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Copyright, vi
Preface, xxv
Chapter 1. The Where, Why, and How of Data Collection, 1
What is Business Statistics?, 2
Descriptive Statistics, 2
Charts and Graphs, 3
Inferential Tools, 5
Estimation, 5
Hypothesis Testing, 6
Tools for Collecting Data, 7
Data Collection Methods, 7
Experiments, 7
Telephone Surveys, 8
Written Questionnaires and Surveys, 9
Direct Observation and Personal Interviews, 12
Other Data Collection Methods, 12
Data Collection Issues, 12
Interviewer Bias, 13
Nonresponse Bias, 13
Selection Bias, 13
Observer Bias, 13
Measurement Error, 13
Internal Validity, 14
External Validity, 14
Populations, Samples, and Sampling Techniques, 15
Populations and Samples, 15
Parameters and Statistics, 16
Sampling Techniques, 16
Nonstatistical Sampling, 16
Statistical Sampling, 16
Simple Random Sampling, 16
Random Number Table, 17
Stratified Random Sampling, 18
Systematic Random Sampling, 19
Cluster Sampling, 19
Data Types and Data Measurement Levels, 21
Quantitative and Qualitative Data, 21
Time-Series Data and Cross-Sectional Data, 22
Data Measurement Levels, 22
Nominal Data, 22
Ordinal Data, 23
Interval Data, 23
Ratio Data, 23
Summary and Conclusions, 27
Key Terms, 28
Chapter Exercises, 29
Video Case 1: Statistical Data Collection @ McDonald’s, 29
References, 30
Chapter 2. Graphs, Charts, and Tables—Describing Your Data, 31
Frequency Distributions and Histograms, 32
Frequency Distribution, 32
Grouped Data Frequency Distributions, 37
Steps for Grouping Data into Classes, 39
Histograms, 43
Issues with Excel, 46
Relative Frequency Histograms and Ogives, 47
Joint Frequency Distributions, 49
Bar Charts, Pie Charts, and Stem and Leaf Diagrams, 57
Bar Charts, 57
Pie Charts, 63
Stem and Leaf Diagrams, 65
Line Charts and Scatter Diagrams, 71
Line Charts, 71
Scatter Diagrams, 75
Summary and Conclusions, 82
Key Terms, 83
Chapter Exercises, 83
Video Case 3: Drive-Thru Service Times @ McDonald’s, 85
Case 2.1: Server Downtime, 86
Case 2.2: Yakima Apples, Inc., 87
Case 2.3: Welco Lumber Company—Part A, 88
References, 90
Chapter 3. Describing Data Using Numerical Measures, 91
Measures of Center and Location, 92
Parameters and Statistics, 92
Population Mean, 92
Sample Mean, 96
The Impact of Extreme Values on the Mean, 98
Median, 98
Skewed and Symmetric Distributions, 100
Mode, 101
Applying the Measures of Central Tendency, 102
Issues with Excel, 104
Other Measures of Location, 104
Weighted Mean, 104
Percentiles, 105
Quartiles, 107
Issues with Excel, 107
Box and Whisker Plots, 107
Data-Level Issues, 110
Measures of Variation, 115
Range, 116
Interquartile Range, 116
Population Variance and Standard Deviation, 118
Sample Variance and Standard Deviation, 121
Using the Mean and the Standard Deviation Together, 128
Coefficient of Variation, 129
The Empirical Rule, 130
Tchebysheff’s Theorem, 132
Standardized Data Values, 132
Summary and Conclusions, 138
Key Terms, 140
Chapter Exercises, 140
Video Case 3: Drive-Thru Service Times @ McDonald’s, 145
Case 3.1: WGI—Human Resources, 146
Case 3.2: National Call Center, 147
Case 3.3: Welco Lumber Company—Part B, 147
Case 3.4: AJ’s Fitness Center, 148
References, 148
Chapters 1–3. Special Review Section, 149
Chapters 1–3, 149
Exercises, 152
Review Case 1: State Department of Insurance, 153
Term Project Assignments, 154
Chapter 4. Using Probability and Probability Distributions, 157
The Basics of Probability, 158
Important Probability Terms, 158
Events and Sample Space, 159
Using Tree Diagrams, 160
Mutually Exclusive Events, 162
Independent and Dependent Events, 163
Methods of Assigning Probability, 164
Classical Probability Assessment, 164
Relative Frequency Assessment, 165
Subjective Probability Assessment, 168
The Rules of Probability, 172
Measuring Probabilities, 172
Possible Values and Sum, 172
Addition Rule for Individual Outcomes, 172
Complement Rule, 175
Addition Rule for Two Events, 176
