Business Statistics PDF

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 iStockphoto.com/alienforce; iStockphoto.com/TommL Statistics for Business & Economics 13e David R. Anderson Jeffrey D. Camm University of Cincinnati Thomas A. Williams Wake Forest University Rochester Institute Dennis J. Sweeney of Technology James J. Cochran University of Cincinnati University of Alabama Australia Brazil Mexico Singapore United Kingdom United States Copyright 2017 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.  This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Statistics for Business and Economics, © 2017, 2015 Cengage Learning® Thirteenth Edition WCN: 02-200-203 David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, ALL RIGHTS RESERVED. No part of this work covered by the copyright James J. Cochran herein may be reproduced or distributed in any form or by any means, except as permitted by U.S. copyright law, without the prior written Vice President, General Manager: Science, permission of the copyright owner. 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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.  Dedicated to Marcia, Cherri, Robbie, Karen, and Teresa Copyright 2017 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 2017 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.  Brief Contents Preface xxiii About the Authors xxix Chapter 1 Data and Statistics 1 Chapter 2 Descriptive Statistics: Tabular and Graphical Displays 32 Chapter 3 Descriptive Statistics: Numerical Measures 102 Chapter 4 Introduction to Probability 171 Chapter 5 Discrete Probability Distributions 217 Chapter 6 Continuous Probability Distributions 269 Chapter 7 Sampling and Sampling Distributions 302 Chapter 8 Interval Estimation 346 Chapter 9 Hypothesis Tests 385 Chapter 10 Inference About Means and Proportions with Two Populations 443 Chapter 11 Inferences About Population Variances 483 Chapter 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit 507 Chapter 13 Experimental Design and Analysis of Variance 544 Chapter 14 Simple Linear Regression 598 Chapter 15 Multiple Regression 681 Chapter 16 Regression Analysis: Model Building 754 Chapter 17 Time Series Analysis and Forecasting 805 Chapter 18 Nonparametric Methods 871 Chapter 19 Statistical Methods for Quality Control 916 Chapter 20 Index Numbers 950 Chapter 21 Decision Analysis (On Website) Chapter 22 Sample Survey (On Website) Appendix A References and Bibliography 972 Appendix B Tables 974 Appendix C Summation Notation 1001 Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 1003 Appendix E Microsoft Excel 2013 and Tools for Statistical Analysis 1070 Appendix F Computing p-Values Using Minitab and Excel 1078 Index 1082 Copyright 2017 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 2017 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.  Contents Preface xxiii About the Authors xxix Chapter 1 Data and Statistics 1 Statistics in Practice: Bloomberg Businessweek 2 1.1 Applications in Business and Economics 3 Accounting 3 Finance 4 Marketing 4 Production 4 Economics 4 Information Systems 5 1.2 Data 5 Elements, Variables, and Observations 5 Scales of Measurement 7 Categorical and Quantitative Data 8 Cross-Sectional and Time Series Data 8 1.3 Data Sources 11 Existing Sources 11 Observational Study 12 Experiment 13 Time and Cost Issues 13 Data Acquisition Errors 13 1.4 Descriptive Statistics 14 1.5 Statistical Inference 16 1.6 Analytics 17 1.7 Big Data and Data Mining 18 1.8 Computers and Statistical Analysis 20 1.9 Ethical Guidelines for Statistical Practice 20 Summary 22 Glossary 23 Supplementary Exercises 24 Chapter 2 Descriptive Statistics: Tabular and Graphical Displays 32 Statistics in Practice: Colgate-Palmolive Company 33 2.1 Summarizing Data for a Categorical Variable 34 Frequency Distribution 34 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  viii Contents Relative Frequency and Percent Frequency Distributions 35 Bar Charts and Pie Charts 35 2.2 Summarizing Data for a Quantitative Variable 41 Frequency Distribution 41 Relative Frequency and Percent Frequency Distributions 43 Dot Plot 43 Histogram 44 Cumulative Distributions 45 Stem-and-Leaf Display 46 2.3 Summarizing Data for