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A ROADMAP FOR SELECTING A STATISTICAL METHOD TYPE OF DATA Type of Analysis Numerical Categorical Describing a group Ordered array, stem-and-leaf display, frequency Summary table, bar chart, pie or several groups distribution, relative frequency distribution, chart, Pareto chart (Sections 2.2, 2.4) percentage distribution, cumulative percentage distribution, histogram, polygon, cumulative percentage polygon (Sections 2.3, 2.5) Mean, median, mode, quartiles, geometric mean, range, interquartile range, standard deviation, variance, coefficient of variation, boxplot (Sections 3.1, 3.2, 3.3) Index numbers (Online Topic 16.8) Inference about one group Confidence interval estimate of the mean Confidence interval estimate of the (Sections 8.1 and 8.2) proportion (Section 8.3) t test for the mean (Section 9.2) Z test for the proportion Chi-square test for a variance (Section 12.5) (Section 9.4) Comparing two groups Tests for the difference in the means of two Z test for the difference between independent populations (Section 10.1) two proportions (Section 10.3) Paired t test (Section 10.2) Chi-square test for the difference F test for the difference between two variances between two proportions (Section 10.4) (Section 12.1) Wilcoxon rank sum test (Section 12.6) McNemar test for the difference Wilcoxon signed ranks test (Online Topic 12.8) between two proportions in related samples (Section 12.4) Comparing more One-way analysis of variance (Section 11.1) Chi-square test for differences than two groups Randomized block design (Section 11.2) among more than two proportions Two-way analysis of variance (Section 11.3) (Section 12.2) Kruskal-Wallis test (Section 12.7) Friedman rank test (Online Topic 12.9) Analyzing the Scatter plot, time series plot (Section 2.6) Contingency table, side-by-side relationship between Covariance, coefficient of correlation (Section 3.5) bar chart, (Sections 2.2, 2.4) two variables Simple linear regression (Chapter 13) Chi-square test of independence t test of correlation (Section 13.7) (Section 12.3) Time series forecasting (Chapter 16) Analyzing the Multiple regression (Chapters 14 and 15) Multidimensional contingency relationship between tables (Section 2.7) two or more variables Logistic regression (Section 14.7) This page intentionally left blank Basic Business Statistics: Concepts and Applications TWELFTH EDITION Basic Business Statistics: Concepts and Applications TWELFTH EDITION Mark L. Berenson Department of Management and Information Systems School of Business, Montclair State University David M. Levine Department of Statistics and Computer Information Systems Zicklin School of Business, Baruch College, City University of New York Timothy C. Krehbiel Department of Management Richard T. Farmer School of Business, Miami University Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editorial Director: Sally Yagan Senior Art Director: Kenny Beck Editor in Chief: Eric Svendsen Text Designers: Dina Curro/Suzanne Behnke Senior Acquisitions Editor: Chuck Synovec Cover Designer and Art: LCI Design Editorial Project Manager: Mary Kate Murray Media Editor: Allison Longley Editorial Assistant: Jason Calcano Media Project Manager: John Cassar Director of Marketing: Patrice Lumumba Jones Full-Service Project Manager: Jen Carley Senior Marketing Manager: Anne Fahlgren Composition: PreMediaGlobal Marketing Assistant: Melinda Jensen Printer/Binder: Courier/Kendallville Senior Managing Editor: Judy Leale Cover Printer: Lehigh-Phoenix Color/Hagerstown Project Manager: Kerri Tomasso Text Font: TimesNewRomanPS Senior Operations Supervisor: Arnold Vila Technical Editor: David Stephan Photo Credits: front, page viii, courtesy of Rudy Krehbiel; pp. 2–3: Photos.com; pp. 3, 7: Maga, Shutterstock; pp. 14–15: Don Farrall, PhotoDisc/Getty Images; pp. 15, 59:Steve Coleccs, iStockphoto; pp. 84–85: Don Farrall, PhotoDisc/Getty Images; pp. 85, 121: Steve Coleccs, iStockphoto; pp. 132–133: Ljupco Smokovski, Shutterstock; pp. 133, 154: © Alan Levenson/Corbis, all rights reserved; pp. 160–161: Sebastian Kaulitzki, Shutterstock; pp. 161, 183: Monkey Business Images, Shutterstock; pp. 192–193: Alexander Kalina, Shutterstock; pp. 193, 215: Lee Morris, Shutterstock; pp. 222–223: R. 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Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on appropriate page within text. Microsoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. Screen shots and icons reprinted with permission from the Microsoft Corporation. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation. Copyright © 2012, 2009, 2006, 2004, 2002 by Pearson Education, Inc., publishing as Prentice Hall, One Lake Street, Upper Saddle River, New Jersey 07458. All rights reserved. Manufactured in the United States of America. This publication is protected by copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, elec- tronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458. Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. CIP data for this title is available on file at the Library of Congress 10 9 8 7 6 5 4 3 2 1 ISBN 10: 0-13-216838-3 ISBN 13: 978-0-13-216838-0 To our wives, Rhoda B., Marilyn L., and, Patti K., and to our children, Kathy, Lori, Sharyn, Ed, Rudy, and Rhonda About the Authors The textbook authors meet to discuss statistics at a Mets baseball game. Shown left to right: David Levine, Mark Berenson, and Tim Krehbiel. Mark L. Berenson is Professor of Management and Information Systems at Montclair State University (Montclair, New Jersey) and also Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (City University of New York). He currently teaches graduate and undergraduate courses in sta- tistics and in operations management in the School of Business and an undergraduate course in international justice and human rights that he co-developed in the College of Humanities and Social Sciences. Berenson received a B.A. in economic statistics and an M.B.A. in business statistics from City College of New York and a Ph.D. in business from the City University of New York. Berenson’s research has been published in Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, Psychometrika, Educational and Psychological Measurement, Journal of Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The New York Statistician, Journal of Health Administration Education, Journal of Behavioral Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The Encyclopedia of Measurement & Statistics and Encyclopedia of Statistical Sciences. He is co-author of 11 statistics texts published by Prentice Hall, including Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, and Business Statistics: A First Course. Over the years, Berenson has received several awards for teaching and for innovative con- tributions to statistics education. In 2005, he was the first recipient of The Catherine A. Becker Service for Educational Excellence Award at Montclair State University. David M. Levine is Professor Emeritus of Statistics and Computer Infor- mation Systems at Baruch College (City University of New York). He received B.B.A. and M.B.A. degrees in Statistics from City College of New York and a Ph.D. from New York viii ABOUT THE AUTHORS ix University in Industrial Engineering and Operations Research. He is nationally recognized as a leading innovator in statistics education and is the co-author of 14 books, including such best-selling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab. He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, currently in its 2nd edition, Six Sigma for Green Belts and Champions and Design for Six Sigma for Green Belts and Champions, and the author of Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and Quality Management, 3rd edition, McGraw-Hill/Irwin. He is also the author of Video Review of Statistics and Video Review of Probability, both published by Video Aided Instruction, and the statistics module of the MBA primer published by Cengage Learning. He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Decision Sciences Journal of Innovative Education, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and given numerous talks at the Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences. Levine has also received several awards for outstanding teaching and curriculum development from Baruch College. Timothy C. Krehbiel is Professor of Management and Senior Associate Dean of the Farmer School of Business at Miami University in Oxford, Ohio. He teaches undergraduate and graduate courses in business statistics. In 1996, he received the presti- gious Instructional Innovation Award from the Decision