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Copyright, vi
Preface, xxi
About the Author, xxxii
UNIT I. INTRODUCTION, 1
Chapter 1. Introduction to Statistics, 2
DECISION DILEMMA: Statistics Describe the State of Business in India’s Countryside, 3
1.1. STATISTICS IN BUSINESS, 4
Marketing, 4
Management, 4
Finance, 4
Economics, 4
Accounting, 4
Management Information Systems, 5
1.2. BASIC STATISTICAL CONCEPTS, 5
1.3. DATA MEASUREMENT, 8
Nominal Level, 8
Ordinal Level, 9
Interval Level, 9
Ratio Level, 10
Comparison of the Four Levels of Data, 10
Statistical Analysis Using the Computer: Excel and MINITAB, 11
Summary, 13
Key Terms, 13
Supplementary Problems, 13
Analyzing the Databases, 14
CASE: DiGiorno Pizza: Introducing a Frozen Pizza to Compete with Carry-Out, 16
Chapter 2. Charts and Graphs, 18
DECISION DILEMMA: Energy Consumption Around the World, 19
2.1. FREQUENCY DISTRIBUTIONS, 20
Class Midpoint, 20
Relative Frequency, 21
Cumulative Frequency, 21
2.2. GRAPHICAL DEPICTION OF DATA, 23
Histograms, 23
Using Histograms to Get an Initial Overview of the Data, 25
Frequency Polygons, 26
Ogives, 26
Pie Charts, 26
Stem and Leaf Plots, 29
Pareto Charts, 31
2.3. GRAPHICAL DEPICTION OF TWO-VARIABLE NUMERICAL DATA: SCATTER PLOTS, 34
Summary, 37
Key Terms, 37
Supplementary Problems, 38
Analyzing the Databases, 41
CASE: Soap Companies Do Battle, 42
Using the Computer, 43
Chapter 3. Descriptive Statistics, 46
DECISION DILEMMA: Laundry Statistics, 47
3.1. MEASURES OF CENTRAL TENDENCY: UNGROUPED DATA, 48
Mode, 48
Median, 48
Mean, 49
Percentiles, 51
Steps in Determining the Location of a Percentile, 51
Quartiles, 52
3.2. MEASURES OF VARIABILITY: UNGROUPED DATA, 55
Range, 56
Interquartile Range, 56
Mean Absolute Deviation, Variance, and Standard Deviation, 57
Mean Absolute Deviation, 59
Variance, 59
Standard Deviation, 60
Meaning of Standard Deviation, 60
Empirical Rule, 60
Chebyshev’s Theorem, 62
Population versus Sample Variance and Standard Deviation, 63
Computational Formulas for Variance and Standard Deviation, 64
z Scores, 66
Coefficient of Variation, 66
3.3. MEASURES OF CENTRAL TENDENCY AND VARIABILITY: GROUPED DATA, 70
Measures of Central Tendency, 70
Mean, 70
Mode, 71
Measures of Variability, 71
3.4. MEASURES OF SHAPE, 76
Skewness, 76
Skewness and the Relationship of the Mean, Median, and Mode, 76
Coefficient of Skewness, 77
Kurtosis, 78
Box and Whisker Plots, 78
3.5. MEASURES OF ASSOCIATION, 80
Correlation, 80
3.6. DESCRIPTIVE STATISTICS ON THE COMPUTER, 84
Summary, 86
Key Terms, 87
Formulas, 87
Supplementary Problems, 88
Analyzing the Databases, 93
CASE: Coca-Cola Goes Small in Russia, 93
Using the Computer, 94
Chapter 4. Probability, 96
DECISION DILEMMA: Equity of the Sexes in the Workplace, 97
4.1. INTRODUCTION TO PROBABILITY, 98
4.2. METHODS OF ASSIGNING PROBABILITIES, 98
Classical Method of Assigning Probabilities, 98
Relative Frequency of Occurrence, 99
Subjective Probability, 100
4.3. STRUCTURE OF PROBABILITY, 100
Experiment, 100
Event, 100
Elementary Events, 100
Sample Space, 101
Unions and Intersections, 102
Mutually Exclusive Events, 102
Independent Events, 102
Collectively Exhaustive Events, 103
