简介
Summary:
Publisher Summary 1
This textbook focuses on how to select the appropriate statistical technique and how to interpret the results, leaving the actual calculations to the computer. The CD-ROM contains 625 data sets, and Data Analysis Plus 3.0 statistical software add-ins for Excel. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Publisher Summary 2
Gerald Keller's new APPLIED STATISTICS WITH MICROSOFT庐 EXCEL integrates Excel into the general introductory statistics course. Keller, the co-author of the market-leading STATISTICS FOR MANAGEMENT AND ECONOMICS, Fifth Edition, incorporates his proven three-step problem-solving process throughout this book. The first step, "Identify," is the work a statistician does before the calculations are performed, which entails organizing the experiment, gathering the data, and deciding which statistical techniques to employ. The second step, "Compute," is the computation with Excel. In this step, Keller shows the manual calculation for the simplest of techniques only. For example, he describes how to calculate the sample mean, variance, and standard deviation, how to compute the z-interval estimate of, and the z-test of. The third step, "Interpret," is the interpretation of the computer output, which requires an understanding of statistical concepts.
目录
1 What is Statistics? p. 1
1.1 Introduction p. 2
1.2 Key Statistical Concepts p. 6
1.3 Statistics and the Computer p. 7
1.4 World Wide Web and Learning Center p. 7
Appendix 1.A Introduction to Microsoft Excel p. 10
2 Graphical Descriptive Techniques p. 15
2.1 Introduction p. 16
2.2 Types of Data p. 16
2.3 Graphically Describing Interval Data: Frequency Distributions and Histograms p. 20
2.4 Graphically Describing Nominal Data: Bar and Pie Charts p. 33
2.5 Describing Time-Series Data: Line Charts p. 38
2.6 Describing the Relationship between Two Interval Variables: Scatter Diagrams p. 42
2.7 Summary p. 49
3 Numerical Descriptive Techniques for Interval Data p. 52
3.1 Introduction p. 53
3.2 Measures of Central Location p. 54
3.3 Measures of Variability p. 60
3.4 Other Measures of Shape (Optional) p. 70
3.5 Measures of Relative Standing and Box Plots p. 71
3.6 Measures of Linear Relationship p. 76
3.7 General Guidelines for Exploring Data p. 84
3.8 Summary p. 85
4 Probability p. 89
4.1 Introduction p. 90
4.2 Assigning Probability to Events p. 90
4.3 Joint, Marginal, and Conditional Probability p. 95
4.4 Probability Rules and Trees p. 103
4.5 Summary p. 113
Case 4.1 Let's Make a Deal p. 116
Case 4.2 To Bunt or Not to Bunt, That Is the Question p. 116
5 Random Variables and Discrete Probability Distributions p. 118
5.1 Introduction p. 119
5.2 Random Variables and Probability Distributions p. 119
5.3 Describing the Population/Probability Distribution p. 124
5.4 Binomial Distribution p. 128
5.5 Poisson Distribution p. 136
5.6 Summary p. 141
Case 5.1 To Bunt or Not to Bunt, That Is the Question, Part II p. 145
6 Continuous Probability Distributions p. 146
6.1 Introduction p. 147
6.2 Probability Density Functions p. 147
6.3 Normal Distribution p. 153
6.4 Other Continuous Distributions p. 170
6.5 Summary p. 187
7 Sampling and Sampling Plans p. 188
7.1 Introduction p. 189
7.2 Sampling p. 189
7.3 Sampling Plans p. 191
7.4 Errors Involved in Sampling p. 196
7.5 Summary p. 198
8 Sampling Distributions p. 199
8.1 Introduction p. 200
8.2 Sampling Distribution of the Mean p. 200
8.3 Creating the Sampling Distribution by Computer Simulation (Optional) p. 212
8.4 Sampling Distribution of a Proportion p. 215
8.5 Sampling Distribution of the Difference between Two Means p. 220
8.6 From Here to Inference p. 223
8.7 Summary p. 224
9 Introduction to Estimation p. 227
9.1 Introduction p. 228
9.2 Concepts of Estimation p. 228
9.3 Estimating the Population Mean when the Population Standard Deviation Is Known p. 232
