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应用统计分析：使用Excel 英文版
作者：（美）杰拉尔德·凯勒（Gerald Keller）著
出版社：机械工业出版社
出版时间：2004-05-01
ISBN：9787111143215
定价：¥68.00
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内容简介
本书的作者有多年概率统计学、管理学和运筹管理学的教学经验，编写了多本畅销教材。在本书中，作者继续体现了他的"三步式"解决问题的方法，第一步是Indentify，包括试验设计、收集数据和选择模型；第二步是Compute，即用Excel做计算；第三步是Interpret，就是分析、解释计算的结果。本书使用较少的概率知识，从各个应用层面，通过丰富的案例分析和读者自己动手的应用实例，讲解了应用统计的基本内容。本书系统地阐述了如何正确收集数据资料，如何使用MicrosoftExcel软件进行统计分析，应女口何从中得到有意义的统计结论。使用此书不需要微积分基础，只要具有高中的数学水平就可以通览全书。此书有以下几个特点：第一，本书更多的用形象思维与直观判断引进统计概念和方法。第二，本书层次清楚，每段都有主要的概念和公式的小结和大量的练习题，应用题的图标分明，利于复习中提高。各章都有光盘数据的案例，有利于实例练习。第三，本书是以MicrosoftExcel带动统计计算，毋需花费时间学习Matlab、SAS、S-Plus等软件。通过使用Excel学会做统计分析，又解除了手工计算的繁琐与枯燥。本书适合作为经济管理专业的大学生、研究生的统计学教材或参考书，也适于人文科学、社会科学、生命科学、考古学、心理学等领域的教师、工程师、技术人员自学之用。
作者简介
暂缺《应用统计分析：使用Excel 英文版》作者简介
目录
I WHAT IS STATISTICS? 1
1.1 Introduction 2
1.2 Key Statistical Concepts 6
1.3 Statistics and the Computer 7
1.4 World Wide Web and Learning Center 7
APPENDIX 1.A: Introduction to Microsoft Excel 10
2 GRAPHICAL DESCRIPTIVE TECHNIQUES 15
2.1 Introduction 16
2.2 Types of Data 16
2.3 Graphically Describing Interval Data: Frequency Distributions and Histograms 20
2.4 Graphically Describing Nominal Data: Bar and Pie Charts 33
2.5 Describing Time-Series Data: Line Charts 38
2.6 Describing the Relationship between Two Interval Variables: Scatter Diagrams 42
2.7 Summary 49
3 NUMERICAL DESCRIPTIVE TECHNIQUES FOR INTERVAL DATA 52
3.1 Introduction 53
3.2 Measures of Central Location 54
3.3 Measures of Variability 60
3.4 Other Measures of Shape (Optional) 70
3.5 Measures of Relative Standing and Box Plots 71
3.6 Measures of Linear Relationship 76
3.7 General Guidelines for Exploring Data 84
3.8 Summary 85
4 PROBABILITY 89
4.1 Introduction 90
4.2 Assigning Probability to Events 90
4.3 Joint, Marginal, and Conditional Probability 95
4.4 Probability Rules and Trees 103
4.5 Summary 113
CASE 4.1 Let's Make a Deal 116
CASE 4.2 To Bunt or Not to Bunt, That Is the Question 116
5 RANDOM VARIABLES AND DISCRETE PROBABILITY
DISTRIBUTIONS 118
5.1 Introduction 119
5.2 Random Variables and Probability Distributions 119
5.3 Describing the Population/Probability Distribution 124
5.4 Binomial Distribution 128
5.5 Poisson Distribution 136
5.6 Summary 141
CASE 5.1 To Bunt or Not to Bunt, That Is the Question, Part II 145
6 CONTINUOUS PROBABILITY DISTRIBUTIONS 146
6.1 Introduction 147
6.2 Probability Density Functions 147
6.3 Normal Distribution 153
6.4 Other Continuous Distributions 170
6.5 Summary 187
7 SAMPLING AND SAMPLING PLANS 188
7.1 Introduction 189
7.2 Sampling 189
7.3 Sampling Plans 191
7.4 Errors Involved in Sampling 196
7.5 Summary 198
8 SAMPLING DISTRIBUTIONS 199
8.1 Introduction 200
8.2 Sampling Distribution of the Mean 200
8.3 Creating the Sampling Distribution by Computer Simulation (Optional) 212
8.4 Sampling Distribution of a Proportion 215
8.5 Sampling Distribution of the Difference between Two Means 220
8.6 From Here to Inference 223
8.7 Summary 224
9 INTRODUCTION TO ESTIMATION 227
9.1 Introduction 228
9.2 Concepts of Estimation 228
9.3 Estimating the Population Mean when the Population Standard Deviation Is Known 232
9.4 Selecting the Sample Size 245
9.5 Simulation Experiments (Optional) 247
9.6 Summary 250
10 INTRODUCTION TO HYPOTHESIS TESTING 253
10.1 Introduction 254
10.2 Concepts of Hypothesis Testing 255
10.3 Testing the Population Mean when the Population Standard Deviation Is Known 257
10.4 Calculating the Probability of a Type II Error 279
