商务与经济统计（精要版）（英文版·原书第7版）

《商务与经济统计（精要版）（英文版·原书第7版）》是2019年11月机械工业出版社出版的图书，作者是戴维R.安德森（David R.Anderson）、丹尼斯J.斯威尼。

商务与经济统计（精要版）（英文版·原书第7版）内容简介

本书是介绍统计学概念及其在商务与经济中应用的经典著作。它结合国际知名公司运用统计知识的具体实例，全面介绍了常用的数据分析方法和统计方法，向读者展示了统计学在商务与经济中的实用性。全书涵盖了统计学的所有基本知识。每章后面都附有适量的练习，并在书后的附录中给出了部分练习的答案，可以帮助读者更加深入地理解书中的内容。本书适用于工商管理及其他相关专业的本科生、研究生、MBA、企业经营管理者及相关领域研究人员。

商务与经济统计（精要版）（英文版·原书第7版）图书目录

出版说明

导　读

前　言

作者简介

第1章　数据与统计 1

1.1　统计在商务和经济领域中的应用 3

1.2　数据 5

1.3　数据来源 11

1.4　描述性统计 14

1.5　统计推断 16

1.6　计算机与统计分析 18

1.7　数据挖掘 18

1.8　统计实践中的道德准则 19

总结 21

关键术语 21

补充练习 22

第2章　描述性统计：表格与图形 25

2.1　分类数据的汇总 27

2.2　数值型数据的汇总 34

2.3　联列表 47

2.4　用图形法对两个变量进行汇总 56

2.5　数据可视化：创建有效图形的最佳实例 62

总结 69

关键术语 70

重要公式 71

补充练习 71

案例一　Pelican商店 73

案例二　电影行业 74

第3章　描述统计学：数值方法 76

3.1　位置指标 78

3.2　变异指标 93

3.3　分布形态、相对位置的度量以及异常值的检测 100

3.4　五数统计和箱形图 107

3.5　两个变量间关系的度量 112

3.6　数据仪表板： 添加数值测度以提高效率 122

总结 126

关键术语 126

重要公式 127

补充练习 129

案例一　Pelican商店 131

案例二　电影行业 132

第7章　抽样和抽样分布 134

7.1　联合电气公司的抽样问题 136

7.2　抽样 137

7.3　点估计 142

7.4　抽样分布简介 146

7.5　x–的抽样分布 148

7.6　p–的抽样分布 158

7.7　其他抽样方法 164

总结 166

关键术语 167

重要公式 168

补充练习 168

第8章　区间估计 170

8.1　总体均值的区间估计：已知的情形 172

8.2　总体均值的区间估计：未知的情形 178

8.3　样本容量的确定 187

8.4　总体比率的区间估计 190

总结 195

关键术语 196

重要公式 197

补充练习 197

案例一　《职业青年》杂志 199

案例二　海湾地区 200

第9章　假设检验 202

9.1　原假设和备择假设的建立 204

9.2　第一类错误和第二类错误 207

9.3　总体均值的检验：已知 210

9.4　总体均值的检验：未知 225

9.5　总体比率的检验 231

总结 236

关键术语 237

重要公式 237

补充练习 237

案例　质量联盟有限公司 238

第10章　总体均值的比较、试验设计及方差分析 240

10.1　两总体均值差的统计推断：1和2已知 242

10.2　两总体均值之差的推断：1和2未知 249

10.3　两总体均值之差的推断：配对样本 257

10.4　试验设计和方差分析简介 263

10.5　方差分析和完全随机化设计 268

总结 279

关键术语 280

重要公式 280

补充练习 282

案例一　Par公司 284

案例二　艾特沃思医疗中心 285

第11章　比率的比较和独立性检验 286

11.1　两个总体比例之差的推断 288

11.2　三个或三个以上总体比率的推断 294

11.3　独立性检验 305

总结 313

关键术语 313

重要公式 313

补充练习 314

第12章　简单线性回归 316

12.1　简单线性回归模型 318

12.2　最小二乘估计 321

12.3　可决系数 332

12.4　回归模型的假定 339

12.5　显著性检验 340

12.6　用回归方程的估计式进行估计和预测 350

12.7　计算机解决方案 357

12.8　残差分析：验证模型的假定条件 361

总结 367

关键术语 368

重要公式 368

补充练习 370

案例一　股市风险度量 373

案例二　美国交通部 374

第13章　多元回归 375

13.1　多元回归模型 377

13.2　最小二乘估计 378

13.3　多重可决系数 387

13.4　回归模型的假定 391

13.5　显著性检验 392

13.6　用回归方程的估计式进行估计和预测 399

13.7　范畴独立变量 402

总结 410

关键术语 410

重要公式 411

补充练习 412

案例一　消费者行为调研公司 413

案例二　校友捐赠 414

案例三　汽车价值的合理评估 415

附录A　部分习题解答 417

Contents

Preface

About the Authors

Chapter 1 Data and Statistics 1

Statistics in Practice: Bloomberg Businessweek 2

1.1 Applications in Business and Economics 3

Accounting 3

Finance 4

Marketing 4

Production 4

Economics 4

Information Systems 5

1.2 Data 5

Elements, Variables, and Observations 5

Scales of Measurement 7

Categorical and Quantitative Data 8

Cross-Sectional and Time Series Data 8

1.3 Data Sources 11

Existing Sources 11

Statistical Studies 12

Data Acquisition Errors 14

1.4 Descriptive Statistics 14

1.5 Statistical Inference 16

1.6 Computers and Statistical Analysis 18

1.7 Data Mining 18

1.8 Ethical Guidelines for Statistical Practice 19

Summary 21

Glossary 21

Supplementary Exercises 22

Chapter 2 Descriptive Statistics: Tabular and Graphical Displays 25

Statistics in Practice: Colgate-Palmolive Company 26

2.1 Summarizing Data for a Categorical Variable 27

Frequency Distribution 27

Relative Frequency and Percent Frequency Distributions 28

Bar Charts and Pie Charts 28

2.2 Summarizing Data for a Quantitative Variable 34

Frequency Distribution 34

Relative Frequency and Percent Frequency Distributions 35

Dot Plot 36

Histogram 36

Cumulative Distributions 38

Stem-and-Leaf Display 39

2.3 Summarizing Data for Two Variables Using Tables 47

Crosstabulation 47

Simpson’s Paradox 50

2.4 Summarizing Data for Two Variables Using Graphical Displays 56

Scatter Diagram and Trendline 56

Side-by-Side and Stacked Bar Charts 57

2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays 62

Creating Effective Graphical Displays 63

