### 基本信息

- 原书名：Statistics for Business: Decision Making and Analysis
- 原出版社： Addison Wesley

### 内容简介

经济管理学书籍

现在商业竞争日益激烈，有效做出商务决策变得至关重要。《商务统计决策与分析(英文版)》从实际的商业问题出发，详细阐述如何利用数据进行信息决策，并将统计概念与实际问题联系起来，告诉读者如何寻找模式从数据建立统计模型，以及如何提供调查结果。《商务统计决策与分析(英文版)》涵盖了应用统计学在当代商务经济领域中几乎所有的重要应用，并且统计软件(包括Excel、Minitab等)的使用贯穿全书。

《商务统计决策与分析(英文版)》特色

·启发性案例：每章都从一个商业案例开始，提出问题并引出该章内容。

·4M示例：4M(动机、方法、实施、结论)的问题解决策略为学生解决商务问题提供了清晰的思路。每个4M示例都先提出一个商业问题，然后引导学生寻求解决该问题的最佳统计方法，使用统计软件实现，并说明分析结果。

·陷阱：为避免发生常见错误，每章结尾处给出一些有用的提示。

·软件提示：每章都有关于运用Excel(2003和2007)、Minitab和JMP进行计算的提示。

·背后的数学：在多数章节的最后，提供了一些有趣的技术细节，以解释某些重要结论，如对某个基本公式的证明或解释。

·实际的统计案例研究：每部分最后都包括两个深度案例研究，这些案例使用真实数据，涉及股票价格、经理人薪酬、企业债券违约、零售额管理和过程控制等方面。

### 目录

Preface iii

Index of Applications xvii

PART ONEVariation

1Introduction2

1.1What is Statistics?2

1.2Previews4

1.3How to Use This Book92Data13

2.1Data Tables14

2.2Categorical and Numerical Data15

2.3Recoding and Aggregation17

2.4Time Series20

2.5Further Attributes of Data21

Chapter Summary24

3Describing Categorical Data28

3.1Looking at Data29

3.2Charts of Categorical Data31

3.3The Area Principle35

3.4Mode and Median40

Chapter Summary43

### 前言

Solving Business Problems. This approach shapes our examples. We open each chapter by framing a business question that motivates the contents of the chapter. For extra practice, worked-out examples within each chapter follow our 4M (Motivation,Method, Mechanics, Message) problem-solving strat-egy. The motivation sets up the problem and explains

the relevance of the question at hand. We then iden-tify the appropriate statistical method and work through the mechanics of its calculation. Finally, the message answers the question in language suitable for a business presentation or report. Through the 4Ms,well show you how a business context guides the sta-tistical procedure and how the results determine a course of action. Motivation and Message are critical.If you do not convey the relevance of the problem at the start of the problem in the Motivation and express the Message in suitable language at the end of the problem, it won't matter how well you do the statisti-cal analysis. Understand the business first, then use statistics to help formulate your conclusion. Notice that we said "heelp." A statistical analysis by itself is not the final answer. You must frame that analysis in terms that others in the business will understand and find persuasive.

Our emphasis on the substantive use of statistics in business shapes our view that the ideal reader for this text is someone with an interest in learning how statistical thinking improves the ability of a man-ager to run or contribute to a business. Whether you're an undergraduate with an interest in busi-ness, an MBA looking to improve your skills, or a business owner looking for another way to get ahead of the competition, the key is a desire to learn how statistics can produce better decisions and insights from the growing amount of data generated in mod-em businesses.

We don't assume that readers have mastered the domains of a business education, such as economics,finance, marketing, or accounting. We do assume,though, that you care about how ideas from these ar-eas can improve a business. If you're interested in these applications--and we think you will be--then our examples provide the background you will need in order to appreciate why we want to solve the chal-lenges that we present in each chapter. Readers with more experience will discover that we've simplified the technical details of some applications, such as those in finance or marketing. Even so, we think that the examples offer those with substantive experience a new perspective on problems that may already be familiar. We hope that you will agree that the exam-ples are realistic and get to the heart of quantitative applications of statistics in business.

