Quantitative Analysis for Management with CD (9th Edition)

Description

Features

Intended for courses in Management Science or Quantitative Methods.

This best–selling text has long been considered one of the most student accessible texts for the management science course. The new edition retains and updates the traditional, comprehensive coverage of past editions but “fine tunes” the coverage and adds many new examples and exercises.

How do you motivate the chapter material?  What types of elements do you look for to drive the concepts home for students? 

Rich Real–world Applications

• Modeling in the Real World boxes
  • Demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
• Procedure boxes
  • Summarize the more complex quantitative techniques, presenting them as a series of easily understandable steps.
• Margin notes
  • Highlight the important topics in the text.
• History boxes
  • Provide interesting asides related to the development of techniques and the people who originated them.
• QA in Action boxes
  • Summarize published articles illustrating how real organizations have used quantitative analysis to solve problems.

What type of problems do you assign? What resources help students review the concepts?

• Solved Problems
  • Serve as models for students in solving their own homework problems.
• Discussion Questions
  • Test student’s understanding of the concepts and definitions.
• Homework Problems are applications–oriented and test the student’s ability to solve exam–type problems. T
  • Provide problems that progress in level of difficulty: introductory (one bullet), moderate (two bullets), and challenging (three bullets).
• Internet Homework Problems
  • Provide additional problems for students to work on the companion Web site.
• Self Tests
  • Allow students to test their knowledge of important terms and concepts in preparation for quizzes and examinations.
• Case Studies
  •  Provide additional challenging managerial applications.
• Glossaries
  • Define important terms.

What type of software do you use to help students problem–solve?

Excel. For students already familiar with Excel, instructions and screen captures are provided to highlight functions and tools that are directly related to quantitative analysis. New appendices provide succinct instructions and examples in several chapters. Excel is used for computations with the normal distribution, the binomial distribution, Bayes’ theorem, simple and multiple regression, the analytic hierarchy process, Markov analysis, matrix operations, and other models. From the previous edition, Excel is also used for linear and nonlinear programming, simulation, and forecasting.

Excel QM. Excel QM is an Excel add–in that makes the use of Excel even easier. It is used to solve many of the problems and examples found in the text. The use of Excel QM is integrated into most chapters.

 

QM for Windows. QM for Windows, developed by Professor Howard Weiss, has long been a preferred software package for quantitative techniques.  It is menu–driven and very easy–to–use so that students with limited computer experience find it very user friendly. The full version of this software is on the Student CD–ROM and updates to this valuable  package are available to the student at www.prenhall.com/weiss. 

 

This text is available for personalization in the PHCBR custom database program.  Select only the chapters you require or supplement with recommended case studies all under one cover.  CLICK HERE to go directly to the PHCBR book–build site or visit our product page for additional information at pearsoncustom.com/business.

New To This Edition

Cohesive Treatment of Decision Models

All models for decision theory have been combined into one chapter.  Decision trees and utility theory are now presented along with decision tables.

New Chapter on Regression Analysis

This includes simple linear regression, multiple regression, and a brief discussion of nonlinear regression.  Statistical inference on the overall model is presented.  Other topics include dummy or indicator variables, model building, and cautions and pitfalls of using regression analysis.

Expanded Coverage of Forecasting

The forecasting chapter has been expanded to include the additive approach to decomposition.  Dummy variables are used in regression models to incorporate seasonal variations into the forecasts.

Enhanced Inventory Chapter

Now includes just–in–time (JIT), materials requirements planning (MRP), and enterprise resource planning (ERP) to provide a more complete introduction to inventory models in one chapter.

CD–ROM MODULES

To streamline the book, six topics are contained in modules available on the student CD–ROM included with the book. The six modules are:

• Analytic Hierarchy Process
• Dynamic Programming
• Decision Theory and the Normal Distribution
• Game Theory
• Mathematical Tools: Matrices and Determinants
• Calculus–Based Optimization