Sciences Institute. He has also received the Farmer School of Business Effective Educator Award and has twice been named MBA professor of the year. Krehbiel’s research interests span many areas of business and applied statistics. His work has appeared in numerous journals, including Quality Management Journal, Ecological Economics, International Journal of Production Research, Journal of Purchasing and Supply Management, Journal of Applied Business Research, Journal of Marketing Management, Communications in Statistics, Decision Sciences Journal of Innovative Education, Journal of Education for Business, Marketing Education Review, Journal of Accounting Education, and Teaching Statistics. He is a co-author of three statistics text- books published by Prentice Hall: Business Statistics: A First Course, Basic Business Statistics, and Statistics for Managers Using Microsoft Excel. Krehbiel is also a co-author of the book Sustainability Perspectives in Business and Resources. Krehbiel graduated summa cum laude with a B.A. in history from McPherson College and earned an M.S. and a Ph.D. in statistics from the University of Wyoming. Brief Contents Preface xxiii 1 Introduction 2 2 Organizing and Visualizing Data 26 3 Numerical Descriptive Measures 94 4 Basic Probability 144 5 Discrete Probability Distributions 180 6 The Normal Distribution and Other Continuous Distributions 216 7 Sampling and Sampling Distributions 248 8 Confidence Interval Estimation 278 9 Fundamentals of Hypothesis Testing: One-Sample Tests 324 10 Two-Sample Tests 364 11 Analysis of Variance 414 12 Chi-Square Tests and Nonparametric Tests 466 13 Simple Linear Regression 520 14 Introduction to Multiple Regression 576 15 Multiple Regression Model Building 628 16 Time-Series Forecasting 664 17 Statistical Applications in Quality Management 716 18 A Road Map for Analyzing Data 762 Online Chapter: 19 Decision Making Appendices A–G 773 Self-Test Solutions and Answers to Selected Even-Numbered Problems 820 Index 850 xi Contents Preface xxiii ORGANIZING DATA 29 2.2 Organizing Categorical Data 30 The Summary Table 30 The Contingency Table 30 1 Introduction 2 2.3 Organizing Numerical Data 33 Stacked and Unstacked Data 33 USING STATISTICS @ Good Tunes & More 3 The Ordered Array 34 The Frequency Distribution 35 1.1 Why Learn Statistics 4 The Relative Frequency Distribution and the Percentage 1.2 Statistics in Business 4 Distribution 37 1.3 Basic Vocabulary of Statistics 5 The Cumulative Distribution 38 1.4 Identifying Type of Variables 7 VISUALIZING DATA 41 Measurement Scales 7 2.4 Visualizing Categorical Data 41 1.5 Statistical Applications for Desktop The Bar Chart 42 Computing 10 The Pie Chart 43 1.6 How to Use This Book 11 The Pareto Chart 44 Checklist for Getting Started 11 The Side-by-Side Bar Chart 46 USING STATISTICS @ Good Tunes & More Revisited 13 2.5 Visualizing Numerical Data 49 SUMMARY 13 The Stem-and-Leaf Display 49 KEY TERMS 13 The Histogram 50 CHAPTER REVIEW PROBLEMS 13 The Percentage Polygon 51 The Cumulative Percentage Polygon (Ogive) 53 END-OF-CHAPTER CASES 15 LEARNING WITH THE DIGITAL CASES 15 2.6 Visualizing Two Numerical Variables 56 The Scatter Plot 56 REFERENCES 16 The Time-Series Plot 58 CHAPTER 1 EXCEL GUIDE 17 2.7 Organizing Multidimensional Data 60 EG1.1 Getting Started with Excel 17 Multidimensional Contingency Tables 60 EG1.2 Entering Data and Variable Type 18 Adding Numerical Variables 61 EG1.3 Opening and Saving Workbooks 18 EG1.4 Creating and Copying Worksheets 19 2.8 Misuses and Common Errors in Visualizing Data 63 EG1.5 Printing Worksheets 19 USING STATISTICS @ Choice Is Yours, Part I Revisited 66 EG1.6 Worksheet Entries and References 20 SUMMARY 67 EG1.7 Absolute and Relative Cell References 21 KEY EQUATIONS 67 EG1.8 Entering Formulas into Worksheets 21 KEY TERMS 68 EG1.9 Using Appendices D and F 21 CHAPTER REVIEW PROBLEMS 68 CHAPTER 1 MINITAB GUIDE 22 MANAGING ASHLAND MULTICOMM SERVICES 74 MG1.1 Getting Started With Minitab 22 DIGITAL CASE 75 MG1.2 Entering Data and Variable Type 22 REFERENCES 75 MG1.3 Opening and Saving Worksheets and CHAPTER 2 EXCEL GUIDE 76 Projects 23 EG2.2 Organizing Categorical Data 76 MG1.4 Creating and Copying Worksheets 24 EG2.3 Organizing Numerical Data 78 MG1.5 Printing Parts of a Project 24 EG2.4 Visualizing Categorical Data 80 MG1.6 Worksheet Entries and References 24 EG2.5 Visualizing Numerical Data 82 MG1.7 Using Appendices D and F 25 EG2.6 Visualizing Two Numerical Variables 84 EG2.7 Organizing Multidimensional Data 85 CHAPTER 2 MINITAB GUIDE 87 2 Organizing and Visualizing MG2.2 Organizing Categorical Data 87 MG2.3 Organizing Numerical Data 87 Data 26 MG2.4 Visualizing Categorical Data 88 MG2.5 Visualizing Numerical Data 89 USING STATISTICS @ Choice Is Yours, Part I 27 MG2.6 Visualizing Two Numerical Variables 92 2.1 Data Collection 28 MG2.7 Organizing Multidimensional Data 93 xiii xiv CONTENTS 3 Numerical Descriptive 4 Basic Probability 144 Measures 94 USING STATISTICS @ M&R Electronics World 145 4.1 Basic Probability Concepts 146 USING STATISTICS @ Choice Is Yours, Part II 95 Events and Sample Spaces 147 3.1 Central Tendency 96 Contingency Tables and Venn Diagrams 148 The Mean 96 Simple Probability 149 The Median 98 Joint Probability 150 The Mode 99 Marginal Probability 150 The Geometric Mean 100 General Addition Rule 151 3.2 Variation and Shape 101 4.2 Conditional Probability 155 The Range 102 The Variance and the Standard Deviation 102 Computing Conditional Probabilities 155 The Coefficient of Variation 106 Decision Trees 156 Z Scores 107 Independence 158 Shape 108 Multiplication Rules 159 VISUAL EXPLORATIONS: Exploring Descriptive Marginal Probability Using the General Multiplication Statistics 110 Rule 160 3.3 Exploring Numerical Data 113 4.3 Bayes’ Theorem 163 Quartiles 113 THINK ABOUT THIS: Divine Providence and Spam 166 The Interquartile Range 115 4.4 Counting Rules 167 The Five-Number Summary 115 Counting Rule 1 167 The Boxplot 117 Counting Rule 2 168 3.4 Numerical Descriptive Measures for a Population 120 Counting Rule 3 168 The Population Mean 121 Counting Rule 4 169 The Population Variance and Standard Deviation 121 Counting Rule 5 169 The Empirical Rule 122 4.5 Ethical Issues and Probability 171 The Chebyshev Rule 123 USING STATISTICS @ M&R Electronics World Revisited 172 3.5 The Covariance and the Coefficient of SUMMARY 172 Correlation 125 KEY EQUATIONS 172 The Covariance 125 KEY TERMS 173 The Coefficient of Correlation 127 CHAPTER REVIEW PROBLEMS 173 3.6 Descriptive Statistics: Pitfalls and Ethical Issues 131 DIGITAL CASE 175 USING STATISTICS @ Choice Is Yours, Part II Revisited 131 REFERENCES 176 SUMMARY 132 CHAPTER 4 EXCEL GUIDE 177 KEY EQUATIONS 132 EG4.1 Basic Probability Concepts 177 KEY TERMS 133 EG4.2 Conditional Probability 177 CHAPTER REVIEW PROBLEMS 133 EG4.3 Bayes’ Theorem 177 MANAGING ASHLAND MULTICOMM SERVICES 138 EG4.4 Counting Rules 178 DIGITAL CASE 138 CHAPTER 4 MINITAB GUIDE 178 REFERENCES 138 MG4.1 Basic Probability Concepts 178 CHAPTER 3 EXCEL GUIDE 139 MG4.2 Conditional Probability 178 EG3.1 Central Tendency 139 MG4.3 Bayes’ Theorem 178 EG3.2 Variation and Shape 139 MG4.4 Counting Rules 178 EG3.3 Exploring Numerical Data 140 EG3.4 Numerical Descriptive Measures for a Population 140 EG3.5 The Covariance and the Coefficient of Correlation 141 5 Discrete Probability CHAPTER 3 MINITAB GUIDE 141 Distributions 180 MG3.1 Central Tendency 141 MG3.2 Variation and Shape 142 USING STATISTICS @ Saxon Home Improvement 181 MG3.3 Exploring Numerical Data 142 5.1 The Probability Distribution for a Discrete Random MG3.4 Numerical Descriptive Measures for a Variable 182 Population 143 Expected Value of a Discrete Random Variable 182 MG3.5 The Covariance and the Coefficient of Variance and Standard Deviation of a Discrete Random Correlation 143 Variable 183 CONTENTS xv 5.2 Covariance and Its Application in Finance 185 USING STATISTICS @ OurCampus! Revisited 240 Covariance 185 SUMMARY 240 Expected Value, Variance, and Standard Deviation of the KEY EQUATIONS 241 Sum of Two Random Variables 187 KEY TERMS 241 Portfolio Expected Return and Portfolio Risk 187 CHAPTER REVIEW PROBLEMS 241 5.3 Binomial Distribution 190 MANAGING ASHLAND MULTICOMM SERVICES 244 5.4 Poisson Distribution 197 DIGITAL CASE 244 5.5 Hypergeometric Distribution 201 REFERENCES 244 5.6 Online Topic Using the Poisson Distribution CHAPTER 6 EXCEL GUIDE 245 to Approximate the Binomial Distribution 204 EG6.1 Continuous Probability Distributions 245 USING STATISTICS @ Saxon Home Improvement EG6.2 The Normal Distribution 245 Revisited 205 EG6.3 Evaluating Normality 245 SUMMARY 205 EG6.4 The Uniform Distribution 246 KEY EQUATIONS 205 EG6.5 The Exponential Distribution 246 