tree above‚ I must fold back the decision tree in order to calculate the expected values and to find the optimal decision. Hence‚ the decision that I will fold back is at node No. 1 which is John accepts the offer of $750 000 and at node No. 4 which is Allied rejects John’s offer of $600 000 because both of them seem unrealistic and gives disadvantage to the both parties. So‚ now I can be able to calculate the expected values as below: EV (Node 3) = (1500 000 X 0.3) + (750 000 X 0.5) + (0 X 0.2)
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in half an hour? 7. You have are offered a contract on a piece of land which is worth 1000000 70% of the time‚ 500000 20% percent of the time and 150000 10% of the time. The contract says you can pay x dollars for someone to determine the land’s value from where you can decide whether or not to pay 300000 for the land. What is x? I.e. how much is this contract worth? Variation: A piece of land has a 30% chance of being located over an oil reserve‚ in which case it is going to be worth 100M. If
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in simple terms‚ to make an informed decision‚ given his/her mental or emotional state and overall ability to understand what is being provided. There are four main areas that should be discussed: (1) the nature of the treatment; (2) the risks and expected benefits associated with the treatment‚ including the likelihood of success; (3) any alternatives to treatment‚ including the alternative of no treatment‚ and their risks and benefits; and (4) any other information that may be required by the standard
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Final Court Decision (0.5) (0.3) Accept $3 Billion Decision Tree and EMV Expected Value (EV) Expected Monetary Value (EMV) Folding back the tree (Averaging-out and folding-back process) Start at the end-points of the branches on the far right-hand side and move to the left Calculate expected value when you encounter a chance node Choose the branch with the highest value or expected value when you encounter a decision node Case: Texaco vs. Penzoil Hue Liedtke’s
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1. 0.2941 Optimal lot-size == NORMINV(0.2941‚100‚40) = 78.34 Given that p = $200‚ s = $30‚ c = $150: Expected profits = (p – s) NORMDIST((O – )/‚ 0‚ 1‚ 1) – (p – s) NORMDIST((O – )/‚ 0‚ 1‚ 0) – O (c – s) NORMDIST(O‚ ‚ ‚ 1) + O (p – c) [1 – NORMDIST(O‚ ‚ ‚ 1)] = $2‚657 Expected overstock = (O – )NORMDIST((O – )/‚ 0‚ 1‚ 1) + NORMDIST((O – )/‚ 0‚ 1‚ 0) = 7.41 Expected understock = ( – O)[1 – NORMDIST((O – )/‚ 0‚ 1‚ 1)] + NORMDIST((O – )/‚ 0‚ 1‚ 0) = 29.07 EXCEL worksheet
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1. Even though independent gasoline stations have been having a difficult time‚ Susan Solomon has been thinking about starting her own independent gas station. Susan’s problem is to decide how large her station should be. The annual returns will depend on both the size of the station and a number of marketing factors related to oil industry and demand for gasoline. After careful analysis‚ Susan developed the following table: Sizes of Gasoline Station Good Market ($) Fair Market ($) Poor Market
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can be considered as the full plastic bending moment Mp. Working: Bending Moment at the root of the cantilever is calculated using the relationship below: Moment = Leaver arm length at P × Applied Load (P) M = lP × P This was used to obtain values of moments of the beam at different lengths when a force is applied. Measurement No. 1 2 3 Load (N) 0 8.9 17.8 26.69 35.59 40.03 44.48 66.72 89 Width (b) 13.02 12.99 12.91 Leaver Arm Leaver
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Chapter 2—Introduction to Probability PROBLEM 1. A market study taken at a local sporting goods store showed that of 20 people questioned‚ 6 owned tents‚ 10 owned sleeping bags‚ 8 owned camping stoves‚ 4 owned both tents and camping stoves‚ and 4 owned both sleeping bags and camping stoves. Let: Event A = owns a tent Event B = owns a sleeping bag Event C = owns a camping stove and let the sample space be the 20 people questioned. a. Find P(A)‚ P(B)‚ P(C)‚ P(A C)‚ P(B C). b
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and write JavaScript® code that addresses the requirements. Copy your code directly into this appendix document and post your assignment. 1. Write JavaScript® code that anticipates and handles an error for an expected numeric field. This code is executed on keypress‚ and the entered value is saved for you in a variable called enteredChar. Include the try block of JavaScript® statements needed to check if the character is not a number or a nonalphanumeric character‚ or if you throw an error message
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Means 2. Expected Values 3. Population & Sample Variances 4. Population & Sample Covariances 5. Population & Sample Correlation Coefficients 6. Estimators Statistic – a numerical characteristic of a sample Statistical inference – drawing conclusion about a population based on information contained in a sample Random sample – every sample of the same size in the population has the same chance of being selected 1 2 POPULATION MEAN AND SAMPLE MEAN Set of all possible values of a random
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