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Contents xi Case Problem 2: Ethical Behavior of Business Students at Bayview University 469 Appendix 9.1 Hypothesis Testing with JMP 471 Appendix 9.2 Hypothesis Testing with Excel 475 Appendix 9.3 Hypothesis Testing with R (MindTap Reader) Chapter 10 inference about Means and proportions with two populations 481 Statistics in Practice: U.S. Food and Drug Administration 482 10.1 Inferences About the Difference Between Two Population Means: s1 and s2 Known 483 Interval Estimation of m1 − m2 483 Hypothesis Tests About m1 − m2 485 Practical Advice 487 10.2 Inferences About the Difference Between Two Population Means: s1 and s2 Unknown 489 Interval Estimation of m1 − m2 489 Hypothesis Tests About m1 − m2 491 Practical Advice 493 10.3 Inferences About the Difference Between Two Population Means: Matched Samples 497 10.4 Inferences About the Difference Between Two Population Proportions 503 Interval Estimation of p1 − p2 503 Hypothesis Tests About p1 − p2 505 Summary 509 Glossary 509 Key Formulas 509 Supplementary Exercises 511 Case Problem: Par, Inc. 514 Appendix 10.1 Inferences About Two Populations with JMP 515 Appendix 10.2 Inferences About Two Populations with Excel 519 Appendix 10.3 Inferences about Two Populations with R (MindTap Reader) Chapter 11 inferences about population Variances 525 Statistics in Practice: U.S. Government Accountability Office 526 11.1 Inferences About a Population Variance 527 Interval Estimation 527 Hypothesis Testing 531 11.2 Inferences About Two Population Variances 537 Summary 544 Key Formulas 544 Supplementary Exercises 544 Case Problem 1: Air Force Training Program 546 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 xii Contents Case Problem 2: Meticulous Drill & Reamer 547 Appendix 11.1 Population Variances with JMP 549 Appendix 11.2 Population Variances with Excel 551 Appendix 11.3 Population Variances with R (MindTap Reader) Chapter 12 Comparing Multiple proportions, test of independence and Goodness of Fit 553 Statistics in Practice: United Way 554 12.1 Testing the Equality of Population Proportions for Three or More Populations 555 A Multiple Comparison Procedure 560 12.2 Test of Independence 565 12.3 Goodness of Fit Test 573 Multinomial Probability Distribution 573 Normal Probability Distribution 576 Summary 582 Glossary 582 Key Formulas 583 Supplementary Exercises 583 Case Problem 1: A Bipartisan Agenda for Change 587 Case Problem 2: Fuentes Salty Snacks, Inc. 588 Case Problem 3: Fresno Board Games 588 Appendix 12.1 Chi-Square Tests with JMP 590 Appendix 12.2 Chi-Square Tests with Excel 593 Appendix 12.3 Chi-Squared Tests with R (MindTap Reader) Chapter 13 experimental design and analysis of Variance 597 Statistics in Practice: Burke Marketing Services, Inc. 598 13.1 An Introduction to Experimental Design and Analysis of Variance 599 Data Collection 600 Assumptions for Analysis of Variance 601 Analysis of Variance: A Conceptual Overview 601 13.2 Analysis of Variance and the Completely Randomized Design 604 Between-Treatments Estimate of Population Variance 605 Within-Treatments Estimate of Population Variance 606 Comparing the Variance Estimates: The F Test 606 ANOVA Table 608 Computer Results for Analysis of Variance 609 Testing for the Equality of k Population Means: An Observational Study 610 13.3 Multiple Comparison Procedures 615 Fisher’s LSD 615 Type I Error Rates 617 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 Contents xiii 13.4 Randomized Block Design 621 Air Traffic Controller Stress Test 621 ANOVA Procedure 623 Computations and Conclusions 623 13.5 Factorial Experiment 627 ANOVA Procedure 629 Computations and Conclusions 629 Summary 635 Glossary 635 Key Formulas 636 Supplementary Exercises 638 Case Problem 1: Wentworth Medical Center 643 Case Problem 2: Compensation for Sales Professionals 644 Case Problem 3: Touristopia Travel 644 Appendix 13.1 Analysis of Variance with JMP 646 Appendix 13.2 Analysis of Variance with Excel 649 Appendix 13.3 Analysis Variance with R (MindTap Reader) Chapter 14 Simple Linear regression 653 Statistics in Practice: Alliance Data Systems 654 14.1 Simple Linear Regression Model 655 Regression Model and Regression Equation 655 Estimated Regression Equation 656 14.2 Least Squares Method 658 14.3 Coefficient of Determination 668 Correlation Coefficient 671 14.4 Model Assumptions 675 14.5 Testing for Significance 676 Estimate of s2 676 t Test 677 Confidence Interval for b1 679 F Test 679 Some Cautions About the Interpretation of Significance Tests 681 14.6 Using the Estimated Regression Equation for Estimation and Prediction 