Decision Analysis Model and Report JaKaiser Smith Southern New Hampshire University Date: 07/09/2015 Abstract In this report following resources have been utilized to establish a relationship between Retail Salesperson’s salaries and their intent to shoplift at their own workplace: * The Larceny theft data from Federal Bureau of Investigation’s official website for the years 2011, 2012 and 2013; * 25th and 26th Annual Retail Theft Surveys by Hayes International for the years
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Presenter Notes for Power-point Decision Analysis Task 4A-E A. (Slide 1) Gloria Rodriguez, Shuzworld's retail development specialist, was looking at some possibilities of placing a Shuzworld store into a major target area and has three options to consider. The first option is to build a proposed stand-alone store, the second option is to open an Auburn Mall store, and the third option is to do nothing at the moment. (Slide 2) With the information given from Ms. Rodriguez, the stand-alone store
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14 Decision Analysis Learning Objectives 1. Learn how to describe a problem situation in terms of decisions to be made, chance events and consequences. 2. Be able to analyze a simple decision analysis problem from both a payoff table and decision tree point of view. 3. Be able to develop a risk profile and interpret its meaning. 4. Be able to use sensitivity analysis to study how changes in problem inputs affect or alter the recommended decision.
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2-1 2.1 Decision Analysis: Expected Value of Perfect Information (EVPI) The Expected Value of Perfect Information (EVPI) will be explained with the following example: Example: “Flip the Coin Game” Richard has to decide whether to play a game of “flip the coin” with Karin. The game is as follows: If the coin ends up heads Karin pays Richard one dollar and if the coin ends up tails Richard pays Karin eighty cents. What is the most Richard should pay a clairvoyant (look up this word if you do not
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MAT 540 Week 2: Decision Analysis |Slide # |Topic |Narration | |1 |Introduction |Welcome to Quantitative Methods. In this lesson, we will discuss the concept of decision | | | |analysis, a valuable technique for decision making process. | | | |
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Business Intelligence and Decision Making of Successful Women Entrepreneurs in Northern States of Malaysia Heng Man Chia, Tan Wen Pei, Lim Wang Ru and Yew Bee Jue (2012,UUM) ABSTRACT Women entrepreneurs increasingly become the important role and make a contribution to the economics of the country. The main purpose of this paper is to determine the effects of business intelligence and decision making to the successful women entrepreneurs. This research is mainly focused on the women entrepreneurs
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|Decision-Making Analysis[1] | The Components of a Decision Problem 2 Decision Making under Uncertainty 3 Maximax Procedure 3 Maximin procedure 4 Equal Likelihood Procedure 5 Minimax Regret Procedure 5 Decision Making under Risk 6 Maximizing Expected Gain 6 Minimizing Expected Regret 7 The Expected Value of Perfect Information 8 Summary 17 Discussion Questions 18 Solved Problems 18 Problems 24 Key Terms 31 |The Components of a Decision Problem
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Science, 10e (Taylor) Chapter 12 Decision Analysis 1) A state of nature is an actual event that may occur in the future. Answer: TRUE Diff: 1 Page Ref: 527 Main Heading: Components of Decision Making Key words: state of nature 2) A payoff table is a means of organizing a decision situation, including the payoffs from different decisions given the various states of nature. Answer: TRUE Diff: 1 Page Ref: 527 Main Heading: Components of Decision Making Key words: payoff table
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Decision Analysis: Decision analysis is a set of quantitative decision making techniques for decision situations in which uncertainty exists. Now, uncertainty can be classified into two ways/ types: 1. Subjective Probability: Subjective probability is the degree of belief to occurrence of the event. 2. Objective Probability: Objective probability is the probability which can be derived either based on historical occurrences or based on experimentation. Alternatively can be derived
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DECISION MODELS Decisions Making Under Uncertainty Decisions Making Under Risk 1. Max-Min Criteria (pessimist) 1. Expected Payoff (Average) - Best of the worst A. Multiply payoffs by probabilities and -Becomes (Min-Max) if loss table add up. (For each action separately) A. Find min (max) in each row B. Pick best action B. Pick the best of the Max (Min) 2. Expected Opportunity Loss A. Set up loss matrix -Subtract all numbers in each column Criteria
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