Complementary Events, 103
Counting the Possibilities, 104
The mn Counting Rule, 104
Sampling from a Population with Replacement, 104
Combinations: Sampling from a Population without Replacement, 104
4.4. MARGINAL, UNION, JOINT, AND CONDITIONAL PROBABILITIES, 105
4.5. ADDITION LAWS, 107
Probability Matrices, 108
Complement of a Union, 111
Special Law of Addition, 112
4.6. MULTIPLICATION LAWS, 115
General Law of Multiplication, 115
Special Law of Multiplication, 117
4.7. CONDITIONAL PROBABILITY, 120
Independent Events, 123
4.8. REVISION OF PROBABILITIES: BAYES’ RULE, 127
Summary, 132
Key Terms, 133
Formulas, 133
Supplementary Problems, 133
Analyzing the Databases, 137
CASE: Colgate-Palmolive Makes a “Total” Effort, 137
UNIT II. DISTRIBUTIONS AND SAMPLING, 139
Chapter 5. Discrete Distributions, 140
DECISION DILEMMA: Life with a Cell Phone, 141
5.1. DISCRETE VERSUS CONTINUOUS DISTRIBUTIONS, 142
5.2. DESCRIBING A DISCRETE DISTRIBUTION, 143
Mean, Variance, and Standard Deviation of Discrete Distributions, 144
Mean or Expected Value, 144
Variance and Standard Deviation of a Discrete Distribution, 144
5.3. BINOMIAL DISTRIBUTION, 147
Solving a Binomial Problem, 148
Using the Binomial Table, 151
Using the Computer to Produce a Binomial Distribution, 152
Mean and Standard Deviation of a Binomial Distribution, 153
Graphing Binomial Distributions, 154
5.4. POISSON DISTRIBUTION, 158
Working Poisson Problems by Formula, 159
Using the Poisson Tables, 161
Mean and Standard Deviation of a Poisson Distribution, 162
Graphing Poisson Distributions, 162
Using the Computer to Generate Poisson Distributions, 163
Approximating Binomial Problems by the Poisson Distribution, 163
5.5. HYPERGEOMETRIC DISTRIBUTION, 168
Using the Computer to Solve for Hypergeometric Distribution Probabilities, 170
Summary, 173
Key Terms, 174
Formulas, 174
Supplementary Problems, 174
Analyzing the Databases, 179
CASE: Kodak Transitions Well into the Digital Camera Market, 179
Using the Computer, 180
Chapter 6. Continuous Distributions, 182
DECISION DILEMMA: The Cost of Human Resources, 183
6.1. THE UNIFORM DISTRIBUTION, 183
Determining Probabilities in a Uniform Distribution, 185
Using the Computer to Solve for Uniform Distribution Probabilities, 187
6.2. NORMAL DISTRIBUTION, 188
History of the Normal Distribution, 189
Probability Density Function of the Normal Distribution, 189
Standardized Normal Distribution, 190
Solving Normal Curve Problems, 191
Using the Computer to Solve for Normal Distribution Probabilities, 197
6.3. USING THE NORMAL CURVE TO APPROXIMATE BINOMIAL DISTRIBUTION PROBLEMS, 200
Correcting for Continuity, 202
6.4. EXPONENTIAL DISTRIBUTION, 206
Probabilities of the Exponential Distribution, 206
Using the Computer to Determine Exponential Distribution Probabilities, 209
Summary, 211
Key Terms, 212
Formulas, 212
Supplementary Problems, 212
Analyzing the Databases, 215
CASE: Mercedes Goes After Younger Buyers, 216
Using the Computer, 217
Chapter 7. Sampling and Sampling Distributions, 218
DECISION DILEMMA: What Is the Attitude of Maquiladora Workers?, 219
7.1. SAMPLING, 219
Reasons for Sampling, 220
Reasons for Taking a Census, 221
Frame, 221
