- Economic Climate Country Decline Stable Improve South Korea Philippines Mexico
|
|
- Norah Burke
- 5 years ago
- Views:
Transcription
1 1) Micro-comp is a Toronto based manufacturer of personal computers. It is planning to build a new manufacturing and distribution facility in South Korea, Philippines, or Mexico. The profit (in $ millions) depends on the financial, labour and political climate, including monetary exchange rates. - Economic Climate Country Decline Stable Improve South Korea Philippines Mexico a. What is the maximax decision? b. What is the maximin decision? c. What is the equally likelihood decision? d. What is the criterion of realism decision. Use alpha of 0.25 e. Develop an opportunity loss table f. What is the minimax regret decision? g. A global economist hired by Micro-comp estimates that the probability that the economic climate overseas and in Mexico will decline is.40, the probability that it will remain approximately the same is 0.50, and the probability that it will improve is Determine Micro-comp s optimal decision using expected value. h. Determine the EOL and the best strategy. i. What is the expected value of perfect information? 2) Susan has been thinking about starting her own independent gasoline station. Susan s problem is to decide how large her station should be. The annual returns will depend both on the size of her station and on a number of marketing factors related to the oil industry and demand for gasoline. After a careful analysis, Susan developed the following table Size of Station Good Market Fair Market Poor Market Small Medium Large Very Large a. What is the maximax decision? b. What is the maximin decision? c. What is the equally likelihood decision? d. What is the criterion of realism decision. Use alpha of 0.8 1
2 e. Develop an opportunity loss table f. What is the minimax regret decision? g. After reading about economic predictions, Susan has assigned the probability that the market will be good, fair, and poor at 0.2, 0.35 and 0.45 respectively. Calculate Susan s best decision using EMV. h. What is Susan s choice using the minimum EOL? i. Compute the EVPI and show that it is the same as minimum EOL 3) A machine shop owner is attempting to decide whether to purchase a new drill press or a grinder. The return from each will be determined by whether the company succeeds in getting a government military contract. Investment Contract (0.7) No Contract (0.3) Drill Press $ $ Grinder $ $ a. Construct a decision tree for this problem. What is the recommended decision using expected value criterion? b. What is the maximum amount of money the investor should pay for additional information regarding the outcomes? c. The machine shop owner is considering hiring a consultant to analyze the situation. The results of the analysis will indicate that either favourable (contract awarded) or unfavourable conditions (not awarded) will occur. There is 0.8 probability that a positive report will result given that favourable conditions actually occur. There is 0.90 probability that a negative report will result given that unfavourable conditions actually occur. Revise the decision tree, and determine best decision using EMV. d. Determine the maximum fee the investor should pay the analyst 4) An investor must decide whether to purchase an apartment building at a cost of $800,000 or purchase land at a cost of $200,000. If the apartment is purchased two states of nature are possible: population growth ($2 M payoff) or no population growth ($225,000 payoff). The probability of growth is 0.6 and the probability of no growth is 0.4. If the investor decides to buy the land he or she can wait three years to see if there is population growth before making a decision about land use. Consequently, there is no payoff for the first three years, as the land is not developed. If the investor decides to buy the land and there is population growth in the three year period (P = 0.6) the investor will then need to decide whether to build apartments at a cost of $800,000 or sell the land for $450,000. If the population continues to grow (P = 0.8) the payoff on the apartment at the end of a ten-year period will be $3 M. If the population does not continue to grow (P = 0.2) the apartment payoff will be only $700,000. 2