10.5 The Road Ahead 288
10.6 Summary 291
11 INFERENCE ABOUT A SINGLE POPULATION 293
1 l. 1 Introduction 294
11.2 Inference about a Population Mean when the Standard Deviation Is Unknown 295
11.3 Inference about a Population Variance 305
11.4 Inference about a Population Proportion 311
11.5 Summary 323
CASE l l.1 Pepsi's Exclusivity Agreement with a University 327
CASE I 1.2 Pepsi's Exclusivity Agreement with a University:
The Coke Side of the Equation 328
CASE 11.3 Number of Uninsured Motorists 328
12 INFERENCE ABOUT TWO POPULATIONS 330
12.1 Introduction 331
12.2 Inference about the Difference between Two Means: Independent Samples 33:
12.3 Observational and Experimental Data 348
12.4 Inference about the Difference betweenTwo Means: Matched Pairs Experiment
12.5 Inference about the Ratio of Two Variances 361
12.6 Inference about the Difference between Two Population Proportions 367
12.7 Summary 378
CASE 12.1 Bonanza International 386
CASE 12.2 Accounting Course Exemptions 387
113 STATISTICAL INFERENCE:
REVIEW OF CHAPTERS 11 AND 12 388
13.1 Introduction 389
13.2 Guide to Identifying the Correct Technique: Chapters 11 and 12 389
CASE 13.1 Quebec Separation: Oui ou non? 403
CASE 13.2 Host Selling and Announcer Commercials 403
14 ANALYSIS OF VARIANCE 405
14.1 Introduction 406
14.2 Single-Factor (One-Way) Analysis of Variance: Independent Samples 407
14.3 Analysis of Variance Experimental Designs 423
14.4 Single-Factor Analysis of Variance: Randqmized Blocks 425
14.5 Two-Factor Analysis of Variance: Independent Samples 434
14.6 Multiple Comparisons 449
14.7 Bartlett's Test 455
14.8 Summary 457
15 CHI-SQUARED TESTS 464
15.1 Introduction 465
15.2 Chi-Squared Goodness-of-Fit Test 465
15.3 Chi-Squared Test of a Contingency Table 472
15.4 Summary of Tests on Nominal Data 482
15.5 Chi-Squared Test for Normality 484
15.6 Summary 489
CASE 15.1 Predicting the Outcomes of Basketball, Baseball, Football, and Hockey Games
from Intermediate Results 493
CASE 15.2 Can Exposure to a Code of Professional Ethics Help Make Managers More
Ethical? 494
16 NONPARAMETRIC STATISTICAL TECHNIQUES 496
16.1 Introduction 497
16.2 Wilcoxon Rank Sum Test 499
16.3 Sign Test and Wilcoxon Signed Rank Sum Test 511
16.4 Kruskal-WallisTest 524
16.5 Friedman Test 529
16.6 Summary 535
17 SIMPLE LINEAR REGRESSION AND CORRELATION 542
17.1 Introduction 543
17.2 Model 544
17.3 Estimating the Cdefficients 546
17.4 Error Variable: Required Conditions 552
17.5 Assessing the Model 555
17.6 Using the Regression Equation 564
17.7 Coefficients of Correlation 568
17.8 Regression Diagnostics I 574
17.9 Summary 580
CASE 17.1 Predicting University Grades from High School Grades 585
CASE 17.2 Insurance Compensation for Lost Revenues 586
18 MULTIPLE REGRESSION 588
18. I Introduction 589
18.2 Model and Required Conditions 589
18.3 Estimating the Coefficients and Assessing the Model 590
18.4 Regression Diagnostics II 605
18.5 Regression Diagnostics III (Time Series) 612
18.6 Nominal Independent Variables 623
18.7 Summary 630
CASE 18.1 Quebec Referendum Vote: Was There Electoral Fraud? 634
CASE 18.2 Quebec Referendum Vote: The Rebuttal 635
19 STATISTICAL INFERENCE: CONCLUSION 636
19.1 Introduction 637
19.2 Identifying the Correct Technique: Summary of Statistical Inference 637
CASE 19.1 Do Banks Discriminate against Women Business Owners? I 644
CASE 19.2 Do Banks Discriminate against Women Business Owners? II 647
19.3 The Last Word 653
CASE 19.3 Ambulance and Fire Department Response Interval Study 665