684 Interval Estimation 685 Confidence Interval for the Mean Value of y 685 Prediction Interval for an Individual Value of y 686 14.7 Computer Solution 691 14.8 Residual Analysis: Validating Model Assumptions 694 Residual Plot Against x 695 Residual Plot Against yˆ 697 Standardized Residuals 698 Normal Probability Plot 699 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 xiv Contents 14.9 Residual Analysis: Outliers and Influential Observations 703 Detecting Outliers 703 Detecting Influential Observations 704 14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression 710 Summary 711 Glossary 711 Key Formulas 712 Supplementary Exercises 714 Case Problem 1: Measuring Stock Market Risk 721 Case Problem 2: U.S. Department of Transportation 721 Case Problem 3: Selecting a Point-and-Shoot Digital Camera 722 Case Problem 4: Finding the Best Car Value 723 Case Problem 5: Buckeye Creek Amusement Park 724 Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 726 Appendix 14.2 A Test for Significance Using Correlation 727 Appendix 14.3 Simple Linear Regression with JMP 727 Appendix 14.4 Regression Analysis with Excel 728 Appendix 14.5 Simple Linear Regression with R (MindTap Reader) Chapter 15 Multiple regression 731 Statistics in Practice: 84.51° 732 15.1 Multiple Regression Model 733 Regression Model and Regression Equation 733 Estimated Multiple Regression Equation 733 15.2 Least Squares Method 734 An Example: Butler Trucking Company 735 Note on Interpretation of Coefficients 737 15.3 Multiple Coefficient of Determination 743 15.4 Model Assumptions 746 15.5 Testing for Significance 747 F Test 747 t Test 750 Multicollinearity 750 15.6 Using the Estimated Regression Equation for Estimation and Prediction 753 15.7 Categorical Independent Variables 755 An Example: Johnson Filtration, Inc. 756 Interpreting the Parameters 758 More Complex Categorical Variables 760 15.8 Residual Analysis 764 Detecting Outliers 766 Studentized Deleted Residuals and Outliers 766 Influential Observations 767 Using Cook’s Distance Measure to Identify Influential Observations 767 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 Contents xv 15.9 Logistic Regression 771 Logistic Regression Equation 772 Estimating the Logistic Regression Equation 773 Testing for Significance 774 Managerial Use 775 Interpreting the Logistic Regression Equation 776 Logit Transformation 778 15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression 782 Summary 783 Glossary 783 Key Formulas 784 Supplementary Exercises 786 Case Problem 1: Consumer Research, Inc. 790 Case Problem 2: Predicting Winnings for NASCAR Drivers 791 Case Problem 3: Finding the Best Car Value 792 Appendix 15.1 Multiple Linear Regression with JMP 794 Appendix 15.2 Logistic Regression with JMP 796 Appendix 15.3 Multiple Regression with Excel 797 Appendix 15.4 Multiple Linear Regression with R (MindTap Reader) Appendix 15.5 Logistics Regression with R (MindTap Reader) Chapter 16 regression analysis: Model Building 799 Statistics in Practice: Monsanto Company 800 16.1 General Linear Model 801 Modeling Curvilinear Relationships 801 Interaction 805 Transformations Involving the Dependent Variable 807 Nonlinear Models That Are Intrinsically Linear 812 16.2 Determining When to Add or Delete Variables 816 General Case 818 Use of p-Values 819 16.3 Analysis of a Larger Problem 822 16.4 Variable Selection Procedures 826 Stepwise Regression 826 Forward Selection 828 Backward Elimination 828 Best-Subsets Regression 828 Making the Final Choice 829 16.5 Multiple Regression Approach to Experimental Design 832 16.6 Autocorrelation and the Durbin-Watson Test 836 Summary 840 Glossary 841 Key Formulas 841 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 xvi Contents Supplementary Exercises 841 Case Problem 1: Analysis of LPGA Tour Statistics 845 Case Problem 2: Rating Wines from the Piedmont Region of Italy 846 Appendix 16.1 Variable Selection Procedures with JMP 848 Appendix 16.2 Variable Selection Procedures with R (MindTap Reader) Chapter 17 time Series analysis and Forecasting 859 Statistics in Practice: Nevada Occupational Health Clinic 860 17.1 Time Series Patterns 861 Horizontal Pattern 861 Trend Pattern 863 Seasonal Pattern 863 Trend and Seasonal Pattern 864 Cyclical Pattern 864 Selecting a Forecasting Method 866 17.2 Forecast Accuracy 867 17.3 Moving Averages and Exponential Smoothing 872 Moving Averages 872 Weighted Moving Averages 874 Exponential Smoothing 875 17.4 Trend Projection 881 Linear Trend Regression 882 Nonlinear Trend Regression 886 17.5 Seasonality and Trend 891 Seasonality Without Trend 892 Seasonality and Trend 894 