3 If there is no population growth in the first three years (P = 0.4) then the investor will either decide to build commercially at a cost of $600,000 or sell the land for only $210,000. If the land is developed commercially and the population grows (P = 0.3) a payoff of $2.3 M is expected and if there is no population growth (P = 0.7) the expected payoff is only $1 M. What is the recommended decision and expected value of the decision? 5) Oscar Weng is planning to raise funds to pay for a scouting trip by running a concession stand during high school soccer game. Oscar needs to decide whether to rent a large insulate thermos from the local rent store for $20 and sell cocoa at the game, or to rent a large refrigerated container for $30 and sell lemonade. Unfortunately, Oscar does not have to the resources to rent both items. Sales depend on whether it is sunny or rainy during the game. The Table below Summarizes, the type of weather and expected profit from each product. Product Sunny Rainy Lemonade $60 $0 Cocoa $20 $80 Based on the local newspaper s prediction, Oscar thinks there is a 60% chance the weather being sunny. a. Draw a decision tree for Oscar s Problem. b. What is the recommended decision using expected value? Oscar s Friend, Susan is budding meteorologist who claims she can predict the weather more accurately than the newspaper. For only $4, she offers to study the weather and tell him is there is a "good chance" or a "bad chance" of it being sunny. The following data are available about the accuracy of Susana s information: The probability that she will say "good chance" is 0.7 If it is sunny, then there is a 0.83 probability that she will say it s a "good chance" If it is rainy, then there is a 0.75 probability that she will say it s a "bad chance" c. Assuming Oscar hires Susana, reconstruct the decision tree and recommend the decision strategy Oscar should follow. d. How much is Susana s information actually worth? 3
4 Answers/Solutions 1) a. Philippines (9.4) b. Mexico (-1.7) c. Mexico (2.57) d. Mexico (0.23) e. Economic Climate Country Decline Stable Improve South Korea Philippines Mexico f. Mexico (3.4) g. Mexico (EMV=1.62) h. Mexico (EOL=0.34) i ) a. Very Large ($ ) b. Small ($ ) c. Very Large ($55 000) d. Very Large ($20 800) e. Size of Station Good Market Fair Market Poor Market Small Medium Large Very Large f. Very Large ($ ) g. Medium (EMV=17 500) h. Medium (EOL=48 500) i. EVPI= ) a. Grinder (EMV=$25 300) 4
5 b. $8 400 c. Hire the analyst. Given positive report purchase a drill press. Given negative report purchase a grinder. d. $5784 4) Buy Land. If growth, Build Apartment. If no growth Build Commercially. EMV=$ ) a. b. Decision: Rent the Large insulated Thermos and Sell Cocoa Posterior Probabilities Table Good Chance Report Prior (P) Conditional (P) Joint (P) Posterior (P) Sunny Rainy
6 Bad Chance Report Prior (P) Conditional (P) Joint (P) Posterior (P) Sunny Rainy c. Decision: Hire Susan. Given a Good Chance report, Rent large Refrigerated Container and Sell Lemonade, EMV Hire Susan. Given a Bad Chance Report, Rent a Large insulated thermos and Sell Cocoa, EMV d. EV with Information-EV without information
Chapter 18 Student Lecture Notes 18-1
Chapter 18 Student Lecture Notes 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 18 Introduction to Decision Analysis 5 Prentice-Hall, Inc. Chap 18-1 Chapter Goals After completing
More informationDecision Analysis. Chapter Topics
Decision Analysis Chapter Topics Components of Decision Making Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility Decision Analysis
More informationModule 15 July 28, 2014
Module 15 July 28, 2014 General Approach to Decision Making Many Uses: Capacity Planning Product/Service Design Equipment Selection Location Planning Others Typically Used for Decisions Characterized by
More informationCauses of Poor Decisions
Lecture 7: Decision Analysis Decision process Decision tree analysis The Decision Process Specify objectives and the criteria for making a choice Develop alternatives Analyze and compare alternatives Select
More informationDecision Analysis. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall
Decision Analysis Chapter 12 12-1 Chapter Topics Components of Decision Making Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility
More informationDecision Making Models
Decision Making Models Prof. Yongwon Seo (seoyw@cau.ac.kr) College of Business Administration, CAU Decision Theory Decision theory problems are characterized by the following: A list of alternatives. A
More informationFull file at CHAPTER 3 Decision Analysis
CHAPTER 3 Decision Analysis TRUE/FALSE 3.1 Expected Monetary Value (EMV) is the average or expected monetary outcome of a decision if it can be repeated a large number of times. 3.2 Expected Monetary Value
More informationChapter 3. Decision Analysis. Learning Objectives
Chapter 3 Decision Analysis To accompany Quantitative Analysis for Management, Eleventh Edition, by Render, Stair, and Hanna Power Point slides created by Brian Peterson Learning Objectives After completing
More informationDecision Analysis. Chapter 12. Chapter Topics. Decision Analysis Components of Decision Making. Decision Analysis Overview
Chapter Topics Components of Decision Making with Additional Information Chapter 12 Utility 12-1 12-2 Overview Components of Decision Making A state of nature is an actual event that may occur in the future.