Two Variables Using Tables 55 Crosstabulation 55 Simpson’s Paradox 58 2.4 Summarizing Data for Two Variables Using Graphical Displays 64 Scatter Diagram and Trendline 64 Side-by-Side and Stacked Bar Charts 65 2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays 71 Creating Effective Graphical Displays 71 Choosing the Type of Graphical Display 72 Data Dashboards 72 Data Visualization in Practice: Cincinnati Zoo and Botanical Garden 74 Summary 77 Glossary 78 Key Formulas 79 Supplementary Exercises 79 Case Problem 1 Pelican Stores 84 Case Problem 2 Motion Picture Industry 85 Case Problem 3 Queen City 86 Appendix 2.1 Using Minitab for Tabular and Graphical Presentations 87 Appendix 2.2 Using Excel for Tabular and Graphical Presentations 90 Chapter 3 Descriptive Statistics: Numerical Measures 102 Statistics in Practice: Small Fry Design 103 3.1 Measures of Location 104 Mean 104 Weighted Mean 106 Median 107 Geometric Mean 109 Mode 110 Percentiles 111 Quartiles 112 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents ix 3.2 Measures of Variability 118 Range 118 Interquartile Range 119 Variance 119 Standard Deviation 120 Coefficient of Variation 121 3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers 125 Distribution Shape 125 z-Scores 125 Chebyshev’s Theorem 127 Empirical Rule 128 Detecting Outliers 130 3.4 Five-Number Summaries and Box Plots 133 Five-Number Summary 133 Box Plot 134 Comparative Analysis Using Box Plots 135 3.5 Measures of Association Between Two Variables 138 Covariance 138 Interpretation of the Covariance 140 Correlation Coefficient 141 Interpretation of the Correlation Coefficient 144 3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness 148 Summary 151 Glossary 152 Key Formulas 153 Supplementary Exercises 155 Case Problem 1 Pelican Stores 160 Case Problem 2 Motion Picture Industry 161 Case Problem 3 Business Schools of Asia-Pacific 162 Case Problem 4 Heavenly Chocolates Website Transactions 164 Case Problem 5 African Elephant Populations 165 Appendix 3.1 Descriptive Statistics Using Minitab 166 Appendix 3.2 Descriptive Statistics Using Excel 168 Chapter 4 Introduction to Probability 171 Statistics in Practice: National Aeronautics and Space Administration 172 4.1 Random Experiments, Counting Rules, and Assigning Probabilities 173 Counting Rules, Combinations, and Permutations 174 Assigning Probabilities 178 Probabilities for the KP&L Project 180 4.2 Events and Their Probabilities 183 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  x Contents 4.3 Some Basic Relationships of Probability 187 Complement of an Event 187 Addition Law 188 4.4 Conditional Probability 194 Independent Events 197 Multiplication Law 197 4.5 Bayes’ Theorem 202 Tabular Approach 205 Summary 208 Glossary 208 Key Formulas 209 Supplementary Exercises 210 Case Problem Hamilton County Judges 214 Chapter 5 Discrete Probability Distributions 217 Statistics in Practice: Citibank 218 5.1 Random Variables 219 Discrete Random Variables 219 Continuous Random Variables 220 5.2 Developing Discrete Probability Distributions 222 5.3 Expected Value and Variance 227 Expected Value 227 Variance 227 5.4 Bivariate Distributions, Covariance, and Financial Portfolios 232 A Bivariate Empirical Discrete Probability Distribution 232 Financial Applications 235 Summary 238 5.5 Binomial Probability Distribution 241 A Binomial Experiment 242 Martin Clothing Store Problem 243 Using Tables of Binomial Probabilities 247 Expected Value and Variance for the Binomial Distribution 248 5.6 Poisson Probability Distribution 252 An Example Involving Time Intervals 253 An Example Involving Length or Distance Intervals 254 5.7 Hypergeometric Probability Distribution 256 Summary 259 Glossary 260 Key Formulas 261 Supplementary Exercises 262 Case Problem Go Bananas! 