Chapter 1 Data and Statistics 1
Statistics in Practice: Business Week 2
1.1 Applications in Business and Economics 3
Accounting 3
Finance 3
Marketing 4
Production 4
Economics 4
1.2 Data 4
Elements, Variables, and Observations 5
Scales of Measurement 6
Qualitative and Quantitative Data 7
Cross-Sectional and Time Series Data 7
1.3 Data Sources 8
Existing Sources 8
Statistical Studies 10
Data Acquisition Errors 11
1.4 Descriptive Statistics 12
1.5 Statistical Inference 14
Summary 16
Glossary 16
Exercises 17
Chapter 2 Descriptive Statistics: Tabular and Graphical Methods 23
Statistics in Practice: Colgate-Palmolive Company 24
2.1 Summarizing Qualitative Data 25
Frequency Distribution 25
Relative Frequency and Percent Frequency Distributions 26
Bar Graphs and Pie Charts 26
Exercises 28
2.2 Summarizing Quantitative Data 30
Frequency Distribution 30
Relative Frequency and Percent Frequency Distributions 32
Dot Plot 32
Histogram 33
Cumulative Distributions 34
Ogive 35
Exercises 36
2.3 Exploratory Data Analysis: The Stem-and-Leaf Display 39
Exercises 42
2.4 Crosstabulations and Scatter Diagrams 44
Crosstabulation 44
Scatter Diagram 45
Exercises 47
Summary 50
Glossary 51
Key Formulas 52
Supplementary Exercises 52
Case Problem: Consolidated Foods 58
Appendix 2.1 Using Minitab for Tabular and Graphical Methods 59
Appendix 2.2 Using Excel for Tabular and Graphical Methods 61
Chapter 3 Descriptive Statistics: Numerical Methods 72
Statistics in Practice: Small Fry Designs 73
3.1 Measures of Location 74
Mean 74
Median 75
Mode 76
Percentiles 77
Quartiles 78
Exercises 79
3.2 Measures of Variability 83
Range 84
Interquartile Range 84
Variance 84
Standard Deviation 86
Coefficient of Variation 87
Exercises 88
3.3 Measures of Relative Location and Detecting Outliers 89
z-Scores 90
Chebyshev's Theorem 90
Empirical Rule 91
Detecting Outliers 92
Exercises 93
3.4 Exploratory Data Analysis 95
Five-Number Summary 95
Box Plot 96
Exercises 97
3.5 Measures of Association Between Two Variables 100
Co variance 101
Interpretation of the Covariance 102
Correlation Coefficient 104
Interpretation of the Correlation Coefficient 105
Exercises 106
3.6 The Weighted Mean and Working With Grouped Data 109
Weighted Mean 109
Grouped Data 110
Exercises 112
Summary 114
Glossary 115
Key Formulas 116
Supplementary Exercises 117
Case Problem 1: Consolidated Foods, Inc. 123
Case Problem 2: National Health Care Association 124
Case Problem 3: Business Schools of Asia-Pacific 125
Appendix 3.1 Descriptive Statistics With Minitab 125
Appendix 3.2 Descriptive Statistics With Excel 129
Chapter 4 Introduction to Probability 132
Statistics in Practice: Morton International 133
4.1 Experiments, Counting Rules, and Assigning Probabilities 134
Counting Rules, Combinations, and Permutations 135
Assigning Probabilities 139
Probabilities for the KP＆L Project 141
Exercises 142
4.2 Events and Their Probabilities 144
Exercises 146
4.3 Some Basic Relationships of Probability 148
Complement of an Event 148
Addition Law 149
Exercises 152
4.4 Conditional Probability 153
Independent Events 156
Multiplication Law 157
Exercises 158
4.5 Bayes'Theorem 161
Tabular Approach 164
Exercises 165
Summary 167
Glossary 167
Key Formulas 168
Supplementary Exercises 169
Case Problem: Hamilton County Judges 173
Chapter 5 Discrete Probability Distributions 175
Statistics in Practice: Citibank 176
5.1 Random Variables 176
Discrete Random Variables 177
Continuous Random Variables 177
Exercises 178
5.2 Discrete Probability Distributions 179
Exercises 182
5.3 Expected Value and Variance 184
Expected Value 184
Variance 185
Exercises 186
5.4 Binomial Probability Distribution 189
A Binomial Experiment 189
Martin Clothing Store Problem 190
Using Tables of Binomial Probabilities 194
Expected Value and Variance for the Binomial Probability Distribution 196
Exercises 197
5.5 Poisson Probability Distribution 199
An Example Involving Time Intervals 199
An Example Involving Length or Distance Intervals 200
Exercises 201
5.6 Hypergeometric Probability Distribution 203
Exercises 204
Summary 205