More informationNext Year s Demand -Alternatives- Low High Do nothing Expand Subcontract 40 70
Lesson 04 Decision Making Solutions Solved Problem #1: see text book Solved Problem #2: see textbook Solved Problem #3: see textbook Solved Problem #6: (costs) see textbook #1: A small building contractor
More informationDr. Abdallah Abdallah Fall Term 2014
Quantitative Analysis Dr. Abdallah Abdallah Fall Term 2014 1 Decision analysis Fundamentals of decision theory models Ch. 3 2 Decision theory Decision theory is an analytic and systemic way to tackle problems
More informationThe Course So Far. Atomic agent: uninformed, informed, local Specific KR languages
The Course So Far Traditional AI: Deterministic single agent domains Atomic agent: uninformed, informed, local Specific KR languages Constraint Satisfaction Logic and Satisfiability STRIPS for Classical
More informationThe Course So Far. Decision Making in Deterministic Domains. Decision Making in Uncertain Domains. Next: Decision Making in Uncertain Domains
The Course So Far Decision Making in Deterministic Domains search planning Decision Making in Uncertain Domains Uncertainty: adversarial Minimax Next: Decision Making in Uncertain Domains Uncertainty:
More informationIntroduction LEARNING OBJECTIVES. The Six Steps in Decision Making. Thompson Lumber Company. Thompson Lumber Company
Valua%on and pricing (November 5, 2013) Lecture 4 Decision making (part 1) Olivier J. de Jong, LL.M., MM., MBA, CFD, CFFA, AA www.olivierdejong.com LEARNING OBJECTIVES 1. List the steps of the decision-making
More informationDecision Making. D.K.Sharma
Decision Making D.K.Sharma 1 Decision making Learning Objectives: To make the students understand the concepts of Decision making Decision making environment; Decision making under certainty; Decision
More informationDecision Making. BUS 735: Business Decision Making and Research. Learn how to conduct regression analysis with a dummy independent variable.
Making BUS 735: Business Making and Research 1 Goals of this section Specific goals: Learn how to conduct regression analysis with a dummy independent variable. Learning objectives: LO5: Be able to use
More information1.The 6 steps of the decision process are:
1.The 6 steps of the decision process are: a. Clearly define the problem Discussion and the factors that Questions influence it. b. Develop specific and measurable objectives. c. Develop a model. d. Evaluate
More informationDecision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques
1 Decision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques Thompson Lumber is looking at marketing a new product storage sheds. Mr. Thompson has identified three decision options (alternatives)
More informationChapter 13 Decision Analysis
Problem Formulation Chapter 13 Decision Analysis Decision Making without Probabilities Decision Making with Probabilities Risk Analysis and Sensitivity Analysis Decision Analysis with Sample Information
More informationDecision Making. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned. 2 Decision Making Without Probabilities
Making BUS 735: Business Making and Research 1 1.1 Goals and Agenda Goals and Agenda Learning Objective Learn how to make decisions with uncertainty, without using probabilities. Practice what we learn.
More informationDecision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to:
CHAPTER 3 Decision Analysis LEARNING OBJECTIVES After completing this chapter, students will be able to: 1. List the steps of the decision-making process. 2. Describe the types of decision-making environments.