266 Appendix 5.1 Discrete Probability Distributions with Minitab 267 Appendix 5.2 Discrete Probability Distributions with Excel 267 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xi Chapter 6 Continuous Probability Distributions 269 Statistics in Practice: Procter & Gamble 270 6.1 Uniform Probability Distribution 271 Area as a Measure of Probability 272 6.2 Normal Probability Distribution 275 Normal Curve 275 Standard Normal Probability Distribution 277 Computing Probabilities for Any Normal Probability Distribution 282 Grear Tire Company Problem 283 6.3 Normal Approximation of Binomial Probabilities 287 6.4 Exponential Probability Distribution 291 Computing Probabilities for the Exponential Distribution 291 Relationship Between the Poisson and Exponential Distributions 292 Summary 294 Glossary 295 Key Formulas 295 Supplementary Exercises 296 Case Problem Specialty Toys 299 Appendix 6.1 Continuous Probability Distributions with Minitab 300 Appendix 6.2 Continuous Probability Distributions with Excel 301 Chapter 7 Sampling and Sampling Distributions 302 Statistics in Practice: Meadwestvaco Corporation 303 7.1 The Electronics Associates Sampling Problem 304 7.2 Selecting a Sample 305 Sampling from a Finite Population 305 Sampling from an Infinite Population 307 7.3 Point Estimation 310 Practical Advice 312 7.4 Introduction to Sampling Distributions 314 7.5 Sampling Distribution of x 316 Expected Value of x 317 Standard Deviation of x 317 Form of the Sampling Distribution of x 318 Sampling Distribution of x for the EAI Problem 319 Practical Value of the Sampling Distribution of x 320 Relationship Between the Sample Size and the Sampling Distribution of x 322 7.6 Sampling Distribution of p 326 Expected Value of p 327 Standard Deviation of p 327 Form of the Sampling Distribution of p 328 Practical Value of the Sampling Distribution of p 329 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  xii Contents 7.7 Properties of Point Estimators 332 Unbiased 332 Efficiency 333 Consistency 334 7.8 Other Sampling Methods 335 Stratified Random Sampling 335 Cluster Sampling 335 Systematic Sampling 336 Convenience Sampling 336 Judgment Sampling 337 Summary 337 Glossary 338 Key Formulas 339 Supplementary Exercises 339 Case Problem Marion Dairies 342 Appendix 7.1 The Expected Value and Standard Deviation of x 342 Appendix 7.2 Random Sampling with Minitab 344 Appendix 7.3 Random Sampling with Excel 345 Chapter 8 Interval Estimation 346 Statistics in Practice: Food Lion 347 8.1 Population Mean: s Known 348 Margin of Error and the Interval Estimate 348 Practical Advice 352 8.2 Population Mean: s Unknown 354 Margin of Error and the Interval Estimate 355 Practical Advice 358 Using a Small Sample 358 Summary of Interval Estimation Procedures 360 8.3 Determining the Sample Size 363 8.4 Population Proportion 366 Determining the Sample Size 368 Summary 372 Glossary 373 Key Formulas 373 Supplementary Exercises 374 Case Problem 1 Young Professional Magazine 377 Case Problem 2 Gulf Real Estate Properties 378 Case Problem 3 Metropolitan Research, Inc. 378 Appendix 8.1 Interval Estimation with Minitab 380 Appendix 8.2 Interval Estimation Using Excel 382 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xiii Chapter 9 Hypothesis Tests 385 Statistics in Practice: John Morrell & Company 386 9.1 Developing Null and Alternative Hypotheses 387 The Alternative Hypothesis as a Research Hypothesis 387 The Null Hypothesis as an Assumption to Be Challenged 388 Summary of Forms for Null and Alternative Hypotheses 389 9.2 Type I and Type II Errors 390 9.3 Population Mean: s Known 393 One-Tailed Test 393 Two-Tailed Test 399 Summary and Practical Advice 401 Relationship Between Interval Estimation and Hypothesis Testing 403 9.4 Population Mean: s Unknown 408 One-Tailed Test 408 Two-Tailed Test 409 Summary and Practical Advice 411 9.5 Population Proportion 414 Summary 416 9.6 Hypothesis Testing and Decision Making 419 9.7 Calculating the Probability of Type II Errors 420 9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean 425 Summary 428 Glossary 429 Key Formulas 430 Supplementary Exercises 430 Case Problem 1 Quality Associates, Inc. 433 Case Problem 2 Ethical Behavior of Business Students at Bayview University 435 Appendix 9.1 Hypothesis Testing with Minitab 436 Appendix 9.2 Hypothesis Testing with Excel 438 Chapter 10 Inference About Means and Proportions with Two Populations 443 Statistics in Practice: U.S. Food and Drug Administration 444 10.1 Inferences About the Difference Between Two Population Means: s1 and s2 Known 445 Interval Estimation of m1 2 m2 445 Hypothesis Tests About m1 2 m2 447 Practical Advice 449 10.2 