Quantitative Methods for Business, International Edition (with Printed Access Card) 12e

ISBN-13: 9781133584469 / ISBN-10: 1133584462

David R. Anderson, University of Cincinnati
Dennis J. Sweeney, University of Cincinnati
Thomas A. Williams, Rochester Institute of Technology
Jeffrey D. Camm, University of Cincinnati
R. Kipp Martin, University of Chicago
912pp
Published by Cengage Learning, ©2013
Available Now
£163.00

Provide your students with a strong conceptual understanding of the critical role that quantitative methods play in today's decision-making process with the well-respected QUANTITATIVE METHODS FOR BUSINESS, 12E, International Edition by award-winning authors Anderson/Sweeney/Williams/Camm/Martin. This text describes the many quantitative methods that have been developed over the years, explains how they work, and shows how the decision-maker can apply and interpret data. Written with the non-mathematician in mind, this text is applications-oriented. Its "Problem-Scenario Approach" motivates and helps students understand and apply mathematical concepts and techniques. In addition, the managerial orientation motivates students by using examples that illustrate situations in which quantitative methods are useful in decision making.

Features

  • Annotations: Annotations that highlight key points and provide additional insights for the student are a continuing feature of this edition. These annotations, which appear in the margins, are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text.
  • Notes & Comments: At the end of many sections, "Notes & Comments" give additional insights about the methodology being discussed and its application. These include warnings about or limitations of the methodology, recommendations for application, and brief descriptions of additional technical considerations.
  • Self-Test Exercises: Certain exercises are identified as self-test exercises. Completely worked-out solutions for these exercises are provided in Appendix G, entitled Self-Test Solutions and Answers to Even-Numbered Problems, located at the end of the book. Students can attempt the self-test problems and immediately check the solutuions to evaluate their understanding of the concepts presented in the chapter. In response to requests from professors using our textbooks, we now provide the answers to even-numbered problems in this same appendix.
  • Q.M. in Action: These articles are presented throughout the text and provide a summary of an application of quantitative methods found in business today. Adaptations of materials from Interfaces and OR/MS Today articles and write-ups provided by practitioners provide the basis for the applications in this feature.

1. Introduction.
2. Introduction to Probability.
3. Probability Distributions.
4. Decision Analysis.
5. Utility and Game Theory.
6. Forecasting.
7. Introduction to Linear Programming.
8. Linear Programming: Sensitivity Analysis and Interpretation of Solution.
9. Linear Programming Applications in Marketing, Finance, and Operations Management.
10. Distribution and Network Models.
11. Integer Linear Programming.
12. Advanced Optimization Applications
13. Project Scheduling: PERT/CPM.
14. Inventory Models.
15. Waiting Line Models.
16. Simulation.
17. Markov Processes.
Appendixes A-G.
Index.
  • New Chapter 12: Advanced Optimization Applications – A new chapter on optimization applications has been added. Applications include portfolio selection, a nonlinear extension of the RMC problem, and selecting stocks to go into an index mutual fund. This chapter introduces the idea of a nonlinear optimization model, but strictly from an applications standpoint. The Management Scientist cannot be used for nonlinear problems, and LINGO or Premium Solver are required.
  • New Documented Solutions – The Management Scientist will not be used in future editions of this book. We encourage adopters of this edition to use either LINGO or Premium Solver when solving optimization problems. To make it easy for new users of LINGO or Excel Premium Solver, we provide both LINGO and Excel files with the model formulation for every optimization problem that appears in the body of the text in Chapters 7 through 12. The model files are well documented and should make it easy for the user to understand the model formulation.
  • New Appendix A: Building Spreadsheet Models – This is not a book on spreadsheet modeling. However, spreadsheets are a very valuable modeling tool. This Appendix will prove useful to professors and students wishing to solve optimization models with Premium Solver. The appendix also contains a section on the principles of good spreadsheet modeling and a section on auditing tips. Exercises are also provided.
  • Updated Chapter 10: Distribution and Network Models – This replaces the old Chapter 10, "Transportation, Assignment, and Transshipment Problems" from the tenth edition. We have added sections on the shortest route problem and the maximal flow problem. However, in keeping with the theme of the book, we do not burden the student with any algorithms. All of the models in the chapter are presented under the unifying theme of linear programming.
  • New Q.M. in Action, Cases, and Problems – Q.M. in Action is the name of the short summaries that describe how the quantitative methods being covered in the chapter have been used in practice. In this edition you will find numerous Q.M. in Action vignettes, cases, and homework problems.
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David R. Anderson
Dr. David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his B.S., M.S., and Ph.D. degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the College's first Executive Program. At the University of Cincinnati, Professor Anderson has taught introductory statistics for business students as well as graduate-level courses in regression analysis, multivariate analysis, and management science. He has also taught statistical courses at the Department of Labor in Washington, D.C. He has been honored with numerous nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 leading textbooks in the areas of statistics, management science, linear programming, and production and operations management. He is an active consultant in the field of sampling and statistical methods.

Dennis J. Sweeney
Dr. Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. 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 served as visiting professor at Duke University. Professor Sweeney also has served as Head of the Department of Quantitative Analysis and 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 coauthored ten leading texts in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams
Dr. Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology (RIT). He earned his BS degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor 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 then served as its coordinator. At RIT, he was the first chairman of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Professor Williams is the coauthor of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics. He 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.

Jeffrey D. Camm
Jeffrey D. Camm is Professor of Quantitative Analysis, Head of the Department of Operations, Business Analytics, and Information Systems, and College of Business REsearch Fellow int he Carl H. Lindner College of Business at the University of Cincinnati. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University and a Ph.D. from Clemson University. He has been at the University of Cincinnati since 1984, and has 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 over 30 papers in the general area of optimization applied to problems in operations management. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. At the University of Cincinnati, he was named the Dornoff Fellow of Teaching Excellence 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 is currently on the editorial board of INFORMS Transactions on Education.

R. Kipp Martin
Dr. Kipp Martin is Professor of Operations Research and Computing Technology at the Graduate School of Business, University of Chicago. Born in St. Bernard, Ohio, he earned a B.A. in Mathematics, an MBA, and a Ph.D. in Management Science from the University of Cincinnati. While at the University of Chicago, Professor Martin has taught courses in Management Science, Operations Management, Business Mathematics, and Information Systems. Research interests include incorporating Web technologies such as XML, XSLT, XQuery, and Web Services into the mathematical modeling process; the theory of how to construct good mixed integer linear programming models; symbolic optimization; polyhedral combinatorics; methods for large scale optimization; bundle pricing models; computing technology and database theory. Dr. Martin has published in INFORMS Journal of Computing, Management Science, Mathematical Programming, Operations Research, The Journal of Accounting Research, and other professional journals. He is also the author of The Essential Guide to Internet Business Technology (with Gail Honda) and Large Scale Linear and Integer Optimization.