Quantitative Analysis for Management, Global Edition, 13th Edition

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Quantitative Analysis for Management, Global Edition, 13th Edition

By Barry Render, Ralph M. Stair, Michael E. Hanna, Trevor S. Hale
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For courses in management science and decision modeling.

Quantitative Analysis for Management helps students to develop a real-world understanding of business analytics, quantitative methods, and management science by emphasising model building, tangible examples, and computer applications. The authors offer an accessible introduction to mathematical models and then students apply those models using step-by-step, how-to instructions. For more intricate mathematical procedures, the 13th Edition offers a flexible approach, allowing instructors to omit specific sections without interrupting the flow of the material. Supporting computer software enables instructors to focus on the managerial problems and solutions, rather than spending valuable class time on the details of algorithms.


Students see how concepts and techniques are applied in real organisations

  • Information and resources ensure that students have the most current and comprehensive understanding of quantitative analytics and management science.
  • Contemporary problems and review questions reflect the present-day state of quantitative business methods, encouraging students to examine key concepts from a real-world perspective.
  • An emphasis on model building and computer applications shows how concepts are used in business.
    • Transportation, assignment, and network models are together in one chapter, focused on modeling with linear programming.
    • Specialised algorithms for the transportation, assignment, and network methods are combined into a single online module for a cohesive learning experience.
  • QA in Action boxes illustrate how real organisations use quantitative analytics to solve problems.
  • Modeling in the Real World boxes demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
  • Case Studies provide additional challenging managerial applications.
  • A warm introduction to business analytics gives students a preview of the concepts covered in the text.

Students develop and check their understanding of each topic, so that they can study efficiently and effectively

  • Procedure boxes summarise complex quantitative techniques, presenting them as a series of digestible steps.
  • Solved Problems, located at the end of each chapter, serve as models for students’ homework problems.
  • Discussion Questions test students’ comprehension of concepts covered in each chapter.
  • Applications-oriented problems in every chapter evaluate students’ abilities to solve exam-type problems. This edition includes 26 new problems.
  • Glossaries at the end of each chapter define key terms covered in the text.
  • Key Equations are listed at the end of each chapter for review.
  • End-of-chapter bibliographies provide a selection of current, relevant books and articles for further learning.
Table of contents
  • 1. Introduction to Quantitative Analysis
  • 2. Probability Concepts and Applications
  • 3. Decision Analysis
  • 4. Regression Models
  • 5. Forecasting
  • 6. Inventory Control Models
  • 7. Linear Programming Models: Graphical and Computer Methods
  • 8. Linear Programming Applications
  • 9. Transportation, Assignment, and Network Models
  • 10. Integer Programming, Goal Programming, and Nonlinear Programming
  • 11. Project Management
  • 12. Waiting Lines and Queuing Theory Models
  • 13. Simulation Modeling
  • 14. Markov Analysis
  • 15. Statistical Quality Control
  • Online Module 1: Analytic Hierarchy Process
  • Online Module 2: Dynamic Programming
  • Online Module 3: Decision Theory and the Normal Distribution
  • Online Module 4: Game Theory
  • Online Module 5: Mathematical Tools: Determinants and Matrices
  • Online Module 6: Calculus-Based Optimization
  • Online Module 7: Linear Programming: The Simplex Method
  • Online Module 8: Transportation, Assignment, and Network Algorithms