Business analytics / Jeffrey Camm (Wake Forest University) [and six others].

Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics 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 served as a visiting scholar at Stanford University and as a visiting Professor of Business Administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 40 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in numerous professional journals, including Science, Management Science, Operations Research and Interfaces. 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, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of 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 is Associate Dean for Research, 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 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 40 papers in the development and application of operations research and statistical methods. He has published in several 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, Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. He received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association and 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 served as operations research consultant to numerous companies and not-for-profit organizations.

Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems (OBAIS) 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 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 chair and has been named a Lindner Research Fellow. He 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. Dr. Fry 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 focus on applying analytics to the areas of supply chain management, sports 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. In 2019 he led the team that was awarded the INFORMS UPS George D. Smith Prize on behalf of the OBAIS Department at the University of Cincinnati.

Jeffrey W. Ohlmann is Associate Professor of Business Analytics 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 M.S. and Ph.D. degrees from the University of Michigan. He has taught 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 and European Journal of Operational Research. He has collaborated with companies such as Transfreight, LeanCor, Cargill and the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to the industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

Dr. David R. Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He 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 has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has 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 actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his B.S., M.S., and Ph.D. degrees from Purdue University.

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 BSBA degree from Drake University and his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor 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 Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

Includes bibliographical references (pages 774-775) and index.

Introduction -- Descriptive Statistics -- Data Visualization -- Descriptive Data Mining -- Probability: An Introduction to Modeling Uncertainty -- Statistical Inference -- Linear Regression -- Time Series Analysis and Forecasting -- Predictive Data Mining -- Spreadsheet Models -- Monte Carlo Simulation -- Linear Optimization Models -- Integer Linear Optimization Models -- Nonlinear Optimization Models -- Decision Analysis -- Basics of Excel -- Database Basics with Microsoft Access -- Solutions to Even-Numbered Questions (MindTap Reader).

Build valuable skills that are in high demand in today’s businesses with BUSINESS ANALYTICS, 3E. You master the full range of analytics as you strengthen your descriptive, predictive and prescriptive analytic skills. Real-world examples and visuals help illustrate data and results for each topic. Clear, step-by-step instructions for various software programs, including Microsoft Excel, Analytic Solver, and JMP Pro, teach you how to perform the analyses discussed. Practical, relevant problems at all levels of difficulty further help you apply what you've learned to succeed in your course.



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.
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.
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.
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.
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.
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.
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.
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.

600-699