9.4 Selecting the Sample Size p. 245
9.5 Simulation Experiments (Optional) p. 247
9.6 Summary p. 250
10 Introduction to Hypothesis Testing p. 253
10.1 Introduction p. 254
10.2 Concepts of Hypothesis Testing p. 255
10.3 Testing the Population Mean when the Population Standard Deviation Is Known p. 257
10.4 Calculating the Probability of a Type II Error p. 279
10.5 The Road Ahead p. 288
10.6 Summary p. 291
11 Inference About A Single Population p. 293
11.1 Introduction p. 294
11.2 Inference about a Population Mean when the Standard Deviation Is Unknown p. 295
11.3 Inference about a Population Variance p. 305
11.4 Inference about a Population Proportion p. 311
11.5 Summary p. 323
Case 11.1 Pepsi's Exclusivity Agreement with a University p. 327
Case 11.2 Pepsi's Exclusivity Agreement with a University: The Coke Side of the Equation p. 328
Case 11.3 Number of Uninsured Motorists p. 328
12 Inference About Two Populations p. 330
12.1 Introduction p. 331
12.2 Inference about the Difference between Two Means: Independent Samples p. 332
12.3 Observational and Experimental Data p. 348
12.4 Inference about the Difference between Two Means: Matched Pairs Experiment p. 349
12.5 Inference about the Ratio of Two Variances p. 361
12.6 Inference about the Difference between Two Population Proportions p. 367
12.7 Summary p. 378
Case 12.1 Bonanza International p. 386
Case 12.2 Accounting Course Exemptions p. 387
13 Statistical Inference: Review of Chapters 11 and 12 p. 388
13.1 Introduction p. 389
13.2 Guide to Identifying the Correct Technique: Chapters 11 and 12 p. 389
Case 13.1 Quebec Separation: Oui ou non? p. 403
Case 13.2 Host Selling and Announcer Commercials p. 403
14 Analysis of Variance p. 405
14.1 Introduction p. 406
14.2 Single-Factor (One-Way) Analysis of Variance: Independent Samples p. 407
14.3 Analysis of Variance Experimental Designs p. 423
14.4 Single-Factor Analysis of Variance: Randomized Blocks p. 425
14.5 Two-Factor Analysis of Variance: Independent Samples p. 434
14.6 Multiple Comparisons p. 449
14.7 Bartlett's Test p. 455
14.8 Summary p. 457
15 Chi-Squared Tests p. 464
15.1 Introduction p. 465
15.2 Chi-Squared Goodness-of-Fit Test p. 465
15.3 Chi-Squared Test of a Contingency Table p. 472
15.4 Summary of Tests on Nominal Data p. 482
15.5 Chi-Squared Test for Normality p. 484
15.6 Summary p. 489
Case 15.1 Predicting the Outcomes of Basketball, Baseball, Football, and Hockey Games from Intermediate Results p. 493
Case 15.2 Can Exposure to a Code of Professional Ethics Help Make Managers More Ethical? p. 494
16 Nonparametric Statistical Techniques p. 496
16.1 Introduction p. 497
16.2 Wilcoxon Rank Sum Test p. 499
16.3 Sign Test and Wilcoxon Signed Rank Sum Test p. 511
16.4 Kruskal-Wallis Test p. 524
16.5 Friedman Test p. 529
16.6 Summary p. 535
17 Simple Linear Regression and Correlation p. 542
17.1 Introduction p. 543
17.2 Model p. 544
17.3 Estimating the Coefficients p. 546
17.4 Error Variable: Required Conditions p. 552
17.5 Assessing the Model p. 555
17.6 Using the Regression Equation p. 564
17.7 Coefficients of Correlation p. 568
17.8 Regression Diagnostics I p. 574
17.9 Summary p. 580
Case 17.1 Predicting University Grades from High School Grades p. 585
Case 17.2 Insurance Compensation for Lost Revenues p. 586
18 Multiple Regression p. 588
18.1 Introduction p. 589
18.2 Model and Required Conditions p. 589
18.3 Estimating the Coefficients and Assessing the Model p. 590
18.4 Regression Diagnostics II p. 605
18.5 Regression Diagnostics III (Time Series) p. 612
18.6 Nominal Independent Variables p. 623
18.7 Summary p. 630
Case 18.1 Quebec Referendum Vote: Was There Electoral Fraud? p. 634
Case 18.2 Quebec Referendum Vote: The Rebuttal p. 635
19 Statistical Inference: Conclusion p. 636
19.1 Introduction p. 637
19.2 Identifying the Correct Technique: Summary of Statistical Inference p. 637