KEY TERMS 206 CHAPTER 6 MINITAB GUIDE 246 CHAPTER REVIEW PROBLEMS 206 MG6.1 Continuous Probability Distributions 246 MANAGING ASHLAND MULTICOMM SERVICES 209 MG6.2 The Normal Distribution 246 MG6.3 Evaluating Normality 247 DIGITAL CASE 210 MG6.4 The Uniform Distribution 247 REFERENCES 210 MG6.5 The Exponential Distribution 247 CHAPTER 5 EXCEL GUIDE 211 EG5.1 The Probability Distribution for a Discrete Random Variable 211 EG5.2 Covariance and Its Application in Finance 211 EG5.3 Binomial Distribution 212 7 Sampling and Sampling EG5.4 Poisson Distribution 212 Distributions 248 EG5.5 Hypergeometric Distribution 213 USING STATISTICS @ Oxford Cereals 249 CHAPTER 5 MINITAB GUIDE 214 MG5.1 The Probability Distribution for a Discrete Random 7.1 Types of Sampling Methods 250 Variable 214 Simple Random Samples 251 MG5.2 Covariance and Its Application in Finance 214 Systematic Samples 253 MG5.3 Binomial Distribution 214 Stratified Samples 253 MG5.4 Poisson Distribution 214 Cluster Samples 254 MG5.5 Hypergeometric Distribution 215 7.2 Evaluating Survey Worthiness 255 Survey Error 255 Ethical Issues 256 THINK ABOUT THIS: New Media Surveys/Old Sampling 6 The Normal Distribution 7.3 Problem 256 Sampling Distributions 258 and Other Continuous 7.4 Sampling Distribution of the Mean 258 The Unbiased Property of the Sample Mean 258 Distributions 216 Standard Error of the Mean 260 USING STATISTICS @ OurCampus! 217 Sampling from Normally Distributed Populations 261 Sampling from Non-Normally Distributed Populations— 6.1 Continuous Probability Distributions 218 The Central Limit Theorem 264 6.2 The Normal Distribution 218 VISUAL EXPLORATIONS: Exploring Sampling Distributions 265 Computing Normal Probabilities 220 7.5 Sampling Distribution of the Proportion 266 THINK ABOUT THIS: What Is Normal? 228 7.6 Online Topic: Sampling from Finite VISUAL EXPLORATIONS: Exploring the Normal Populations 269 Distribution 229 6.3 Evaluating Normality 230 USING STATISTICS @ Oxford Cereals Revisited 270 Comparing Data Characteristics to Theoretical SUMMARY 270 Properties 231 KEY EQUATIONS 270 Constructing the Normal Probability Plot 232 KEY TERMS 271 6.4 The Uniform Distribution 235 CHAPTER REVIEW PROBLEMS 271 6.5 The Exponential Distribution 237 MANAGING ASHLAND MULTICOMM SERVICES 273 6.6 Online Topic: The Normal Approximation to the DIGITAL CASE 273 Binomial Distribution 240 REFERENCES 274 xvi CONTENTS CHAPTER 7 EXCEL GUIDE 275 CHAPTER 8 MINITAB GUIDE 322 EG7.1 Types of Sampling Methods 275 MG8.1 Confidence Interval Estimate for the EG7.2 Evaluating Survey Worthiness 275 Mean (s Known) 322 EG7.3 Sampling Distributions 275 MG8.2 Confidence Interval Estimate for the EG7.4 Sampling Distribution of the Mean 275 Mean (s Unknown) 323 EG7.5 Sampling Distribution of the Proportion 276 MG8.3 Confidence Interval Estimate for the Proportion 323 MG8.4 Determining Sample Size 323 CHAPTER 7 MINITAB GUIDE 276 MG8.5 Applications of Confidence Interval Estimation MG7.1 Types of Sampling Methods 276 in Auditing 323 MG7.2 Evaluating Survey Worthiness 277 MG7.3 Sampling Distributions 277 MG7.4 Sampling Distribution of the Mean 277 9 Fundamentals of Hypothesis Testing: One-Sample Tests 324 8 Confidence Interval USING STATISTICS @ Oxford Cereals, Part II 325 Estimation 278 9.1 Fundamentals of Hypothesis-Testing Methodology 326 The Null and Alternative Hypotheses 326 USING STATISTICS @ Saxon Home Improvement 279 The Critical Value of the Test Statistic 327 8.1 Confidence Interval Estimate for the Mean Regions of Rejection and Nonrejection 328 (s Known) 280 Risks in Decision Making Using Hypothesis Testing 328 Can You Ever Know the Population Standard Hypothesis Testing Using the Critical Value Approach 331 Deviation? 285 Hypothesis Testing Using the p-Value Approach 333 8.2 Confidence Interval Estimate for the A Connection Between Confidence Interval Estimation and Mean (s Unknown) 286 Hypothesis Testing 336 Student’s t Distribution 286 Can You Ever Know the Population Standard Deviation? 336 Properties of the t Distribution 287 9.2 t Test of Hypothesis for the Mean (s Unknown) 338 The Concept of Degrees of Freedom 288 The Critical Value Approach 338 The Confidence Interval Statement 288 The p-Value Approach 340 8.3 Confidence Interval Estimate for the Proportion 294 Checking the Normality Assumption 340 8.4 Determining Sample Size 297 9.3 One-Tail Tests 344 Sample Size Determination for the Mean 297 The Critical Value Approach 345 Sample Size Determination for the Proportion 299 The p-Value Approach 346 8.5 Applications of Confidence Interval Estimation in 9.4 Z Test of Hypothesis for the Proportion 349 Auditing 303 The Critical Value Approach 350 Estimating the Population Total Amount 304 The p-Value Approach 351 Difference Estimation 305 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues 353 One-Sided Confidence Interval Estimation of the Rate of Statistical Significance Versus Practical Significance 353 Noncompliance with Internal Controls 308 Reporting of Findings 353 8.6 Confidence Interval Estimation and Ethical Issues 310 Ethical Issues 354 8.7 Online Topic: Estimation and Sample Size 9.6 Online Topic: The Power of a Test 354 Determination for Finite Populations 311 USING STATISTICS @ Oxford Cereals, Part II Revisited 354 USING STATISTICS @ Saxon Home Improvement SUMMARY 355 Revisited 311 KEY EQUATIONS 355 SUMMARY 311 KEY TERMS 355 KEY EQUATIONS 312 CHAPTER REVIEW PROBLEMS 355 KEY TERMS 313 MANAGING ASHLAND MULTICOMM SERVICES 358 CHAPTER REVIEW PROBLEMS 313 DIGITAL CASE 358 MANAGING ASHLAND MULTICOMM SERVICES 317 REFERENCES 358 DIGITAL CASE 318 CHAPTER 9 EXCEL GUIDE 359 REFERENCES 318 EG9.1 Fundamentals of Hypothesis-Testing Methodology 359 CHAPTER 8 EXCEL GUIDE 319 EG9.2 t Test of Hypothesis for the Mean (s Unknown) 359 EG8.1 Confidence Interval Estimate for the EG9.3 One-Tail Tests 360 Mean (s Known) 319 EG9.4 Z Test of Hypothesis for the Proportion 361 EG8.2 Confidence Interval Estimate for the Mean (s Unknown) 319 CHAPTER 9 MINITAB GUIDE 362 EG8.3 Confidence Interval Estimate for the Proportion 320 MG9.1 Fundamentals of Hypothesis-Testing Methodology 362 EG8.4 Determining Sample Size 320 MG9.2 t Test of Hypothesis for the Mean (s Unknown) 362 EG8.5 Applications of Confidence Interval Estimation in MG9.3 One-Tail Tests 362 Auditing 321 MG9.4 Z Test of Hypothesis for the Proportion 363 CONTENTS xvii 10 Two-Sample Tests 364 11.2 The Randomized Block Design 430 Testing for Factor and Block Effects 430 Multiple Comparisons: The Tukey Procedure 436 USING STATISTICS @ BLK Beverages 365 11.3 The Factorial Design: Two-Way Analysis of Variance 438 10.1 Comparing the Means of Two Independent Testing for Factor and Interaction Effects 439 Populations 366 Multiple Comparisons: The Tukey Procedure 444 Pooled-Variance t Test for the Difference Between Two Means 366 Visualizing Interaction Effects: The Cell Means Plot 445 Confidence Interval Estimate for the Difference Between Interpreting Interaction Effects 446 Two Means 371 USING STATISTICS @ Perfect Parachutes Revisited 451 t Test for the Difference Between Two Means Assuming SUMMARY 451 Unequal Variances 372 KEY EQUATIONS 451 THINK ABOUT THIS: “This Call May Be Monitored ... ” 374 KEY TERMS 453 10.2 Comparing the Means of Two Related Populations 377 CHAPTER REVIEW PROBLEMS 453 Paired t Test 378 MANAGING ASHLAND MULTICOMM SERVICES 457 Confidence Interval Estimate for the Mean Difference 383 DIGITAL CASE 458 10.3 Comparing the Proportions of Two Independent REFERENCES 458 Populations 385 CHAPTER 11 EXCEL GUIDE 459 Z Test for the Difference Between Two Proportions 386 EG11.1 The Completely Randomized Design: One-Way Confidence Interval Estimate for the Difference Between Analysis of Variance 459 Two Proportions 390 EG11.2 The Randomized Block Design 461 10.4 F Test for the Ratio of Two Variances 392 EG11.3 The Factorial Design: Two-Way Analysis of Variance 462 USING STATISTICS @ BLK Beverages Revisited 397 CHAPTER 11 MINITAB GUIDE 464 SUMMARY 398 MG11.1 The Completely Randomized Design: One-Way KEY EQUATIONS 399 Analysis of Variance 464 KEY TERMS 400 MG11.2 The Randomized Block Design 465 CHAPTER REVIEW PROBLEMS 400 MG11.3 The Factorial Design: Two-Way Analysis of MANAGING ASHLAND MULTICOMM SERVICES 404 Variance 465 DIGITAL CASE 405 REFERENCES 405 CHAPTER 10 EXCEL GUIDE 406 12 Chi-Square Tests and EG10.1 Comparing the Means of Two Independent Nonparametric Tests 466 Populations 406 EG10.2 Comparing the Means of Two Related USING STATISTICS @ T.C. Resort Properties 467 Populations 408 12.1 Chi-Square Test for the Difference Between Two EG10.3 Comparing the Proportions of Two Independent Proportions 468 Populations 409 12.2 Chi-Square Test for Differences Among More Than Two