Choosing the Type of Graphical Display 64

Data Dashboards 64

Data Visualization in Practice: Cincinnati Zoo and Botanical Garden 66

Summary 69

Glossary 70

Key Formulas 71

Supplementary Exercises 71

Case Problem 1 Pelican Stores 73

Case Problem 2 Motion Picture Industry 74

Chapter 3 Descriptive Statistics: Numerical Measures 76

Statistics in Practice: Small Fry Design 77

3.1 Measures of Location 78

Mean 78

Weighted Mean 80

Median 81

Geometric Mean 83

Mode 84

Percentiles 85

Quartiles 86

3.2 Measures of Variability 93

Range 93

Interquartile Range 94

Variance 94

Standard Deviation 95

Coefficient of Variation 96

3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers 100

Distribution Shape 100

z-Scores 100

Chebyshev’s Theorem 102

Empirical Rule 103

Detecting Outliers 104

3.4 Five-Number Summaries and Box Plots 107

Five-Number Summary 108

Box Plot 108

3.5 Measures of Association Between Two Variables 112

Covariance 113

Interpretation of the Covariance 115

Correlation Coefficient 117

Interpretation of the Correlation Coefficient 118

3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness 122

Summary 126

Glossary 126

Key Formulas 127

Supplementary Exercises 129

Case Problem 1 Pelican Stores 131

Case Problem 2 Motion Picture Industry 132

Chapter 7 Sampling and Sampling Distributions 134

Statistics in Practice: Meadwestvaco Corporation 135

7.1 The Electronics Associates Sampling Problem 136

7.2 Selecting a Sample 137

Sampling from a Finite Population 137

Sampling from an Infinite Population 139

7.3 Point Estimation 142

Practical Advice 144

7.4 Introduction to Sampling Distributions 146

7.5 Sampling Distribution of x 148

Expected Value of x 148

Standard Deviation of x 149

Form of the Sampling Distribution of x 150

Sampling Distribution of x for the EAI Problem 152

Practical Value of the Sampling Distribution of x 153

Relationship Between the Sample Size and the

Sampling Distribution of x 154

7.6 Sampling Distribution of p 158

Expected Value of p 159

Standard Deviation of p 159

Form of the Sampling Distribution of p 160

Practical Value of the Sampling Distribution of p 160

7.7 Other Sampling Methods 164

Stratified Random Sampling 164

Cluster Sampling 165

Systematic Sampling 165

Convenience Sampling 165

Judgment Sampling 166

Summary 166

Glossary 167

Key Formulas 168

Supplementary Exercises 168

Chapter 8 Interval Estimation 170

Statistics in Practice: Food Lion 171

8.1 Population Mean: σ Known 172

Margin of Error and the Interval Estimate 172

Practical Advice 176

8.2 Population Mean: σ Unknown 178

Margin of Error and the Interval Estimate 179

Practical Advice 182

Using a Small Sample 182

Summary of Interval Estimation Procedures 184

8.3 Determining the Sample Size 187

8.4 Population Proportion 190

Determining the Sample Size 192

Summary 195

Glossary 196

Key Formulas 197

Supplementary Exercises 197

Case Problem 1 Young Professional Magazine 199

Case Problem 2 Gulf Real Estate Properties 200

Chapter 9 Hypothesis Tests 202

Statistics in Practice: John Morrell & Company 203

9.1 Developing Null and Alternative Hypotheses 204

The Alternative Hypothesis as a Research Hypothesis 204

The Null Hypothesis as an Assumption to Be Challenged 205

Summary of Forms for Null and Alternative Hypotheses 206

9.2 Type I and Type II Errors 207

9.3 Known 210

One-Tailed Test 210

Two-Tailed Test 216

Summary and Practical Advice 218

Relationship Between Interval Estimation and Hypothesis Testing 220

9.4 Population Mean: σ Unknown 225

One-Tailed Test 225

Two-Tailed Test 226

Summary and Practical Advice 228

9.5 Population Proportion 231

Summary 233

Summary 236

Glossary 237

Key Formulas 237

Supplementary Exercises 237

Case Problem Quality Associates, Inc. 238

Chapter 10 Comparisons Involving Means, Experimental Design,

