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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。 本书特色  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。 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。

CONTENTS 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 IndexI-1

Robert Stine 于普林斯顿大学获得博士学位。自1983年以来他一直在宾夕法尼亚大学沃顿商学院讲授商务统计学课程。在任教期间，他获得了多项教学奖，包括MBA核心教学奖、David W. Hauck优秀教学奖。他的研究领域包括计算机软件、时间序列分析和预测、与模型识别和选择相关的一般问题等。 Dean Foster 于马里兰大学获得博士学位。他曾在芝加哥大学任教，自1992年以来任教于宾夕法尼亚大学沃顿商学院。他讲授的课程有商务统计初步、概率论与马尔可夫链、统计计算和高等统计学等。其研究领域包括随机过程的统计推断、博弈论、机器学习和变量选择。

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