商务统计学
2010-6
电子工业出版社
（美）夏普，（美）德维克斯，（美）维尔曼 著
761
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本书是为商科学生而写的，它将回答一个简单的问题：“怎样才能做出更好的决策？”作为企业家和顾问，应该知道为了在今天这样的竞争环境下生存和发展，统计学是至关重要的。作为教育工作者，我们看到了向商科学生讲授统计学的方式与商业决策制定中统计学的使用方式之间的脱节。本书将试图通过介绍统计方法来缩短理论与实践之间的距离。所以对学生来说，统计方法既重要又有趣。根据数据做出一个商业决策有一个故事要讲，统计学在其中所扮演的角色是帮助听清楚这个故事。像其他教材一样，本书将讲授如何计算一个特定的统计量或检验，并且强调定义和公式。但是，与其他教材不同的是，本书也将讲解“为什么”，并坚持在商业决策的背景下给出结果。学生们将会了解到，为了做出更好的商业决策，应该如何进行统计思考、如何有效地表达分析结果并将决策告知他人。在写作本书时，我们知道当今时代的统计学是用技术来实践的。这种见解的结果是：从对方程形式（比计算形式更喜欢直觉形式）的选择中得到的一切东西，都运用到了对真实数据的广泛使用中。但是更重要的是，对技术价值的理解，使本书将重点集中于讲授统计思维而不是计算上。书中几百个例子关注的不是“怎么找出答案”，而是“如何思考答案以及它如何有助于制定出一个更好的决策”。对统计思维的关注将书中的各章联系起来。初级商务统计学课程包含大量的新术语、概念和方法，但是它们有一个核心部分：通过理解数据告诉如何更加了解这个世界，怎样做出更好的决策。从这个角度来看，学生们能够知道从数据中得出推断的许多方式都是相同的核心概念的一些应用。
统计学是一门工具性学科，在众多的学科领域有着广泛的应用。本书将统计学的概念与方法应用于商务领域，从应用层面对统计学的基本方法进行了系统的讲解。全书包括探索和收集数据、理解数据和分布、探索变量间的关系以及为决策建立模型四部分内容，共24章，将方法的讲解与商务领域中的现实案例紧密结合起来，让读者掌握如何利用统计方法解决商务中的实际问题。本书还将统计软件与统计方法的应用结合起来，详细介绍各种统计方法在Excel、Minitab、JMP、SPSS和DataDesk等软件中的操作实现步骤。 本书可作为大学本科生和研究生的教材，也可供从事工商管理和经济分析的人士参考。
作者：（美国）夏普（Norean Radke Sharpe） （美国）德维克斯（Richard D.De Veaux） （美国）维尔曼（Paul F.Velleman）
Part I Exploring and Collecting Data Chapter 1 Statistics and Variation 1.1 So, What Is Statistics？ 1.2 How Will This Book Help？ Chapter 2 Data 9 2.1 What Are Data？ 2.2 Variable Types 2.3 Where, How, and When Mini Case Study Project: Credit Card Bank Chapter 3 Surveys and Sampling 3.1 Three Ideas of Sampling 3.2 A Census—Does It Make Sense？ 3.3 Populations and Parameters 3.4 Simple Random Sample (SRS) 3.5 Other Sample Designs 3.6 Defining the Population 3.7 The Valid Survey Mini Case Study Projects: Market Survey Research The GfK Roper Reports Worldwide Survey Chapter 4 Displaying and Describing Categorical Data 4.1 The Three Rules of Data Analysis 4.2 Frequency Tables 4.3 Charts 4.4 Contingency Tables Mini Case Study Project: KEEN Footwear Chapter 5 Randomness and Probability 85 5.1 Random Phenomena and Probability 5.2 The Nonexistent Law of Averages 5.3 Different Types of Probability 5.4 Probability Rules 5.5 Joint Probability and Contingency Tables 5.6 Conditional Probability 5.7 Constructing Contingency Tables Mini Case Study Project: Market Segmentation 103 Chapter 6 Displaying and Describing Quantitative Data 6.1 Displaying Distributions 6.2 Shape 6.3 Center 6.4 Spread of the Distribution 6.5 Shape, Center, and Spread—A Summary 6.6 Five-Number Summary and Boxplots 6.7 Comparing Groups 6.8 Identifying Outliers 6.9 Standardizing 6.10 Time Series Plots *6.11 Transforming Skewed Data Mini Case Study Projects: Hotel Occupancy Rates 143, Value and Growth Stock Returns 143Part II Understanding Data and Distributions 157 Chapter 7 Scatterplots, Association, and Correlation 159 7.1 Looking at Scatterplots 7.2 Assigning Roles to Variables in Scatterplots 7.3 Understanding Correlation *7.4 Straightening Scatterplots 7.5 Lurking Variables and Causation Mini Case Study Projects: *Fuel Efficiency 181, The U.S. Economy and Home Depot Stock Prices Chapter 8 Linear Regression 193 8.1 The Linear Model 8.2 Correlation and the Line 8.3 Regression to the Mean 8.4 Checking the Model 8.5 Learning More from the Residuals 8.6 Variation in the Model and R2 8.7 Reality Check: Is the Regression Reasonable？ Mini Case Study Projects: Cost of Living 213, Mutual Funds Chapter 9 Sampling Distributions and the Normal Model 223 9.1 Modeling the Distribution of Sample Proportions 9.2 Simulations 9.3 The Normal Distribution 9.4 Practice with Normal Distribution Calculations 9.5 The Sampling Distribution for Proportions 9.6 Assumptions and Conditions 9.7 The Central Limit Theorem—The Fundamental Theorem of Statistics 9.8 The Sampling Distribution of the Mean 9.9 Sample Size—Diminishing Returns 9.10 How Sampling Distribution Models Work Mini Case Study Project: Real Estate Simulation 247 Chapter 10 Confidence Intervals for Proportions 255 10.1 A Confidence Interval 10.2 Margin of Error: Certainty vs. Precision 10.3 Critical Values 10.4 Assumptions and Conditions *10.5 A Confidence Interval for Small Samples 10.6 Choosing the Sample Size Mini Case Study Projects: Investment 272, Forecasting Demand 272 Chapter 11 Testing Hypotheses about Proportions 279 11.1 Hypotheses 11.2 A Trial as a Hypothesis Test 11.3 P-values 11.4 The Reasoning of Hypothesis Testing 11.5 Alternative Hypotheses 11.6 Alpha Levels and Significance 11.7 Critical Values 11.8 Confidence Intervals and Hypothesis Tests 11.9 Two Types of Errors *11.10 Power Mini Case Study Projects: Metal Production 305, Loyalty Program 305 Chapter 12 Confidence Intervals and Hypothesis Tests for Means 313 12.1 The Sampling Distribution for the Mean 12.2 A Confidence Interval for Means 12.3 Assumptions and Conditions 12.4 Cautions About Interpreting Confidence Intervals 12.5 One-Sample t-Test 12.6 Sample Size *12.7 Degrees of Freedom—Why n – 1？ Mini Case Study Projects: Real Estate 333, Donor Profiles 333 Chapter 13 Comparing Two Means 343 13.1 Testing Differences Between Two Means 13.2 The Two-Sample t-Test 13.3 Assumptions and Conditions 13.4 A Confidence Interval for the Difference Between Two Means 13.5 The Pooled t-Test *13.6 Tukey’s Quick Test Mini Case Study Project: Real Estate 364 Chapter 14 Paired Samples and Blocks 375 14.1 Paired Data 14.2 Assumptions and Conditions 14.3 The Paired t-Test 14.4 How the Paired t-Test Works Mini Case Study Projects: A Taste Test (Data Collection and Analysis) 389, Consumer Spending Patterns (Data Analysis) 389 Chapter 15 Inference for Counts: Chi-Square Tests 401 15.1 Goodness-of-Fit Tests 15.2 Interpreting Chi-Square Values 15.3 Examining the Residuals 15.4 The Chi-Square Test of Homogeneity 15.5 Comparing Two Proportions 15.6 Chi-Square Test of Independence Mini Case Study Projects: Health Insurance 424, Loyalty Program 424Part III Exploring Relationships Among Variables 435 Chapter 16 Inference for Regression 437 16.1 The Population and the Sample 16.2 Assumptions and Conditions 16.3 The Standard Error of the Slope 16.4 A Test for the Regression Slope 16.5 A Hypothesis Test for Correlation 16.6 Standard Errors for Predicted Values 16.7 Using Confidence and Prediction Intervals Mini Case Study Projects: Frozen Pizza 461, Global Warming？ 461 Chapter 17 Understanding Residuals 473 17.1 Examining Residuals for Groups 17.2 Extrapolation and Prediction 17.3 Unusual and Extraordinary Observations 17.4 Working with Summary Values 17.5 Autocorrelation 17.6 Linearity 17.7 Transforming (Re-expressing) Data 17.8 The Ladder of Powers Mini Case Study Projects: Gross Domestic Product 497, Energy Sources 498 Chapter 18 Multiple Regression 509 18.1 The Multiple Regression Model 18.2 Interpreting Multiple Regression Coefficients 18.3 Assumptions and Conditions for the Multiple Regression Model 18.4 Testing the Multiple Regression Model 18.5 Adjusted R2, and the F-statistic *18.6 The Logistic Regression Model Mini Case Study Project: Golf Success 536 Chapter 19 Building Multiple Regression Models 547 19.1 Indicator (or Dummy) Variables 19.2 Adjusting for Different Slopes—Interaction Terms 19.3 Multiple Regression Diagnostics 19.4 Building Regression Models 19.5 Collinearity 19.6 Quadratic Terms Mini Case Study Project: Paralyzed Veterans of America 577 Chapter 20 Time Series Analysis 589 20.1 What Is a Time Series？ 