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。

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