Addition Rule for Mutually Exclusive Events, 180
Conditional Probability, 180
Tree Diagrams, 183
Conditional Probability for Independent Events, 184
Multiplication Rules, 185
Multiplication Rule for Two Events, 185
Using a Tree Diagram, 186
Multiplication Rule for Independent Events, 187
Bayes’ Theorem, 188
Bayes’ Theorem Using a Tree Diagram, 190
Summary and Conclusions, 197
Key Terms, 198
Chapter Exercises, 199
Case 4.1: Great Air Commuter Service, 201
Case 4.2: Let’s Make a Deal, 202
References, 202
Chapter 5. Discrete Probability Distributions, 203
Introduction to Discrete Probability Distributions, 204
Random Variables, 204
Displaying Discrete Probability Distributions Graphically, 205
Mean and Standard Deviation of Discrete Distributions, 205
Calculating the Mean, 205
Calculating the Standard Deviation, 206
The Binomial Probability Distribution, 212
The Binomial Distribution, 213
Characteristics of the Binomial Distribution, 213
Combinations, 215
Binomial Formula, 216
Using the Binomial Distribution Table, 217
Mean and Standard Deviation of the Binomial Distribution, 220
Mean of a Binomial Distribution, 220
Standard Deviation of a Binomial Distribution, 222
Additional Information about the Binomial Distribution, 223
Other Discrete Probability Distributions, 228
The Poisson Distribution, 228
Characteristics of the Poisson Distribution, 229
Poisson Probability Distribution Table, 230
The Mean and Standard Deviation of the Poisson Distribution, 233
The Hypergeometric Distribution, 235
The Hypergeometric Distribution with More Than Two Possible Outcomes per Trial, 238
Summary and Conclusions, 243
Key Terms, 244
Chapter Exercises, 244
Case 5.1: SaveMor Pharmacies, 247
Case 5.2: Arrowmark Vending, 248
Case 5.3: Boise Cascade Corporation, 249
References, 249
Chapter 6. Introduction to Continuous Probability Distributions, 251
The Normal Probability Distribution, 252
The Normal Distribution, 253
The Standard Normal Distribution, 254
Using the Standard Normal Table, 256
Approximate Areas Under the Normal Curve, 264
Other Continuous Probability Distributions, 269
Uniform Probability Distribution, 270
The Exponential Probability Distribution, 272
Summary and Conclusions, 277
Key Terms, 278
Chapter Exercises, 278
Case 6.1: State Entitlement Programs, 282
Case 6.2: Credit Data, Inc., 282
Case 6.3: American Oil Company, 282
References, 283
Chapter 7. Introduction to Sampling Distributions, 285
Sampling Error: What It Is and Why It Happens, 286
Calculating Sampling Error, 286
The Role of Sample Size in Sampling Error, 290
Sampling Distribution of the Mean, 296
Simulating the Sampling Distribution for x̄, 297
Sampling from Normal Populations, 300
The Central Limit Theorem, 305
Sampling Distribution of a Proportion, 313
Working with Proportions, 313
Sampling Distribution of p, 315
Summary and Conclusions, 323
Key Terms, 323
Chapter Exercises, 324
Case 7.1: Carpita Bottling Company, 327
Case 7.2: Truck Safety Inspection, 328
References, 328
Chapter 8. Estimating Single Population Parameters, 329
Point and Confidence Interval Estimates for a Population Mean, 330
Point Estimates and Confidence Intervals, 330
Confidence Interval Estimate for the Population Mean, σ Known, 332
Confidence Interval Calculation, 334
Impact of the Confidence Level on the Interval Estimate, 337
Impact of the Sample Size on the Interval Estimate, 339
Confidence Interval Estimates for the Population Mean, σ Unknown, 340
Student’s t-Distribution, 340
Estimation with Larger Sample Sizes, 346
Determining the Required Sample Size for Estimating a Population Mean, 351
Determining the Required Sample Size for Estimating μ, σ Known, 351
Determining the Required Sample Size for Estimating μ, σ Unknown, 353
Estimating a Population Proportion, 358
Confidence Interval Estimate for a Population Proportion, 359
Determining the Required Sample Size for Estimating a Population Proportion, 361
Summary and Conclusions, 366