20.2 Components of a Time Series 20.3 Smoothing Methods 20.4 Simple Moving Average Methods 20.5 Weighted Moving Averages 20.6 Exponential Smoothing Methods 20.7 Summarizing Forecast Error 20.8 Autoregressive Models 20.9 Random Walks 20.10 Multiple Regression-based Models 20.11 Additive and Multiplicative Models 20.12 Cyclical and Irregular Components 20.13 Forecasting with Regressionbased Models 20.14 Choosing a Time Series Forecasting Method 20.15 Interpreting Time Series Models: The Whole Foods Data Revisited Mini Case Study Projects: Intel Corporation 624, Tiffany & Co. 624Part IV Building Models for Decision Making 637 Chapter 21 Random Variables and Probability Models 639 21.1 Expected Value of a Random Variable 21.2 Standard Deviation of a Random Variable 21.3 Properties of Expected Values and Variances 21.4 Discrete Probability Models 21.5 Continuous Random Variables Mini Case Study Project: Investment Options 668 Chapter 22 Decision Making and Risk 675 22.1 Actions, States of Nature, and Outcomes 22.2 Payoff Tables and Decision Trees 22.3 Minimizing Loss and Maximizing Gain 22.4 The Expected Value of an Action 22.5 Expected Value with Perfect Information 22.6 Decisions Made with Sample Information 22.7 Estimating Variation 22.8 Sensitivity 22.9 Simulation 22.10 Probability Trees *22.11 Reversing the Conditioning: Bayes’s Rule 22.12 More Complex Decisions Mini Case Study Projects: Texaco-Pennzoil 693, Insurance Services, Revisited 694 Chapter 23 Design and Analysis of Experiments and Observational Studies 699 23.1 Observational Studies 23.2 Randomized, Comparative Experiments 23.3 The Four Principles of Experimental Design 23.4 Experimental Designs 23.5 Blinding and Placebos 23.6 Confounding and Lurking Variables 23.7 Analyzing a Design in One Factor—The Analysis of Variance 23.8 Assumptions and Conditions for ANOVA *23.9 Multiple Comparisons 23.10 ANOVA on Observational Data 23.11 Analysis of Multifactor Designs Mini Case Study Project: A Multifactor Experiment 736 Chapter 24 Introduction to Data Mining 747 24.1 Direct Marketing 24.2 The Data 24.3 The Goals of Data Mining 24.4 Data Mining Myths 24.5 Successful Data Mining 24.6 Data Mining Problems 24.7 Data Mining Algorithms 24.8 The Data Mining Process 24.9 SummaryAppendixes A Answers A-1 B Photo Acknowledgments A-37 C Tables and Selected Formulas A-41 D Index A-57
插图：Selecting a sample to represent the population fairly is more difficult than it sounds.Polls or surveys most often fail because t11e sample fails to represent part ofthe population.The wav the sample is drawn may overlook subgroups that are hardto find.For example，a telephone survey may get no responses from people withcaller ID and may favor other groups，such as the retired or tlle homebound，who would be more likely to be near their Dhones when the interviewer calls.Samplesthat over-or underemphasize some characteristics of the population are said to bebiased.the corresponding characteristics of the population it is trying to represent.Conclusions based on biased samples are inherently flawed.There is usually no way to fixbias after the sample is drawn and no way to salvage useful information from it. That are the basic techniques for making sure that a sample is representative？To make the sample as representative as possible，you might be tempted to hand-pick t}1e individuals included in the sample.But the best strategY is to do some-thing quite different：We should select individuals for the sample at random.
《商务统计学(英文版)》特点：1.强调统计知识和开发统计思维；2.使用真实数据；3.强调概念的理解而不仅仅是获取知识的过程；4.培养主动学习；5.在理解概念和分析数据时使用软件技术；6.强调对统计结果的分析过程。
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