AE Business Analytics,
4th Edition

1. Introduction.
2. Descriptive Statistics.
3. Data Visualization.
4. Probability: An Introduction to Modeling Uncertainty.
5. Descriptive Data Mining.
6. Statistical Inference.
7. Linear Regression.
8. Time Series Analysis and Forecasting.
9. Predictive Data Mining.
10. Spreadsheet Modeling.
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.

• NEW ONLINE APPENDIX INTRODUCES HOW TO USE TABLEAU FOR DATA VISUALIZATION. This brand-new online appendix details how to maximize the features of Tableau for data visualization. The authors apply their proven, step-by-step presentation methods to clearly guide students through using this powerful software to produce useful charts for analytics.

• NEW HOMEWORK PROBLEMS AND CASES HIGHLIGHT DATA MINING AND CUMULATIVE KNOWLEDGE. The chapters on data mining in this edition contain even more problems that do not require specialized software. This gives you the flexibility to introduce these important topics, even if you do not want students to have to learn additional software to solve the problems. This edition also introduces numerous additional cases throughout the text, including cases that integrate topics from multiple chapters to emphasize how various analytics topics interact and build upon each another.

• NEW SECTION HIGHLIGHTS LEGAL AND ETHICAL ISSUES IN THE USE OF DATA AND ANALYTICS. Chapter 1 includes a new section that addresses common legal and ethical issues related to the use of data and analytics. This new legal and ethical section discusses recent data privacy laws as well as ethical issues that both practitioners and consumers of analytics models should consider.

• REVISED DATA MINING CHAPTERS OFFER CLEARER PRESENTATION OF CONCEPTS. The authors have reorganized and updated this edition's data mining chapters to ensure students thoroughly understand the presentation. The descriptive data mining chapter (Ch. 5) now appears after the probability chapter so that the data mining discussion can directly integrate notions of probability within the explanations.

• STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Clear instructions show students how to use a variety of leading software programs to perform the analyses discussed in the text.

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

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

• ONLINE DATA FILES AND MODEL FILES SAVE TIME. All data sets used as examples and used within student exercises are provided online for convenient student download. DATAfiles are files that contain data that corresponds to examples and problems given in the text. MODELfiles contain additional modeling features that highlight the extensive use of Excel formulas or the use of other software.

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