Inferences About the Difference Between Two Population Means: s1 and s2 Unknown 452 Interval Estimation of m1 2 m2 452 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  xiv Contents Hypothesis Tests About m1 2 m2 454 Practical Advice 456 10.3 Inferences About the Difference Between Two Population Means: Matched Samples 460 10.4 Inferences About the Difference Between Two Population Proportions 466 Interval Estimation of p1 2 p2 466 Hypothesis Tests About p1 2 p2 468 Summary 472 Glossary 472 Key Formulas 473 Supplementary Exercises 474 Case Problem Par, Inc. 477 Appendix 10.1 Inferences About Two Populations Using Minitab 478 Appendix 10.2 Inferences About Two Populations Using Excel 480 Chapter 11 Inferences About Population Variances 483 Statistics in Practice: U.S. Government Accountability Office 484 11.1 Inferences About a Population Variance 485 Interval Estimation 485 Hypothesis Testing 489 11.2 Inferences About Two Population Variances 495 Summary 502 Key Formulas 502 Supplementary Exercises 502 Case Problem Air Force Training Program 504 Appendix 11.1 Population Variances with Minitab 505 Appendix 11.2 Population Variances with Excel 506 Chapter 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit 507 Statistics in Practice: United Way 508 12.1 Testing the Equality of Population Proportions for Three or More Populations 509 A Multiple Comparison Procedure 514 12.2 Test of Independence 519 12.3 Goodness of Fit Test 527 Multinomial Probability Distribution 527 Normal Probability Distribution 530 Summary 536 Glossary 536 Key Formulas 537 Supplementary Exercises 537 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xv Case Problem A Bipartisan Agenda for Change 540 Appendix 12.1 Chi-Square Tests Using Minitab 541 Appendix 12.2 Chi-Square Tests Using Excel 542 Chapter 13 Experimental Design and Analysis of Variance 544 Statistics in Practice: Burke Marketing Services, Inc. 545 13.1 An Introduction to Experimental Design and Analysis of Variance 546 Data Collection 547 Assumptions for Analysis of Variance 548 Analysis of Variance: A Conceptual Overview 548 13.2 Analysis of Variance and the Completely Randomized Design 551 Between-Treatments Estimate of Population Variance 552 Within-Treatments Estimate of Population Variance 553 Comparing the Variance Estimates: The F Test 554 ANOVA Table 556 Computer Results for Analysis of Variance 557 Testing for the Equality of k Population Means: An Observational Study 558 13.3 Multiple Comparison Procedures 562 Fisher’s LSD 562 Type I Error Rates 565 13.4 Randomized Block Design 568 Air Traffic Controller Stress Test 569 ANOVA Procedure 570 Computations and Conclusions 571 13.5 Factorial Experiment 575 ANOVA Procedure 577 Computations and Conclusions 577 Summary 582 Glossary 583 Key Formulas 583 Supplementary Exercises 586 Case Problem 1 Wentworth Medical Center 590 Case Problem 2 Compensation for Sales Professionals 591 Appendix 13.1 Analysis of Variance with Minitab 592 Appendix 13.2 Analysis of Variance with Excel 594 Chapter 14 Simple Linear Regression 598 Statistics in Practice: Alliance Data Systems 599 14.1 Simple Linear Regression Model 600 Regression Model and Regression Equation 600 Estimated Regression Equation 601 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  xvi Contents 14.2 Least Squares Method 603 14.3 Coefficient of Determination 614 Correlation Coefficient 617 14.4 Model Assumptions 621 14.5 Testing for Significance 622 Estimate of s2 623 t Test 623 Confidence Interval for b1 625 F Test 626 Some Cautions About the Interpretation of Significance Tests 628 14.6 Using the Estimated Regression Equation for Estimation and Prediction 631 Interval Estimation 632 Confidence Interval for the Mean Value of y 633 Prediction Interval for an Individual Value of y 634 14.7 Computer Solution 639 14.8 Residual Analysis: Validating Model Assumptions 643 Residual Plot Against x 644 Residual Plot Against ŷ 645 Standardized Residuals 647 Normal Probability Plot 649 14.9 Residual Analysis: Outliers and Influential Observations 652 Detecting Outliers 652 Detecting Influential Observations 654 Summary 660 Glossary 661 Key Formulas 662 Supplementary Exercises 664 Case Problem 1 Measuring Stock Market Risk 670 Case