Key Terms, 368
Chapter Exercises, 368
Video Case 4: New Product Introductions @ McDonald’s, 370
Case 8.1: Management Solutions, Inc., 371
Case 8.2: Federal Aviation Administration, 372
Case 8.3: Cell Phone Use, 372
References, 373
Chapter 9. Introduction to Hypothesis Testing, 375
Hypothesis Tests for Means, 376
Formulating the Hypotheses, 376
Null and Alternative Hypotheses, 376
Testing the Status Quo, 377
Testing a Research Hypothesis, 377
Testing a Claim About the Population, 378
Types of Statistical Errors, 380
Significance Level and Critical Value, 381
Hypothesis Tests for μ, σ Known, 382
Calculating Critical Values, 382
Decision Rules and Test Statistics, 383
p-value Approach, 387
Types of Hypothesis Tests, 389
p-Value for Two-Tailed Tests, 389
Hypothesis Test for μ, σ Unknown, 391
Hypothesis Tests for Proportions, 400
Testing a Hypothesis About a Single Population Proportion, 400
Type II Errors, 407
Calculating Beta, 407
Controlling Alpha and Beta, 408
Power of the Test, 414
Summary and Conclusions, 419
Key Terms, 420
Chapter Exercises, 421
Video Case 4: New Product Introductions @ McDonald’s, 426
Case 9.1: Campbell Brewery, Inc., Part 1, 427
Case 9.2: Wings of Fire, 427
References, 428
Chapter 10. Estimation and Hypothesis Testing for Two Population Parameters, 429
Estimation for Two Population Means Using Independent Samples, 430
Estimating the Difference Between Two Population Means When σ_1 and σ_2 Are Known, Using Independent Samples, 430
Estimating the Difference Between Two Means When σ_1 and σ_2 Are Unknown, Using Independent Samples, 432
What If the Population Variances Are Not Equal?, 436
Hypothesis Tests for Two Population Means Using Independent Samples, 441
Testing for μ_1 - μ_2 When σ_1 and σ_2 Are Known, Using Independent Samples, 442
Using p-values, 444
Testing μ_1 - μ_2 When σ_1 and σ_2 Are Unknown, Using Independent Samples, 444
What If the Population Variances Are Not Equal?, 452
Interval Estimation and Hypothesis Tests for Paired Samples, 456
Why Use Paired Samples?, 457
Hypothesis Testing for Paired Samples, 460
Estimation and Hypothesis Tests for Two Population Proportions, 465
Estimating the Difference Between Two Population Proportions, 465
Hypothesis Tests for the Difference Between Two Population Proportions, 466
Summary and Conclusions, 473
Key Terms, 475
Chapter Exercises, 475
Case 10.1: Motive Power Company—Part 1, 478
Case 10.2: Hamilton Marketing Services, 479
Case 10.3: Green Valley Assembly Company, 479
Case 10.4: U-Need-It Rental Agency, 480
References, 480
Chapter 11. Hypothesis Tests and Estimation for Population Variances, 481
Hypothesis Tests and Estimation for a Single Population Variance, 482
Chi-Square Test for One Population Variance, 482
Interval Estimation for a Population Variance, 488
Hypothesis Tests for Two Population Variances, 492
F-Test for Two Population Variances, 492
Additional F-test Considerations, 501
Summary and Conclusions, 504
Key Term, 504
Chapter Exercises, 504
Case 11.1: Motive Power Company—Part 2, 507
References, 508
Chapter 12. Analysis of Variance, 509
One-Way Analysis of Variance, 510
The Logic Behind One-Way ANOVA, 510
Partitioning the Sum of Squares, 513
The ANOVA Assumptions, 514
Applying One-Way ANOVA, 517
The Tukey-Kramer Procedure for Multiple Comparisons, 525
Fixed Effects Versus Random Effects in Analysis of Variance, 530
Randomized Complete Block Analysis of Variance, 535
Randomized Complete Block ANOVA, 535
Was Blocking Necessary?, 538
Fisher’s Least Significant Difference Test, 543
Two-Factor Analysis of Variance with Replication, 548
Two-Factor ANOVA with Replications, 548
Interaction Explained, 550
A Caution About Interaction, 555
Summary and Conclusions, 559
Key Terms, 560
Chapter Exercises, 560
Video Case 3: Drive-Thru Service Times @ McDonald’s, 563
Case 12.1: Agency for New Americans, 564
Case 12.2: McLaughlin Salmon Works, 565
Case 12.3: NW Pulp and Paper, 566
