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Chapter 1. An Introduction to Business Statistics, 2
1.1. Populations and Samples, 3
1.2. Selecting a Random Sample, 4
1.3. Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional), 17
1.4. An Introduction to Survey Sampling (Optional), 18
1.5. More About Data Acquisition and Survey Sampling (Optional), 20
App1.1. Getting Started with MINITAB, 29
App1.2. Getting Started with Excel, 36
App1.3. Getting Started with MegaStat, 41
App1.4. Introduction to Internet Exercises, 46
Chapter 2. Descriptive Statistics: Tabular and Graphical Methods, 48
2.1. Graphically Summarizing Qualitative Data, 49
2.2. Graphically Summarizing Quantitative Data, 56
2.3. Dot Plots, 68
2.4. Stem-and-Leaf Displays, 70
2.5. Crosstabulation Tables (Optional), 75
2.6. Scatter Plots (Optional), 81
2.7. Misleading Graphs and Charts (Optional), 84
App2.1. Tabular and Graphical Methods Using MINITAB, 94
App2.2. Tabular and Graphical Methods Using Excel, 102
App2.3. Tabular and Graphical Methods Using MegaStat, 110
Chapter 3. Descriptive Statistics: Numerical Methods, 114
3.1. Describing Central Tendency, 115
3.2. Measures of Variation, 125
3.3. Percentiles, Quartiles, and Box-and-Whiskers Displays, 136
3.4. Covariance, Correlation, and the Least Squares Line (Optional), 144
3.5. Weighted Means and Grouped Data (Optional), 150
3.6. The Geometric Mean (Optional), 154
App3.1. Numerical Descriptive Statistics Using MINITAB, 162
App3.2. Numerical Descriptive Statistics Using Excel, 164
App3.3. Numerical Descriptive Statistics Using MegaStat, 168
Chapter 4. Probability, 170
4.1. The Concept of Probability, 171
4.2. Sample Spaces and Events, 173
4.3. Some Elementary Probability Rules, 180
4.4. Conditional Probability and Independence, 187
Chapter 5. Discrete Random Variables, 202
5.1. Two Types of Random Variables, 203
5.2. Discrete Probability Distributions, 204
5.3. The Binomial Distribution, 215
5.4. The Poisson Distribution (Optional), 226
App5.1. Binomial and Poisson Probabilities Using MINITAB, 234
App5.2. Binomial and Poisson Probabilities Using Excel, 235
App5.3. Binomial and Poisson Probabilities Using MegaStat, 237
Chapter 6. Continuous Random Variables, 238
6.1. Continuous Probability Distributions, 239
6.2. The Uniform Distribution, 241
6.3. The Normal Probability Distribution, 244
6.4. Approximating the Binomial Distribution by Using the Normal Distribution (Optional), 262
6.5. The Exponential Distribution (Optional), 266
6.6. The Normal Probability Plot (Optional), 269
App6.1. Normal Distribution Using MINITAB, 275
App6.2. Normal Distribution Using Excel, 277
App6.3. Normal Distribution Using MegaStat, 278
Chapter 7. Sampling Distributions, 280
7.1. The Sampling Distribution of the Sample Mean, 281
7.2. The Sampling Distribution of the Sample Proportion, 294
App7.1. Simulating Sampling Distributions Using MINITAB, 303
Chapter 8. Confidence Intervals, 304
8.1. z-Based Confidence Intervals for a Population Mean: σ Known, 305
8.2. t-Based Confidence Intervals for a Population Mean: σ Unknown, 313
8.3. Sample Size Determination, 321
8.4. Confidence Intervals for a Population Proportion, 325
8.5. Confidence Intervals for Parameters of Finite Populations (Optional), 332
8.6. A Comparison of Confidence Intervals and Tolerance Intervals (Optional), 337
App8.1. Confidence Intervals Using MINITAB, 342
App8.2. Confidence Intervals Using Excel, 344
App8.3. Confidence Intervals Using MegaStat, 345
Chapter 9. Hypothesis Testing, 346
9.1. The Null and Alternative Hypotheses and Errors in Hypothesis Testing, 347
9.2. z Tests about a Population Mean: σ Known, 353
9.3. t Tests about a Population Mean: σ Unknown, 366
9.4. z Tests about a Population Proportion, 371
9.5. Type II Error Probabilities and Sample Size Determination (Optional), 376
9.6. The Chi-Square Distribution (Optional), 382
9.7. Statistical Inference for a Population Variance (Optional), 383
App9.1. One-Sample Hypothesis Testing Using MINITAB, 389
App9.2. One-Sample Hypothesis Testing Using Excel, 391
App9.3. One-Sample Hypothesis Testing Using MegaStat, 392
Chapter 10. Statistical Inferences Based on Two Samples, 394
10.1. Comparing Two Population Means by Using Independent Samples: Variances Known, 395
10.2. Comparing Two Population Means by Using Independent Samples: Variances Unknown, 401
10.3. Paired Difference Experiments, 409
10.4. Comparing Two Population Proportions by Using Large, Independent Samples, 417
10.5. Comparing Two Population Variances by Using Independent Samples, 423
App10.1. Two-Sample Hypothesis Testing Using MINITAB, 434
App10.2. Two-Sample Hypothesis Testing Using Excel, 437
App10.3. Two-Sample Hypothesis Testing Using MegaStat, 438
Chapter 11. Experimental Design and Analysis of Variance, 440
11.1. Basic Concepts of Experimental Design, 441
11.2. One-Way Analysis of Variance, 444