EG10.4 F Test for the Ratio of Two Variances 410 Proportions 475 CHAPTER 10 MINITAB GUIDE 411 The Marascuilo Procedure 478 MG10.1 Comparing the Means of Two Independent Online Topic: The Analysis of Proportions (ANOP) 480 Populations 411 12.3 Chi-Square Test of Independence 481 MG10.2 Comparing the Means of Two Related Populations 411 12.4 McNemar Test for the Difference Between Two Proportions MG10.3 Comparing the Proportions of Two Independent (Related Samples) 487 Populations 412 12.5 Chi-Square Test for the Variance or Standard MG10.4 F Test for the Ratio of Two Variances 412 Deviation 490 12.6 Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations 494 11 Analysis of Variance 414 12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way ANOVA 500 USING STATISTICS @ Perfect Parachutes 415 12.8 Online Topic: Wilcoxon Signed Ranks test: 11.1 The Completely Randomized Design: One-Way Analysis Nonparametric Analysis for Two Related Populations 505 of Variance 416 12.9 Online Topic: Friedman Rank Test: Nonparametric One-Way ANOVA F Test for Differences Among More Than Analysis for the Randomized Block Design 506 Two Means 416 Multiple Comparisons: The Tukey-Kramer Procedure 422 USING STATISTICS @ T.C. Resort Properties Revisited 506 Online Topic: The Analysis of Means (ANOM) 424 SUMMARY 506 ANOVA Assumptions 424 KEY EQUATIONS 507 Levene Test for Homogeneity of Variance 425 KEY TERMS 508 xviii CONTENTS CHAPTER REVIEW PROBLEMS 508 Confidence Interval Estimate for the Slope 550 MANAGING ASHLAND MULTICOMM SERVICES 511 t Test for the Correlation Coefficient 551 DIGITAL CASE 512 13.8 Estimation of Mean Values and Prediction of Individual REFERENCES 513 Values 554 CHAPTER 12 EXCEL GUIDE 514 The Confidence Interval Estimate 554 EG12.1 Chi-Square Test for the Difference Between The Prediction Interval 556 Two Proportions 514 13.9 Pitfalls in Regression 558 EG12.2 Chi-Square Test for Differences Among More Than THINK ABOUT THIS: By Any Other Name 561 Two Proportions 514 USING STATISTICS @ Sunflowers Apparel EG12.3 Chi-Square Test of Independence 515 Revisited 561 EG12.4 McNemar Test for the Difference Between Two SUMMARY 562 Proportions (Related Samples) 515 KEY EQUATIONS 563 EG12.5 Chi-Square Test for the Variance or Standard Deviation 516 KEY TERMS 564 EG12.6 Wilcoxon Rank Sum Test: Nonparametric Analysis CHAPTER REVIEW PROBLEMS 564 for Two Independent Populations 516 MANAGING ASHLAND MULTICOMM SERVICES 569 EG12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis DIGITAL CASE 569 for the One-Way ANOVA 517 REFERENCES 570 CHAPTER 12 MINITAB GUIDE 518 CHAPTER 13 EXCEL GUIDE 571 MG12.1 Chi-Square Test for the Difference Between Two EG13.1 Types of Regression Models 571 Proportions 518 EG13.2 Determining the Simple Linear Regression MG12.2 Chi-Square Test for Differences Among More Equation 571 Than Two Proportions 518 EG13.3 Measures of Variation 572 MG12.3 Chi-Square Test of Independence 518 EG13.4 Assumptions 572 MG12.4 McNemar Test for the Difference Between Two EG13.5 Residual Analysis 572 Proportions (Related Samples) 518 EG13.6 Measuring Autocorrelation: The Durbin-Watson MG12.5 Chi-Square Test for the Variance or Standard Statistic 572 Deviation 518 EG13.7 Inferences About the Slope and Correlation MG12.6 Wilcoxon Rank Sum Test: Nonparametric Coefficient 573 Analysis for Two Independent Populations 519 EG13.8 Estimation of Mean Values and Prediction EG12.7 Kruskal-Wallis Rank Test: Nonparametric Analysis of Individual Values 573 for the One-Way ANOVA 519 CHAPTER 13 MINITAB GUIDE 574 MG13.1 Types of Regression Models 574 MG13.2 Determining the Simple Linear Regression 13 Simple Linear Equation 574 MG13.3 Measures of Variation 574 Regression 520 MG13.4 Assumptions 574 MG13.5 Residual Analysis 574 USING STATISTICS @ Sunflowers Apparel 521 MG13.6 Measuring Autocorrelation: The Durbin-Watson 13.1 Types of Regression Models 522 Statistic 575 13.2 Determining the Simple Linear Regression Equation 524 MG13.7 Inferences About the Slope and Correlation The Least-Squares Method 525 Coefficient 575 Predictions in Regression Analysis: Interpolation Versus MG13.8 Estimation of Mean Values and Prediction Extrapolation 527 of Individual Values 575 Computing the Y Intercept, b0 and the Slope, b1 528 VISUAL EXPLORATIONS: Exploring Simple Linear Regression Coefficients 530 14 Introduction to Multiple 13.3 Measures of Variation 533 Computing the Sum of Squares 533 Regression 576 The Coefficient of Determination 534 USING STATISTICS @ OmniFoods 577 Standard Error of the Estimate 536 14.1 Developing a Multiple Regression Model 578 13.4 Assumptions 538 Visualizing Multiple Regression Data 578 13.5 Residual Analysis 539 Interpreting the Regression Coefficients 578 Evaluating the Assumptions 539 Predicting the Dependent Variable Y 581 13.6 Measuring Autocorrelation: The Durbin-Watson 14.2 r2, Adjusted r2, and the Overall F Test 584 Statistic 543 Coefficient of Multiple Determination 584 Residual Plots to Detect Autocorrelation 543 Adjusted r2 585 The Durbin-Watson Statistic 544 Test for the Significance of the Overall Multiple 13.7 Inferences About the Slope and Correlation Coefficient 547 Regression Model 585 t Test for the Slope 548 14.3 Residual Analysis for the Multiple Regression F Test for the Slope 549 Model 588 CONTENTS xix 14.4 Inferences Concerning the Population Regression 15.3 Collinearity 642 Coefficients 590 15.4 Model Building 644 Tests of Hypothesis 590 The Stepwise Regression Approach to Model Building 646 Confidence Interval Estimation 591 The Best-Subsets Approach to Model Building 647 14.5 Testing Portions of the Multiple Regression Model Validation 652 Model 593 15.5 Pitfalls in Multiple Regression and Ethical Issues 653 Coefficients of Partial Determination 597 Pitfalls in Multiple Regression 653 14.6 Using Dummy Variables and Interaction Terms in Ethical Issues 654 Regression Models 599 15.6 ( Online Topic) Influence Analysis 654 Dummy variables 599 Interactions 602 15.7 ( Online Topic) Analytics and Data Mining 654 14.7 Logistic Regression 609 USING STATISTICS @ WHIT-DT Revisited 654 SUMMARY 655 USING STATISTICS @ OmniFoods Revisited 614 KEY EQUATIONS 656 SUMMARY 614 KEY TERMS 656 KEY EQUATIONS 616 CHAPTER REVIEW PROBLEMS 656 KEY TERMS 617 THE MOUNTAIN STATES POTATO COMPANY 658 CHAPTER REVIEW PROBLEMS 617 DIGITAL CASE 659 MANAGING ASHLAND MULTICOMM SERVICES 620 REFERENCES 659 DIGITAL CASE 620 CHAPTER 15 EXCEL GUIDE 660 REFERENCES 621 EG15.1 The Quadratic Regression Model 660 CHAPTER 14 EXCEL GUIDE 622 EG15.2 Using Transformations in Regression Models 660 EG14.1 Developing a Multiple Regression Model 622 EG15.3 Collinearity 660 EG14.2 r2, Adjusted r2, and the Overall F Test 623 EG15.4 Model Building 660 EG14.3 Residual Analysis for the Multiple Regression Model 623 CHAPTER 15 MINITAB GUIDE 661 EG14.4 Inferences Concerning the Population Regression MG15.1 The Quadratic Regression Model 661 Coefficients 624 MG15.2 Using Transformations in Regression Models 662 EG14.5 Testing Portions of the Multiple Regression MG15.3 Collinearity 662 Model 624 MG15.4 Model Building 662 EG14.6 Using Dummy Variables and Interaction Terms in Regression Models 624 EG14.7 Logistic Regression 624 CHAPTER 14 MINITAB GUIDE 625 MG14.1 Developing a Multiple Regression Model 625 16 Time-Series Forecasting 664 MG14.2 r2, Adjusted r2, and the Overall F Test 626 USING STATISTICS @ The Principled 665 MG14.3 Residual Analysis for the Multiple Regression 16.1 The Importance of Business Forecasting 666 Model 626 16.2 Component Factors of Time-Series Models 666 MG14.4 Inferences Concerning the Population Regression Coefficients 626 16.3 Smoothing an Annual Time Series 667 MG14.5 Testing Portions of the Multiple Regression Moving Averages 668 Model 626 Exponential Smoothing 670 MG14.6 Using Dummy Variables and Interaction Terms 16.4 Least-Squares Trend Fitting and Forecasting 673 in Regression Models 626 The Linear Trend Model 673 MG14.7 Logistic Regression 627 The Quadratic Trend Model 675 The Exponential Trend Model 676 Model Selection Using First, Second, and Percentage 15 Multiple Regression Model Differences 678 16.5 Autoregressive Modeling for Trend Fitting and Building 628 Forecasting 684 16.6 Choosing an Appropriate Forecasting Model 692 USING STATISTICS @ WHIT-DT 629 Performing a Residual Analysis 693 15.1 The Quadratic Regression Model 630 Measuring the Magnitude of the Residuals Through Squared Finding the Regression Coefficients and Predicting Y 630 or Absolute Differences 693 Testing for the Significance of the Quadratic Model 633 Using the Principle of Parsimony 694 Testing the Quadratic Effect 633 A Comparison of Four Forecasting Methods 694 The Coefficient of Multiple Determination 635 