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Quantitative Methods for Business 12th Edition

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

  • Published
  • Previous Editions 2010, 2006, 2004
  • 936 Pages
Starting At 135.00 See pricing and ISBN options

Overview

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 by award-winning authors Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann. Written with the non-mathematician in mind, this applications-oriented text clearly introduces today’s many quantitative methods, how they work, and how savvy decision makers can most effectively apply and interpret data. A strong managerial orientation motivates learning by weaving pertinent, actual examples throughout that illustrate key situations where quantitative methods make a difference in decision making. QUANTITATIVE METHODS FOR BUSINESS, 12E provides everything your students need for success from the authors’ hallmark "Problem-Scenario Approach" that helps readers understand and apply mathematical concepts and techniques. Instant online access provides students with Excel® worksheets, TreePlan, Crystal Ball, Premium Solver for Excel®, and LINGO.

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.

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.
  • UPDATED CHAPTER ON FORECASTING NOW USES REGRESSION TO ESTIMATE LINEAR TRENDS AND SEASONAL EFFECTS. An updated discussion in Chapter 6 now focuses on the use of regression to estimate linear trends and seasonal effects. A new chapter appendix details how to use the Excel® LINEST function to estimate linear trends and seasonal effects. With these revisions the chapter better represents current industry approaches to these important topics.
  • UPDATED OPTIMIZATION OUTPUT NOW ALIGNS WITH THE POPULAR EXCEL® SOLVER. Rather than using The Management Scientist output for optimization as in previous editions, Chapters 8-12 now use output aligned with today's popular Excel® solver to better prepare students for success in business today.
  • UPDATED CHAPTER ON SENSITIVITY ANALYSIS REFLECTS THIS EDITION'S NEW, CONSISTENT COMPUTER OUTPUT FORMAT. This edition's Chapter 8 on Sensitivity Analysis incorporates numerous changes that more closely align the chapter descriptions with the new computer output format used in this edition. An informative new discussion explores the limitations of classical sensitivity analysis to give students an understanding of both the benefits and potential drawbacks to this approach.
  • UPDATED CHAPTER ON DISTRIBUTION AND NETWORK MODELS EMPHASIZES IMPORTANCE OF SUPPLY CHAIN APPLICATIONS. Chapter 10's content is now revised and rearranged to more effectively reflect the current importance of supply chain models and supply chain applications for business managers and quantitative methods in business. The authors group transportation and trans-shipment models into a single section on supply chain models for a clearer approach. All chapter models are presented as linear programs. In keeping with the theme of this book, the authors do not burden students with solution algorithms.
  • NEW Q.M. IN ACTION, CASES, AND PROBLEMS DEMONSTRATE PRINCIPLES IN PRACTICE. Short, captivating new Q.M. in Action summaries describe how the quantitative methods from the chapter are currently used in actual business practices. This edition provides 19 Q.M. in Action vignettes, two new cases, and more than 30 new homework problems to ensure all practice reflects actual business challenges.
  • HELPFUL MARGIN ANNOTATIONS CLARIFY KEY POINTS FOR STUDENTS. Popular, informative annotations in the margins of the book highlight key information and provide additional insights for readers who wish to know more. These clear annotations provide appropriate emphasis and enhance students' understanding of the book's most important terms and concepts.
  • NOTES & COMMENTS PROVIDE ADDITIONAL INSIGHTS AND WARNINGS ABOUT METHODOLOGY. At the end of many sections, "Notes & Comments" offer additional insights about the methodology being discussed and its application. Notes & Comments may include warnings or highlight limitations of the methodology, may offer recommendations for applications, or provide brief, additional technical considerations.
  • SELF-TEST EXERCISES LET STUDENTS INSTANTLY CHECK COMPREHENSION BEFORE PROGRESSING. Helpful Self-Test Exercises let students immediately complete and evaluate their understanding of the concepts presented in the chapter. Completely worked-out Self-Test solutions appear in an appendix in addition to the solutions for even-numbered problems, as requested by past users.
  • ENGAGING Q.M.IN ACTION ARTICLES SUMMARIZE APPLICATIONS FROM TODAY'S BUSINESS. Interesting Q.M. in Action articles throughout the text offer practical summaries of how quantitative methods apply in business today. The articles feature adaptations from Interfaces and OR/MS Today articles as well as written contributions from leading practitioners.
1. Introduction.
2. Introduction to Probability.
3. Probability Distributions.
4. Decision Analysis.
5. Utility and Game Theory.
6. Time Series Analysis and 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.
Appendix A: Building Spreadsheet Models.
Appendix B: Binomial Probabilities.
Appendix C: Poisson Probabilities.
Appendix D: Areas for the Standard Normal Distribution.
Appendix E: Values for e-λ.
Appendix F: References and Bibliography.
Appendix G: Self-Test Solutions and Answers to Even-Numbered Problems.
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