and Analysis of Variance 240

Statistics in Practice: U.S. Food and Drug Administration 241

10.1 Inferences About the Difference Between Two Population Means:

σ1 and σ2 Known 242

Interval Estimation of μ1 2 μ2 242

Hypothesis Tests About μ1 2 μ2 245

Practical Advice 246

10.2 Inferences About the Difference Between Two Population Means:

σ1 and σ2 Unknown 249

Interval Estimation of μ1 2 μ2 249

Hypothesis Tests About μ1 2 μ2 251

Practical Advice 253

10.3 Inferences About the Difference Between Two Population Means:

Matched Samples 257

10.4 An Introduction to Experimental Design and Analysis of Variance 263

Analysis of Variance: A Conceptual Overview 265

10.5 Analysis of Variance and the Completely Randomized Design 268

Between-Treatments Estimate of Population Variance 265

10.5 Analysis of Variance and the Completely Randomized Design 268

Between-Treatments Estimate of Population Variance 269

Within-Treatments Estimate of Population Variance 270

Comparing the Variance Estimates: The F Test 271

ANOVA Table 272

Computer Results for Analysis of Variance 273

Testing for the Equality of k Population Means: An

Observational Study 275

Key Formulas 280

Supplementary Exercises 282

Case Problem 1 Par, Inc. 284

Case Problem 2 Wentworth Medical Center 285

Chapter 11 Comparisons Involving Proportions and a Test of Independence 286

Statistics in Practice: United Way 287

11.1 Inferences About the Difference Between Two Population Proportions 288

Inferences About the Difference Between 288

Hypothesis Tests About p1 2 p2 290

11.2 Testing the Equality Population Proportions for Three or More Populations 294

A Multiple Comparison Procedure 300

11.3 Test of Independence 305

Summary 313

Glossary 313

Key Formulas 313

Supplementary Exercises 314

Chapter 12 Simple Linear Regression 316

Statistics in Practice: Alliance Data Systems 317

12.1 Simple Linear Regression Model 318

Regression Model and Regression Equation 318

Estimated Regression Equation 319

12.2 Least Squares Method 321

12.3 Coefficient of Determination 332

Correlation Coefficient 335

12.4 Model Assumptions 339

12.5 Testing for Significance 340

Estimate of σ2 340

t Test 342

Confidence Interval for β1 344

F Test 344

Some Cautions About the Interpretation of Significance Tests 346

12.6 Using the Estimated Regression Equation for Estimation and Prediction 350

Interval Estimation 351

Confidence Interval for the Mean Value of y 351

Prediction Interval for an Individual Value of y 352

12.7 Computer Solution 357

12.8 Residual Analysis: Validating Model Assumptions 361

Residual Plot Against x 362

Residual Plot Against y^ 365

Summary 367

Glossary 368

Key Formulas 368

Supplementary Exercises 370

Case Problem 1 Measuring Stock Market Risk 373

Case Problem 2 U.S. Department of Transportation 374

Chapter 13 Multiple Regression 375

Statistics in Practice: dunnhumby 376

13.1 Multiple Regression Model 377

Regression Model and Regression Equation 377

Estimated Multiple Regression Equation 377

13.2 Least Squares Method 378

An Example: Butler Trucking Company 379

Note on Interpretation of Coefficients 381

13.3 Multiple Coefficient of Determination 387

13.4 Model Assumptions 391

13.5 Testing for Significance 392

F Test 392

t Test 395

Multicollinearity 396

13.6 Using the Estimated Regression Equation for Estimation and Prediction 399

13.7 Categorical Independent Variables 402

An Example: Johnson Filtration, Inc. 402

Interpreting the Parameters 404

More Complex Categorical Variables 406

Summary 410

Glossary 410

Key Formulas 411

Supplementary Exercises 412

Case Problem 1 Consumer Research, Inc. 413

Case Problem 2 Predicting Winnings for NASCAR Drivers 414

Case Problem 3 Finding the Best Car Value 415

Appendix A: Self-Test Solutions and Answers to Even-Numbered Exercises 417