商务统计

2011-6  

机械工业出版社  

（美）Robert A. Stine,（美）Dean P. Foster  

832  

无  

　　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。

作者：（美国）斯泰恩（Robert A.Stine） （美国）福斯特（Dean P.Foster）斯泰恩，Robert A.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。福斯特，Dean P.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。

preface iii
index of applications xvii
part onevariation
　1introduction
　1.1what is statistics?
　1.2previews
　1.3how to use this book92data
　2.1data tables
　2.2categorical and numerical data
　2.3recoding and aggregation
　2.4time series
　2.5further attributes of data
　chapter summary
　3describing categorical data
　3.1looking at data
　3.2charts of categorical data
　3.3the area principle
　3.4mode and median
　chapter summary
　4describing numerical data
　4.1summaries of numerical variables
　4.2histograms and the distribution of numerical data
　4.3boxplot
　4.4shape of a distribution
　4.5epilog
　chapter summary
　5association between categorical variables
　5.1contingency tables
　5.2lurking variables and simpson’s paradox
　5.3strength of association
　chapter summary
　6association between quantitative variables
　6.1scatterplots
　6.2association in scatterplots
　6.3measuring association
　6.4summarizing association with a line
　6.5spurious correlation
　chapter summary
　statistics in action casefinancial time series
　statistics in action caseexecutive compensation
parttwo probability
　7probability
　7.1from data to probability
　7.2rules for probability
　7.3independent events
　chapter summary
　8conditional probability
　8.1from tables to probabilities
　8.2dependent events
　8.3organizing probabilities
　8.4order in conditional probabilities
　chapter summary
　9random variables
　9.1random variables
　9.2properties of random variables
　9.3properties of expected values
　9.4comparing random variables
　chapter summary
　10association between random variables
　10.1portfolios and random variables
　10.2joint probability distribution
　10.3sums of random variables
　10.4dependence between random variables
　10.5iid random variables
　10.6weighted sums
　chapter summary
　11probability models for counts
　11.1random variables for counts
　11.2binomial model
　11.3properties of binomial random variables
　11.4poisson model
　chapter summary
　12the normal probability model
　12.1normal random variable
　12.2the normal model
　12.3percentiles
　12.4de　partures from normality
　chapter summary
　statistics in action casemanaging financial risk
　statistics in action casemodeling sampling variation
part three inference
　13samples and surveys
　13.1two surprising properties of sampling
　13.2variation
　13.3alternative sampling methods
　13.4checklist for surveys
　chapter summary
　14sampling variation and quality
　14.1sampling distribution of the mean
　14.2control limits
　14.3using a control chart
　14.4control charts for variation
　chapter summary
　15confidence intervals
　15.1ranges for parameters
　15.2confidence interval for the mean
　15.3interpreting confidence intervals
　15.4manipulating confidence intervals
　15.5margin of error
　chapter summary
　16statistical tests
　16.1concepts of statistical tests
　16.2testing the proportion
　16.3testing the mean
　16.4other properties of tests
　chapter summary
　17alternative approaches to inference
　17.1a confidence interval for the median
　17.2transformations
　17.3prediction intervals
　17.4proportions based on small samples
　chapter summary
　18comparison
　18.1data for comparisons
　18.2two-sample t-test
　18.3confidence interval for the difference
　18.4other comparisons
　chapter summary
　statistics in action caserare events
　statistics in action casetesting association
part four regression models
　19linear patterns
　19.1fitting a line to data
　19.2interpreting the fitted line
　19.3properties of residuals
　19.4explaining variation
　19.5conditions for simple regression
　chapter summary
　20curved patterns
　20.1detecting nonlinear patterns
　20.2transformations
　20.3reciprocal transformation
　20.4logarithm transformation
　chapter summary
　21the simple regression model
　21.1the simple regression model
　21.2conditions for the simple regression model
　21.3inference in regression
　21.4prediction intervals
　chapter summary
　22regression diagnostics
　22.1problem 1:changing variation
　22.2problem 2: leveraged outliers
　22.3problem 3:dependent errors and time series
　chapter summary
　23multiple regression
　23.1the multiple regression model
　23.2interpreting multiple regression
　23.3checking conditions
　23.4inference in multiple regression
　23.5steps in fitting a multiple regression
　chapter summary
　24building regression models
　24.1identifying explanatory variables
　24.2collinearity
　24.3removing explanatory variables
　chapter summary
　25categorical explanatory variables
　25.1two-sample comparisons
　25.2analysis of covariance
　25.3checking conditions
　25.4interactions and inference
　25.5regression with several groups
　chapter summary
　26analysis of variance
　26.1comparing several groups
　26.2inference in anova regression models
　26.3multiple comparisons
　26.4groups of different size
　chapter summary
　27time series
　27.1decomposing a time series
　27.2regression models
　27.3checking the model
　chapter summary
statistics in action caseanalyzing experiments
statistics in action caseautomated modeling
appendix: tables
answersa-
photo acknowledgmentsc-
indexi-

