Statistics for Business and Economics,
15th Edition

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. Microsoft Excel and Tools for Statistical Analysis.
Appendix E. Computing p-Values Using JMP and Excel.
Index.

• Jeffrey D. Camm

  Jeffrey D. Camm is the Inmar Presidential Chair 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, he 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 many professional journals, including Science, Management Science, Operations Research and the INFORMS Journal on Applied Analytics. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati, and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, Dr. Camm has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016 Dr. Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS fellow.

• James J. Cochran

  James J. Cochran is associate dean for research, a professor of applied statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has served as 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 50 papers in the development and application of operations research and statistical methods. He has published in numerous journals, including 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 on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. Dr. Cochran received the 2008 INFORMS prize for the Teaching of Operations Research Practice, the 2010 Mu Sigma Rho Statistical Education Award and the 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and was named a fellow of the American Statistical Association in 2011 and a fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In addition, he received the INFORMS President's Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has also served as an operations research or statistics consultant to numerous companies and not-for-profit organizations.

• Michael J. Fry

  Michael J. Fry is a professor of operations, business analytics and information systems as well as 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, Dr. Fry earned his B.S. from Texas A&M University and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. He has also been named a Lindner Research Fellow. Dr. Fry 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.He has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine and 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 different 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 & Botanical Garden. He 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 business analytics and a 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 M.S. and Ph.D. degrees from the University of Michigan. Dr. Ohlmann has been at the University of Iowa since 2003. His research on the modeling and solution of decision-making problems has produced more than two dozen research papers published in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science and the European Journal of Operational Research. He has collaborated with organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and three National Football League franchises. Because of the relevance of his work to industry, Dr. Ohlmann received the George B. Dantzig Dissertation Award, and he was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

• 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.

• Thomas A. Williams

  N/A

• NEW LEARNING OBJECTIVES DRAW STUDENT ATTENTION TO KEY CONCEPTS. This edition's new learning objectives, that now appear at the beginning of each chapter, detail and explain the key concepts that are covered in each chapter. These new learning objectives are also mapped to each problem so you can easily identify which problems are addressing specific individual learning objective for focused instruction.

• REORGANIZED AND EXPANDED CONTENT IN REGRESSION ANALYSIS AND MODEL BUILDING (CH. 16) CLARIFIES CONCEPTS. The authors have strengthened content throughout this chapter. For instance, the authors have added discussion that compares a regression model with a transformed dependent variable to a regression model using the untransformed dependent variable in the original units. In addition, this chapter includes a new example that illustrates the use of the Durbin-Watson statistic to test the presence of first-order autocorrelation.

• SIGNIFICANTLY EXPANDED SOFTWARE SUPPORT FOR R PREPARES STUDENTS TO USE THIS IMPORTANT TOOL. Revised eBook and WebAssign digital chapter appendices now include relevant examples for easy reference. In addition, all scripts are updated to ensure compatibility with the most recent versions of R. The authors have also expanded the number of the scripts and .csv data sets to support the major chapter examples, application problems and cases.

• UPDATED JMP CHAPTER APPENDICES REFLECT THE MOST RECENT VERSION OF JMP STATISTICAL SOFWARE. All JMP chapter appendices in both the printed or eBook incorporate changes to the most recent student version of JMP® -- JMP® Student Edition 16. You can be sure your students are able to work with the latest statistics digital support.

• NEW PROBLEMS, CASES AND VIGNETTES KEEP CONTENT FRESH AND CURRENT. This edition includes more than 100 additional new problems as well as three new cases. In addition, the authors have added three new Statistic in Practice vignettes that reflect current challenges in statistics.

• 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 reference the latest versions of Excel, JMP® Student Edition and R (online only). Students gain important hands-on experience using these popular professional statistical analysis software tools.

• ENGAGING CASE PROBLEMS PROVIDE ADDITIONAL OPPORTUNITIES TO PRACTICE SKILLS. Approximately 50 case problems in this edition provide students with opportunities 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.

• INTRIGUING EXAMPLES INCORPORATE REAL MEANINGFUL DATA. More than 100 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 helpful examples and exercises.

• WEBASSIGN COURSE MANAGEMENT SOLUTION OFFERS A COMPREHENSIVE TEACHING TOOL FOR BUSINESS STATISTICS. This flexible and fully customizable platform puts powerful, time-saving tools in your hands. You can easily deploy assignments, instantly assess individual student and class performance and help struggling students master the course concepts. With WebAssign’s powerful digital platform and this edition's 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.

• TRUSTED TEAM OF DISTINGUISHED AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. Prominent leaders and active consultants in business and statistics, this edition's authors Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney and Thomas A. Williams work seamlessly to deliver an accurate, real-world presentation of statistical concepts that you can trust for accuracy and comprehensive, engaging content.

• PROVEN, SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and 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 the use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

Cengage Testing, powered by Cognero® for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Statistics for Business & Economics
9780357715963

Cengage Testing, powered by Cognero® for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Statistics for Business & Economics, Instant Access
9780357715956

Instructor Companion Website for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Statistics for Business & Economics
9780357715949

XLSTAT Education Edition for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Statistics for Business & Economics, 2 terms Instant Access
9780357715871