Problem 2 U.S. Department of Transportation 671 Case Problem 3 Selecting a Point-and-Shoot Digital Camera 672 Case Problem 4 Finding the Best Car Value 673 Case Problem 5 Buckeye Creek Amusement Park 674 Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 675 Appendix 14.2 A Test for Significance Using Correlation 676 Appendix 14.3 Regression Analysis with Minitab 677 Appendix 14.4 Regression Analysis with Excel 678 Chapter 15 Multiple Regression 681 Statistics in Practice: dunnhumby 682 15.1 Multiple Regression Model 683 Regression Model and Regression Equation 683 Estimated Multiple Regression Equation 683 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xvii 15.2 Least Squares Method 684 An Example: Butler Trucking Company 685 Note on Interpretation of Coefficients 688 15.3 Multiple Coefficient of Determination 694 15.4 Model Assumptions 697 15.5 Testing for Significance 699 F Test 699 t Test 702 Multicollinearity 703 15.6 Using the Estimated Regression Equation for Estimation and Prediction 706 15.7 Categorical Independent Variables 709 An Example: Johnson Filtration, Inc. 709 Interpreting the Parameters 711 More Complex Categorical Variables 713 15.8 Residual Analysis 718 Detecting Outliers 720 Studentized Deleted Residuals and Outliers 720 Influential Observations 721 Using Cook’s Distance Measure to Identify Influential Observations 721 15.9 Logistic Regression 725 Logistic Regression Equation 726 Estimating the Logistic Regression Equation 727 Testing for Significance 730 Managerial Use 730 Interpreting the Logistic Regression Equation 731 Logit Transformation 734 Summary 738 Glossary 738 Key Formulas 739 Supplementary Exercises 741 Case Problem 1 Consumer Research, Inc. 748 Case Problem 2 Predicting Winnings for NASCAR Drivers 749 Case Problem 3 Finding the Best Car Value 750 Appendix 15.1 Multiple Regression with Minitab 751 Appendix 15.2 Multiple Regression with Excel 751 Appendix 15.3 Logistic Regression with Minitab 753 Chapter 16 Regression Analysis: Model Building 754 Statistics in Practice: Monsanto Company 755 16.1 General Linear Model 756 Modeling Curvilinear Relationships 756 Interaction 759 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  xviii Contents Transformations Involving the Dependent Variable 763 Nonlinear Models That Are Intrinsically Linear 767 16.2 Determining When to Add or Delete Variables 771 General Case 773 Use of p-Values 774 16.3 Analysis of a Larger Problem 780 16.4 Variable Selection Procedures 782 Stepwise Regression 782 Forward Selection 784 Backward Elimination 784 Best-Subsets Regression 785 Making the Final Choice 786 16.5 Multiple Regression Approach to Experimental Design 788 16.6 Autocorrelation and the Durbin-Watson Test 793 Summary 797 Glossary 798 Key Formulas 798 Supplementary Exercises 798 Case Problem 1 Analysis of PGA Tour Statistics 801 Case Problem 2 Rating Wines from the Piedmont Region of Italy 802 Appendix 16.1 Variable Selection Procedures with Minitab 803 Chapter 17 Time Series Analysis and Forecasting 805 Statistics in Practice: Nevada Occupational Health Clinic 806 17.1 Time Series Patterns 807 Horizontal Pattern 807 Trend Pattern 809 Seasonal Pattern 809 Trend and Seasonal Pattern 810 Cyclical Pattern 810 Selecting a Forecasting Method 812 17.2 Forecast Accuracy 813 17.3 Moving Averages and Exponential Smoothing 818 Moving Averages 818 Weighted Moving Averages 821 Exponential Smoothing 821 17.4 Trend Projection 828 Linear Trend Regression 828 Nonlinear Trend Regression 833 17.5 Seasonality and Trend 839 Seasonality Without Trend 839 Seasonality and Trend 841 Models Based on Monthly Data 844 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xix 17.6 Time Series Decomposition 848 Calculating the Seasonal Indexes 849 Deseasonalizing the Time Series 853 Using the Deseasonalized Time Series to Identify Trend 853 Seasonal Adjustments 855 Models Based on Monthly Data 855 Cyclical Component 855 Summary 858 Glossary 859 Key Formulas 860 Supplementary Exercises 860 Case Problem 1 Forecasting Food and Beverage Sales 864 Case Problem 2 Forecasting Lost Sales 865 Appendix 17.1 Forecasting with Minitab 866 Appendix 17.2 Forecasting with Excel 869 Chapter 18 Nonparametric Methods 871 Statistics in