CASE 19.4 PC Magazine Survey 666
CASE 19.5 WLU Graduate Survey 667
CASE 19.6 Evaluation of a New Antidepressant Drug 668
CASE 19.7 Nutrition Education Programs 669
CASE 19.8 Do Banks Discriminate against Women Business Owners? III 670
Appendix A Sample Statistics from Data Files in Chapters 9 and 10 A-1
AppendixB Tables B-1
Appendix C Answers to Selected Even-Numbered Exercises C-1
Index I-1
1.1 Introduction 2
1.2 Key Statistical Concepts 6
1.3 Statistics and the Computer 7
1.4 World Wide Web and Learning Center 7
APPENDIX 1.A: Introduction to Microsoft Excel 10
2 GRAPHICAL DESCRIPTIVE TECHNIQUES 15
2.1 Introduction 16
2.2 Types of Data 16
2.3 Graphically Describing Interval Data: Frequency Distributions and Histograms 20
2.4 Graphically Describing Nominal Data: Bar and Pie Charts 33
2.5 Describing Time-Series Data: Line Charts 38
2.6 Describing the Relationship between Two Interval Variables: Scatter Diagrams 42
2.7 Summary 49
3 NUMERICAL DESCRIPTIVE TECHNIQUES FOR INTERVAL DATA 52
3.1 Introduction 53
3.2 Measures of Central Location 54
3.3 Measures of Variability 60
3.4 Other Measures of Shape (Optional) 70
3.5 Measures of Relative Standing and Box Plots 71
3.6 Measures of Linear Relationship 76
3.7 General Guidelines for Exploring Data 84
3.8 Summary 85
4 PROBABILITY 89
4.1 Introduction 90
4.2 Assigning Probability to Events 90
4.3 Joint, Marginal, and Conditional Probability 95
4.4 Probability Rules and Trees 103
4.5 Summary 113
CASE 4.1 Let's Make a Deal 116
CASE 4.2 To Bunt or Not to Bunt, That Is the Question 116
5 RANDOM VARIABLES AND DISCRETE PROBABILITY
DISTRIBUTIONS 118
5.1 Introduction 119
5.2 Random Variables and Probability Distributions 119
5.3 Describing the Population/Probability Distribution 124
5.4 Binomial Distribution 128
5.5 Poisson Distribution 136
5.6 Summary 141
CASE 5.1 To Bunt or Not to Bunt, That Is the Question, Part II 145
6 CONTINUOUS PROBABILITY DISTRIBUTIONS 146
6.1 Introduction 147
6.2 Probability Density Functions 147
6.3 Normal Distribution 153
6.4 Other Continuous Distributions 170
6.5 Summary 187
7 SAMPLING AND SAMPLING PLANS 188
7.1 Introduction 189
7.2 Sampling 189
7.3 Sampling Plans 191
7.4 Errors Involved in Sampling 196
7.5 Summary 198
8 SAMPLING DISTRIBUTIONS 199
8.1 Introduction 200
8.2 Sampling Distribution of the Mean 200
8.3 Creating the Sampling Distribution by Computer Simulation (Optional) 212
8.4 Sampling Distribution of a Proportion 215
8.5 Sampling Distribution of the Difference between Two Means 220
8.6 From Here to Inference 223
8.7 Summary 224
9 INTRODUCTION TO ESTIMATION 227
9.1 Introduction 228
9.2 Concepts of Estimation 228
9.3 Estimating the Population Mean when the Population Standard Deviation Is Known 232
9.4 Selecting the Sample Size 245
9.5 Simulation Experiments (Optional) 247
9.6 Summary 250
10 INTRODUCTION TO HYPOTHESIS TESTING 253
10.1 Introduction 254
10.2 Concepts of Hypothesis Testing 255
10.3 Testing the Population Mean when the Population Standard Deviation Is Known 257
10.4 Calculating the Probability of a Type II Error 279
10.5 The Road Ahead 288
10.6 Summary 291
11 INFERENCE ABOUT A SINGLE POPULATION 293
1 l. 1 Introduction 294
11.2 Inference about a Population Mean when the Standard Deviation Is Unknown 295
11.3 Inference about a Population Variance 305
11.4 Inference about a Population Proportion 311
11.5 Summary 323
CASE l l.1 Pepsi's Exclusivity Agreement with a University 327
CASE I 1.2 Pepsi's Exclusivity Agreement with a University:
The Coke Side of the Equation 328
