商务统计 : 决策与分析

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

4Describing Numerical Data52

4.1Summaries of Numerical Variables53

4.2Histograms and the Distribution of Numerical Data57

4.3Boxplot60

4.4Shape of a Distribution62

4.5Epilog66

Chapter Summary69

5Association between Categorical Variables77

5.1Contingency Tables78

5.2Lurking Variables and Simpson’s Paradox85

5.3Strength of Association89

Chapter Summary95

6Association between Quantitative Variables104

6.1Scatterplots105

6.2Association in Scatterplots107

6.3Measuring Association109

6.4Summarizing Association with a Line115

6.5Spurious Correlation118

Chapter Summary123

STATISTICS IN ACTION CASEFinancial time series134

STATISTICS IN ACTION CASEExecutive compensation142

PARTTWO Probability

7Probability150

7.1From Data to Probability151

7.2Rules for Probability156

7.3Independent Events161

Chapter Summary165

8Conditional Probability174

8.1From Tables to Probabilities175

8.2Dependent Events178

8.3Organizing Probabilities182

8.4Order in Conditional Probabilities185

Chapter Summary190

9Random Variables196

9.1Random Variables197

9.2Properties of Random Variables200

9.3Properties of Expected Values205

9.4Comparing Random Variables207

Chapter Summary209

10Association between Random Variables218

10.1Portfolios and Random Variables219

10.2Joint Probability Distribution221

10.3Sums of Random Variables224

10.4Dependence between Random Variables225

10.5IID Random Variables230

10.6Weighted Sums232

Chapter Summary236

11Probability Models for Counts243

11.1Random Variables for Counts244

11.2Binomial Model246

11.3Properties of Binomial Random Variables247

11.4Poisson Model251

Chapter Summary257

12The Normal Probability Model261

12.1Normal Random Variable262

12.2The Normal Model265

12.3Percentiles271

12.4Departures from Normality272

Chapter Summary278

STATISTICS IN ACTION CASEManaging Financial Risk287

STATISTICS IN ACTION CASEModeling Sampling Variation296

PART THREE Inference

13Samples and Surveys304

13.1Two Surprising Properties of Sampling305

13.2Variation310

13.3Alternative Sampling Methods314

13.4Checklist for Surveys317

Chapter Summary321

14Sampling Variation and Quality325

14.1Sampling Distribution of the Mean326

14.2Control Limits331

14.3Using a Control Chart334

14.4Control Charts for Variation337

Chapter Summary343

15Confidence Intervals351

15.1Ranges for Parameters352

15.2Confidence Interval for the Mean357

15.3Interpreting Confidence Intervals360

15.4Manipulating Confidence Intervals362

15.5Margin of Error364

Chapter Summary371

16Statistical Tests378

16.1Concepts of Statistical Tests379

16.2Testing the Proportion384

16.3Testing the Mean388

16.4Other Properties of Tests393

Chapter Summary397

17Alternative Approaches to Inference403

17.1A Confidence Interval for the Median404

17.2Transformations410

17.3Prediction Intervals411

17.4Proportions Based on Small Samples415

Chapter Summary419

18Comparison424

18.1Data for Comparisons425

18.2Two-sample t-test427

18.3Confidence Interval for the Difference432

18.4Other Comparisons435

Chapter Summary444

STATISTICS IN ACTION CASERare Events450

STATISTICS IN ACTION CASETesting Association456

PART FOUR Regression Models

19Linear Patterns464

19.1Fitting a Line to Data465

19.2Interpreting the Fitted Line467

19.3Properties of Residuals472

19.4Explaining Variation474

19.5Conditions for Simple Regression475

Chapter Summary481

20Curved Patterns488

20.1Detecting Nonlinear Patterns489

20.2Transformations491

20.3Reciprocal Transformation492

20.4Logarithm Transformation497

Chapter Summary506

21The Simple Regression Model513

21.1The Simple Regression Model514

21.2Conditions for the Simple Regression Model518

21.3Inference in Regression521

21.4Prediction Intervals529

Chapter Summary537

22Regression Diagnostics545

22.1Problem 1:Changing Variation546

22.2Problem 2: Leveraged Outliers555

22.3Problem 3:Dependent Errors and Time Series559

Chapter Summary566

23Multiple Regression573

23.1The Multiple Regression Model574

23.2Interpreting Multiple Regression575

23.3Checking Conditions581

23.4Inference in Multiple Regression584

23.5Steps in Fitting a Multiple Regression588

Chapter Summary594

24Building Regression Models605

24.1Identifying Explanatory Variables606

24.2Collinearity611

24.3Removing Explanatory Variables616

Chapter Summary627

25Categorical Explanatory Variables635

25.1Two-sample Comparisons636

25.2Analysis of Covariance639

25.3Checking Conditions642

25.4Interactions and Inference644

25.5Regression with Several Groups651

Chapter Summary656

26Analysis of Variance665

26.1Comparing Several Groups666

26.2Inference in Anova Regression Models673

26.3Multiple Comparisons677

26.4Groups of Different Size680

Chapter Summary686

27Time Series694

27.1Decomposing a Time Series695

27.2Regression Models698

27.3Checking the Model708

Chapter Summary719

STATISTICS IN ACTION CASEAnalyzing Experiments728

STATISTICS IN ACTION CASEAutomated Modeling736

Appendix: Tables743

AnswersA-1

Photo AcknowledgmentsC-1

现在商业竞争日益激烈，有效做出商务决策变得至关重要。本书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。书中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）》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。
随书光盘中包括纯文本、Excel、Minitab 14、Minitab 15和SPSS（PASW）格式的数据集文件以及Excel的一个统计学插件DDXL。