More informationDecision Making. DKSharma
Decision Making DKSharma Decision making Learning Objectives: To make the students understand the concepts of Decision making Decision making environment; Decision making under certainty; Decision making
More informationA B C D E F 1 PAYOFF TABLE 2. States of Nature
Chapter Decision Analysis Problem Formulation Decision Making without Probabilities Decision Making with Probabilities Risk Analysis and Sensitivity Analysis Decision Analysis with Sample Information Computing
More informationDecision Analysis REVISED TEACHING SUGGESTIONS ALTERNATIVE EXAMPLES
M03_REND6289_0_IM_C03.QXD 5/7/08 3:48 PM Page 7 3 C H A P T E R Decision Analysis TEACHING SUGGESTIONS Teaching Suggestion 3.: Using the Steps of the Decision-Making Process. The six steps used in decision
More informationTextbook: pp Chapter 3: Decision Analysis
1 Textbook: pp. 81-128 Chapter 3: Decision Analysis 2 Learning Objectives After completing this chapter, students will be able to: List the steps of the decision-making process. Describe the types of decision-making
More informationIX. Decision Theory. A. Basic Definitions
IX. Decision Theory Techniques used to find optimal solutions in situations where a decision maker is faced with several alternatives (Actions) and an uncertain or risk-filled future (Events or States
More informationChapter 12. Decision Analysis
Page 1 of 80 Chapter 12. Decision Analysis [Page 514] [Page 515] In the previous chapters dealing with linear programming, models were formulated and solved in order to aid the manager in making a decision.
More informationChapter 4: Decision Analysis Suggested Solutions
Chapter 4: Decision Analysis Suggested Solutions Fall 2010 Que 1a. 250 25 75 b. Decision Maximum Minimum Profit Profit 250 25 75 Optimistic approach: select Conservative approach: select Regret or opportunity
More information19 Decision Making. Expected Monetary Value Expected Opportunity Loss Return-to-Risk Ratio Decision Making with Sample Information
19 Decision Making USING STATISTICS @ The Reliable Fund 19.1 Payoff Tables and Decision Trees 19.2 Criteria for Decision Making Maximax Payoff Maximin Payoff Expected Monetary Value Expected Opportunity
More informationSCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research
SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT BF360 Operations Research Unit 5 Moses Mwale e-mail: moses.mwale@ictar.ac.zm BF360 Operations Research Contents Unit 5: Decision Analysis 3 5.1 Components
More informationUNIT 5 DECISION MAKING
UNIT 5 DECISION MAKING This unit: UNDER UNCERTAINTY Discusses the techniques to deal with uncertainties 1 INTRODUCTION Few decisions in construction industry are made with certainty. Need to look at: The
More informationObjective of Decision Analysis. Determine an optimal decision under uncertain future events
Decision Analysis Objective of Decision Analysis Determine an optimal decision under uncertain future events Formulation of Decision Problem Clear statement of the problem Identify: The decision alternatives
More informationDecision making under uncertainty
Decision making under uncertainty 1 Outline 1. Components of decision making 2. Criteria for decision making 3. Utility theory 4. Decision trees 5. Posterior probabilities using Bayes rule 6. The Monty
More informationAgenda. Lecture 2. Decision Analysis. Key Characteristics. Terminology. Structuring Decision Problems
Agenda Lecture 2 Theory >Introduction to Making > Making Without Probabilities > Making With Probabilities >Expected Value of Perfect Information >Next Class 1 2 Analysis >Techniques used to make decisions
More informationINTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations
Hun Myoung Park (5/2/2018) Decision Analysis: 1 INTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations DCC5350/ADC5005 (2 Credits) Public
More informationDecision Analysis. Introduction. Job Counseling
Decision Analysis Max, min, minimax, maximin, maximax, minimin All good cat names! 1 Introduction Models provide insight and understanding We make decisions Decision making is difficult because: future
More informationEconomic order quantity = 90000= 300. The number of orders per year
Inventory Model 1. Alpha industry needs 5400 units per year of a bought out component which will be used in its main product. The ordering cost is Rs. 250 per order and the carrying cost per unit per year
More informationstake and attain maximum profitability. Therefore, it s judicious to employ the best practices in
1 2 Success or failure of any undertaking mainly lies with the decisions made in every step of the undertaking. When it comes to business the main goal would be to maximize shareholders stake and attain
More informationDecision-making under conditions of risk and uncertainty
Decision-making under conditions of risk and uncertainty Solutions to Chapter 12 questions (a) Profit and Loss Statement for Period Ending 31 May 2000 Revenue (14 400 000 journeys): 0 3 miles (7 200 000
More informationMaster of Business Administration - General. Cohort: MBAG/14/PT Mar. Examinations for Semester II / 2014 Semester I
Master of Business Administration - General Cohort: MBAG/14/PT Mar Examinations for 2013 2014 Semester II / 2014 Semester I MODULE: OPERATIONS RESEARCH MODULE CODE: MGMT5214 DURATION: 3 HOURS Instructions
More informationDecision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne
Decision Analysis under Uncertainty Christopher Grigoriou Executive MBA/HEC Lausanne 2007-2008 2008 Introduction Examples of decision making under uncertainty in the business world; => Trade-off between
More informationAn Introduction to Decision Theory
20 An Introduction to Decision Theory BLACKBEARD S PHANTOM FIRE- WORKS is considering introducing two new bottle rockets. The company can add both to the current line, neither, or just one of the two.