Technology. The growing power of computer soft-ware has had a dramatic impact on the field of statis-tics, and it's our intention to take advantage of this progress in our textbook. A casual glance at the table of contents of a recent research journal in statistics shows that most of this research relies on computers.You simply cannot do research in modern applied statistics without computing. Data sets have grown in size and complexity, making it impossible to work out the calculations by hand. Rather than dwell on routine calculations, we rely on software (often re-ferred to as a statistics package) to compute the re-sults. That said, we do not treat this software as a black box; we give the formulas and illustrate the cal-culations introduced in each chapter so you will al-ways know what is being done by software. It is essential to appreciate what happens in the calcula-tions, and it is also a crucial part of decision-making:You need to understand how the calculations are done in order to recognize when they are appropriate and when they fail. That does not mean, however,that you need to spend hours doing routine calcula-tions. Your time is precious and there's only so much of it to go around. We think it makes good economic sense to take advantage of the relatively inexpensive

cost of computation in order to give us more time to think harder and more thoroughly about the moti-vating context for an application and successfully present the business message. To help you learn how to use software, each chapter includes hints on using Excel~, Minitab~, and JMP~, for calculations. These hints won't replace the help provided by your soft-ware, but they will point you in the right direction so that you don't spin your wheels figuring out how to get started with an analysis.

Data. Statistical analysis uses data, and we've pro-vided lots of that to give you the opportunity to have some real hands-on experience. As you read through the chapters, you'll discover a variety of data sets that include real estate markets, stocks and bonds,technology, retail sales, human resource manage-ment, and fundamental economics. These data come from a range of sources, and each chapter includes a discussion about where we collected the data used in examples. We hope you'll use our suggestions and find more.

Prerequisite Knowledge. To appreciate the illus-trative calculations and formulas, readers will need

to be familiar with basic algebra. Portions of chap-ters that introduce a statistical method often in-clude some algebra to show where a formula comes from. Usually, we only use basic algebra (up through topics such as exponents and square roots).Several chapters make extensive use of the loga-rithm function. If you're interested in business and economics, this is a function worth getting to know a lot better. The applications we've provided, such as modeling sales or finding the best price, show why the logarithm is so important. Occasionally, we give credit to calculus for solving a problem, but we don't present derivations using calculus. You'll do fine if you are willing to accept that calculus is a branch of more advanced mathematics that pro-vides, among other things, the ability to derive for-mulas that have special properties. If you do know calculus, you'll be able to see where these expres-sions come from.

Coverage & Organization

We have organized the chapters of this book into

four parts:

1. Variation

2. Probability

3. Inference

4. Regression Models

Part 1. Readers who have worked with data will be able to skip portions of Part 1 or move through it fairly quickly. These chapters introduce summary statistics such as the mean and important graphical summaries such as bar charts, histograms, and scat-terplots. Even if you are familiar with these methods,we encourage you to skim the examples in these chapters. These examples introduce important termi-nology that appears in subsequent chapters. A quick review will introduce the notation that we use (which is rather standard) as well as give you a chance to look at some interesting data. If you do skip past these, take advantage of the index of Key Terms in each chapter to find definitions and examples.

Part 2. The contents of Part 2 also will be familiar to some readers; many courses in mathematics now include topics from probability. Even if you have seen basic probability, you might benefit from re-viewing how methods, such as Bayes rule, can be used to improve business processes (Chapter 8). If you plan to skip or move briskly through the rest of the chapters in Part 2, be sure that you're familiar with the concept of a random variable (Chapter 9).Statistical models use random variables to present an idealized description of the data in applications.Unless you're familiar with random variables, you won't appreciate the important assumptions that come with their use in practice. Chapter 11 de-scribes special random variables used to model counts, and Chapter 12 defines normal random vari-ables that appear so often in statistical models.

Part 3. This part presents the foundations for statis-tical inference, the process of inferring properties of an entire population from those of a subset known as a sample. Even if you are not interested in quality con-trol, we encourage you to read Chapter 14. Chapter 14 uses quality control to introduce a fundamental con-cept of inferential statistics, the sampling distribution and standard error. You can get by in statistics with a basic understanding of the concept of a sampling dis-tribution, but the more you know about sampling dis-tributions, the better. If you are in a hurry, you can also skip Chapter 17; it offers methods for situations in which the standard procedures don't work. How will you know that you need these? Each inferential procedure comes with a checklist of conditions that tell you whether your data and situation match up to the various inferential techniques in these chapters.