Business decision analysis : an active learning approach
著者
書誌事項
Business decision analysis : an active learning approach
Blackwell, 1999
大学図書館所蔵 全14件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
At head of title: The Open Learning Foundation
内容説明・目次
内容説明
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.
目次
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.
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