More informationBERNARD WILLIAM TAYLOR III - DECISION MAKING MODELS CHAPTER 12
BERNARD WILLIAM TAYLOR III - DECISION MAKING MODELS CHAPTER 12 l. A farmer in Iowa is considering either leasing some extra land or investing in savings certificates at the local bank. If weather conditions
More informationM G T 2251 Management Science. Exam 3
M G T 2251 Management Science Exam 3 Professor Chang November 8, 2012 Your Name (Print): ID#: Read each question carefully before you answer. Work at a steady pace, and you should have ample time to finish.
More informationDecision Analysis Models
Decision Analysis Models 1 Outline Decision Analysis Models Decision Making Under Ignorance and Risk Expected Value of Perfect Information Decision Trees Incorporating New Information Expected Value of
More informationSubject : Computer Science. Paper: Machine Learning. Module: Decision Theory and Bayesian Decision Theory. Module No: CS/ML/10.
e-pg Pathshala Subject : Computer Science Paper: Machine Learning Module: Decision Theory and Bayesian Decision Theory Module No: CS/ML/0 Quadrant I e-text Welcome to the e-pg Pathshala Lecture Series
More informationLearning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h
Learning Objectives After reading Chapter 15 and working the problems for Chapter 15 in the textbook and in this Workbook, you should be able to: Distinguish between decision making under uncertainty and
More informationDecision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g.
Decision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g. casinos, weather forecasting) Subjective Probability Often, the decision maker
More informationTECHNIQUES FOR DECISION MAKING IN RISKY CONDITIONS
RISK AND UNCERTAINTY THREE ALTERNATIVE STATES OF INFORMATION CERTAINTY - where the decision maker is perfectly informed in advance about the outcome of their decisions. For each decision there is only
More informationDECISION MAKING. Decision making under conditions of uncertainty
DECISION MAKING Decision making under conditions of uncertainty Set of States of nature: S 1,..., S j,..., S n Set of decision alternatives: d 1,...,d i,...,d m The outcome of the decision C ij depends
More informationStatistics for Managers Using Microsoft Excel Chapter 5 Decision Making
Statistics for Managers Using Microsoft Excel Chapter 5 Decision Making 1999 Prentice-Hall, Inc. Chap. 5-1 Chapter Topics The Payoff Table and Decision Trees Opportunity Loss Criteria for Decision Making
More informationDECISION ANALYSIS. Decision often must be made in uncertain environments. Examples:
DECISION ANALYSIS Introduction Decision often must be made in uncertain environments. Examples: Manufacturer introducing a new product in the marketplace. Government contractor bidding on a new contract.
