MS&E 252 Decision Analysis I Handout #16 November 8th‚ 2007 Case Study Due: Friday November 30th‚ 2007 The case study will give you practice applying the concepts you are learning in MS&E 252. . You are from the XYZ consulting company (you can create your own name). William Jaeger of Freemark Abbey Winery has hired your company to help him make a production decision. A storm is approaching the winery and Jaeger is not sure if he should wait for the storm or harvest the grapes immediately
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Abstract This case study examines the decision making process of the Kowloon Development Company to the PrecisionTree decision tree software from Palisade. The Kowloon Development Company was faced with a major decision about their future investments. The General Manager of the Kowloon Development Company is usually involved in billion dollar investments‚ accurate decisions are needed. The company has to make a decision over the decision to purchase a new development project the total site area
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edu/ml/datasets/Tic-Tac-Toe+Endgame]. Irvine‚ CA: University of California‚ School of Information and Computer Science. http://en.wikipedia.org/wiki/Tic-tac-toe http://www.infoacumen.com/ http://www.kdnuggets.com/software/classification-decision-tree.html#tree-free o http://www.geocities.com/adotsaha/CTree/CtreeinExcel.html o http://www.tetris1d.org/zigah/mangrove/ Data Mining – A tutorial based primer‚ Richard J. Riger and Michael W. Geatz‚ Second impression 2008‚ Pearson Education Inc.
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Avalanche Corporation Decision Analysis and Strategic Recommendation Table of Contents Table of Contents 1 Overview 2 Question 1: Production Strategy 2 Question 2: Sensitivity Analysis 3 Question 3: Influence of Outside Vendor 5 Question 4: Alternative Risk Profiles 6 Question 5: Are Fantastic Forecasters Worth It? 7 Conclusions 7 Appendix 8 Figure A: Precision Tree (Question 1) 8 Figure B: Cost Calculation Table 9 Figure C: Profit Calculation Table 9 Figure D: Tornado
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Expected Value of Perfect Information (EVPI) In decision-making under risk ‚ each state of nature is associated with probability of its occurrence ; If the decision-maker can acquire perfect (complete) information about the occurrence of various states of nature ‚ he will be able to select a strategy that yields the desired pay-off for whatever state of nature that actually occurs EMV /EOL criterion helps the decision-maker select a strategy that optimises the expected pay-off without
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Table of Content I. Introduction II. Fault Tree One III. Discussion of Fault Tree One IV. Fault Tree Two V. Discussion of Fault Tree Two VI. Conclusions VII. Works cited I. Introduction I will be the lead Project Manager in building one of the largest buildings in the world. This 1‚453-foot building will have a 103-story structure and should be built in just over 13 months. It’s important to know some key facts about risks associated with construction of the
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Negotiation Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Decision Tree Analyses Help Develop and Test Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Decision Trees Are Used to Analyze Complex Business Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Legal Claims Share Similarities with Complex Business Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Car Buyer Becomes
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|Lovely Professional University‚ Punjab | | | | | | |
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RISK MANAGEMENT ESSAY The following essay has been written by analyzing the risks associated from the construction managers/ project managers’ point of view. Citing the possible risks associated while working on international or varied geographical location. Risks are associated with almost all levels of the project life cycle and is mutually shared and mitigated by all parties employed within the construction industry. There are many evidences to state that poor risk mitigation leads to poor performance
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Basic Concepts of Classification and Prediction 2.1.1 Definition 2.1.2 Classification vs. Prediction 2.1.3 Classification Steps 2.1.4 Issues of Classification and Prediction 2.2 2 2 Decision Tree Induction 2.2.1 The Algorithm 2.2.2 Attribute Selection Measures 2.2.3 Tree P 223T Pruning i 2.2.4 Scalability and Decision Tree Induction 2.3 Bayes Classification Methods 2.4 Rule Based Classification 2.5 Lazy Learners 2.6 2 6 Prediction 2.7 How to Evaluate and Improve Classification 2.1.1 Definition
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