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Essentials of Business Analytics 2nd Edition

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney, Thomas A. Williams

  • Published
  • Previous Editions 2015
  • 896 Pages
Starting At 135.00 See pricing and ISBN options

Overview

ESSENTIALS OF BUSINESS ANALYTICS, 2e provides coverage over the full range of analytics--descriptive, predictive, and prescriptive--not covered by any other single book. It includes step-by-step instructions to help students learn how to use Excel and powerful but easy to use Excel add-ons such as XL Miner for data mining. Extensive solutions to problems help instructors master material and grade student assignments.

Jeffrey D. Camm, Wake Forest University

Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University in 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 30 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. 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 and has also served on the editorial board of INFORMS Transactions on Education.

James J. Cochran, University of Alabama

James J. Cochran is 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. degrees from Wright State University and a 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.

Michael J. Fry, University of Cincinnati

Dr. Michael J. Fry is Associate Professor and Lindner Research Fellow in the Department of Operations, Business Analytics, and Information Systems in the Carl H. Lindner College of Business at the University of Cincinnati, where he also serves as Assistant Director for the Center for Business Analytics. At the University of Cincinnati since 2002, he has 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 twenty research publications in such journals as OPERATIONS RESEARCH, M&SOM, TRANSPORTATION SCIENCE, NAVAL RESEARCH LOGISTICS, IIE TRANSACTIONS, and INTERFACES. His research interests include applying management science 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., Copeland Corporation, Starbucks Coffee Company, The Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals, and the Cincinnati Zoo and Botanical Gardens. Professor Fry's teaching awards include the 2013 Michael L. Dean Excellence in Graduate Teaching Award and the 2006 Daniel J. Westerbeck Junior Faculty Teaching Award. 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.

Jeffrey W. Ohlmann, University of Iowa

Jeffrey W. Ohlmann is Associate Professor of Management Sciences in the Tippie College of Business at the University of Iowa, where he has been since 2003. Professor Ohlmann’s research on the modeling and solution of decision-making problems has produced more than a dozen research papers in such journals as MATHEMATICS OF OPERATIONS RESEARCH, INFORMS JOURNAL ON COMPUTING, TRANSPORTATION SCIENCE, and INTERFACES. He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and the Cincinnati Bengals. Due to the relevance of his work to industry, he received the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. Born in Valentine, Nebraska, he earned a BS from the University of Nebraska and MS and PhD degrees from the University of Michigan.

David R. Anderson, University of Cincinnati

Dr. David R. Anderson is a textbook 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. Professor Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the coauthor of ten 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 BS, MS, and PhD degrees from Purdue University.

Dennis J. Sweeney, University of Cincinnati

Dr. Dennis J. Sweeney is a leading textbook author, Professor Emeritus of Quantitative Analysis, and founder of the Center for Productivity Improvement at the University of Cincinnati. He also served five years as head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration. In addition, Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Dr. Sweeney has published more than 30 articles 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 respected journals. Dr. Sweeney is the co-author of ten textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a B.S. degree from Drake University, graduating summa cum laude. He received his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA Fellow.