More informationA Taxonomy of Decision Models
Decision Trees and Influence Diagrams Prof. Carlos Bana e Costa Lecture topics: Decision trees and influence diagrams Value of information and control A case study: Drilling for oil References: Clemen,
More information36106 Managerial Decision Modeling Decision Analysis in Excel
36106 Managerial Decision Modeling Decision Analysis in Excel Kipp Martin University of Chicago Booth School of Business October 19, 2017 Reading and Excel Files Reading: Powell and Baker: Sections 13.1,
More informationAt the operational level, hundreds of decisions are made in order to achieve local outcomes
BMAppendixA.indd Page 592 14/03/14 9:46 PM user APPENDIXA Operational Decision-Making Tools: Decision Analysis LEARNING OBJECTIVES < Decision Analysis (With and Without Probabilities) At the operational
More informationDecision Making Supplement A
Decision Making Supplement A Break-Even Analysis Break-even analysis is used to compare processes by finding the volume at which two different processes have equal total costs. Break-even point is the
More informationUNIT 10 DECISION MAKING PROCESS
UIT 0 DECISIO MKIG PROCESS Structure 0. Introduction Objectives 0. Decision Making Under Risk Expected Monetary Value (EMV) Criterion Expected Opportunity Loss (EOL) Criterion Expected Profit with Perfect
More informationChapter 2 supplement. Decision Analysis
Chapter 2 supplement At the operational level hundreds of decisions are made in order to achieve local outcomes that contribute to the achievement of the company's overall strategic goal. These local outcomes
More informationExaminations for Semester II. / 2011 Semester I
PROGRAMME MBA-Human Resources & knowledge Management MBA- Project Management Master of Business Administration General MBA-Marketing Management COHORT MBAHR/11/PT MBAPM/11/PT MBAG/11/PT MBAMM/11/PT Examinations
More informationDECISION ANALYSIS: INTRODUCTION. Métodos Cuantitativos M. En C. Eduardo Bustos Farias 1
DECISION ANALYSIS: INTRODUCTION Cuantitativos M. En C. Eduardo Bustos Farias 1 Agenda Decision analysis in general Structuring decision problems Decision making under uncertainty - without probability
More informationSCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME END SEMESTER EXAMINATION JULY 2016
All Rights Reserved No. of Pages - 17 No of Questions - 07 SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME END SEMESTER EXAMINATION JULY 2016 AFM 31130 Strategic
More informationMBF1413 Quantitative Methods
MBF1413 Quantitative Methods Prepared by Dr Khairul Anuar 4: Decision Analysis Part 1 www.notes638.wordpress.com 1. Problem Formulation a. Influence Diagrams b. Payoffs c. Decision Trees Content 2. Decision
More informationDECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition)
DECISION ANALYSIS (Hillier & Lieberman Introduction to Operations Research, 8 th edition) Introduction Decision often must be made in uncertain environments Examples: Manufacturer introducing a new product
More informationJohan Oscar Ong, ST, MT
Decision Analysis Johan Oscar Ong, ST, MT Analytical Decision Making Can Help Managers to: Gain deeper insight into the nature of business relationships Find better ways to assess values in such relationships;
More informationDecision Trees and Influence Diagrams
29/10/15 Decision Trees and Influence Diagrams Carlos Bana e Costa and Mónica Oliveira REFERENCES: CLEMEN, R. (1996), MAKING HARD DECISIONS: AN INTRODUCTION TO DECISION ANALYSIS (2 ND EDITION). DUXBURY.
More informationEnergy and public Policies
Energy and public Policies Decision making under uncertainty Contents of class #1 Page 1 1. Decision Criteria a. Dominated decisions b. Maxmin Criterion c. Maximax Criterion d. Minimax Regret Criterion
More informationEngineering Risk Benefit Analysis
Engineering Risk Benefit Analysis 1.155, 2.943, 3.577, 6.938, 10.816, 13.621, 16.862, 22.82, ES.72, ES.721 A 1. The Multistage ecision Model George E. Apostolakis Massachusetts Institute of Technology
More informationSUGGESTED SOLUTIONS. December KB 2 Business Management Accounting. All Rights Reserved. KB2 - Suggested Solutions December 2016, Page 1 of 18
SUGGESTED SOLUTIONS KB 2 Business Management Accounting December 2016 December 2016, Page 1 of 18 All Rights Reserved SECTION 1 Answer 01 Relevant Learning Outcome/s: 1.1.1 Assess the key features of the
More informationMgtOp 470 Business Modeling with Spreadsheets Washington State University Sample Final Exam
MgtOp 470 Business Modeling with Spreadsheets Washington State University Sample Final Exam Section 1 Multiple Choice 1. An information desk at a rest stop receives requests for assistance (from one server).
More informationThe Islamic University of Gaza Faculty of Commerce Quantitative Analysis - Prof. Dr. Samir Safi Midterm #1-15/3/2015. Name
The Islamic University of Gaza Faculty of Commerce Quantitative Analysis - Prof. Dr. Samir Safi Midterm #1-15/3/2015 Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or
More informationMathematics 235 Robert Gross Homework 10 Answers 1. Joe Plutocrat has been approached by 4 hedge funds with 4 different plans to minimize his taxes.