Case 19.1 Do Banks Discriminate against Women Business Owners? I p. 644
Case 19.2 Do Banks Discriminate against Women Business Owners? II p. 647
19.3 The Last Word p. 653
Case 19.3 Ambulance and Fire Department Response Interval Study p. 665
Case 19.4 PC Magazine Survey p. 666
Case 19.5 WLU Graduate Survey p. 667
Case 19.6 Evaluation of a New Antidepressant Drug p. 668
Case 19.7 Nutrition Education Programs p. 669
Case 19.8 Do Banks Discriminate against Women Business Owners? III p. 670
Appendix A Sample Statistics from Data Files in Chapters 9 and 10 p. 1
Appendix B Tables p. 1
Appendix C Answers to Selected Even-Numbered Exercises p. 1
Index p. 1
1.1 Introduction p. 2
1.2 Key Statistical Concepts p. 6
1.3 Statistics and the Computer p. 7
1.4 World Wide Web and Learning Center p. 7
Appendix 1.A Introduction to Microsoft Excel p. 10
2 Graphical Descriptive Techniques p. 15
2.1 Introduction p. 16
2.2 Types of Data p. 16
2.3 Graphically Describing Interval Data: Frequency Distributions and Histograms p. 20
2.4 Graphically Describing Nominal Data: Bar and Pie Charts p. 33
2.5 Describing Time-Series Data: Line Charts p. 38
2.6 Describing the Relationship between Two Interval Variables: Scatter Diagrams p. 42
2.7 Summary p. 49
3 Numerical Descriptive Techniques for Interval Data p. 52
3.1 Introduction p. 53
3.2 Measures of Central Location p. 54
3.3 Measures of Variability p. 60
3.4 Other Measures of Shape (Optional) p. 70
3.5 Measures of Relative Standing and Box Plots p. 71
3.6 Measures of Linear Relationship p. 76
3.7 General Guidelines for Exploring Data p. 84
3.8 Summary p. 85
4 Probability p. 89
4.1 Introduction p. 90
4.2 Assigning Probability to Events p. 90
4.3 Joint, Marginal, and Conditional Probability p. 95
4.4 Probability Rules and Trees p. 103
4.5 Summary p. 113
Case 4.1 Let's Make a Deal p. 116
Case 4.2 To Bunt or Not to Bunt, That Is the Question p. 116
5 Random Variables and Discrete Probability Distributions p. 118
5.1 Introduction p. 119
5.2 Random Variables and Probability Distributions p. 119
5.3 Describing the Population/Probability Distribution p. 124
5.4 Binomial Distribution p. 128
5.5 Poisson Distribution p. 136
5.6 Summary p. 141
Case 5.1 To Bunt or Not to Bunt, That Is the Question, Part II p. 145
6 Continuous Probability Distributions p. 146
6.1 Introduction p. 147
6.2 Probability Density Functions p. 147
6.3 Normal Distribution p. 153
6.4 Other Continuous Distributions p. 170
6.5 Summary p. 187
7 Sampling and Sampling Plans p. 188
7.1 Introduction p. 189
7.2 Sampling p. 189
7.3 Sampling Plans p. 191
7.4 Errors Involved in Sampling p. 196
7.5 Summary p. 198
8 Sampling Distributions p. 199
8.1 Introduction p. 200
8.2 Sampling Distribution of the Mean p. 200
8.3 Creating the Sampling Distribution by Computer Simulation (Optional) p. 212
8.4 Sampling Distribution of a Proportion p. 215
8.5 Sampling Distribution of the Difference between Two Means p. 220
8.6 From Here to Inference p. 223
8.7 Summary p. 224
9 Introduction to Estimation p. 227
9.1 Introduction p. 228
9.2 Concepts of Estimation p. 228
9.3 Estimating the Population Mean when the Population Standard Deviation Is Known p. 232
9.4 Selecting the Sample Size p. 245
9.5 Simulation Experiments (Optional) p. 247
9.6 Summary p. 250
10 Introduction to Hypothesis Testing p. 253
10.1 Introduction p. 254
10.2 Concepts of Hypothesis Testing p. 255
10.3 Testing the Population Mean when the Population Standard Deviation Is Known p. 257
10.4 Calculating the Probability of a Type II Error p. 279
10.5 The Road Ahead p. 288
10.6 Summary p. 291
11 Inference About A Single Population p. 293
11.1 Introduction p. 294
11.2 Inference about a Population Mean when the Standard Deviation Is Unknown p. 295
11.3 Inference about a Population Variance p. 305
11.4 Inference about a Population Proportion p. 311
11.5 Summary p. 323
