• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

• Cengage digital App includes:

  Student Downloads

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 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.

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

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. 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. 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).

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

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.