Random versus Nonrandom Sampling, 221
Random Sampling Techniques, 222
Simple Random Sampling, 222
Stratified Random Sampling, 223
Systematic Sampling, 224
Cluster (or Area) Sampling, 225
Nonrandom Sampling, 226
Convenience Sampling, 226
Judgment Sampling, 227
Quota Sampling, 227
Snowball Sampling, 228
Sampling Error, 228
Nonsampling Errors, 228
7.2. SAMPLING DISTRIBUTION OF x̄, 230
Sampling from a Finite Population, 237
7.3. SAMPLING DISTRIBUTION OF p, 239
Summary, 243
Key Terms, 244
Formulas, 244
Supplementary Problems, 244
Analyzing the Databases, 247
CASE: Shell Attempts to Return to Premiere Status, 247
Using the Computer, 248
UNIT III. MAKING INFERENCES ABOUT POPULATION PARAMETERS, 249
Chapter 8. Statistical Inference: Estimation for Single Populations, 252
DECISION DILEMMA: Compensation for Purchasing Managers, 253
8.1. ESTIMATING THE POPULATION MEAN USING THE z STATISTIC (σ KNOWN), 255
Finite Correction Factor, 258
Estimating the Population Mean Using the z Statistic when the Sample Size is Small, 259
Using the Computer to Construct z Confidence Intervals for the Mean, 260
8.2. ESTIMATING THE POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN), 262
The t Distribution, 263
Robustness, 263
Characteristics of the t Distribution, 263
Reading the t Distribution Table, 263
Confidence Intervals to Estimate the Population Mean Using the t Statistic, 264
Using the Computer to Construct t Confidence Intervals for the Mean, 266
8.3. ESTIMATING THE POPULATION PROPORTION, 269
Using the Computer to Construct Confidence Intervals of the Population Proportion, 271
8.4. ESTIMATING THE POPULATION VARIANCE, 273
8.5. ESTIMATING SAMPLE SIZE, 277
Sample SizeWhen Estimating µ, 277
Determining Sample SizeWhen Estimating p, 279
Summary, 282
Key Terms, 282
Formulas, 282
Supplementary Problems, 283
Analyzing the Databases, 286
CASE: Thermatrix, 286
Using the Computer, 287
Chapter 9. Statistical Inference: Hypothesis Testing for Single Populations, 290
DECISION DILEMMA: Business Referrals, 291
9.1. INTRODUCTION TO HYPOTHESIS TESTING, 292
Types of Hypotheses, 293
Research Hypotheses, 294
Statistical Hypotheses, 294
Substantive Hypotheses, 296
Using the HTAB System to Test Hypotheses, 297
Rejection and Nonrejection Regions, 299
Type I and Type II Errors, 300
9.2. TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE z STATISTIC (σ KNOWN), 301
Testing the Mean with a Finite Population, 303
Using the p -Value to Test Hypotheses, 304
Using the Critical Value Method to Test Hypotheses, 305
Using the Computer to Test Hypotheses About a Population Mean Using the z Statistic, 308
9.3. TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN), 310
Using the Computer to Test Hypotheses About a Population Mean Using the t Test, 313
9.4. TESTING HYPOTHESES ABOUT A PROPORTION, 316
Using the Computer to Test Hypotheses About a Population Proportion, 321
9.5. TESTING HYPOTHESES ABOUT A VARIANCE, 322
9.6. SOLVING FOR TYPE II ERRORS, 326
Some Observations About Type II Errors, 330
Operating Characteristic and Power Curves, 330
Effect of Increasing Sample Size on the Rejection Limits, 331
Summary, 335
Key Terms, 336
Formulas, 336
Supplementary Problems, 337