Mathematic35 Robert Gross Homework 10 Answers 1. Joe Plutocrat has been approached by 4 hedge funds with 4 different plans to minimize his taxes. The unknown state of nature is a combination of what the
More informationP1: PBU/OVY P2: PBU/OVY QC: PBU/OVY T1: PBU GTBL GTBL032-Black-v13 January 22, :43
CHAPTER19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Learn about decision making under certainty,
More informationSensitivity = NPV / PV of key input
SECTION A 20 MARKS Question One 1.1 The answer is D 1.2 The answer is C Sensitivity measures the percentage change in a key input (for example initial outlay, direct material, direct labour, residual value)
More informationDecision Analysis CHAPTER 19
CHAPTER 19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Learn about decision making under certainty,
More informationDecision Analysis CHAPTER 19 LEARNING OBJECTIVES
CHAPTER 19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Make decisions under certainty by constructing
More informationChapter 17 Student Lecture Notes 17-1
Chapter 17 Student Lecture Notes 17-1 Basic Business Statistics (9 th Edition) Chapter 17 Decision Making 2004 Prentice-Hall, Inc. Chap 17-1 Chapter Topics The Payoff Table and Decision Trees Opportunity
More informationSample Final. DSc 3120 Departmental Final Examination
Instructor's Name Sample Final Your Name All Class Hour / DSc 3120 Departmental Final Examination Sample Questions You may take the full examination period for this final exam, but may also leave early
More informationDECISION ANALYSIS WITH SAMPLE INFORMATION
DECISION ANALYSIS WITH SAMPLE INFORMATION In the previous section, we saw how probability information about the states of nature affects the expected value calculations and therefore the decision recommendation.
More informationReview of Expected Operations
Economic Risk and Decision Analysis for Oil and Gas Industry CE81.98 School of Engineering and Technology Asian Institute of Technology January Semester Presented by Dr. Thitisak Boonpramote Department
More informationPerformance Pillar. P1 Performance Operations. Wednesday 31 August 2011
Performance Pillar P1 Performance Operations Instructions to candidates Wednesday 31 August 2011 You are allowed three hours to answer this question paper. You are allowed 20 minutes reading time before
More informationACCA Paper F5 Performance Management
ACCA Paper F5 Performance Management Mock Exam Question Paper Time allowed 3 hours 15 minutes This paper is divided into three sections Section A Section B Section C ALL FIFTEEN questions are compulsory
More informationDECISION THEORY AND THE NORMAL DISTRIBUTION M ODULE 3 LEARNING OBJECTIVE MODULE OUTLINE
M ODULE 3 DECISION THEORY AND THE NORMAL DISTRIBUTION LEARNING OBJECTIVE After completing this module, students will be able to: 1. Understand how the normal curve can be used in performing break-even
More informationUsing the Maximin Principle
Using the Maximin Principle Under the maximin principle, it is easy to see that Rose should choose a, making her worst-case payoff 0. Colin s similar rationality as a player induces him to play (under
More informationHandling Uncertainty. Ender Ozcan given by Peter Blanchfield
Handling Uncertainty Ender Ozcan given by Peter Blanchfield Objectives Be able to construct a payoff table to represent a decision problem. Be able to apply the maximin and maximax criteria to the table.
More information56:171 Operations Research Homework #8 Solution -- Fall Estimated resale price A: Private $ $600 B: Dealer $
56:171 Operations Research Homework #8 Solution -- Fall 2002 1. Decision Analysis (an exercise from Operations Research: a Practical Introduction, by M. Carter & C. Price) Suppose that you are in the position
More informationEVPI = EMV(Info) - EMV(A) = = This decision tree model is saved in the Excel file Problem 12.2.xls.
1...1 EMV() = 7...6.1 1 EMV() = 6. 6 Perfect Information EMV(Info) = 8. =.1 = 1. =.6 =.1 EVPI = EMV(Info) - EMV() = 8. - 7. = 1.. This decision tree model is saved in the Excel file Problem 1..xls. 1.3.
