ntroduction to Monte Carlo simulation This article was adapted from Microsoft Office Excel 2007 Data Analysis and Business Modeling by Wayne L. Winston. Visit Microsoft Learning to learn more about this book. This classroom-style book was developed from a series of presentations by Wayne Winston‚ a well known statistician and business professor who specializes in creative‚ practical applications of Excel. So be prepared — you may need to put your thinking cap on. In this article * Overview
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charging its client for execution of a project. Mr. Clark‚ Director‚ Central Region Appshop Inc had to make a decision on either accepting any one of the prices suggested by the client or participate in the bidding process. The case involves using Monte Carlo Simulation and Triangle Distribution to figure out the best possible option for Appshop Inc. Executive Summary Appshop Inc was a privately held‚ independent full-service Oracle consulting‚ applications and outsourcing company with revenues of
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hunting grounds over time. For the time dimension‚ the focus may be on periods or discrete occasions. In other cases‚ our ’population’ may be even less tangible. For example‚ Joseph Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo‚ and used this to identify a biased wheel. In this case‚ the ’population’ Jagger wanted to investigate was the overall behaviour of the wheel (i.e. the probability distribution of its results over infinitely many trials)‚ while his ’sample’ was
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experimental method uses what is known as “flicker paradigm”‚ this method uses a flicker simulation to find the reaction time of the participant’s change blindness which is affected by the simulation. (Hewlett‚ Oezbek. “How Stimulus Variables Combine to Affect Change Blindness”. 8 November 2012. 337-338. Print.) Computer simulations are also used today in the medical field for training purposes. In the article‚ “Comparing the Effectiveness of Clinical Simulation versus Didactic Methods to Teach Undergraduate
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Final Simulation.vi item to open the Final Simulation example VI. 4. Press the Run button to run the Final Simulation example VI. 5. Select Automatic from the Method drop-down menu and observe the changes on the Pictures‚ Power‚ and Turbine Parameters pages as the Wind Speed and Wind Direction change. You also can select Manual from the Method drop-down menu to modify the Wind Speed and Wind Direction manually. Requirements Filename: windturbinesim.zip Software Requirements Application Software:
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Chapter 9 Monte Carlo methods 183 184 CHAPTER 9. MONTE CARLO METHODS Monte Carlo means using random numbers in scientific computing. More precisely‚ it means using random numbers as a tool to compute something that is not random. For example1 ‚ let X be a random variable and write its expected value as A = E[X]. If we can generate X1 ‚ . . . ‚ Xn ‚ n independent random variables with the same distribution‚ then we can make the approximation A ≈ An = 1 n n Xk . k=1
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Two Rainfall-Runoff Models were earlier identified for application and for the generation of a more reliable discharge data. These are the Thornwaite Water Balance Model (WBM) and the IHACRES. The input requirement of both models differ. For the WBM‚ generation of runoff requires seven input parameters. These are the Runoff Factor (RF)‚ Direct Runoff factor (DRF)‚ Soil Moisture Storage Capacity (SMSC)‚ Latitude of location‚ Rain Temperature Threshold‚ Snow Temperature Threshold and Maximum Snow Melt
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Chapter 1: Types of Simulation Contents Introduction ................................................................................................................................ 2 The Basic Simulation Process................................................................................................... 2 Figure 1.01: Basic Simulation Process............................................................................ 2 Figure 1.02: Decision Cycle.........................................
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Definition of Value at Risk (VaR) Value at risk is a statistical technique which measures the level of financial risk in a portfolio over a specific time frame. For example‚ if a firm states that it has a 1% one week value at risk of $5 million; this would mean that for any given week‚ the firm would have a 1% chance of losing $5 million. In order words‚ 1 out of every 100 weeks‚ the firm would expect to have a loss of $5 million. This can be viewed as the standard deviation of portfolio value
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REAL OPTIONS: STATE OF THE PRACTICE by Alex Triantis‚ University of Maryland‚ and Adam Borison‚ Applied Decision Analysis/ PricewaterhouseCoopers1 n an economic environment characterized by rapid change‚ great uncertainty‚ and the need for flexibility‚ it has become increasingly important for corporate managers to use investment evaluation tools and processes that properly account for both uncertainty and the company’s ability to react to new information. Real options has emerged as an approach
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