Glossary 206
Key Formulas 206
Supplementary Exercises 207
Appendix 5.1 Discrete Probability Distributions With Minitab 210
Appendix 5.2 Discrete Probability Distributions With Excel 210
Chapter 6 Continuous Probability Distributions 212
Statistics in Practice: Procter ＆ Gamble 213
6.1 Uniform Probability Distribution 214
Area as a Measure of Probability 215
Exercises 217
6.2 Normal Probability Distribution 218
Normal Curve 219
Standard Normal Probability Distribution 221
Computing Probabilities for Any Normal Probability Distribution 226
Grear Tire Company Problem 227
Exercises 229
6.3 Exponential Probability Distribution 232
Computing Probabilities for the Exponential Distribution 232
Relationship Between the Poisson and Exponential Distributions 234
Exercises 234
Summary 236
Glossary 236
Key Formulas 236
Supplementary Exercises 237
Appendix 6.1 Continuous Probability Distributions With Minitab 239
Appendix 6.2 Continuous Probability Distributions With Excel 240
Chapter 7 Sampling and Sampling Distributions 241
Statistics in Practice: Mead Corporation 242
7.1 The Electronics Associates Sampling Problem 243
7.2 Simple Random Sampling 244
Sampling from a Finite Population 244
Sampling from an Infinite Population 246
Exercises 247
7.3 Point Estimation 249
Exercises 251
7.4 Introduction to Sampling Distributions 252
7.5 Sampling Distribution of x 255
Expected Value of x 256
Standard Deviation of x 256
Central Limit Theorem 258
Sampling Distribution of x of the EAI Sampling Problem 259
Practical Value of the Sampling Distribution of x 260
Relationship Between the Sample Size and the Sampling Distribution of x 261
Exercises 263
7.6 Sampling Distribution of p 265
Expected Value of p 266
Standard Deviation of p 267
Form of the Sampling Distribution of p 267
Practical Value of the Sampling Distribution of p 268
Exercises 269
7.7 Properties of Point Estimators 271
Unbiasedness 271
Efficiency 272
Consistency 273
7.8 Other Sampling Methods 273
Stratified Random Sampling 274
Cluster Sampling 274
Systematic Sampling 275
Convenience Sampling 275
Judgment Sampling 276
Summary 276
Glossary 277
Key Formulas 278
Supplementary Exercises 278
Appendix 7.1 The Expected Value and Standard Deviation of x 280
Appendix 7.2 Random Sampling With Minitab 282
Appendix 7.3 Random Sampling With Excel 283
Chapter 8 Interval Estimation 284
Statistics in Practice: Dollar General Corporation 285
8.1 Interval Estimation of a Population Mean: Large-Sample Case 286
CJW Estimation Problem 286
Sampling Error 287
Large-Sample Case With a Assumed Known 288
Large-Sample Case With a Estimated by s 291
Exercises 293
8.2 Interval Estimation of a Population Mean: Small-Sample Case 294
Small-Sample Case With a Assumed Known 295
Small-Sample Case With a Estimated by s 295
The Role of the Population Distribution 299
Exercises 300
8.3 Determining the Sample Size 303
Exercises 304
8.4 Interval Estimation of a Population Proportion 305
Determining the Sample Size 307
Exercises 309
Summary 310
Glossary 311
Key Formulas 312
Supplementary Exercises 312
Case Problem 1: Bock Investment Services 315
Case Problem 2: Gulf Real Estate Properties 317
Case Problem 3: Metropolitan Research, Inc. 317
Appendix 8.1 Interval Estimation of a Population Mean With Minitab 319
Appendix 8.2 Interval Estimation of a Population Mean With Excel 320
Chapter 9 Hypothesis Testing 323
Statistics in Practice: Harris Corporation 324
9.1 Developing Null and Alternative Hypotheses 325
Testing Research Hypotheses 325
Testing the Validity of a Claim 325
Testing in Decision-Making Situations 326
A Summary of Forms for Null and Alternative Hypotheses 326
Exercises 327
9.2 Type I and Type II Errors 327
Exercises 329
9.3 One-Tailed Tests About a Population Mean: Large-Sample Case 329
Using the Test Statistic 332
Using the p-Value 333
Summary: One-Tailed Tests About a Population Mean 335
Steps of Hypothesis Testing 336
Exercises 337
9.4 Two-Tailed Tests About a Population Mean: Large-Sample Case 339
p-Values for Two-Tailed Tests 341
Summary: Two-Tailed Tests About a Population Mean 342
