本书将讲授如何去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报告结果。学生们将会了解，为做出更好的商业决策怎样进行统计思考以及如何有效地表达分析结果并将决策告知他人。

　　 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。

Part I Exploring and Collecting Data

 Chapter 1 Statistics and Variation  3

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  22

 Chapter 3 Surveys and Sampling  27

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 47

The GfK Roper Reports Worldwide Survey 47

 Chapter 4 Displaying and Describing Categorical Data 53

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 74

 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 111

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 143

Part 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 182

 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 213

 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 424

Part 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. 624

Part 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 Summary

Appendixes

 A Answers A-1

 B Photo Acknowledgments A-37

 C Tables and Selected Formulas A-41

 D Index A-57