Analyzing the Databases, 339
CASE: Frito-Lay Target the Hispanic Market, 340
Using the Computer, 341
Chapter 10. Statistical Inferences about Two Populations, 344
DECISION DILEMMA: Comparing Austria to France on Labor Statistics, 345
10.1. HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS USING THE z STATISTIC (POPULATION VARIANCES KNOWN), 348
Hypothesis Testing, 348
Confidence Intervals, 352
Using the Computer to Test Hypotheses About the Difference in Two Population Means Using the z Test, 354
10.2. HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS: INDEPENDENT SAMPLES AND POPULATION VARIANCES UNKNOWN, 357
Hypothesis Testing, 357
Using the Computer to Test Hypotheses and Construct Confidence Intervals About the Difference in Two Population Means Using the t Test, 360
Confidence Intervals, 362
10.3. STATISTICAL INFERENCES FOR TWO RELATED POPULATIONS, 367
Hypothesis Testing, 368
Using the Computer to Make Statistical Inferences About Two Related Populations, 370
Confidence Intervals, 372
10.4. STATISTICAL INFERENCES ABOUT TWO POPULATION PROPORTIONS, p₁ – p₂, 377
Hypothesis Testing, 377
Confidence Intervals, 381
Using the Computer to Analyze the Difference in Two Proportions, 382
10.5. TESTING HYPOTHESES ABOUT TWO POPULATION VARIANCES, 384
Using the Computer to Test Hypotheses About Two Population Variances, 388
Summary, 393
Key Terms, 393
Formulas, 393
Supplementary Problems, 394
Analyzing the Databases, 399
CASE: Seitz Corporation: Producing Quality Gear-Driven and Linear-Motion Products, 399
Using the Computer, 400
Chapter 11. Analysis of Variance and Design of Experiments, 404
DECISION DILEMMA: Job and Career Satisfaction of Foreign Self-Initiated Expatriates, 405
11.1. INTRODUCTION TO DESIGN OF EXPERIMENTS, 406
11.2. THE COMPLETELY RANDOMIZED DESIGN (ONE-WAY ANOVA), 409
One-Way Analysis of Variance, 409
Reading the F Distribution Table, 413
Using the Computer for One-Way ANOVA, 414
Comparison of F and t Values, 414
11.3. MULTIPLE COMPARISON TESTS, 421
Tukey’s Honestly Significant Difference (HSD) Test: The Case of Equal Sample Sizes, 421
Using the Computer to Do Multiple Comparisons, 423
Tukey-Kramer Procedure: The Case of Unequal Sample Sizes, 425
11.4. THE RANDOMIZED BLOCK DESIGN, 429
Using the Computer to Analyze Randomized Block Designs, 433
11.5. A FACTORIAL DESIGN (TWO-WAY ANOVA), 439
Advantages of the Factorial Design, 439
Factorial Designs with Two Treatments, 440
Applications, 440
Statistically Testing the Factorial Design, 441
Interaction, 442
Using a Computer to Do a Two-Way ANOVA, 447
Summary, 456
Key Terms, 457
Formulas, 457
Supplementary Problems, 458
Analyzing the Databases, 462
CASE: Tyco Valves & Controls Sells Clarkson Products, 462
Using the Computer, 464
Chapter 12. Analysis of Categorical Data, 466
DECISION DILEMMA: Selecting Suppliers in the Electronics Industry, 467
12.1. CHI-SQUARE GOODNESS-OF-FIT TEST, 468
Testing a Population Proportion by Using the Chi-Square Goodness-of-Fit Test as an Alternative Technique to the z Test, 474
12.2. CONTINGENCY ANALYSIS: CHI-SQUARE TEST OF INDEPENDENCE, 479
Summary, 488
Key Terms, 488
Formulas, 488
Supplementary Problems, 488
