Nelson Higher Education

Higher Education

Quantitative Methods for Business, 13th Edition

  • David R. Anderson
  • Dennis J. Sweeney
  • Thomas A. Williams
  • Jeffrey D. Camm
  • James J. Cochran
  • Michael J. Fry
  • Jeffrey W. Ohlmann
  • ISBN-10: 1285866312
  • ISBN-13: 9781285866314
  • 936 Pages | Hardcover
  • Previous Editions: 2013, 2010, 2006
  • COPYRIGHT: 2016 Published
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Overview

About the Product

Written with the non-mathematician in mind, QUANTITATIVE METHODS FOR BUSINESS, 13E by award-winning authors Anderson, Sweeney, Williams, Camm, Cochran, Fry, and Ohlmann equips your students with a strong conceptual understanding of the critical role that quantitative methods play in today's decision-making process. This applications-oriented text clearly introduces current 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 relevant, real-world examples throughout. The authors' hallmark "Problem-Scenario Approach" helps readers understand and apply mathematical concepts and techniques. Instant online access provides students with Excel® worksheets, LINGO, and the Excel add-in Analytic Solver Platform. Using Microsoft Excel to develop spreadsheet simulation models, the thoroughly revised Chapter 16 explains how to construct a spreadsheet simulation model using only native Excel functionality, while the chapter appendix covers how the use of Excel add-in Analytic Solver Platform facilitates more sophisticated simulation analyses. Data Tables and Goal Seek Excel features were also added to Appendix A to help in the construction of spreadsheet simulation models. The 13th Edition includes a more holistic description of how variable activity times affect the probability of a project meeting a deadline, while maintaining simplicity by showing when using the critical path for these calculations is reasonable. In addition, numerous all-new Q.M. in Action vignettes, homework problems, and end-of-chapter cases are included throughout.

Features

  • Helpful Margin Annotations Clarify Key Points For Students. Brief, informative annotations in the margins of the book highlight key information and offer additional insights for readers who wish to know more. Providing appropriate emphasis, these clear annotations enhance students' understanding of key terms and concepts.

  • Notes and Comments Provide Additional Insights and Warnings About Methodology. At the end of many sections, "Notes & Comments" offer added information about the methodology being discussed and its application. Notes & Comments may include warnings or highlight limitations of the methodology, offer recommendations for applications, or provide brief technical considerations.

  • Self-Test Exercises Let Students Instantly Check Comprehension Before Progressing. Helpful Self-Test Exercises enable students to immediately evaluate their understanding of chapter concepts before advancing to the next topic. 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 Real-World Practice. 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 as well as contributions from leading practitioners.

About the Author

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

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.

Table of Contents

Preface.
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.

New to this edition

  • Completely Revised and Updated Simulation Chapter. While the authors maintain Chapter 16's intuitive introduction by continuing the use of best-, worst-, and base-case scenarios, they also added a more elaborate treatment of uncertainty by using Microsoft Excel to develop spreadsheet simulation models. Chapter 16 thoroughly explains how to construct a spreadsheet simulation model using only native Excel functionality, while the chapter appendix covers how the use of an Excel add-in−Analytic Solver Platform−facilitates more sophisticated simulation analyses. This new appendix replaces the previous edition's coverage of Crystal Ball, which is no longer paired with the textbook.
  • Data Tables and Goal Seek in Appendix A. These two Excel features were added to Appendix A, Building Spreadsheet Models, as they are particularly useful in the construction of spreadsheet simulation models in the completely revised Chapter 16.
  • New Section on Variability in Project Management. The 13th Edition's new section on variability provides a more holistic description of how variable activity times affect the probability of a project meeting a deadline, while maintaining simplicity by showing when using the critical path for these calculations is reasonable. In contract, traditional coverage has focused solely on the critical path to estimate the probability of a project meeting a deadline (on average, the longest sequence of activities). However, this calculation is based on the implicit assumption that no other "non-critical" activity will become a bottleneck. In the presence of highly variable activities, the assumption may not be accurate, yet traditional coverage provides no insight on this.
  • Adjustment of Forecasting Notation in Chapter 6. The notation in Chapter 6, Time Series Analysis and Forecasting, was adjusted to be more in line with "regression-style" standard notation for forecasting.
  • Updated Q.M. in Action. The 13th Edition includes 15 all-new Q.M. in Action vignettes to provide the most recent examples available.
  • New Cases: End-of-chapter student cases offer more in-depth and open-ended exercises than homework problems, giving students plenty of experience applying what they learn to real-world practice. This edition includes new cases on linear programming applications in Chapter 9, distribution and network models in Chapter 10, and integer programming in Chapter 11. Solutions to all cases are provided to instructors.
  • New And Updated Homework Problems: The 13th Edition added more than 35 new homework problems as well as updated numerous others to ensure the timeliest references available.

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

Website  (ISBN-10: 1305503171 | ISBN-13: 9781305503175)

Accessible through Cengage.com/login with your faculty account, this website for instructors features all of the assets available to students at no charge, plus an Instructor's Resource Manual (instructional objectives, chapter outlines, discussion questions, suggested lecture topics, suggested paper topics, and related Internet resources) and PowerPoint® presentations. Additionally, the following test bank format types are available for download from the Instructor Companion Website: Blackboard, Angel, Moodle, Canvas, Desire2Learn, and PDF.

Solutions Manual  (ISBN-10: 1305503244 | ISBN-13: 9781305503243)

This trusted Solutions Manual includes verified solutions and answers to all problems in the text that enable you to efficiently grade student homework. For your convenience and in response to instructors' requests, a printed version of the Solutions Manual may be packaged with the textbook for student purchase.

Cengage Testing, powered by Cognero® Instant Access  (ISBN-10: 1305503260 | ISBN-13: 9781305503267)

Cengage Learning Testing Powered by Cognero is a flexible, online system that allows you to author, edit, and manage test bank content from multiple Cengage Learning solutions; create multiple test versions in an instant; and deliver tests from your learning management system, your classroom, or wherever you want!

CengageNOWv2, 1 term (6 months) Instant Access  (ISBN-10: 1305503201 | ISBN-13: 9781305503205)

With its engaging learning and assessment tools, CengageNOW supports the entire student workflow, from motivation to mastery. For instructors, CengageNOW provides control and customization with the opportunity to tailor the learning experience to improve outcomes.

CengageNOWv2, 2 terms (12 months) Instant Access  (ISBN-10: 130550321X | ISBN-13: 9781305503212)

With its engaging learning and assessment tools, CengageNOW supports the entire student workflow, from motivation to mastery. For instructors, CengageNOW provides control and customization with the opportunity to tailor the learning experience to improve outcomes.

Student Supplements

CengageNOWv2, 1 term (6 months) Instant Access  (ISBN-10: 1305503201 | ISBN-13: 9781305503205)

With its engaging learning and assessment tools, CengageNOW supports your entire workflow, from motivation to mastery.

CengageNOWv2, 2 terms (12 months) Instant Access  (ISBN-10: 130550321X | ISBN-13: 9781305503212)

With its engaging learning and assessment tools, CengageNOW supports your entire workflow, from motivation to mastery.