Nelson Higher Education

Higher Education

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
  • ISBN-10: 1305627733
  • ISBN-13: 9781305627734
  • 896 Pages | Hardcover
  • Previous Editions: 2015
  • COPYRIGHT: 2017 Published
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Overview

About the Product

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.

Features

  • 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.

  • 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.

About the Author

Jeffrey D. Camm

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

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

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

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.

David R. Anderson

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

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.

Thomas A. Williams

N/A

Table of Contents

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).

New to this edition

  • 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.

Supplements

All supplements have been updated in coordination with the Main title.
Please see Main title page for new to this edition information.

Instructor Supplements

Instructor's Solutions Manual  (ISBN-10: 1305861728 | ISBN-13: 9781305861725)
Website  (ISBN-10: 1305861736 | ISBN-13: 9781305861732)
Cengage Testing, powered by Cognero® Instant Access  (ISBN-10: 1305861752 | ISBN-13: 9781305861756)

Cengage Learning Testing, powered by Cognero®, is a flexible, online system that allows you to import, edit, and manipulate content from the text’s test bank or elsewhere, including your own favorite test questions. Create multiple test versions in an instant and deliver tests from your LMS, your classroom, or wherever you want.

Cengage Testing, powered by Cognero®  (ISBN-10: 1305861744 | ISBN-13: 9781305861749)

Cengage Learning Testing, powered by Cognero®, is a flexible, online system that allows you to import, edit, and manipulate content from the text’s test bank or elsewhere, including your own favorite test questions. Create multiple test versions in an instant and deliver tests from your LMS, your classroom, or wherever you want.

MindTap Business Analytics, 1 term (6 months) Instant Access for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics  (ISBN-10: 1305861787 | ISBN-13: 9781305861787)

MindTap Business Analytics for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics, 2nd Edition is the digital learning solution that powers students from memorization to mastery. It gives you complete control of your course—to provide engaging content, to challenge every individual, and to build their confidence. Empower students to accelerate their progress with MindTap. MindTap: Powered by You. MindTap gives you complete ownership of your content and learning experience. Customize the interactive syllabi, emphasize the most important topics, and add your own material or notes in the eBook.

MindTap Business Analytics, 2 terms (12 months) Instant Access for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics  (ISBN-10: 1305861809 | ISBN-13: 9781305861800)

MindTap Business Analytics for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics, 2nd Edition is the digital learning solution that powers students from memorization to mastery. It gives you complete control of your course—to provide engaging content, to challenge every individual, and to build their confidence. Empower students to accelerate their progress with MindTap. MindTap: Powered by You. MindTap gives you complete ownership of your content and learning experience. Customize the interactive syllabi, emphasize the most important topics, and add your own material or notes in the eBook.

Student Supplements

MindTap Business Analytics, 1 term (6 months) Instant Access for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics  (ISBN-10: 1305861787 | ISBN-13: 9781305861787)

MindTap Business Analytics for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics, 2nd Edition helps you learn on your terms. INSTANT ACCESS IN YOUR POCKET. Take advantage of the MindTap Mobile App. Read or listen to textbooks and study with the aid of instructor notifications, flashcards, and practice quizzes. MINDTAP HELPS YOU CREATE YOUR OWN POTENTIAL. GEAR UP FOR ULTIMATE SUCCESS. Track your scores and stay motivated toward goals. Whether you have more work to do or are ahead of the curve, you’ll know where to focus efforts. And the MindTap Green Dot will charge your confidence along the way. MINDTAP HELPS YOU OWN YOUR PROGRESS. MAKE YOUR TEXTBOOK YOURS. No one knows what works for you better than you. Highlight key text, add notes, and create custom flashcards. When it’s time to study, gather everything you’ve flagged into a guide you organize.

MindTap Business Analytics, 2 terms (12 months) Instant Access for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics  (ISBN-10: 1305861809 | ISBN-13: 9781305861800)

MindTap Business Analytics for Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' Essentials of Business Analytics, 2nd Edition helps you learn on your terms. INSTANT ACCESS IN YOUR POCKET. Take advantage of the MindTap Mobile App. Read or listen to textbooks and study with the aid of instructor notifications, flashcards, and practice quizzes. MINDTAP HELPS YOU CREATE YOUR OWN POTENTIAL. GEAR UP FOR ULTIMATE SUCCESS. Track your scores and stay motivated toward goals. Whether you have more work to do or are ahead of the curve, you’ll know where to focus efforts. And the MindTap Green Dot will charge your confidence along the way. MINDTAP HELPS YOU OWN YOUR PROGRESS. MAKE YOUR TEXTBOOK YOURS. No one knows what works for you better than you. Highlight key text, add notes, and create custom flashcards. When it’s time to study, gather everything you’ve flagged into a guide you organize.