Case 12.4: Quinn Restoration, 567
Business Statistics Capstone Project, 568
References, 569
Chapters 8–12. Special Review Section, 571
Chapters 8–12, 571
Using the Flow Diagrams, 584
Exercises, 585
Term Project Assignments, 587
Business Statistics Capstone Project, 587
Chapter 13. Goodness-of-Fit Tests and Contingency Analysis, 589
Introduction to Goodness-of-Fit Tests, 590
Chi-Square Goodness-of-Fit Test, 591
Introduction to Contingency Analysis, 605
2 × 2 Contingency Tables, 605
r × c Contingency Tables, 610
Chi-Square Test Limitations, 612
Summary and Conclusions, 616
Key Term, 616
Chapter Exercises, 616
Case 13.1: American Oil Company, 619
Case 13.2: Bentford Electronics, Part 1, 619
References, 620
Chapter 14. Introduction to Linear Regression and Correlation Analysis, 621
Scatter Plots and Correlation, 622
Correlation Versus Regression, 623
The Correlation Coefficient, 623
Significance Test for the Correlation, 627
Cause-and-Effect Interpretations, 629
Simple Linear Regression Analysis, 633
The Regression Model and Assumptions, 633
Meaning of the Regression Coefficients, 634
Least Squares Regression Properties, 640
Significance Tests in Regression Analysis, 643
The Coefficient of Determination, R^2, 644
Significance of the Slope Coefficient, 648
Uses for Regression Analysis, 657
Regression Analysis for Description, 657
Regression Analysis for Prediction, 660
Confidence Interval for the Average y, Given x, 661
Prediction Interval for a Particular y, Given x, 661
Residual Analysis, 663
Common Problems Using Regression Analysis, 664
Summary and Conclusions, 670
Key Terms, 671
Chapter Exercises, 671
Case 14.1: A & A Industrial Products, 675
Case 14.2: Sapphire Coffee—Part 1, 676
Case 14.3: Alamar Industries, 676
Case 14.4: Continental Trucking, 677
References, 677
Chapter 15. Multiple Regression Analysis and Model Building, 679
Introduction to Multiple Regression Analysis, 680
Basic Model-Building Concepts, 682
Model Specification, 683
Model Building, 683
Model Diagnosis, 683
Computing the Regression Equation, 687
The Coefficient of Determination, 689
Is the Model Significant?, 690
Are the Individual Variables Significant?, 692
Is the Standard Deviation of the Regression Model Too Large?, 693
Is Multicollinearity a Problem?, 695
Confidence Interval Estimation for Regression Coefficients, 697
Using Qualitative Independent Variables, 703
Possible Improvements to the First City Appraisal Model, 706
Working with Nonlinear Relationships, 711
Analyzing Interaction Effects, 718
Stepwise Regression, 725
Forward Selection, 725
Backward Elimination, 726
Standard Stepwise Regression, 730
Best Subsets Regression, 731
Determining the Aptness of the Model, 737
Analysis of Residuals, 737
Checking for Linearity, 738
Do the Residuals Have Equal Variances at all Levels of Each x Variable?, 740
Are the Residuals Independent?, 740
Checking for Normally Distributed Error Terms, 740
Corrective Actions, 743
Summary and Conclusions, 748
Key Terms, 749
Chapter Exercises, 749
Case 15.1: Dynamic Scales, Inc., 752
Case 15.2: Glaser Machine Works, 753
Case 15.3: Hawlins Manufacturing, 754
Case 15.4: Sapphire Coffee—Part 2, 754
Case 15.5: Wendell Motors, 755
References, 755
Chapter 16. Analyzing and Forecasting Time-Series Data, 757
Introduction to Forecasting, Time-Series Data, and Index Numbers, 758
General Forecasting Issues, 758
Components of a Time Series, 759
Trend Component, 760
Seasonal Component, 760
Cyclical Component, 761
Random Component, 762
Introduction to Index Numbers, 762
Aggregate Price Indexes, 764
Weighted Aggregate Price Indexes, 765
The Paasche Index, 765
The Laspeyres Index, 766
Commonly Used Index Numbers, 768
Consumer Price Index, 768
Producer Price Index, 768
Stock Market Indexes, 769
Using Index Numbers to Deflate a Time Series, 769
Trend-Based Forecasting Techniques, 772
Developing a Trend-Based Forecasting Model, 772
Comparing the Forecast Values to the Actual Data, 774
