The eleventh edition of Quantitative Analysis for Management continues to provide both graduate and undergraduate students with a solid foundation in quantitative methods and management science. Thanks to the comments and suggestions from numerous users and reviewers of this textbook over the last thirty years, we are able to make this best selling textbook even better.
We continue to place emphasis on model building and computer applications to help students understand how the techniques presented in this book are actually used in business today. In each chapter, managerial problems are presented to provide motivation for learning the techniques that can by used to address these problems. Next, the mathematical models, with all necessary assumptions, are presented in a clear and concise fashion.
The techniques are applied to the sample problems with complete details provide. We have found that this method of presentation is very effective, and students are very appreciative of this approach. If the mathematical computations for a technique are very detailed, the mathematical details are presented in such a way that the instructor can easily omit these sections without interrupting the flow of the material. The use of computer software allows the instructor to focus on the manage4rial problem and spend less time on the mathematical details of the algorithms. Computer output is provided for many examples.
The only mathematical prerequisite for this textbook is algebra. One chapter on probability and another chapter on regression analysis provide introductory coverage of these topics. We use standard notation, terminology, and equations throughout the book. Careful verbal explanation is provided for the mathematical notation and equations used.
1. Introduction to quantitative
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 and assignment models
10. Integer programming, goal programming, and nonlinear programming
, etc.