16.7 Time-Series Forecasting of Seasonal Data 696 15.2 Using Transformations in Regression Models 638 Least-Squares Forecasting with Monthly or Quarterly Data 697 The Square-Root Transformation 638 16.8 Online Topic: Index Numbers 703 The Log Transformation 639 THINK ABOUT THIS: Let the Model User Beware 703 xx CONTENTS USING STATISTICS @ The Principled Revisited 703 MANAGING ASHLAND MULTICOMM SERVICES 753 SUMMARY 704 REFERENCES 754 KEY EQUATIONS 704 CHAPTER 17 EXCEL GUIDE 755 KEY TERMS 705 EG17.1 The Theory of Control Charts 755 CHAPTER REVIEW PROBLEMS 706 EG17.2 Control Chart for the Proportion: The p Chart 755 MANAGING ASHLAND MULTICOMM SERVICES 707 EG17.3 The Red Bead Experiment: Understanding Process Variability 756 DIGITAL CASE 708 EG17.4 Control Chart for an Area of Opportunity: The c REFERENCES 708 Chart 756 CHAPTER 16 EXCEL GUIDE 709 EG17.5 Control Charts for the Range and the Mean 757 EG16.1 The Importance of Business Forecasting 709 EG17.6 Process Capability 758 EG16.2 Component Factors of Time-Series Models 709 EG17.7 Total Quality Management 759 EG16.3 Smoothing an Annual Time Series 709 EG17.8 Six Sigma 759 EG16.4 Least-Squares Trend Fitting and Forecasting 710 CHAPTER 17 MINITAB GUIDE 759 EG16.5 Autoregressive Modeling for Trend Fitting and MG17.1 The Theory of Control Charts 759 Forecasting 711 MG17.2 Control Chart for the Proportion: EG16.6 Choosing an Appropriate Forecasting Model 711 The p Chart 759 EG16.7 Time-Series Forecasting of Seasonal Data 712 MG17.3 The Red Bead Experiment: Understanding CHAPTER 16 MINITAB GUIDE 713 Process Variability 759 MG16.1 The Importance of Business Forecasting 713 MG17.4 Control Chart for an Area of Opportunity: The c MG16.2 Component Factors of Time-Series Models 713 Chart 756 MG16.3 Smoothing an Annual Time Series 713 MG17.5 Control Charts for the Range and the Mean 760 MG16.4 Least-Squares Trend Fitting and Forecasting 713 MG17.6 Process Capability 761 MG16.5 Autoregressive Modeling for Trend Fitting and MG17.7 Total Quality Management 761 Forecasting 714 MG17.8 Six Sigma 761 MG16.6 Choosing an Appropriate Forecasting Model 714 MG16.7 Time-Series Forecasting of Seasonal Data 714 18 A Roadmap for Analyzing 17 Statistical Applications in Data 762 Quality Management 716 USING STATISTICS @ YourBusiness 763 USING STATISTICS @ Beachcomber Hotel 717 18.1 Analyzing Numerical Variables 765 How to Describe the Characteristics of a Numerical 17.1 The Theory of Control Charts 718 Variable 766 17.2 Control Chart for the Proportion: The p Chart 720 How to Draw Conclusions About the Population Mean 17.3 The Red Bead Experiment: Understanding Process or Standard Deviation 766 Variability 726 How to Determine Whether the Mean or Standard Deviation Differs Depending on the Group 766 17.4 Control Chart for an Area of Opportunity: The c Chart 728 How to Determine Which Factors Affect the Value of a Variable 767 17.5 Control Charts for the Range and the Mean 732 How to Predict the Value of a Variable Based on the Value The R Chart 732 of Other Variables 767 The X Chart 734 How to Determine Whether the Values of a Variable Are 17.6 Process Capability 737 Stable over Time 767 Customer Satisfaction and Specification Limits 737 18.2 Analyzing Categorical Variables 767 Capability Indices 739 How to Describe the Proportion of Items of Interest in Each CPL, CPU, and Cpk 740 Category 768 17.7 Total Quality Management 742 How to Reach Conclusions About the Proportion of Items of Interest 768 17.8 Six Sigma 744 How to Determine Whether the Proportion of Items The DMAIC Model 744 of Interest Differs Depending on the Group 768 Roles in a Six Sigma Organization 745 How to Predict the Proportion of Items of Interest Based USING STATISTICS @ Beachcomber Hotel Revisited 746 on the Value of Other Variables 768 SUMMARY 747 How to Determine Whether the Proportion of Items of Interest Is Stable over Time 769 KEY EQUATIONS 747 KEY TERMS 748 USING STATISTICS @ YourBusiness Revisited 769 CHAPTER REVIEW PROBLEMS 748 DIGITAL CASE 769 THE HARNSWELL SEWING MACHINE COMPANY CASE 751 CHAPTER REVIEW PROBLEMS 769 CONTENTS xxi Online Chapter: D.3 Configuring Excel for PHStat2 Usage 793 D.4 Using the Visual Explorations Add-in 19 Decision Making Workbook 795 D.5 Checking for the Presence of the Analysis USING STATISTICS @ Reliable Fund ToolPak 795 19.1 Payoff Tables and Decision Trees E. Tables 796 19.2 Criteria for Decision Making E.1 Table of Random Numbers 796 Maximax Payoff E.2 The Cumulative Standardized Normal Maximin Payoff Distribution 798 Expected Monetary Value E.3 Critical Values of t 800 Expected Opportunity Loss Return-to-Risk Ratio E.4 Critical Values of x2 802 19.3 Decision Making with Sample Information E.5 Critical values of F 803 19.4 Utility E.6 Lower and Upper Critical Values, T1, of Wilcoxon Rank Sum Test 807 THINK ABOUT THIS: Risky Business E.7 Critical Values of the Studentized Range, Q 808 USING STATISTICS @ Reliable Fund Revisited E.8 Critical Values dL and du of the Durbin-Watson CHAPTER 19 EXCEL GUIDE Statistic, D 810 EG19.1 Payoff Tables and Decision Trees EG19.2 Criteria for Decision Making E.9 Control Chart Factors 811 E.10 The Standardized Normal Distribution 812 Appendices 773 F. Additional Excel Procedures 813 A. Basic Math Concepts and Symbols 774 F.1 Enhancing Workbook Presentation 813 A.1 Rules for Arithmetic Operations 774 F.2 Useful Keyboard Shortcuts 814 A.2 Rules for Algebra: Exponents and Square Roots 774 F.3 Verifying Formulas and Worksheets 815 A.3 Rules for Logarithms 775 F.4 Chart Formatting 815 A.4 Summation Notation 776 F.5 Creating Histograms for Discrete Probability Distributions 816 A.5 Statistical Symbols 779 F.6 Pasting with Paste Special 816 A.6 Greek Alphabet 779 G. PHStat2, Excel, and Minitab FAQs 818 B. Basic Computing Skills 780 G.1 PHStat2 FAQs 818 B.1 Objects in a Window 780 G.2 Excel FAQs 818 B.2 Basic Mouse Operations 781 G.3 FAQs for Minitab 819 B.3 Dialog Box Interactions 781 B.4 Unique Features 783 C. Companion Website Resources 784 C.1 Visiting the Companion Website for This Book 784 Self-Test Solutions and Answers to Selected C.2 Downloading the Files for This Book 784 Even-Numbered Problems 820 C.3 Accessing the Online Topics Files 784 C.4 Details of Downloadable Files 784 Index 850 D. Software Configuration Details 792 D.1 Checking for and Applying Excel Updates 792 D.2 Concise Instructions for Installing PHStat2 792 Preface Educational Philosophy Seeking ways to continuously improve the teaching of business statistics is the core value that guides our works. We actively participate in Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences. We use the Guidelines for Assessment and Instruction (GAISE) reports as well as our reflections on teaching business statistics to a diverse student body at several large universities. These experiences have helped us identify the following key principles: 1. Show students the relevance of statistics Students need a frame of reference when learning statistics, especially when statistics is not their major. That frame of reference for business students should be the functional areas of business, such as accounting, finance, information systems, management, and marketing. Each statistics topic needs to be presented in an applied context related to at least one of these functional areas. The focus in teaching each topic should be on its application in business, the interpretation of results, the evaluation of the assumptions, and the discussion of what should be done if the assumptions are violated. 2. Familiarize students with the statistical applications used in the business world Integrating these programs into all aspects of an introductory statistics course allows the course to focus on interpretation of results instead of computations. Introductory business statistics courses should recognize that programs with statistical functions are commonly found on a business decision maker’s desktop computer, therefore making the interpretation of results more important than the tedious hand calculations required to produce them. 3. Provide clear instructions to students for using statistical applications Books should explain clearly how to use programs such as Excel and Minitab with the study of statistics, without having those instructions dominate the book or distract from the learning of statisti- cal concepts. 