Business Analytics,
5th Edition

1. Introduction.
2. Descriptive Statistics.
3. Data Visualization.
4. Data Wrangling.
5. Probability: An Introduction to Modeling Uncertainty.
6. Descriptive Data Mining.
7. Statistical Inference.
8. Linear Regression.
9. Time Series Analysis and Forecasting.
10. Predictive Data Mining: Regression.
11. Predictive Data Mining: Classification.
12. Spreadsheet Modeling.
13. Monte Carlo Simulation.
14. Linear Optimization Models.
15. Integer Linear Optimization Models.
16. Nonlinear Optimization Models.
17. 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 CHAPTER ON DATA WRANGLING (CH. 4) PROVIDES CRITICAL INSIGHTS INTO THIS IMPORTANT TOPIC. New content covers issues such as how to access and structure data for exploration, how to clean and enrich data to facilitate analysis and how to validate data.

• SIGNIFICANTLY EXPANDED COVERAGE OF DATA MINING ADDRESSES CRUCIAL CONTENT. This edition's coverage of descriptive data mining techniques now includes a discussion of how to conduct dimension reduction with principal component analysis (PCA). Thorough revisions also offer updated coverage of clustering, association rules and text mining.

• COVERAGE OF PREDICTIVE DATA MINING TECHNIQUES NOW INCLUDES TWO SEPARATE CHAPTERS (CHS. 10, 11) One chapter now focuses on predicting quantitative outcomes with k-nearest neighbors regression, regression trees and neural network regression. A second chapter discusses predicting binary categorical outcomes with k-nearest neighbors classification, classification trees, support vector classifiers and neural network classifiers.

• NEW ONLINE APPENDIXES INTRODUCE THE SOFTWARE PACKAGE ORANGE FOR DESCRIPTIVE AND PREDICTIVE DATA-MINING MODELS. Students learn this easy-to-use, yet powerful, workflow-based approach to building analytics models using Orange, the open-source machine learning and data visualization software package built using Python. This coverage of Orange and Python complements the book's existing coverage of R for solving descriptive and predictive analytics models. New practice problems and solutions in the R and Orange appendices strengthen students' problem-solving skills.

• EXPANDED COVERAGE OF DESCRIPTIVE ANALYTICS METHODS, INCLUDING DATA VISUALIZATION, DISCUSSES NEW TOPICS. Coverage of histograms (Ch. 2) now includes a discussion of frequency polygons as a way of exploring data. Chapter 3’s coverage of data visualization now offers a more comprehensive discussion of best practices in data visualization, including the use of preattentive attributes and the data-ink ratio to create effective tables and charts.

• REORDERED CHAPTER CONTENT AND NEW COVERAGE OF CHARTS AND MAPS FURTHER STRENGTHEN THE BOOK'S COMPREHENSIVE APPROACH. The authors have carefully rearranged key chapter material for clarity. This edition also includes new coverage of table lens, waterfall charts, stock charts, choropleth maps and cartograms.

• LARGER, MORE REALISTIC DATA SETS BETTER PREPARE STUDENTS FOR SUCCESS ON THE JOB. The authors have increased the size of many data sets in Chapter 8 on linear regression and Chapter 9 on time series analysis and forecasting. This data sets now better represent real data sets students will encounter in practice.

• NEW LEARNING OBJECTIVES IN EACH CHAPTER DIRECT STUDENT ATTENTION TO KEY CONCEPTS. These new Learning Objectives appear at the beginning of each chapter and preview the important concepts that are covered in that chapter. Each problem is now identified by Learning Objectives so you can easily determine which problems to assign for additional practice and review.

• COMPLETELY INTEGRATED COVERAGE OF EXCEL DEMONSTRATES THE LATEST METHODS FOR SOLVING PRACTICAL PROBLEMS. Clear, step-by-step instructions teach students to use Excel as a tool for applying concepts in the book. The authors also include by-hand calculations to highlight specific analytical insights, when appropriate. Fully updated Excel instructions correspond to the latest versions of Excel.

• STEP-BY-STEP INSTRUCTIONS EXPLAIN IMPORTANT ANALYTICAL STEPS. Helpful 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 various levels of difficulty. Time-saving data sets are available for most exercises and cases.

• ANALYTICS IN ACTION EFFECTIVELY 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. These timely, engaging examples are drawn from organizations in a variety of areas, including healthcare, finance, manufacturing and marketing.

• 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 such as R and Orange.

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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