Analyzing the Databases, 490
CASE: Foot Locker in the Shoe Mix, 490
Using the Computer, 491
Chapter 13. Nonparametric Statistics, 492
DECISION DILEMMA: How Is the Doughnut Business?, 493
13.1. RUNS TEST, 495
Small-Sample Runs Test, 496
Large-Sample Runs Test, 497
13.2. MANN-WHITNEY U TEST, 500
Small-Sample Case, 500
Large-Sample Case, 502
13.3. WILCOXON MATCHED-PAIRS SIGNED RANK TEST, 508
Small-Sample Case (n ≤ 15), 508
Large-Sample Case (n > 15), 510
13.4. KRUSKAL-WALLIS TEST, 516
13.5. FRIEDMAN TEST, 521
13.6. SPEARMAN’S RANK CORRELATION, 527
Summary, 532
Key Terms, 533
Formulas, 533
Supplementary Problems, 533
Analyzing the Databases, 538
CASE: Schwinn, 539
Using the Computer, 540
UNIT IV. REGRESSION, FORECASTING, AND QUALITY: RELATIONSHIPS, PREDICTION, AND PROCESS IMPROVEMENT, 541
Chapter 14. Simple Regression Analysis, 542
DECISION DILEMMA: Predicting International Hourly Wages by the Price of a Big Mac™, 543
14.1. INTRODUCTION TO SIMPLE REGRESSION ANALYSIS, 544
14.2. DETERMINING THE EQUATION OF THE REGRESSION LINE, 545
14.3. RESIDUAL ANALYSIS, 552
Using Residuals to Test the Assumptions of the Regression Model, 554
Using the Computer for Residual Analysis, 555
14.4. STANDARD ERROR OF THE ESTIMATE, 558
14.5. COEFFICIENT OF DETERMINATION, 562
Relationship between r and r², 564
14.6. HYPOTHESIS TESTS OF SLOPE OF THE REGRESSION MODEL AND TESTING THE OVERALL MODEL, 564
Testing the Slope, 564
Testing the Overall Model, 568
14.7. ESTIMATION, 569
Confidence Intervals to Estimate the Conditional Mean of y: µ_{y | x}, 569
Prediction Intervals to Estimate a Single Value of y, 570
14.8. USING REGRESSION TO DEVELOP A FORECASTING TREND LINE, 573
Determining the Equation of the Trend Line, 574
Forecasting Using the Equation of the Trend Line, 575
Alternate Coding for Time Periods, 576
14.9. INTERPRETING THE OUTPUT, 579
Summary, 583
Key Terms, 583
Formulas, 583
Supplementary Problems, 584
Analyzing the Databases, 587
CASE: Delta Wire Uses Training as a Weapon, 588
Using the Computer, 589
Chapter 15. Multiple Regression Analysis, 592
DECISION DILEMMA: Are You Going to Hate Your New Job?, 593
15.1. THE MULTIPLE REGRESSION MODEL, 594
Multiple Regression Model with Two Independent Variables (First-Order), 595
Determining the Multiple Regression Equation, 596
A Multiple Regression Model, 596
15.2. SIGNIFICANCE TESTS OF THE REGRESSION MODEL AND ITS COEFFICIENTS, 601
Testing the Overall Model, 601
Significance Tests of the Regression Coefficients, 603
15.3. RESIDUALS, STANDARD ERROR OF THE ESTIMATE, AND R², 606
Residuals, 606
SSE and Standard Error of the Estimate, 607
Coefficient of Multiple Determination (R²), 608
Adjusted R², 609
15.4. INTERPRETING MULTIPLE REGRESSION COMPUTER OUTPUT, 611
A Reexamination of the Multiple Regression Output, 611
Summary, 615
Key Terms, 616
Formulas, 616
Supplementary Problems, 616
Analyzing the Databases, 619
CASE: Starbucks Introduces Debit Card, 619
Using the Computer, 620
Chapter 16. Building Multiple Regression Models, 622
DECISION DILEMMA: Determining Compensation for CEOs, 623
16.1. NONLINEAR MODELS: MATHEMATICAL TRANSFORMATION, 624
Polynomial Regression, 624
Tukey’s Ladder of Transformations, 627
