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Business Decision Analysis: An Active Learning Approach

Business Decision Analysis: An Active Learning Approach

Graham Hackett, Peter Luffrum

ISBN: 978-0-631-20176-2

Jun 1999

644 pages

Select type: Paperback

In Stock



Business Decision Analysis is part of a major new national programme of texts and modules designed for undergraduate students on Business Studies degree courses. It provides 150 hours of high quality study to be used in a supported learning environment.

The module provides a comprehensive introduction to the quantitative analysis and solution of business problems and covers some of the key topics in the field, including an introduction to model building for business decision analysis, linear programming, regression analysis, time-series analysis and simulation techniques. Business Decision Analysis contains numerous activities and exercises to develop an understanding of the subject, including many utilizing Microsoft Excel in version 5.0 or later (not supplied with this publication). The module provides the most effective teaching and learning resource available at this level.

Part I: An Introduction to Business Decision Analysis:.

1. What is Business Decision Analysis?.

2. Model-Building in Business Decision Analysis.

3. The Components of a Mathematical Model.

4. Deterministic and Stochastic Models.

5. Single-attribute and Multi-attribute Problems.

6. Sensitivity Analysis and Model Building.

Part II: Decision Analysis:.

7. Decision Trees and Payoff Matrices.

8. Decision-Making under Conditions of Uncertainty.

9. Decision-Making under Conditions of Risk.

10. Multi-Stage Decision Problems.

11. Revising Probabilities.

12. Extensions.

Part III: Linear Programming:.

13. Formulating a Linear Programming Problem.

14. Solving Linear Programming Problems Using a Graphical Method.

15. Sensitivity Analysis of Solutions.

16. Computer Solution of Linear Programming Problems.

17. The Transportation Problem.

18. The Assignment Problem.

19. Linear Programming - Limitations and Extensions.

Part IV: Regression Analysis:.

20. Functional Relationships.

21. Bi-Variate Causal Models.

22. The Technique of Regression Analysis.

23. Regression Models and Predictive Accuracy.

24. The Analysis of Residuals.

25. Confidence Intervals and Regression Analysis.

26. The Multivariate Model.

27. The Performance of the Multivariate Model.

28. Refining the Multiple Regression Model.

29. Extending Regression Analysis.

Part V: Time Series Analysis:.

30. Time Series: an Overview.

31. Decomposition of a Time Series.

32. Non-Centred Moving Averages and Forecasting Error.

33. Exponential Smoothing.

34. Introduction to ARIMA.

Part VI: Simulation:.

35. What is Simulation?.

36. The Technique of Simulation.

37. Refining the Simulation Model.

38. Waiting Lines and Scheduling Problems.

39. Inventory Problems.

40. Waiting Lines: The Time Element.

41. Additional Topics in Simulation.

  • Provides 150 hours of self-study material for students.

  • Checks students understanding through numerous exercises, worked examples and assignments.