Relationship Between Interval Estimation and Hypothesis Testing 342
Exercises 345
9.5 Tests About a Population Mean: Small-Sample Case 347
p-Values and the t Distribution 348
A Two-Tailed Test 349
Exercises 350
9.6 Test About a Population Proportion 353
Exercises 357
9.7 Hypothesis Testing and Decision Making 359
9.8 Calculating the Probability of Type II Errors 360
Exercises 363
9.9 Determining the Sample Size for a Hypothesis Test About a Population Mean 365
Exercises 368
Summary 369
Glossary 370
Key Formulas 371
Supplementary Exercises 371
Case Problem 1: Quality Associates, Inc. 374
Case Problem 2: Unemployment Study 375
Appendix 9.1 Hypothesis Testing With Minitab 376
Appendix 9.2 Hypothesis Testing With Excel 377
Chapter 10 Statistical Inference About Means and Proportions With Two Populations 380
Statistics in Practice: Fisons Corporation 381
10.1 Estimation of the Difference Between the Means of Two Populations: Independent Samples 382
Sampling Distributions of x1-x2, 383
Interval Estimate of u1-u2: Large-Sample Case 384
Interval Estimate of u1-u2: Small-Sample Case 386
Exercises 389
10.2 Hypothesis Tests About the Difference Between the Means of Two Populations: Independent Samples 391
Large-Sample Case 391
Small-Sample Case 394
Exercises 397
10.3 Inferences About the Difference Between the Means of Two Populations: Matched Samples 399
Exercises 402
10.4 Inferences About the Difference Between the Proportions of Two Populations 405
Sampling Distribution of px -- p2 405
Interval Estimation of px -- p2 406
Hypothesis Tests About p1 - p2 407
Exercises 409
Summary 411
Glossary 411
Key Formulas 412
Supplementary Exercises 414
Case Problem: Par, Inc. 416
Appendix 10.1 Two Population Means With Minitab 417
Appendix 10.2 Two Population Means With Excel 418
Chapter 11 Inferences About Population Variances 420
Statistics in Practice: U.S. General Accounting Office 421
11.1 Inferences About a Population Variance 422
Interval Estimation of a2 422
Hypothesis Testing 426
Exercises 430
11.2 Inferences About the Variances of Two Populations 432
Exercises 437
Summary 439
Key Formulas 439
Supplementary Exercises 440
Case Problem: Air Force Training Program 441
Appendix 11.1 Population Variances With Minitab 442
Appendix 11.2 Population Variances With Excel 444
Chapter 12 Tests of Goodness of Fit and Independence 446
Statistics in Practice: United Way 447
12.1 Goodness of Fit Test: A Multinomial Population 448
Exercises 451
12.2 Test of Independence 453
Exercises 457
12.3 Goodness of Fit Test: Poisson and Normal Distributions 460
Poisson Distribution 460
Normal Distribution 464
Exercises 467
Summary 469
Glossary 469
Key Formulas 469
Supplementary Exercises 470
Case Problem: A Bipartisan Agenda for Change 473
Appendix 12.1 Tests of Goodness of Fit and Independence With Minitab 474
Appendix 12.2 Tests of Goodness of Fit and Independence With Excel 475
Chapter 13 Analysis of Variance and Experimental Design 477
Statistics in Practice: Burke Marketing Services, Inc. 478
13.1 An Introduction to Analysis of Variance 478
Assumptions for Analysis of Variance 480
A Conceptual Overview 480
13.2 Analysis of Variance: Testing for the Equality of k Population Means 482
Between-Treatments Estimate of Population Variance 483
Within-Treatments Estimate of Population Variance 484
Comparing the Variance Estimates: The F Test 485
ANOVA Table 487
Computer Results for Analysis of Variance 487
Exercises 489
13.3 Multiple Comparison Procedures 492
Fisher's LSD 492
Type I Error Rates 495
Exercises 496
13.4 An Introduction to Experimental Design 497
Data Collection 499
13.5 Completely Randomized Designs 500
Between-Treatments Estimate of Population Variance 500
Within-Treatments Estimate of Population Variance 500
Comparing the Variance Estimates: The F Test 501
ANOVA Table 501
Pairwise Comparisons 501
Exercises 502
13.6 Randomized Block Design 505
Air Traffic Controller Stress Test 505
ANOVA Procedure 506
Computations and Conclusions 508
Exercises 510
13.7 Factorial Experiments 511
ANOVA Procedure 513
Computations and Conclusions 513