版权页：插图：Suddenly, the initial pricing question branches into several questions, andthe answers depend on whom you ask. There's variation among customers;customers react differently. One customer might be willing to pay $300whereas another would pay only $200. Once you recognize these differencesamong customers, how are you going to set one price？ Statistics shows howto use your data——what you know about your product and your customers——to set a price that will attract business and earn a profit. Here's another interesting question: Why does a shopper choose a particu-lar box of cereal？ Modern grocers have become information-rich retailers,tracking every item purchased by each patron. That's why they give out per-sonalized shopping cards; they're paying customers with discounts in returnfor tracking purchases. Customers keep retailers off balance because theydon't buy the same things every time they shop. Did the customer buy that boxof cereal because it was conveniently positioned at the end of an aisle,because he or she had a discount coupon, or simply because a six-year-old justsaw a commercial while watching Sponge Bob？ Again, variation makes thequestion hard to answer. If they find that coupons improve sales, store man-agers might decide to place more advertising in the local newspaper.Patterns and HodelsStatistics helps you answer questions by providing methods designed to han-dle variation. These methods filter out the clutter by revealing patterns. Apattern in data is a systematic, predictable feature. If customers who receivecoupons buy more cereal than customers without coupons, there's a pattern. Patterns form one part of a statistical model. A statistical model describesthe variation in data as the combination of a pattern plus a background ofremaining, unexplained variation. The pattern in a statistical model describesthe variation that we claim to understand. The pattern tells us what we cananticipate in new data and thus goes beyond describing the data we observe.Often, an equation can summarize the pattern in a precise mathematicalform. Background variation represents variation due to factors we cannot ex-plain because we lack enough information to do so. For instance, retail salesincrease during holiday seasons. Retailers recognize this pattern and prepareby increasing inventories and hiring extra employees. It's impossible, though,for retailers to know exactly which items customers will want and how muchthey ~ spend. The pattern does not explain everything. Good statistical models simplify reality to help us answer questions. Indeed,the word mode/once meant the blueprints, the plans, for a building. Plans answersome questions about the building. How large is the building？ Where are thebathrooms？ The model isn't the building, but we can learn a lot from the model.A model of an airplane in a wind tunnel provides insights about flight eventhough it doesn't mimic every detail of flight. Models of data provide answers toquestions even though those answers may not be entirely right. A famous statisti-cian, George Box, once said, "All models are wrong, but some are useful."

《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）、Mi rlitab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。

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作者简介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。内容简介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。 阅读更多 ›

有兴趣的可以看一看，是全英的教材。没有翻译的

这是一本商务统计方面很好的书，前些天在海图买的，花了92，整整贵了10元啊！不过，这本书确实很好，我今天看了第一章，通俗易懂，是初学者学习商务统计的明智之选！