Autocorrelation, 777
True Forecasts, 781
Nonlinear Trend Forecasting, 782
Some Words of Caution, 786
Adjusting for Seasonality, 786
Computing Seasonal Indexes, 787
The Need to Normalize the Indexes, 790
Deseasonalizing, 791
Using Dummy Variables to Represent Seasonality, 793
Forecasting Using Smoothing Methods, 799
Exponential Smoothing, 799
Single Exponential Smoothing, 800
Double Exponential Smoothing, 805
Summary and Conclusions, 812
Key Terms, 813
Chapter Exercises, 813
Video Case 2: Restaurant Location & Re-imaging Decisions @ McDonald’s, 816
Case 16.1: Park Falls Chamber of Commerce, 817
Case 16.2: The St. Louis Companies, 818
Case 16.3: Wagner Machine Works, 818
References, 819
Chapter 17. Introduction to Nonparametric Statistics, 821
The Wilcoxon Signed Rank Test for One Population Median, 822
The Wilcoxon Signed Rank Test—Single Population, 822
Nonparametric Tests for Two Population Medians, 828
The Mann–Whitney U-Test, 828
Mann–Whitney U-Test—Large Samples, 832
The Wilcoxon Matched-Pairs Signed Rank Test, 834
Ties in the Data, 836
Large-Sample Wilcoxon Test, 836
Kruskal–Wallis One-Way Analysis of Variance, 842
Limitations and Other Considerations, 846
Summary and Conclusion, 849
Chapter Exercises, 851
Case 17.A: Bentford Electronics, 854
References, 855
Chapter 18. Introduction to Quality and Statistical Process Control, 857
Quality Management and Tools for Process Improvement, 858
The Tools of Quality for Process Improvement, 860
Process Flowcharts, 860
Brainstorming, 860
Fishbone Diagram, 860
Histograms, 860
Trend Charts, 861
Scatter Plots, 861
Statistical Process Control Charts, 861
Introduction to Statistical Process Control Charts, 862
The Existence of Variation, 862
Sources of Variation, 863
Types of Variation, 863
The Predictability of Variation: Understanding the Normal Distribution, 863
The Concept of Stability, 864
Introducing Statistical Process Control Charts, 864
x̄-Chart and R-Chart, 865
Using the Control Charts, 871
p-Charts, 874
Using the p-Chart, 877
c-Charts, 878
Other Control Charts, 881
Summary and Conclusions, 884
Key Terms, 886
Chapter Exercises, 886
Case 18.1: Izbar Precision Casters, Inc., 888
References, 889
Chapter 19. Introduction to Decision Analysis, 891
Decision-Making Environments and Decision Criteria, 892
Certainty, 892
Uncertainty, 893
Decision Criteria, 895
Nonprobabilistic Decision Criteria, 896
Probabilistic Decision Criteria, 897
Expected-Value Criterion, 897
Cost of Uncertainty, 902
Decision-Tree Analysis, 905
Harris Publishing Company, 908
Sensitivity Analysis, 920
Summary and Conclusions, 922
Key Terms, 922
Chapter Exercises, 922
Case 19.1: Rockstone International, 926
Case 19.2: Hadden Materials and Supplies, Inc., 927
References, 928
Appendixes, 929
Appendix A. Random Numbers Table, 930
Appendix B. Binomial Distribution Table, 931
Appendix C. Poisson Probability Distribution Table, 938
Appendix D. Standard Normal Distribution Table, 943
Appendix E. Exponential Distribution Table, 944
Appendix F. Values of t for Selected Probabilities, 945
Appendix G. Values of χ^2 for Selected Probabilities, 946
Appendix H. F-Distribution Table, 947
Appendix I. Critical Values of Hartley’s F_max Test, 953
Appendix J. Distribution of the Studentized Range (q-values), 954
Appendix K. Critical Values of r in the Runs Test, 956
Appendix L. Mann-Whitney U Test Probabilities (n < 9), 957
Appendix M. Mann-Whitney U Test Critical Values (9 ≤ n ≤ 20), 959
Appendix N. Critical Values of T in the Wilcoxon Matched-Pairs Signed-Ranks Test (n ≤ 25), 961
Appendix O. Critical Values d_L and d_U of the Durbin-Watson Statistic D, 962
Appendix P. Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test, 964
Appendix Q. Control Chart Factors, 965
Answers to Selected Odd-Numbered Problems, 967
Glossary, 1029
Index, 1035
Values of t for Selected Probabilities, IBC-1
Standard Normal Distribution Table, IBC-2

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