4. Give students ample practice in understanding how to apply statistics to business Both classroom examples and homework exercises should involve actual or realistic data as much as possible. Students should work with data sets, both small and large, and be encouraged to look beyond the statistical analysis of data to the interpretation of results in a managerial context. New to This Edition: MyStatLab Custom MyStatLab course materials designed for specific use with this book are available. MyStatLab is Pearson Education’s online learning, homework, and assessment tool that provides a rich and flexible set of course materials, including free-response exercises that are algorithmically generated for unlimited practice and mastery. MyStatLab provides students with a personalized, interactive learning environment that helps them to independently improve their understanding and performance in a course. MyStatLab allows instructors to deliver portions of a course online, to per- form course management functions, and to create a supportive online community. In addition, instructors can use the MyStatLab homework and test manager to select and assign their own online exercises as well as import TestGen tests. The MyStatLab for Basic Business Statistics features several improvements over earlier versions including a more intuitive user design that presents a simpler interface with fewer pop-up windows. This MyStatLab also provides mobile device access through free apps that can be downloaded for iPhones, iPads, and Andriod phones. (iPad users can even download a free app to access all of their Pearson eTexts, seeing their instructors annotations and gaining links to Do Homework, Take a Test, and Study Plan functions.) xxiii xxiv PREFACE New to This Edition: Enhanced Statistical Coverage This 12th edition of Basic Business Statistics builds on previous editions with these new and enhanced features: • New chapter-ending “Using Statistics ... Revisited” sections that reinforce the statistical methods and applications discussed in each chapter. • The use of the DCOVA (Define, Collect, Organize, Visualize, and Analyze) framework as an integrated approach for applying statistics to help solve business problems. • Many new applied examples and exercises, with data from The Wall Street Journal, USA Today, and other sources. • “Managing Ashland MultiComm Services,” a new integrated case that appears at the ends of chapters throughout the book (replacing the Springville Herald case). • “Digital Cases,” interactive PDF files that create a new series of cases that appear at the ends of chapters throughout the book (replacing the Web Cases). • An expanded discussion of using Excel and Minitab to summarize and explore multidimen- sional data. • Revised and updated “Think About This” essays (formerly entitled “From the Author’s Desktop”) that provide greater insight into what has just been learned and raise important issues about the application of statistical knowledge. • Additional in-chapter Excel and Minitab results. • A new online section that discusses analytics and data mining. New to This Edition: Expanded Excel and Minitab Guides In this 12th edition of Basic Business Statistics, the instructions for using Excel and Minitab have been revised, reorganized, and enhanced in new end-of-chapter guides and back-of-the book appen- dices. These sections support students by: • Providing a readiness checklist and orientation that guide students through the process of get- ting ready to use Excel or Minitab (see Chapter 1 and the Chapter 1 Excel and Minitab Guides). • Incorporating Excel Guide workbooks that serve as models and templates for using Excel for statistical problem solving. These free and reusable workbooks, annotated examples of which appear throughout the chapters of this book, can be used by students in their other courses or in their jobs. • Allowing students to use Excel with or without PHStat2 and with or without the Analysis ToolPak (an Excel component that is not available in Mac Excel 2008). • Expanding the scope of Minitab Guide instructions. • Reviewing common operations, such as opening, saving, and printing results (see Chapter 1 Excel and Minitab Guides). • Explaining the different types of files available online that support this book and how to download those free files from this book’s companion website (Appendix C). • Providing a separate appendix that discusses software configuration issues, including how to check for Excel and Minitab updates and how to configure Excel for use with PHStat2 or the Analysis ToolPak (Appendix D). • An appendix that discusses formatting and other intermediate-level Excel operations (Appendix F). • Answering frequently asked questions about Excel, PHStat2, the Pearson statistical add-in for Microsoft Windows–based Excel versions, and Minitab (the new Appendix G). • In Appendix Section C.4, offering a complete list of all downloadable files and programs for this book. (See “Student Resources” on page xxvi for more details about the files and programs that can be downloaded.) Chapter-by-Chapter Changes in the 12th Edition Chapters begin with a redesigned opening page that displays the chapter sections and subsections and conclude with the new Excel and Minitab Guides that discuss how to apply Excel and Minitab to the statistical methods discussed in a chapter. Minitab Guides have been expanded to better match the scope of the Excel Guides. End-of-chapter Digital Cases that use interactive documents, in lieu PREFACE xxv of simulated web pages, update the former Web Cases. There is a new integrated case, “Managing Ashland MultiComm Services,” that replaces the “Managing the Springville Herald” case (see Chapters 2, 3, 5 through 7, 9 through 14, 16, and 17). Appendices B through D and F and G have been revised, reorganized, and updated. Highlights of changes to individual chapters follow. Chapter 1 The 11th edition’s Section 1.4 has been moved to Chapter 2. Section 1.6 has been rewritten and retitled “How to Use This Book” and now includes the “Checklist for Getting Started” (with Excel or Minitab). There are new undergraduate and graduate surveys. Chapter 2 This chapter has been completely reorganized. Sections 1.4 of the previous edition, concerning data collection, has been moved to this chapter. The Define, Collect, Organize, Visualize, and Analyze approach to solving business problems has been incorporated. The material on tables and charts has been reorganized so that the sections on organizing data into tables is presented first, in Sections 2.2 and 2.3, followed by sections on visualizing data in graphs in Sections 2.4–2.7. There is a new section on organizing multidimensional data (Section 2.7). There are new Excel and Minitab Guide sections that discuss multidimensional data. The Minitab Guide that replaces the Minitab Appendix has been greatly expanded. In addition, there are new examples throughout the chapter, and a new data set on bond funds has been created. Chapter 3 A new data set on bond funds has been created. The section “Numerical Measures for a Population” has been moved after the section on quartiles and boxplots. “Numerical Descriptive Measures from a Population” has been deleted. Chapter 4 The chapter example has been updated. There are new problems throughout the chapter. The “Think About This” essay about Bayes’ theorem has been condensed and updated. In combinations and permutations, x is used instead of X to be consistent with bino- mial notation in Chapter 5. Chapter 5 This chapter has revised notation for the binomial, Poisson, and hypergeometric distributions. It uses lower-case x and includes the parameter after an | sign in the equa- tion. To reduce the size of the book, the tables of the binomial and Poisson distributions (Tables E.6 and E.7) have been placed online. There are new problems throughout the chapter. Chapter 6 This chapter has an updated Using Statistics scenario. The “Think About This” essay on the importance of the normal distribution has been revised. The discussion of the exponential distribution has been revised. Chapter 7 A new “Think About This” essay replaces and expands on the pros and cons of web- based surveys, using a famous historical example. “Sampling from Finite Populations” is now an online topic. Chapter 8 This chapter includes problems on sigma known in Section 8.1. Chapter 9 This chapter includes problems on sigma known in Section 9.1. “Power of a Test” is now an online topic. Chapter 10 This chapter has a new example on the paired t-test on textbook prices. Chapter 11 This chapter has an “Online Topic” subsection titled “The Analysis of Means (ANOM).” Chapter 12 This chapter has an “Online Topic” subsection titled “The Analysis of Proportions (ANOP).” The Wilcoxon signed ranks test and the Friedman test are now online topicss. Chapter 13 The “Think About This” essay has been revised. There are new problems through- out the chapter. Chapter 14 This chapter has various new problems. Chapter 15 This chapter has a new “Online Topic” section titled “Analytics and Data Mining.” There are new problems throughout the chapter. Chapter 16 This chapter has updated examples throughout the chapter. “Index Numbers” is now an online topic. Chapter 17 This chapter has been edited for conciseness without any loss of concepts or clarity. Chapter 18 This chapter now includes an interactive roadmap for analyzing data as part of a new Digital Case. There are many new problems in the chapter. Chapter 19 This chapter (formerly Chapter 17) has become an online chapter and is available for download through this book’s companion website. xxvi PREFACE Hallmark Features We have continued many of the traditions of past editions and have highlighted some of these features below. Using Statistics Business Scenarios—Each chapter begins with a Using Statistics example that shows how statistics is used in the functional areas of business—accounting, finance, infor- mation systems, management, and marketing. Each scenario is used throughout the chapter to provide an applied context for the concepts. Emphasis on Data Analysis and Interpretation of Software Results—We believe that the use of computer software is an integral part of learning statistics. Our focus empha- sizes analyzing data by interpreting results while reducing emphasis on doing compu- tations. For example, in the coverage of tables and charts in Chapter 2, the focus is on the interpretation of various charts and on when to use each chart. In our coverage of hypothesis testing in Chapters 9 through 12, and regression and time series forecasting in Chapters 13–16, extensive computer results have been included so that the p-value approach can be emphasized. Pedagogical Aids—An active writing style is used, with boxed numbered equations, set-off examples to provide reinforcement for learning concepts, problems divided into “Learning the Basics” and “Applying the Concepts,” key equations, and key terms. Answers—Many answers to the even-numbered exercises are included at the end of the book. Flexibility Using Excel—For almost every statistical method discussed, this book presents more than one way of using Excel. Students can use In-Depth Excel instructions to directly work with the worksheet cell-level details or they can use the PHStat2 instructions or use the Analysis ToolPak instructions to automate the creation of those same details. Digital Cases—An end-of-chapter Digital Case is included for each of the first 16 chapters. Most Digital Cases extend a Using Statistics business scenario by posing additional ques- tions and raising issues about the scenario. Students examine interactive documents to sift through claims and assorted information in order to discover the data most relevant to a sce- nario. Students then determine whether the conclusions and claims are supported by the data. In doing so, students discover and learn how to identify common misuses of statistical infor- mation. (Instructional tips for using the Digital Cases and solutions to the Digital Cases are included in the Instructor’s Solutions Manual.) Case Studies and Team Projects—Detailed case studies are included in numerous chapters. A “Managing Ashland MultiComm Services” continuing case, a team project related to bond funds, and undergraduate and graduate student surveys are included at the end of most chap- ters, and these serve to integrate learning across the chapters. Visual Explorations—The Excel add-in workbook allows students to interactively explore important statistical concepts in descriptive statistics, the normal distribution, sampling dis- tributions, and regression analysis. For example, in descriptive statistics, students observe the effect of changes in the data on the mean, median, quartiles, and standard deviation. With the normal distribution, students see the effect of changes in the mean and standard deviation on the areas under the normal curve. In sampling distributions, students use simulation to explore the effect of sample size on a sampling distribution. In regression analysis, students have the opportunity to fit a line and observe how changes in the slope and intercept affect the goodness of fit. Student Resources Student Solutions Manual—Created by Professor Pin Tian Ng of Northern Arizona University, this manual provides detailed solutions to virtually all the even-numbered exercises and worked-out solutions to the self-test problems. Companion website—This book comes with a companion website from which the following resources can be downloaded for free (see Appendix C that starts on page 784 for more details about these resources, including how to visit the companion website): PREFACE xxvii • Data files Excel and Minitab data files used by in-chapter examples and problems (in .xls and .mtw formats). • Online Chapter The electronic-only Chapter 19: Decision Making in PDF format. • Online Topics Online topics are PDF files that discuss additional topics for Chapters 5, 6, 7, 8, 9, 11, 12, 15, and 16. • Excel Guide workbooks Self-documenting Excel Guide workbooks illustrate solutions for more than 60 statistical topics that serve as freely reusable templates for future problem solv- ing. • Case files Supporting files are provided for the Digital Cases and the Managing Ashland MultiComm Services Case. • Visual Explorations The files needed to use the Visual Explorations Excel add-in work- book. • Using Excel 2003 Guide This guide presents, where necessary, alternate Excel Guide instructions for users of this older version of Excel. • PHStat2 The latest version of PHStat2, the Pearson statistical add-in for Windows-based Excel, version 2003 and later. This version eliminates the use of the Excel Analysis ToolPak add-ins, thereby simplifying installation and setup. MyStatLab—MyStatLab provides students with direct access to the companion website resources as well as the following exclusive online features and tools: • Interactive tutorial exercises A comprehensive set of exercises have been written espe- cially for use with this book that are algorithmically generated for unlimited practice and mastery. Most exercises are free-response exercises and provide guided solutions, sample problems, and learning aids for extra help at point of use. • Personalized study plan A plan indicates which topics have been mastered and creates direct links to tutorial exercises for topics that have not been mastered. MyStatLab manages the study plan, updating its content based on the results of future online assessments. • Pearson Tutor Center (www.pearsontutorservices.com) The MyStatlab student access code grants access to this online resource, staffed by qualified instructors who provide book- specific tutoring via phone, fax, e-mail, and interactive web sessions. • Integration with Pearson eTexts iPad users can download a free app at www.apple.com/ ipad/apps-for-ipad/ and then sign in using their MyStatLab account to access a bookshelf of all their Pearson eTexts. The iPad app also allows access to the Do Homework, Take a Test, and Study Plan pages of their MyStatLab course. • Mobile Dashboard Allows students to use their mobile devices to log in and review informa- tion from the dashboard of their courses: announcements, assignments, results, and progress bars for completed work. This app is available for iPhones, iPads, and Android phones, and is designed to promote effective study habits rather than to allow students to complete assign- ments on their mobile devices. @RISK trial Palisade Corporation, the maker of the market-leading risk and decision analysis Excel add-ins, @RISK and the DecisionTools® Suite, provides special academic versions of its software to students (and faculty). Its flagship product, @RISK, debuted in 1987 and performs risk analysis using Monte Carlo simulation. @RISK and the DecisionTools Suite are used widely in undergraduate and graduate business programs worldwide. Thanks to the company’s generous academic sales program, more than 40,000 students learn to make better decisions using @RISK and the DecisionTools Suite each year. To download a trial version of @RISK software, visit www.palisadecom/academic/. Instructor Resources Instructor’s Resource Center—Reached through a link at www.pearsonhighered.com/levine, the Instructor’s Resource Center contains the electronic files for the complete Instructor’s Solutions Manual, the Test Item File, and PowerPoint lecture presentations. • Register, redeem, log in At www.pearsonhighered.com/irc, instructors can access a vari- ety of print, media, and presentation resources that are available with this book in downloadable xxviii PREFACE digital format. Resources are also available for course