使用三维散点图来直观展示多重共线性的影响，这比纯粹看数学公式要容易理解一百倍。最让我印象深刻的是关于时间序列分析的部分。书中用一个跨越二十年的零售销售数据为例，展示了如何通过分解趋势、季节性和随机波动，预测下一季度的库存需求。更绝的是，它还对比了不同预测模型（ARIMA, 平滑法等）的适用场景和预测误差的可视化对比。这种将复杂的建模过程转化为清晰、可操作的视觉化流程的功 W 方式，让数据分析不再是少数专家的特权，而是可以赋能给更多业务部门的通用语言。我甚至开始尝试用书中教的方法，重构了我部门内部原有的月度业绩报告，效果立竿见影，管理层对报告的接受度和理解度都大幅提升。

如果非要说这本书有什么让我感到“挑战”的地方，那可能就是它对读者基础数学素养的隐含要求。虽然作者努力用商业语言来软化统计概念，但在讲解中心极限定理的推导过程或是最大似然估计法的原理时，对微积分和线性代数的基础知识还是有一定依赖的。对于那些完全没有统计学背景，数学基础又相对薄弱的读者来说，可能需要在阅读这些特定章节时，要格外放慢速度，并结合一些外部的数学复习材料。但这反过来看，也正是这种不妥协于数学深度的态度，保证了这本书的学术高度和专业性。它没有为了追求“人人可读”而牺牲掉对统计学核心精神的阐述。对我而言，这种略带强度的学习过程，反而带来了一种扎实的成就感。读完这本书，我感觉自己不仅仅是学会了如何“使用”统计软件，更重要的是，我开始理解为什么在某些情况下，特定的统计检验是唯一正确的选择。它培养了一种严谨的数据思维，远超出了任何操作层面的技能。

比中文的好懂。。。

比中文的好懂。。。

比中文的好懂。。。

比中文的好懂。。。

比中文的好懂。。。