More informationEXPECTED MONETARY VALUES ELEMENTS OF A DECISION ANALYSIS QMBU301 FALL 2012 DECISION MAKING UNDER UNCERTAINTY
QMBU301 FALL 2012 DECISION MAKING UNDER UNCERTAINTY ELEMENTS OF A DECISION ANALYSIS Although there is a wide variety of contexts in decision making, all decision making problems have three elements: the
More informationElements of Decision Theory
Chapter 1 Elements of Decision Theory Key words: Decisions, pay-off, regret, decision under uncertainty, decision under risk, expected value of perfect information, expected value of sample information,
More informationProject Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7)
Project Risk Evaluation and Management Exercises (Part II, Chapters 4, 5, 6 and 7) Chapter II.4 Exercise 1 Explain in your own words the role that data can play in the development of models of uncertainty
More informationMGS 3100 Business Analysis. Chapter 8 Decision Analysis II. Construct tdecision i Tree. Example: Newsboy. Decision Tree
MGS 3100 Business Analysis Chapter 8 Decision Analysis II Decision Tree An Alternative e (Graphical) Way to Represent and Solve Decision Problems Under Risk Particularly l Useful lfor Sequential Decisions
More informationTEST TWO QUANTITATIVE METHODS BSTA 450 March 17, 2003
TEST TWO QUANTITATIVE METHODS BSTA 450 March 17, 2003 Name:(Print neatly) Student Number: -Put all your solutions on the paper provided. -make sure you READ the question and provide the solution it asks
More informationLearning Objectives 6/2/18. Some keys from yesterday
Valuation and pricing (November 5, 2013) Lecture 12 Decisions Risk & Uncertainty Olivier J. de Jong, LL.M., MM., MBA, CFD, CFFA, AA www.centime.biz Some keys from yesterday Learning Objectives v Explain
More informationPerformance Pillar. P1 Performance Operations. 24 November 2010 Wednesday Morning Session
Performance Pillar P1 Performance Operations 24 November 2010 Wednesday Morning Session Instructions to candidates You are allowed three hours to answer this question paper. You are allowed 20 minutes
More informationProject Risk Analysis and Management Exercises (Part II, Chapters 6, 7)
Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7) Chapter II.6 Exercise 1 For the decision tree in Figure 1, assume Chance Events E and F are independent. a) Draw the appropriate
More informationhp calculators HP 12C Platinum Statistics - Weighted mean Weighted mean HP12C Platinum weighted mean Practice finding average price sales
HP 12C Platinum Statistics - Weighted mean Weighted mean HP12C Platinum weighted mean Practice finding average price sales Practice finding averages and standard deviations with two variables Weighted
More informationBSc (Hons) Software Engineering BSc (Hons) Computer Science with Network Security
BSc (Hons) Software Engineering BSc (Hons) Computer Science with Network Security Cohorts BCNS/ 06 / Full Time & BSE/ 06 / Full Time Resit Examinations for 2008-2009 / Semester 1 Examinations for 2008-2009
More informationESD.71 Engineering Systems Analysis for Design
ESD.71 Engineering Systems Analysis for Design Assignment 4 Solution November 18, 2003 15.1 Money Bags Call Bag A the bag with $640 and Bag B the one with $280. Also, denote the probabilities: P (A) =
More informationThe May 2012 examination produced the highest pass rate so far achieved on the P1, Performance Operations paper within the Russian Diploma at 78%.
General Comments The May 2012 examination produced the highest pass rate so far achieved on the P1, Performance Operations paper within the Russian Diploma at 78%. The objective questions within Section
More informationPERT 12 Quantitative Tools (1)
PERT 12 Quantitative Tools (1) Proses keputusan dalam operasi Fundamental Decisin Making, Tabel keputusan. Konsep Linear Programming Problem Formulasi Linear Programming Problem Penyelesaian Metode Grafis
More informationResource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Decision Analysis
Resource Allocation and Decision Analysis (ECON 800) Spring 04 Foundations of Decision Analysis Reading: Decision Analysis (ECON 800 Coursepak, Page 5) Definitions and Concepts: Decision Analysis a logical
More information