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Business Analytics, Cengage International Edition, 5th Edition

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

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Starting At £45.00 See pricing and ISBN options
Business Analytics, Cengage International Edition 5th Edition by Jeffrey D. Camm/James J. Cochran/Michael J. Fry/Jeffrey W. Ohlmann

Overview

Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, CENGAGE INTERNATIONAL EDITION 5E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R or the Python-based Orange data mining software to solve more advanced analytics concepts. As instructor, you choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time while providing students with critical practice. Updates throughout this edition cover topics beyond the traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable online learning platforms offer an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.

Jeffrey D. Camm

Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean for Faculty 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, The INFORMS Journal on Applied Analytics and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as a consultant to numerous companies and government agencies. Dr. Camm served as editor-in-chief of INFORMS Journal on Applied Analytics and is an INFORMS fellow.

James J. Cochran

James J. Cochran is Professor of Applied Statistics, the Mike and Cathy Mouron Research Chair 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 50 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 on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Association’s Waller Distinguished Teaching Career Award and in 2018 he received the INFORMS President’s Award. Dr. Cochran is an elected member of the International Statistics Institute, a fellow of the American Statistical Association and a fellow of INFORMS. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world.

Michael J. Fry

Michael J. Fry is Professor of Operations, Business Analytics and Information Systems, Lindner Research Fellow and Managing 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. He has also been a visiting professor at Cornell University and 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, IISE Transactions, Critical Care Medicine and INFORMS Journal on Applied Analytics. 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 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 and Botanical Garden. 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 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 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 on Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to 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.
  • 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.
  • 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.
  • 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.
  • 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.
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).
MindTap
Each MindTap product offers the full, mobile-ready textbook combined with superior and proven learning tools at one affordable price. Students who purchase digital access can add a print option at any time when a print option is available for their course.

This Cengage solution can be seamlessly integrated into most Learning Management Systems (Blackboard, Brightspace by D2L, Canvas, Moodle, and more) but does require a different ISBN for access codes. Please work with your Cengage Learning Consultant to ensure the proper course set up and ordering information. For additional information, please visit the LMS Integration site.

Standalone Digital Access — Ultimate Value

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  • ISBN-10: 0357990323
  • ISBN-13: 9780357990322
  • RETAIL £45.00

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  • ISBN-10: 8214493560
  • ISBN-13: 9798214493565
  • RETAIL £53.00

  • ISBN-10: 8214050294
  • ISBN-13: 9798214050294
  • RETAIL £86.99

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.

FOR STUDENTS

International MindTap Instant Access for Camm/Cochran/Fry/Ohlmann's Business Analytics

ISBN: 9780357990322
International MindTap Instant Access for Camm/Cochran/Fry/Ohlmann's Business Analytics, 5th 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. All online text media materials accessible through this access code are available in EMEA, Latin America, Asia, and India only.