Exercises 517
Summary 519
Glossary 519
Key Formulas 520
Supplementary Exercises 523
Case Problem 1: Wentworth Medical Center 529
Case Problem 2: Compensation for ID Professionals 530
Appendix 13.1 Analysis of Variance and Experimental Design With Minitab 531
Appendix 13.2 Analysis of Variance and Experimental Design With Excel 532
Chapter 14 Simple Linear Regression 537
Statistics in Practice: Polaroid Corporation 538
14.1 Simple Linear Regression Model 539
Regression Model and Regression Equation 539
Estimated Regression Equation 540
14.2 Least Squares Method 541
Exercises 546
14.3 Coefficient of Determination 551
Correlation Coefficient 555
Exercises 556
14.4 Model Assumptions 558
14.5 Testing for Significance 560
Estimate of a2 560
t Test 561
Confidence Interval for B1 563
F Test 564
Some Cautions About the Interpretation of Significance Tests 566
Exercises 567
14.6 Using the Estimated Regression Equation for Estimation and Prediction 569
Point Estimation 569
Interval Estimation 569
Confidence Interval Estimate of the Mean Value of y 570
Prediction Interval Estimate of the Individual Value of y 572
Exercises 573
14.7 Computer Solution 575
Exercises 577
14.8 Residual Analysis: Validating Model Assumptions 579
Residual Plot Against x 580
Residual Plot Against y 583
Standardized Residuals 584
Normal Probability Plot 585
Exercises 587
14.9 Residual Analysis: Outliers and Influential Observations 588
Detecting Outliers 588
Detecting Influential Observations 591
Exercises 593
Summary 595
Glossary 596
Key Formulas 597
Supplementary Exercises 599
Case Problem 1: Spending and Student Achievement 604
Case Problem 2: U.S. Department of Transportation 606
Case Problem 3: Alumni Giving 607
Appendix 14.1 Calculus-Based Derivation of Least-Squares Formulas 607
Appendix 14.2 A Test for Significance Using Correlation 609
Appendix 14.3 Regression Analysis With Minitab 610
Appendix 14.4 Regression Analysis With Excel 611
Chapter 15 Multiple Regression 614
Statistics in Practice: Champion International Corporation 615
15.1 Multiple Regression Model 616
Regression Model and Regression Equation 616
Estimated Multiple Regression Equation 616
15.2 Least Squares Method 617
An Example: Butler Trucking Company 618
Note on Interpretation of Coefficients 621
Exercises 621
15.3 Multiple Coefficient of Determination 626
Exercises 627
15.4 Model Assumptions 629
15.5 Testing for Significance 630
F Test 630
t Test 633
Multicollinearity 634
Exercises 635
15.6 Using the Estimated Regression Equation for Estimation and Prediction 637
Exercises 638
15.7 Qualitative Independent Variables 639
An Example: Johnson Filtration, Inc. 639
Interpreting the Parameters 640
More Complex Qualitative Variables 642
Exercises 644
15.8 Residual Analysis 647
Detecting Outliers 648
Studentized Deleted Residuals and Outliers 649
Influential Observations 650
Using Cook's Distance Measure to Identify Influential Observations 650
Exercises 652
Summary 654
Glossary 655
Key Formulas 656
Supplementary Exercises 657
Case Problem 1: Consumer Research, Inc. 662
Case Problem 2: NFL Quarterback Rating 663
Case Problem 3: Predicting Student Proficiency Test Scores 665
Case Problem 4: Alumni Giving 665
Chapter 16 Regression Analysis: Model Building 668
Statistics in Practice: Monsanto Company 669
16.1 General Linear Model 670
Modeling Curvilinear Relationships 670
Interaction 674
Transformations Involving the Dependent Variable 676
Nonlinear Models That Are Intrinsically Linear 680
Exercises 681
16.2 Determining When to Add or Delete Variables 685
General Case 686
Use of p-Values 688
Exercises 688
16.3 Analysis of a Larger Problem 691
16.4 Variable Selection Procedures 695
Stepwise Regression 695
Forward Selection 697
Backward Elimination 697
Best-Subsets Regression 697
Making the Final Choice 698
Exercises 699
16.5 Residual Analysis 702
Autocorrelation and the Durbin-Watson Test 702
Exercises 708
16.6 Multiple Regression Approach to Analysis of Variance and Experimental Design 708
Exercises 711
Summary 712
Glossary 713
Key Formulas 713
Supplementary Problems 713