Regression Models with Interaction, 628
Model Transformation, 630
16.2. INDICATOR (DUMMY) VARIABLES, 636
16.3. MODEL-BUILDING: SEARCH PROCEDURES, 642
Search Procedures, 644
All Possible Regressions, 644
Stepwise Regression, 644
Forward Selection, 648
Backward Elimination, 648
16.4. MULTICOLLINEARITY, 652
Summary, 656
Key Terms, 657
Formulas, 657
Supplementary Problems, 657
Analyzing the Databases, 660
CASE: Virginia Semiconductor, 661
Using the Computer, 662
Chapter 17. Time-Series Forecasting and Index Numbers, 664
DECISION DILEMMA: Forecasting Air Pollution, 665
17.1. INTRODUCTION TO FORECASTING, 666
Time-Series Components, 666
The Measurement of Forecasting Error, 667
Error, 667
Mean Absolute Deviation (MAD), 667
Mean Square Error (MSE), 668
17.2. SMOOTHING TECHNIQUES, 670
Naïve Forecasting Models, 670
Averaging Models, 671
Simple Averages, 671
Moving Averages, 671
Weighted Moving Averages, 673
Exponential Smoothing, 675
17.3. TREND ANALYSIS, 680
Linear Regression Trend Analysis, 680
Regression Trend Analysis Using Quadratic Models, 682
Holt’s Two-Parameter Exponential Smoothing Method, 685
17.4. SEASONAL EFFECTS, 687
Decomposition, 687
Finding Seasonal Effects with the Computer, 690
Winters’ Three-Parameter Exponential Smoothing Method, 690
17.5. AUTOCORRELATION AND AUTOREGRESSION, 692
Autocorrelation, 692
Ways to Overcome the Autocorrelation Problem, 695
Addition of Independent Variables, 695
Transforming Variables, 696
Autoregression, 696
17.6. INDEX NUMBERS, 699
Simple Index Numbers and Unweighted Aggregate Price Indexes, 700
Unweighted Aggregate Price Index Numbers, 700
Weighted Aggregate Price Index Numbers, 701
Laspeyres Price Index, 702
Paasche Price Index, 703
Summary, 708
Key Terms, 709
Formulas, 709
Supplementary Problems, 709
Analyzing the Databases, 714
CASE: Debourgh Manufacturing Company, 714
Using the Computer, 716
Chapter 18. Statistical Quality Control, 718
DECISION DILEMMA: Italy’s Piaggio Makes a Comeback, 719
18.1. INTRODUCTION TO QUALITY CONTROL, 720
What Is Quality Control?, 720
Total Quality Management, 721
Some Important Quality Concepts, 723
Benchmarking, 723
Just-in-Time Inventory Systems, 723
Reengineering, 725
Failure Mode and Effects Analysis, 725
Poka-Yoke, 726
Six Sigma, 727
Design for Six Sigma, 729
Lean Manufacturing, 729
Team Building, 730
18.2. PROCESS ANALYSIS, 731
Flowcharts, 732
Pareto Analysis, 733
Cause-and-Effect (Fishbone) Diagrams, 733
Control Charts, 735
Check Sheets or Checklists, 736
Histogram, 737
Scatter Chart or Scatter Diagram, 737
18.3. CONTROL CHARTS, 738
Variation, 738
Types of Control Charts, 739
x Chart, 739
R Charts, 742
p Charts, 744
c Charts, 747
Interpreting Control Charts, 749
18.4. ACCEPTANCE SAMPLING, 754
Single-Sample Plan, 755
Double-Sample Plan, 755
Multiple-Sample Plan, 756
Determining Error and OC Curves, 756
Summary, 762
Key Terms, 763
Formulas, 763
Supplementary Problems, 764
Analyzing the Databases, 768
CASE: Robotron-Elotherm, 769
Using the Computer, 770
Appendix A. TABLES, 773
Appendix B. ANSWERS TO SELECTED ODD-NUMBERED QUANTITATIVE PROBLEMS, 813
GLOSSARY, 823
INDEX, 833

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