Models Based on Monthly Data 897 17.6 Time Series Decomposition 900 Calculating the Seasonal Indexes 902 Deseasonalizing the Time Series 905 Using the Deseasonalized Time Series to Identify Trend 905 Seasonal Adjustments 907 Models Based on Monthly Data 908 Cyclical Component 908 Summary 910 Glossary 911 Key Formulas 912 Supplementary Exercises 913 Case Problem 1: Forecasting Food and Beverage Sales 917 Case Problem 2: Forecasting Lost Sales 918 Appendix 17.1 Forecasting with JMP 920 Appendix 17.2 Forecasting with Excel 926 Appendix 17.3 Forecasting with R (MindTap Reader) Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 Contents xvii Chapter 18 nonparametric Methods 931 Statistics in Practice: West Shell Realtors 932 18.1 Sign Test 933 Hypothesis Test About a Population Median 933 Hypothesis Test with Matched Samples 938 18.2 Wilcoxon Signed-Rank Test 941 18.3 Mann-Whitney-Wilcoxon Test 947 18.4 Kruskal-Wallis Test 956 18.5 Rank Correlation 961 Summary 966 Glossary 966 Key Formulas 967 Supplementary Exercises 968 Case Problem: RainOrShine.Com 971 Appendix 18.1 Nonparametric Methods with JMP 972 Appendix 18.2 Nonparametric Methods with Excel 979 Appendix 18.3 Nonparametric Methods with R (MindTap Reader) Chapter 19 decision analysis 981 Statistics in Practice: Ohio Edison Company 982 19.1 Problem Formulation 983 Payoff Tables 983 Decision Trees 984 19.2 Decision Making with Probabilities 985 Expected Value Approach 985 Expected Value of Perfect Information 987 19.3 Decision Analysis with Sample Information 992 Decision Tree 993 Decision Strategy 994 Expected Value of Sample Information 998 19.4 Computing Branch Probabilities Using Bayes’ Theorem 1002 Summary 1006 Glossary 1007 Key Formulas 1008 Supplementary Exercises 1008 Case Problem 1: Lawsuit Defense Strategy 1010 Case Problem 2: Property Purchase Strategy 1011 Chapter 20 index numbers 1013 Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics 1014 20.1 Price Relatives 1014 20.2 Aggregate Price Indexes 1015 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 xviii Contents 20.3 Computing an Aggregate Price Index from Price Relatives 1019 20.4 Some Important Price Indexes 1021 Consumer Price Index 1021 Producer Price Index 1021 Dow Jones Averages 1022 20.5 Deflating a Series by Price Indexes 1023 20.6 Price Indexes: Other Considerations 1026 Selection of Items 1026 Selection of a Base Period 1026 Quality Changes 1027 20.7 Quantity Indexes 1027 Summary 1029 Glossary 1029 Key Formulas 1029 Supplementary Exercises 1030 Chapter 21 Statistical Methods for Quality Control 1033 Statistics in Practice: Dow Chemical Company 1034 21.1 Philosophies and Frameworks 1035 Malcolm Baldrige National Quality Award 1036 ISO 9000 1036 Six Sigma 1036 Quality in the Service Sector 1038 21.2 Statistical Process Control 1039 Control Charts 1040 x Chart: Process Mean and Standard Deviation Known 1041 x Chart: Process Mean and Standard Deviation Unknown 1043 R Chart 1045 p Chart 1046 np Chart 1049 Interpretation of Control Charts 1049 21.3 Acceptance Sampling 1052 KALI, Inc.: An Example of Acceptance Sampling 1053 Computing the Probability of Accepting a Lot 1054 Selecting an Acceptance Sampling Plan 1056 Multiple Sampling Plans 1057 Summary 1059 Glossary 1060 Key Formulas 1060 Supplementary Exercises 1061 Appendix 21.1 Control Charts with JMP 1064 Appendix 21.2 Control Charts with R (MindTap Reader) Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203 Contents xix Chapter 22 Sample Survey (Mindtap reader) 22-1 Statistics in Practice: Duke Energy 22-2 22.1 Terminology Used in Sample Surveys 22-2 22.2 Types of Surveys and Sampling Methods 22-3 22.3 Survey Errors 22-5 Nonsampling Error 22-5 Sampling Error 22-5 22.4 Simple Random Sampling 22-6 Population Mean 22-6 Population Total 22-7 Population Proportion 22-8 Determining the Sample Size 22-9 22.5 Stratified Simple Random Sampling 22-12 Population Mean 22-12 Population Total 22-14 Population Proportion 22-15 Determining the Sample Size 22-16 22.6 Cluster Sampling 22-21 Population Mean 22-23 Population Total 22-25 Population Proportion 22-25 Determining the Sample Size 22-27 22.7 Systematic Sampling 22-29 Summary 22-29 Glossary 22-30 Key Formulas 22-30 Supplementary Exercises 22-34 Case Problem: Medicament’s Predicament 22-36 appendix a References and Bibliography 1068 appendix B Tables 1070 appendix C Summation Notation 1097 appendix d Answers to Even-Numbered Exercises (MindTap Reader) appendix e Microsoft Excel 2016 and Tools for Statistical Analysis 1099 appendix F Computing p-Values with JMP and Excel 1107 index 1111

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