Case Problem 1: Unemployment Study 717
Case Problem 2: Analysis of PGA Tour Statistics 718
Case Problem 3: Predicting Graduation Rates for Colleges and Universities 719
Chapter 17 Index Numbers 721
Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics 722
17.1 Price Relatives 723
17.2 Aggregate Price Indexes 723
Exercises 726
17.3 Computing an Aggregate Price Index from Price Relatives 727
Exercises 728
17.4 Some Important Price Indexes 729
Consumer Price Index 729
Producer Price Index 730
Dow Jones Averages 730
17.5 Deflating a Series by Price Indexes 731
Exercises 733
17.6 Price Indexes: Other Considerations 734
Selection of Items 735
Selection of a Base Period 735
Quality Changes 735
17.7 Quantity Indexes 736
Exercises 737
Summary 737
Glossary 738
Key Formulas 738
Supplementary Exercises 739
Chapter 18 Forecasting 742
Statistics in Practice: Nevada Occupational Health Clinic 743
18.1 Components of a Time Series 744
Trend Component 744
Cyclical Component 745
Seasonal Component 746
Irregular Component 747
18.2 Smoothing Methods 747
Moving Averages 748
Weighted Moving Averages 750
Exponential Smoothing 751
Exercises 756
18.3 Trend Projection 758
Exercises 761
18.4 Trend and Seasonal Components 763
Multiplicative Model 764
Calculating the Seasonal Indexes 764
Deseasonalizing the Time Series 768
Using the Deseasonalized Time Series to Identify Trend 768
Seasonal Adjustments 771
Models Based on Monthly Data 771
Cyclical Component 771
Exercises 772
18.5 Regression Analysis 773
18.6 Qualitative Approaches 775
Delphi Method 775
Expert Judgment 776
Scenario Writing 776
Intuitive Approaches 776
Summary 776
Glossary 777
Key Formulas 778
Supplementary Exercises 778
Case Problem 1: Forecasting Food and Beverage Sales 782
Case Problem 2: Forecasting Lost Sales 783
Appendix 18.1 Forecasting With Minitab 785
Appendix 18.2 Forecasting With Excel 786
Chapter 19 Nonparametric Methods 788
Statistics in Practice: West Shell Realtors 789
19.1 Sign Test 791
Small-Sample Case 791
Large-Sample Case 793
Hypothesis Testing About a Median 794
Exercises 795
19.2 Wilcoxon Signed-Rank Test 797
Exercises 800
19.3 Mann-Whitney-Wilcoxon Test 802
Small-Sample Case 802
Large-Sample Case 804
Exercises 807
19.4 Kruskal-Wallis Test 810
Exercises 812
19.5 Rank Correlation 813
Test for Significant Rank Correlation 815
Exercises 815
Summary 817
Glossary 818
Key Formulas 818
Supplementary Exercises 819
Chapter 20 Statistical Methods for Quality Control 821
Statistics in Practice: Dow Chemical U.S.A. 822
20.1 Statistical Process Control 823
Control Charts 824
x Chart: Process Mean and Standard Deviation Known 825
x Chart: Process Mean and Standard Deviation Unknown 827
R Chart 830
p Chart 831
np Chart 833
Interpretation of Control Charts 834
Exercises 834
20.2 Acceptance Sampling 836
KALI, Inc.: An Example of Acceptance Sampling 838
Computing the Probability of Accepting a Lot 838
Selecting an Acceptance Sampling Plan 842
Multiple Sampling Plans 843
Exercises 844
Summary 845
Glossary 845
Key Formulas 846
Supplementary Exercises 847
Appendix 20.1 Control Charts With Minitab 849
Chapter 21 Sample Survey 850
Statistics in Practice: Cinergy 851
21.1 Terminology Used in Sample Surveys 851
21.2 Types of Surveys and Sampling Methods 852
21.3 Survey Errors 854
Nonsampling Error 854
Sampling Error 854
21.4 Simple Random Sampling 855
Population Mean 855
Population Total 856
Population Proportion 858
Determining the Sample Size 858
Exercises 860
21.5 Stratified Simple Random Sampling 861
Population Mean 862
Population Total 864
Population Proportion 864
Determining the Sample Size 865
Exercises 869
21.6 Cluster Sampling 870
Population Mean 872
Population Total 874
Population Proportion 874
Determining the Sample Size 876
Exercises 876
21.7 Systematic Sampling 878
Summary 878
Glossary 879
Key Formulas 879
Supplementary Exercises 883
Appendix A References and Bibliography 886
Appendix B Tables 888
Appendix C Summation Notation 916
Appendix D Answers to Even-Numbered Exerclses 918
Appendix E Solutions to Self-Test Exercises 936
Index 969