management platforms such as Blackboard, WebCT, and CourseCompass. • Need help? Pearson Education’s dedicated technical support team is ready to assist instruc- tors with questions about the media supplements that accompany this text. Visit http:// 247.prenhall.com for answers to frequently asked questions and toll-free user support phone numbers. The supplements are available to adopting instructors. Detailed descriptions are provided at the Instructor’s Resource Center. Instructor’s Solutions Manual—Created by Professor Pin Tian Ng of Northern Arizona University, this manual includes solutions for end-of-section and end-of-chapter problems, answers to case questions, where applicable, and teaching tips for each chapter. Electronic solutions are provided in PDF and Word formats. Lecture PowerPoint Presentations—A PowerPoint presentation, created by Professor Patrick Schur of Miami University, is available for each chapter. The PowerPoint slides provide an instruc- tor with individual lecture outlines to accompany the text. The slides include many of the figures and tables from the text. Instructors can use these lecture notes as is or can easily modify the notes to reflect specific presentation needs. Test Item File—Created by Professor Pin Tian Ng of Northern Arizona University, the Test Item File contains true/false, multiple-choice, fill-in, and problem-solving questions based on the defini- tions, concepts, and ideas developed in each chapter of the text. TestGen—The computerized TestGen package allows instructors to customize, save, and generate classroom tests. The test program permits instructors to edit, add, and delete questions from the test bank; edit existing graphics and create new graphics; analyze test results; and organize a database of test and student results. This software provides ease of use and extensive flexibility, and it provides many options for organizing and displaying tests, along with search and sort features. The software and the test banks can be downloaded from the Instructor’s Resource Center. MathXL for Statistics—MathXL for Statistics is a powerful online homework, tutorial, and assess- ment system that accompanies Pearson Education statistics textbooks. With MathXL for Statistics, instructors can create, edit, and assign online homework and tests using algorithmically generated exercises correlated at the objective level to the textbook. They can also create and assign their own online exercises and import TestGen tests for added flexibility. All student work is tracked in ’s MathXL online grade book. Students can take chapter tests in MathXL and receive personalized study plans based on their test results. Each study plan diagnoses weaknesses and links the student directly to tutorial exercises for the objectives he or she needs to study and retest. Students can also access supplemental animations and video clips directly from selected exercises. MathXL for Statistics is available to qualified adopters. For more information, visit www.mathxl.com or con- tact your sales representative. MyStatLab—Part of the MyMathLab and MathXL product family, MyStatLab is a text-specific, easily customizable online course that integrates interactive multimedia instruction with textbook content. MyStatLab gives you the tools you need to deliver all or a portion of your course online, whether your students are in a lab setting or working from home. The latest version of MyStatLab offers a new, intuitive design that features more direct access to MathXL for Statistics pages (Gradebook, Homework & Test Manager, Home Page Manager, etc.) and provides enhanced func- tionality for communicating with students and customizing courses. Other key features include: • Assessment manager An easy-to-use assessment manager lets instructors create online homework, quizzes, and tests that are automatically graded and correlated directly to your textbook. Assignments can be created using a mix of questions from the MyStatLab exercise bank, instructor-created custom exercises, and/or TestGen test items. • Grade book Designed specifically for mathematics and statistics, the MyStatLab grade book automatically tracks students’ results and gives you control over how to calculate final grades. You can also add offline (paper-and-pencil) grades to the grade book. • MathXL Exercise Builder You can use the MathXL Exercise Builder to create static and algorithmic exercises for your online assignments. A library of sample exercises provides an easy starting point for creating questions, and you can also create questions from scratch. • eText-MathXL for Statistics Full Integration Students using appropriate mobile devices can use your eText annotations and highlights for each course, and iPAd users can download PREFACE xxix a free app that allows them access to the Do Homework, Take a Test, and Study Plan pages of their course. • “Ask the Publisher” Link in “Ask My Instructor” Email You can easily notify the con- tent team of any irregularities with specific questions by using the “Ask the Publisher” func- tionality in the “Ask My Instructor” emails you receive from students. • Tracking Time Spent on Media Because the latest version of MyStatLab requires students to explicitly click a “Submit” button after viewing the media for their assignments, you will be able to track how long students are spending on each media file. Palisade Corporation software—Palisade Corporation, the maker of the market-leading risk and decision analysis Excel add-ins, @RISK and the DecisionTools® Suite, provides special academic versions of its software. Its flagship product, @RISK, debuted in 1987 and performs risk analysis using Monte Carlo simulation. With an estimated 150,000 users, Palisade software can be found in more than 100 countries and has been translated into five languages. @RISK and the DecisionTools Suite are used widely in undergraduate and graduate business programs worldwide and can be bundled with this textbook. To download a trial version of @RISK software, visit www.palisade.com/academic/. Acknowledgments We are extremely grateful to the Biometrika Trustees, American Cyanamid Company, the RAND Corporation, and the American Society for Testing and Materials for their kind permission to pub- lish various tables in Appendix E, and the American Statistical Association for its permission to publish diagrams from the American Statistician. Also, we are grateful to Professors George A. Johnson and Joanne Tokle of Idaho State University and Ed Conn, Mountain States Potato Company, for their kind permission to incorporate parts of their work as our Mountain States Potato Company case in Chapter 15. A Note of Thanks We would like to thank Kevin Caskey, SUNY–New Paltz; Zhi Min Huang, Adelphi University; David Huff, Wayne State University; Eugene Jones, Ohio State University; Glen Miller, Piedmont College; Angela Mitchell, Wilmington College; Daniel Montgomery, Delta State University; Patricia Mullins, University of Wisconsin–Madison; Robert Pred, Temple University; Gary Smith, Florida State University; and Robert Wharton, Fordham University for their comments, which have made this a better book. We would especially like to thank Chuck Synovec, Mary Kate Murray, Jason Calcano, Judy Leale, Anne Fahlgren, Melinda Jensen, and Kerri Tomasso of the editorial, marketing, and produc- tion teams at Prentice Hall. We would like to thank our statistical reader and accuracy checker Annie Puciloski for her diligence in checking our work; Susan Pariseau, Merrimack College, for assisting in the reading of the page proofs; Kitty Wilson for her copyediting; Lori Cavanaugh for her proof- reading; and Jen Carley of PreMediaGlobal for her work in the production of this text. Finally, we would like to thank our families for their patience, understanding, love, and assis- tance in making this book a reality. It is to them that we dedicate this book. Concluding Remarks We have gone to great lengths to make this text both pedagogically sound and error free. If you have any suggestions or require clarification about any of the material, or if you find any errors, please contact us at davidlevine@davidlevinestatistics.com. Include the phrase “BBS edition 12” in the subject line of your e-mail. For technical support for PHStat2 beyond what is presented in the appendices and in the PHStat2 readme file that accompanies PHStat2, visit the PHStat2 website, www.pearsonhighered.com/phstat and click on the Contact Pearson Technical Support link. Mark L. Berenson David M. Levine Timothy C. Krehbiel Basic Business Statistics: Concepts and Applications TWELFTH EDITION

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