CASE 11.3 Number of Uninsured Motorists 328
12 INFERENCE ABOUT TWO POPULATIONS 330
12.1 Introduction 331
12.2 Inference about the Difference between Two Means: Independent Samples 33:
12.3 Observational and Experimental Data 348
12.4 Inference about the Difference betweenTwo Means: Matched Pairs Experiment
12.5 Inference about the Ratio of Two Variances 361
12.6 Inference about the Difference between Two Population Proportions 367
12.7 Summary 378
CASE 12.1 Bonanza International 386
CASE 12.2 Accounting Course Exemptions 387
113 STATISTICAL INFERENCE:
REVIEW OF CHAPTERS 11 AND 12 388
13.1 Introduction 389
13.2 Guide to Identifying the Correct Technique: Chapters 11 and 12 389
CASE 13.1 Quebec Separation: Oui ou non? 403
CASE 13.2 Host Selling and Announcer Commercials 403
14 ANALYSIS OF VARIANCE 405
14.1 Introduction 406
14.2 Single-Factor (One-Way) Analysis of Variance: Independent Samples 407
14.3 Analysis of Variance Experimental Designs 423
14.4 Single-Factor Analysis of Variance: Randqmized Blocks 425
14.5 Two-Factor Analysis of Variance: Independent Samples 434
14.6 Multiple Comparisons 449
14.7 Bartlett's Test 455
14.8 Summary 457
15 CHI-SQUARED TESTS 464
15.1 Introduction 465
15.2 Chi-Squared Goodness-of-Fit Test 465
15.3 Chi-Squared Test of a Contingency Table 472
15.4 Summary of Tests on Nominal Data 482
15.5 Chi-Squared Test for Normality 484
15.6 Summary 489
CASE 15.1 Predicting the Outcomes of Basketball, Baseball, Football, and Hockey Games
from Intermediate Results 493
CASE 15.2 Can Exposure to a Code of Professional Ethics Help Make Managers More
Ethical? 494
16 NONPARAMETRIC STATISTICAL TECHNIQUES 496
16.1 Introduction 497
16.2 Wilcoxon Rank Sum Test 499
16.3 Sign Test and Wilcoxon Signed Rank Sum Test 511
16.4 Kruskal-WallisTest 524
16.5 Friedman Test 529
16.6 Summary 535
17 SIMPLE LINEAR REGRESSION AND CORRELATION 542
17.1 Introduction 543
17.2 Model 544
17.3 Estimating the Cdefficients 546
17.4 Error Variable: Required Conditions 552
17.5 Assessing the Model 555
17.6 Using the Regression Equation 564
17.7 Coefficients of Correlation 568
17.8 Regression Diagnostics I 574
17.9 Summary 580
CASE 17.1 Predicting University Grades from High School Grades 585
CASE 17.2 Insurance Compensation for Lost Revenues 586
18 MULTIPLE REGRESSION 588
18. I Introduction 589
18.2 Model and Required Conditions 589
18.3 Estimating the Coefficients and Assessing the Model 590
18.4 Regression Diagnostics II 605
18.5 Regression Diagnostics III (Time Series) 612
18.6 Nominal Independent Variables 623
18.7 Summary 630
CASE 18.1 Quebec Referendum Vote: Was There Electoral Fraud? 634
CASE 18.2 Quebec Referendum Vote: The Rebuttal 635
19 STATISTICAL INFERENCE: CONCLUSION 636
19.1 Introduction 637
19.2 Identifying the Correct Technique: Summary of Statistical Inference 637
CASE 19.1 Do Banks Discriminate against Women Business Owners? I 644
CASE 19.2 Do Banks Discriminate against Women Business Owners? II 647
19.3 The Last Word 653
CASE 19.3 Ambulance and Fire Department Response Interval Study 665
CASE 19.4 PC Magazine Survey 666
CASE 19.5 WLU Graduate Survey 667
CASE 19.6 Evaluation of a New Antidepressant Drug 668
CASE 19.7 Nutrition Education Programs 669
CASE 19.8 Do Banks Discriminate against Women Business Owners? III 670
Appendix A Sample Statistics from Data Files in Chapters 9 and 10 A-1
AppendixB Tables B-1
Appendix C Answers to Selected Even-Numbered Exercises C-1
Index I-1
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