Case 11.1 Pepsi's Exclusivity Agreement with a University p. 327
Case 11.2 Pepsi's Exclusivity Agreement with a University: The Coke Side of the Equation p. 328
Case 11.3 Number of Uninsured Motorists p. 328
12 Inference About Two Populations p. 330
12.1 Introduction p. 331
12.2 Inference about the Difference between Two Means: Independent Samples p. 332
12.3 Observational and Experimental Data p. 348
12.4 Inference about the Difference between Two Means: Matched Pairs Experiment p. 349
12.5 Inference about the Ratio of Two Variances p. 361
12.6 Inference about the Difference between Two Population Proportions p. 367
12.7 Summary p. 378
Case 12.1 Bonanza International p. 386
Case 12.2 Accounting Course Exemptions p. 387
13 Statistical Inference: Review of Chapters 11 and 12 p. 388
13.1 Introduction p. 389
13.2 Guide to Identifying the Correct Technique: Chapters 11 and 12 p. 389
Case 13.1 Quebec Separation: Oui ou non? p. 403
Case 13.2 Host Selling and Announcer Commercials p. 403
14 Analysis of Variance p. 405
14.1 Introduction p. 406
14.2 Single-Factor (One-Way) Analysis of Variance: Independent Samples p. 407
14.3 Analysis of Variance Experimental Designs p. 423
14.4 Single-Factor Analysis of Variance: Randomized Blocks p. 425
14.5 Two-Factor Analysis of Variance: Independent Samples p. 434
14.6 Multiple Comparisons p. 449
14.7 Bartlett's Test p. 455
14.8 Summary p. 457
15 Chi-Squared Tests p. 464
15.1 Introduction p. 465
15.2 Chi-Squared Goodness-of-Fit Test p. 465
15.3 Chi-Squared Test of a Contingency Table p. 472
15.4 Summary of Tests on Nominal Data p. 482
15.5 Chi-Squared Test for Normality p. 484
15.6 Summary p. 489
Case 15.1 Predicting the Outcomes of Basketball, Baseball, Football, and Hockey Games from Intermediate Results p. 493
Case 15.2 Can Exposure to a Code of Professional Ethics Help Make Managers More Ethical? p. 494
16 Nonparametric Statistical Techniques p. 496
16.1 Introduction p. 497
16.2 Wilcoxon Rank Sum Test p. 499
16.3 Sign Test and Wilcoxon Signed Rank Sum Test p. 511
16.4 Kruskal-Wallis Test p. 524
16.5 Friedman Test p. 529
16.6 Summary p. 535
17 Simple Linear Regression and Correlation p. 542
17.1 Introduction p. 543
17.2 Model p. 544
17.3 Estimating the Coefficients p. 546
17.4 Error Variable: Required Conditions p. 552
17.5 Assessing the Model p. 555
17.6 Using the Regression Equation p. 564
17.7 Coefficients of Correlation p. 568
17.8 Regression Diagnostics I p. 574
17.9 Summary p. 580
Case 17.1 Predicting University Grades from High School Grades p. 585
Case 17.2 Insurance Compensation for Lost Revenues p. 586
18 Multiple Regression p. 588
18.1 Introduction p. 589
18.2 Model and Required Conditions p. 589
18.3 Estimating the Coefficients and Assessing the Model p. 590
18.4 Regression Diagnostics II p. 605
18.5 Regression Diagnostics III (Time Series) p. 612
18.6 Nominal Independent Variables p. 623
18.7 Summary p. 630
Case 18.1 Quebec Referendum Vote: Was There Electoral Fraud? p. 634
Case 18.2 Quebec Referendum Vote: The Rebuttal p. 635
19 Statistical Inference: Conclusion p. 636
19.1 Introduction p. 637
19.2 Identifying the Correct Technique: Summary of Statistical Inference p. 637
Case 19.1 Do Banks Discriminate against Women Business Owners? I p. 644
Case 19.2 Do Banks Discriminate against Women Business Owners? II p. 647
19.3 The Last Word p. 653
Case 19.3 Ambulance and Fire Department Response Interval Study p. 665
Case 19.4 PC Magazine Survey p. 666
Case 19.5 WLU Graduate Survey p. 667
Case 19.6 Evaluation of a New Antidepressant Drug p. 668
Case 19.7 Nutrition Education Programs p. 669
Case 19.8 Do Banks Discriminate against Women Business Owners? III p. 670
Appendix A Sample Statistics from Data Files in Chapters 9 and 10 p. 1
Appendix B Tables p. 1
Appendix C Answers to Selected Even-Numbered Exercises p. 1
Index p. 1
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