Business Analytics,
3rd Edition

1. Introduction
2. Descriptive Statistics.
3. Data Visualization.
4. Descriptive Data Mining.
5. Probability: An Introduction to Modeling Uncertainty.
6. Statistical Inference.
7. Linear Regression.
8. Time Series Analysis and Forecasting.
9. Predictive Data Mining.
10. Spreadsheet Models.
11. Monte Carlo Simulation.
12. Linear Optimization Models.
13. Integer Linear Optimization Models.
14. Nonlinear Optimization Models.
15. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (online).

• 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

• FULLY UPDATED ANALYTIC SOLVER REFLECTS THE LATEST SOFTWARE. This edition's coverage of using Analytic Solver now details the newest version of this Excel Add-in. Coverage of Analytic Solver continues to appear in chapter appendices, allowing those who do not wish to use Analytic Solver to cover data mining topics without relying on a specific software. Updated chapter appendices, homework problems and solutions also correspond with the latest version of Analytic Solver.

• COVERAGE OF JMP SOFTWARE NOW APPEARS IN MATERIAL ADDRESSING DATA MINING. The authors now introduce a second software package, JMP Pro, within the MindTap Reader eBook that supports the coverage of data mining concepts in Chapters 4 and 9. This helpful online supplement provides problems and solutions using JMP software. This supplement allows you, the instructor, to use JMP Pro for data mining rather than Analytic Solver, if you prefer.

• NEW SOFTWARE-INDEPENDENT PROBLEMS NOW APPEAR IN DATA MINING CHAPTERS. The authors have seamlessly woven additional "conceptual" problems into Chapters 4 and 9 that cover descriptive and predictive data mining chapters. These new problems do not require the use of a specific software package. This enables you, as the instructor, to introduce data mining concepts and assign homework problems without having to introduce any software, such as Analytic Solver or JMP Pro.

• NEW COVERAGE OF TEXT MINING ADDRESSES THE LATEST DEVELOPMENTS. Text mining is now introduced in Chapter 4 on descriptive data mining. Students learn the details of using this extremely powerful and growing application area of unstructured data mining.

• EXPANDED BIG DATA COVERAGE ADDRESSES SPECIAL CONCERNS AND TOPICS. These topics, related to the use of "big data" in Chapters 6 and 7 on statistical inference and linear regression, introduce students to the impact that a large number of observations has on precision and inference. Students learn why this increase in precision does not necessarily mean the associated inference is more likely to be correct. Additional exercises include much larger data sets than are generally used in introductory statistics books.

• STRONGER DECISION-MAKING ORIENTATION EMPHASIZES RELEVANCE OF CONCEPTS. Throughout this edition, the authors have further emphasized how readers can use the results of mathematical models in decision making. Specifically, new material in linear regression (Ch. 7), in time series analysis and forecasting (Ch. 8) and predictive data mining (Ch. 9) emphasizes the importance of using analyses results in business decision making. Many exercises in these chapters now stress the use of models in decision making.

• REORDERED TOPICS OFFER MORE LOGICAL PROGRESSION. This edition's Monte Carlo Simulation now appears before coverage of optimization models (in chapters 12, 13 and 14). This allows a better transition of topics from the "what-if" analysis in Chapter 10 to the Monte Carlo Simulation in Chapter 11, since the Simulation can be considered a more sophisticated form of "what-if" analysis. The appendix on Simulation-Optimization now appears in Chapter 14, covering this advanced topic only after students have been introduced to both simulation and optimization models.

• ENHANCED MINDTAP FUNCTIONS PROVIDE FLEXIBILITY AND MORE ASSIGNMENT OPTIONS. This customizable digital course solution offers an interactive eBook, algorithmically-generated exercises from the text, and rich solutions feedback with suggested Excel formulas to use for every exercise. Students complete assignments whenever and wherever they are ready with customized material in one, proven, easy-to-use interface. MindTap gives students a roadmap to master decision-making in business analytics using resources, tools, and apps -- including videos, practice opportunities, note taking and flashcards.

• FULLY INTEGRATED COVERAGE OF EXCEL 2016 HIGHLIGHTS THE LATEST ADVANCEMENTS. New coverage of Excel 2016 demonstrates how to create box plots, tree maps and several other data visualization tools not available in previous versions of Excel. These updates are seamlessly integrated into this edition with new examples and homework problems for students to complete in Excel.

• COMPLETELY INTEGRATED COVERAGE OF EXCEL DEMONSTRATES THE LATEST METHODS FOR SOLVING PRACTICAL PROBLEMS. Clear, step-by-step instructions teach students to use Excel 2016 when applying concepts in the book. The authors also include by-hand calculation approaches to highlight specific analytical insights when appropriate.

• STEP-BY-STEP INSTRUCTIONS CLARIFY IMPORTANT STEPS. Clear instructions show students how to use various software programs to perform the analyses discussed in the text. This edition integrates coverage of easy-to-use, but powerful, software such as JMP Pro and the Excel Add-in Analytic Solver.

• PRACTICAL, RELEVANT PROBLEMS HELP STUDENTS MASTER CONCEPTS AND SKILLS. Applications drawn from all functional business areas, including finance, marketing and operations, provide important practice at a variety of difficulty levels. Time-saving data sets are available for most exercises and cases.

• ANALYTICS IN ACTION CLEARLY DEMONSTRATE THE IMPORTANCE OF CONCEPTS IN BUSINESS TODAY. Each chapter contains an Analytics in Action feature that presents interesting examples of how professionals use business analytics in actual practice. The examples are drawn from many different organizations in a variety of areas, including healthcare, finance, manufacturing and marketing.

• ONLINE DATAFILES AND MODELFILES OFFER TIME-SAVING CONVENIENCES. All data sets used as examples and in student exercises are also provided online for convenient student download. DATAfiles are files that contain data needed for the examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of Excel Solver, JMP or Analytic Solver.

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.

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Instructor's Website for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Business Analytics, 3rd
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MindTap: Business Analytics 12 Months
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Cengage eBook: Business Analytics 12 Months
9788000028200