Thomas A. Williams, Rochester Institute of Technology

Dr. Thomas A. Williams is a well respected textbook author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology, where he was the first chairman of the Decision Sciences Department. He taught courses in management science and statistics, as well as graduate courses in regression and decision analysis. Before joining the College of Business at RIT, Dr. Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and served as its coordinator. The co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics, Dr. Williams has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models. He earned his B.S. degree at Clarkson University and completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.
  • New Chapters on Probability and Statistical Inference. Chapters 5 and 6 are new to this edition. Chapter 5 covers an introduction to probability for those students who are not familiar with basic probability concepts such as random variables, conditional probability, Bayes’ theorem, and probability distributions. Chapter 6 presents statistical inference topics such as sampling, sampling distributions, interval estimation, and hypothesis testing. These two chapters extend the basic statistical coverage of Essentials of Business Analytics so that the book includes a full coverage of introductory business statistics for students who are unfamiliar with these concepts.
  • Expanded Data Mining Coverage. The Data Mining chapter from the first edition has been broken into two chapters: Chapter 4 on Descriptive Data Mining and Chapter 9 on Predictive Data Mining. Chapter 4 on Descriptive Data Mining covers unsupervised learning methods such as clustering and association rules where the user is interested in identifying relationships among observations rather than predicting specific outcome variables. Chapter 4 also covers very important topics related to data preparation including missing data, outliers, and variable representation. Chapter 9 on Predictive Data Mining introduces supervised learning methods that are used to predict an outcome based on a set of input variables. The methods covered in Chapter 9 include logistic regression, k-nearest neighbors clustering, and classification and regression trees.
  • New Appendix to Chapter 8. Chapter 8 on Time Series Analysis and Forecasting now includes an appendix on Excel 2016’s new Forecast Sheet tool for implementing Holt-Winters additive seasonal smoothing model.
  • First Mindtap for Business Analytics. MindTap is a customizable digital course solution that includes an interactive eBook, autograded exercises from the textbook, and author-created video walkthroughs of key chapter concepts and select examples that use Analytic Solver platform. Students can complete assignments whenever and wherever they are ready to learn with course material specially customized for students by you streamlined in one proven, easy-to-use interface. MindTap gives students a roadmap to master decision-making in business analytics. With an array of resources, tools, and apps -- including videos, practice opportunities, note taking, and flashcards.
  • Coverage of Analytic Solver Platform (ASP) Moved to Chapter Appendices. All coverage of the Excel add-in, Analytics Solver Platform, has been moved to the chapter appendices. This means that instructors can now cover all the material in the bodies of the chapters using only native Excel functionality. However, this change makes it easier for an instructor to tailor a course’s coverage of data mining concepts and the execution of these concepts.
  • Updates to ASP. All examples, problems, and solutions have been updated in response to changes in the ASP software. Frontline Systems, the developer of ASP, implemented a major rewrite of the code base that powers ASP shortly after the release of the first edition of Essentials of Business Analytics. All the material related to ASP is updated to correspond to Analytic Solver Platform V2016 (16.0.0).
  • Incorporation of Excel 2016. Most updates in Excel 2016 are relatively minor as they relate to its use for statistics and analytics. However, Excel 2016 does have new options for creating Charts in Excel and for implementing forecasting methods. Excel 2016 allows for the creation of box plots, tree maps, and several other data visualization tools that could not be created in previous versions of Excel.
  • New Style and More Color. The second edition of Essentials of Business Analytics includes full color figures and a new color template throughout the text. This makes much of the material covered much easier for students to interpret and understand.
  • Step-by-step instructions show students how to use various software programs to perform the analyses discussed in the text. It uses easy-to-use but powerful Excel add-ons such as XL Miner for data mining.
  • Practical, relevant problems at a variety of difficulty levels help students learn the material. Applications are drawn from all functional business areas: finance, marketing, operations, etc. Data sets are available for most exercises and cases.
  • Analytics in Action: Each chapter contains an Analytics in Action that present interesting examples of the use of business analytics in practice. The examples are drawn from many different organizations in a variety of areas including healthcare, finance, manufacturing, marketing, and others.
  • DATAfiles and MODELfiles: All data sets used as examples and in student exercises are also provided online as files available for download by the student. DATAfiles are Excel files that contain data needed for the examples and problems given in the textbook. MODELfiles contain additional modeling features such as extensive use of Excel formulas or the use of Excel Solver or Analytic Solver Platform.
  • Excel is completely integrated throughout the book, so students learn the latest methods for solving practical problems. It includes step-by-step instructions to help students learn how to use Excel 2016 to apply material in the book. It also includes by-hand calculation approaches to convey insights when this is appropriate.
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. Linear Optimization Models.
12. Integer Linear Optimization Models.
13. Nonlinear Optimization Models.
14. Monte Carlo Simulation.
15. Decision Analysis.
Appendix A: Basics of Excel.
Appendix B: Database Basics with Microsoft Access.
Appendix C: Solutions to Even-Numbered Questions (online).
MindTap
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