Practice: West Shell Realtors 872 18.1 Sign Test 873 Hypothesis Test About a Population Median 873 Hypothesis Test with Matched Samples 878 18.2 Wilcoxon Signed-Rank Test 881 18.3 Mann-Whitney-Wilcoxon Test 886 18.4 Kruskal-Wallis Test 897 18.5 Rank Correlation 901 Summary 906 Glossary 906 Key Formulas 907 Supplementary Exercises 908 Appendix 18.1 Nonparametric Methods with Minitab 911 Appendix 18.2 Nonparametric Methods with Excel 913 Chapter 19 Statistical Methods for Quality Control 916 Statistics in Practice: Dow Chemical Company 917 19.1 Philosophies and Frameworks 918 Malcolm Baldrige National Quality Award 919 ISO 9000 919 Six Sigma 919 Quality in the Service Sector 922 19.2 Statistical Process Control 922 Control Charts 923 x Chart: Process Mean and Standard Deviation Known 924 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  xx Contents x Chart: Process Mean and Standard Deviation Unknown 926 R Chart 929 p Chart 931 np Chart 933 Interpretation of Control Charts 933 19.3 Acceptance Sampling 936 KALI, Inc.: An Example of Acceptance Sampling 937 Computing the Probability of Accepting a Lot 938 Selecting an Acceptance Sampling Plan 941 Multiple Sampling Plans 943 Summary 944 Glossary 944 Key Formulas 945 Supplementary Exercises 946 Appendix 19.1 Control Charts with Minitab 948 Chapter 20 Index Numbers 950 Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics 951 20.1 Price Relatives 952 20.2 Aggregate Price Indexes 952 20.3 Computing an Aggregate Price Index from Price Relatives 956 20.4 Some Important Price Indexes 958 Consumer Price Index 958 Producer Price Index 958 Dow Jones Averages 959 20.5 Deflating a Series by Price Indexes 960 20.6 Price Indexes: Other Considerations 963 Selection of Items 963 Selection of a Base Period 963 Quality Changes 964 20.7 Quantity Indexes 964 Summary 966 Glossary 966 Key Formulas 967 Supplementary Exercises 967 Chapter 21 Decision Analysis (On Website) Statistics in Practice: Ohio Edison Company 21-2 21.1 Problem Formulation 21-3 Payoff Tables 21-4 Decision Trees 21-4 21.2 Decision Making with Probabilities 21-5 Copyright 2017 Cengage Learning. 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Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.  Contents xxi Expected Value Approach 21-5 Expected Value of Perfect Information 21-7 21.3 Decision Analysis with Sample Information 21-13 Decision Tree 21-14 Decision Strategy 21-15 Expected Value of Sample Information 21-18 21.4 Computing Branch Probabilities Using Bayes’ Theorem 21-24 Summary 21-28 Glossary 21-29 Key Formulas 21-30 Supplementary Exercises 21-30 Case Problem Lawsuit Defense Strategy 21-33 Appendix: Self-Test Solutions and Answers to Even-Numbered Exercises 21-34 Chapter 22 Sample Survey (On Website) 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 Copyright 2017 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.  xxii Contents Appendix A References and Bibliography 972 Appendix B Tables 974 Appendix C Summation Notation 1001 Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 1003 Appendix E Microsoft Excel 2013 and Tools for Statistical Analysis 1070 Appendix F Computing p-Values Using Minitab and Excel 1078 Index 1082 Copyright 2017 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.  Preface This text is the 13th edition of STATISTICS FOR BUSINESS AND ECONOMICS. The purpose of Statistics for Business and Economics is to give students, primarily those in the fields of business administration and economics, a conceptual introduction to the field of statistics and its many applications. The text is applications oriented and written with the needs of the nonmathematician in mind; the mathematical prerequisite is knowledge of algebra. Applications of data analysis and statistical methodology are an integral part of the organization and presentation of the text material. The discussion and development of each technique is presented in an application setting, with the statistical results providing insights to decisions and solutions to problems. Although the book is applications oriented, we have taken care to provide sound method- ological development and to use notation that is generally accepted for the topic being cov- ered. Hence, students