11.3. The Randomized Block Design, 455
11.4. Two-Way Analysis of Variance, 464
App11.1. Experimental Design and Analysis of Variance Using MINITAB, 479
App11.2. Experimental Design and Analysis of Variance Using Excel, 482
App11.3. Experimental Design and Analysis of Variance Using MegaStat, 483
Chapter 12. Chi-Square Tests, 486
12.1. Chi-Square Goodness of Fit Tests, 487
12.2. A Chi-Square Test for Independence, 496
App12.1. Chi-Square Tests Using MINITAB, 506
App12.2. Chi-Square Tests Using Excel, 509
App12.3. Chi-Square Tests Using MegaStat, 511
Chapter 13. Simple Linear Regression Analysis, 514
13.1. The Simple Linear Regression Model and the Least Squares Point Estimates, 515
13.2. Model Assumptions and the Standard Error, 530
13.3. Testing the Significance of the Slope and y-Intercept, 533
13.4. Confidence and Prediction Intervals, 541
13.5. Simple Coefficients of Determination and Correlation, 546
13.6. Testing the Significance of the Population Correlation Coefficient (Optional), 552
13.7. An F Test for the Model, 553
13.8. Residual Analysis (Optional), 556
13.9. Some Shortcut Formulas (Optional), 567
App13.1. Simple Linear Regression Analysis Using MINITAB, 576
App13.2. Simple Linear Regression Analysis Using Excel, 577
App13.3. Simple Linear Regression Analysis Using MegaStat, 578
Chapter 14. Multiple Regression, 580
14.1. The Multiple Regression Model and the Least Squares Point Estimates, 581
14.2. Model Assumptions and the Standard Error, 593
14.3. R² and Adjusted R², 595
14.4. The Overall F Test, 597
14.5. Testing the Significance of an Independent Variable, 599
14.6. Confidence and Prediction Intervals, 603
14.7. Using Dummy Variables to Model Qualitative Independent Variables, 606
14.8. The Partial F Test: Testing the Significance of a Portion of a Regression Model, 618
14.9. Residual Analysis in Multiple Regression, 621
App14.1. Multiple Regression Analysis Using MINITAB, 629
App14.2. Multiple Regression Analysis Using Excel, 630
App14.3. Multiple Regression Analysis Using MegaStat, 632
Chapter 15. Model Building and Model Diagnostics, 634
15.1. The Quadratic Regression Model, 635
15.2. Interaction, 642
15.3. Logistic Regression, 646
15.4. Model Building and the Effects of Multicollinearity, 650
15.5. Improving the Regression Model I: Diagnosing and Using Information about Outlying and Influential Observations, 663
15.6. Improving the Regression Model II: Transforming the Dependent and Independent Variables, 669
15.7. Improving the Regression Model III: The Durbin–Watson Test and Dealing with Autocorrelation, 676
App15.1. Model Building Using MINITAB, 686
App15.2. Model Building Using Excel, 690
App15.3. Model Building Using MegaStat, 691
Chapter 16. Time Series Forecasting, 694
16.1. Time Series Components and Models, 695
16.2. Time Series Regression: Basic Models, 696
16.3. Time Series Regression: More Advanced Models (Optional), 702
16.4. Multiplicative Decomposition, 706
16.5. Simple Exponential Smoothing, 713
16.6. Holt–Winters' Models, 718
16.7. Forecast Error Comparisons, 727
16.8. Index Numbers, 728
App16.1. Time Series Analysis Using MINITAB, 737
App16.2. Time Series Analysis Using Excel, 738
App16.3. Time Series Analysis Using MegaStat, 739
Chapter 17. Process Improvement Using Control Charts, 742
17.1. Quality: Its Meaning and a Historical Perspective, 743
17.2. Statistical Process Control and Causes of Process Variation, 747
17.3. Sampling a Process, Rational Subgrouping, and Control Charts, 749
17.4. x and R Charts, 754
17.5. Pattern Analysis, 770
17.6. Comparison of a Process with Specifications: Capability Studies, 775
17.7. Charts for Fraction Nonconforming, 783
17.8. Cause-and-Effect and Defect Concentration Diagrams (Optional), 789
App17.1. Control Charts Using MINITAB, 797
App17.2. Control Charts Using MegaStat, 799
Chapter 18. Nonparametric Methods, 800
18.1. The Sign Test: A Hypothesis Test about the Median, 802
18.2. The Wilcoxon Rank Sum Test, 806
18.3. The Wilcoxon Signed Ranks Test, 812
18.4. Comparing Several Populations Using the Kruskal–Wallis H Test, 816
18.5. Spearman's Rank Correlation Coefficient, 818
App18.1. Nonparametric Methods Using MINITAB, 824
App18.2. Nonparametric Methods Using MegaStat, 827
Chapter 19. Decision Theory, 830
19.1. Bayes' Theorem, 831
19.2. Introduction to Decision Theory, 836
19.3. Decision Making Using Posterior Probabilities, 842
19.4. Introduction to Utility Theory, 848
Appendix A. Statistical Tables, 854
Appendix B. Counting Rules, 880
Appendix C. The Hypergeometric Distribution, 882
Appendix D. Properties of the Mean and the Variance of a Random Variable, and the Covariance, 883
Appendix E. Derivations of the Mean and Variance of x and p, 886
Answers to Most Odd-Numbered Exercises, 888
References, 897
Photo Credits, 899
Index, 900
Inside Front Cover, FM-1
Inside Back Cover, BM-1

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