• SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and thoroughly master the use of techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems.
• TRUSTED TEAM OF DISTINGUISHED AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. Prominent leaders and active consultants in business and statistics, authors David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry and Jeffrey W. Ohlmann deliver an accurate, real-world presentation of statistical concepts you can trust with every edition.
• NEW EXAMPLES INCORPORATE REAL MEANINGFUL DATA. More than 150 new examples and exercises incorporate real data and reference timely sources to bring statistical concepts to life. The authors draw data from sources used by The Wall Street Journal, USA Today, Barron's and other leading publications. Using actual studies and applications, the authors present clear explanations and create exercises that demonstrate the many uses of statistics in business and economics today. In total, this edition provides more than 350 examples and exercises.
• UPDATED APPENDICES AND FIGURES HIGHLIGHT TODAY'S LATEST PROFESSIONAL SOFTWARE. All step-by-step instructions in this edition's software appendices and all textbook figures featuring software output now reflect Excel 2016, JMP® Student Edition 14, and R (online only). This edition provides important hands-on experience that prepares your students to work with current versions of these two popular software choices for statistical analysis in business today.
• NEW COVERAGE OF BIG DATA EQUIPS STUDENTS TO WORK WITH INFORMATION ON A MASSIVE SCALE. This edition provides a solid framework for thinking about big data and its potential ramifications on sampling distributions, confidence intervals and hypothesis testing for means and proportions. This important information on Big Data is presented in separate sections at the ends of Chapters 7, 8 and 9.
• WEBASSIGN COURSE MANAGEMENT SOLUTION. WebAssign is the complete teaching tool for Business Statistics. This flexible and fully customizable platform puts powerful tools in your hands. Deploy assignments, instantly assess individual student and class performance, and help your students master the course concepts. With WebAssign’s powerful digital platform and Statistics for Business & Economics' specific content, you can tailor your course with a wide range of assignment settings, add your own questions and content, and access student and course analytics and communication tools.
• TEN ALL-NEW CASE PROBLEMS PROVIDE ADDITIONAL OPPORTUNITIES TO PRACTICE SKILLS. The 44 case problems in this edition provide students with the opportunity to put what they’ve learned into action. Students work on more complex problems, analyze larger data sets and prepare managerial reports based on the results of their analyses.

Preface.

1. Data and Statistics.

2. Descriptive Statistics: Tabular and Graphical Displays.

3. Descriptive Statistics: Numerical Measures.

4. Introduction to Probability.

5. Discrete Probability Distributions.

6. Continuous Probability Distributions.

7. Sampling and Sampling Distributions.

8. Interval Estimation.

9. Hypothesis Tests.

10. Inference about Means and Proportions with Two Populations.

11. Inferences about Population Variances.

12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.

13. Experimental Design and Analysis of Variance.

14. Simple Linear Regression.

15. Multiple Regression.

16. Regression Analysis: Model Building.

17. Time Series Analysis and Forecasting.

18. Decision Analysis

19. Nonparametric Methods.

20. Statistical Methods for Quality Control.

21. Index Numbers.

22. Sample Survey (online).

Appendix A. References and Bibliography.

Appendix B. Tables.

Appendix C. Summation Notation.

Appendix D. Solutions to Even–Numbered Exercises (Available Online Only).

Appendix E. Microsoft Excel 2016 and Tools for Statistical Analysis.

Appendix F. Computing p-Values Using JMP and Excel.

Index.

David R. Anderson

David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati. Dr. Anderson has served as head of the Department of Quantitative Analysis and Operations Management and as associate dean of the College of Business Administration. He was also coordinator of the college’s first executive program. In addition to introductory statistics for business students, Dr. Anderson taught graduate-level courses in regression analysis, multivariate analysis and management science. He also taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences, and he actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, Dr. Anderson earned his B.S., M.S. and Ph.D. degrees from Purdue University.

Dennis J. Sweeney

Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA fellow. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. He also served as head of the Department of Quantitative Analysis and served four years as associate dean of the College of Business Administration at the University of Cincinnati. Dr. Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

Jeffrey D. Camm

Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean of Business Analytics programs in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, Dr. Camm served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, Interfaces and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of

Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010, Dr. Camm served as editor-in-chief of INFORMS Journal of Applied Analytics (formerly Interfaces). In 2017, he was named an INFORMS fellow.

James J. Cochran

James J. Cochran is Professor of Applied Statistics, the Rogers-Spivey Faculty Fellow and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. Dr. Cochran has served at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 45 papers in the development and application of operations research and statistical methods. He has published his research in Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal of Applied Analytics and Statistics and Probability Letters. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. Dr. Cochran was elected to the International Statistics Institute in 2005 and named a fellow of the American Statistical Association in 2011. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Association’s Waller Distinguished Teaching Career Award and was named a fellow of INFORMS. In 2018 he received the INFORMS President’s Award. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world.

Michael J. Fry

Michael J. Fry is Professor of Operations, Business Analytics and Information Systems and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University and his M.S.E. and Ph.D. from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. Dr. Fry has been named a Lindner Research Fellow. He has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia. Dr. Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine and INFORMS Journal of Applied Analytics (formerly Interfaces). His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo and Botanical Garden. Dr. Fry was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati.

Jeffrey W. Ohlmann

Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and his M.S. and Ph.D. from the University of Michigan. He has been at the University of Iowa since 2003. Dr. Ohlmann’s research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research and INFORMS Journal of Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.