will find that this text provides good preparation for the study of more advanced statistical material. A bibliography to guide further study is included as an appendix. The text introduces the student to the software packages of Minitab 17 and Microsoft® Office Excel 2013 and emphasizes the role of computer software in the application of statistical analysis. Minitab is illustrated as it is one of the leading statistical software packages for both education and statistical practice. Excel is not a statistical software package, but the wide avail- ability and use of Excel make it important for students to understand the statistical capabilities of this package. Minitab and Excel procedures are provided in appendixes so that instructors have the flexibility of using as much computer emphasis as desired for the course. Changes in the Thirteenth Edition We appreciate the acceptance and positive response to the previous editions of Statistics for Business and Economics. Accordingly, in making modifications for this new edition, we have maintained the presentation style and readability of those editions. There have been many changes made throughout the text to enhance its educational effectiveness. The most substantial changes in the new edition are summarized here. Content Revisions Data and Statistics—Chapter 1. We have expanded our section on data mining to include a discussion of big data. We have added a new section on analytics. We have also placed greater emphasis on the distinction between observed and experi- mental data. Descriptive Statistics: Tabular and Graphical Displays—Chapter 2. We have added instructions on how to use Excel’s recommended charts option to Appendix 2.2 at the end of this chapter. This new Excel functionality produces a gallery of suggested charts based on the data selected by the user and can help students iden- tify the most appropriate chart(s) to use to depict their data. Descriptive Statistics: Numerical Measures—Chapter 3. We now use the method for calculating percentiles that is recommended by the National Institute of Standards and Technology (NIST). In addition to being the standard recommended by NIST, this approach is also used by a wide variety of software. The NIST rec- ommended approach for calculating percentiles is used throughout the textbook Copyright 2017 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.  xxiv Preface wherever percentiles are used (for example, when creating a box plot or when cal- culating quantiles or an interquartile range). Introduction to Probability—Chapter 4. The discussion on experiments has been updated to draw a more clear distinction between random and designed experi- ments. This distinction makes it easier to understand the differences in the dis- cussion of experiments in the probability chapters (Chapters 4, 5, and 6) and the experimental design chapter (Chapter 13). Software. We have revised all step-by-step instructions in the software appendices and all figures throughout the book that feature software output to reflect Excel 2013 and Minitab 17. This provides students exposure to and experience with the current versions of two of the most commonly used software for statistical analysis in busi- ness. In this latest edition, we no longer provide discussion of the use of StatTools. Case Problems. We have added two new case problems in this addition; the total num- ber of cases is 33. One new probability modeling case has been added to Chapter 5, and one new simple linear regression case appears in Chapter 14. The 33 case problems in this book provide students the opportunity to work on more complex problems, analyze larger data sets, and prepare managerial reports based on the results of their analyses. Examples and Exercises Based on Real Data. We continue to make a substantial effort to update our text examples and exercises with the most current real data and referenced sources of statistical information. In this edition, we have added more than 180 new examples and exercises based on real data and referenced sources. Using data from sources also used by The Wall Street Journal, USA Today, Barron’s, and others, we have drawn from actual studies and applications to develop