《商務與經濟統計（精要版）（英文版·原書第7版）》是2019年11月機械工業出版社出版的圖書，作者是戴維R.安德森（David R.Anderson）、丹尼斯J.斯威尼。
基本介紹
- 中文名：商務與經濟統計（精要版）（英文版·原書第7版）
- 作者：戴維R.安德森（David、R.Anderson）、丹尼斯J.斯威尼
- 出版社：機械工業出版社
- 出版時間：2019年11月
- 定價：79 元
- 開本：16 開
- 裝幀：平裝
- ISBN：9787111603863
內容簡介,圖書目錄,
內容簡介
本書是介紹統計學概念及其在商務與經濟中套用的經典著作。它結合國際知名公司運用統計知識的具體實例，全面介紹了常用的數據分析方法和統計方法，向讀者展示了統計學在商務與經濟中的實用性。全書涵蓋了統計學的所有基本知識。每章後面都附有適量的練習，並在書後的附錄中給出了部分練習的答案，可以幫助讀者更加深入地理解書中的內容。本書適用於工商管理及其他相關專業的本科生、研究生、MBA、企業經營管理者及相關領域研究人員。
圖書目錄
出版說明
導 讀
前 言
作者簡介
第1章 數據與統計 1
1.1 統計在商務和經濟領域中的套用 3
1.2 數據 5
1.3 數據來源 11
1.4 描述性統計 14
1.5 統計推斷 16
1.6 計算機與統計分析 18
1.7 數據挖掘 18
1.8 統計實踐中的道德準則 19
總結 21
關鍵術語 21
補充練習 22
第2章 描述性統計：表格與圖形 25
2.1 分類數據的匯總 27
2.2 數值型數據的匯總 34
2.3 聯列表 47
2.4 用圖形法對兩個變數進行匯總 56
2.5 數據可視化：創建有效圖形的最佳實例 62
總結 69
關鍵術語 70
重要公式 71
補充練習 71
案例一 Pelican商店 73
案例二 電影行業 74
第3章 描述統計學：數值方法 76
3.1 位置指標 78
3.2 變異指標 93
3.3 分布形態、相對位置的度量以及異常值的檢測 100
3.4 五數統計和箱形圖 107
3.5 兩個變數間關係的度量 112
3.6 數據儀錶板： 添加數值測度以提高效率 122
總結 126
關鍵術語 126
重要公式 127
補充練習 129
案例一 Pelican商店 131
案例二 電影行業 132
第7章 抽樣和抽樣分布 134
7.1 聯合電氣公司的抽樣問題 136
7.2 抽樣 137
7.3 點估計 142
7.4 抽樣分布簡介 146
7.5 x–的抽樣分布 148
7.6 p–的抽樣分布 158
7.7 其他抽樣方法 164
總結 166
關鍵術語 167
重要公式 168
補充練習 168
第8章 區間估計 170
8.1 總體均值的區間估計：已知的情形 172
8.2 總體均值的區間估計：未知的情形 178
8.3 樣本容量的確定 187
8.4 總體比率的區間估計 190
總結 195
關鍵術語 196
重要公式 197
補充練習 197
案例一 《職業青年》雜誌 199
案例二 海灣地區 200
第9章 假設檢驗 202
9.1 原假設和備擇假設的建立 204
9.2 第一類錯誤和第二類錯誤 207
9.3 總體均值的檢驗：已知 210
9.4 總體均值的檢驗：未知 225
9.5 總體比率的檢驗 231
總結 236
關鍵術語 237
重要公式 237
補充練習 237
案例 質量聯盟有限公司 238
第10章 總體均值的比較、試驗設計及方差分析 240
10.1 兩總體均值差的統計推斷：1和2已知 242
10.2 兩總體均值之差的推斷：1和2未知 249
10.3 兩總體均值之差的推斷：配對樣本 257
10.4 試驗設計和方差分析簡介 263
10.5 方差分析和完全隨機化設計 268
總結 279
關鍵術語 280
重要公式 280
補充練習 282
案例一 Par公司 284
案例二 艾特沃思醫療中心 285
第11章 比率的比較和獨立性檢驗 286
11.1 兩個總體比例之差的推斷 288
11.2 三個或三個以上總體比率的推斷 294
11.3 獨立性檢驗 305
總結 313
關鍵術語 313
重要公式 313
補充練習 314
第12章 簡單線性回歸 316
12.1 簡單線性回歸模型 318
12.2 最小二乘估計 321
12.3 可決係數 332
12.4 回歸模型的假定 339
12.5 顯著性檢驗 340
12.6 用回歸方程的估計式進行估計和預測 350
12.7 計算機解決方案 357
12.8 殘差分析：驗證模型的假定條件 361
總結 367
關鍵術語 368
重要公式 368
補充練習 370
案例一 股市風險度量 373
案例二 美國交通部 374
第13章 多元回歸 375
13.1 多元回歸模型 377
13.2 最小二乘估計 378
13.3 多重可決係數 387
13.4 回歸模型的假定 391
13.5 顯著性檢驗 392
13.6 用回歸方程的估計式進行估計和預測 399
13.7 範疇獨立變數 402
總結 410
關鍵術語 410
重要公式 411
補充練習 412
案例一 消費者行為調研公司 413
案例二 校友捐贈 414
案例三 汽車價值的合理評估 415
附錄A 部分習題解答 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
