was high in 1990 with the range of 75.01 compare to 42.76 for 1998. Another high variability for 1990 was the standard deviation of 9.30 compare to 5.17 for 1998. (For Excel instructions see pages 28 and 61 of the textbook.) Question 2. (Statistical Inferences: Single Population) Feasibility Study: Companies that sell groceries over the Internet are called e-grocers. Customers enter their orders‚ pay by credit card and receive delivery by truck. To determine whether an e-grocery would be
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project is to apply simulation in optimizing the cycle time and logistics of various activities involved in construction of a breakwater. The main benefit of this simulation analysis is a detailed look at the techniques for improving breakwater construction The scope of this project is limited to breakwater construction sites. The project scope includes modeling‚ simulation and optimization of construction methods used in breakwater Methodology In this project suitable simulation techniques are used
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TECHNOLOGY ELECTRONICS AND TELECOMMUNICATION FINAL YEAR [2004-2005] A REPORT ON BLUETOOTH TECHNOLOGY PREPARED BY JANHAVI KHANOLKAR NAVEEN BITRA YASHESH MANKAD TABLE OF CONTENTS 1. INTRODUCTION 2. HISTORY 3. WHAT IS BLUETOOTH? 4. BLUETOOTH SPECIFICATIONS 5. BLUETOOTH NETWORKS 6. HOW DOES BLUETOOTH WORK? 7. BLUETOOTH PROTOCOL STACK 8. BLUETOOTH SECURITY 9. APPLICATIONS 10. MERITS AND DEMERITS INTRODUCTION:
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The simulation wasn’t as intuitive as I thought at first; it took three tries and 20 steps in order to get the 61% buy in order to complete the simulation. There was one steps taken that gave a 0 or neutral buy‚ basically a waste of money and time. I quickly evaluated that in order to start a change process and achieve the buy in and support‚ activities need to be done in a certain order. I think the simulation does take into account the perceived human emotions surrounding change‚ however‚ it only
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(among a group of 7 – 8 students) 2. Material Time is precious You do not have to include everything Tell a coherent story Present your results clearly What to say Structure your presentation •Title page •Introduction •Aims and objectives •Methodology •Results •Summary •Conclusions •Further Work •Questions What not to say First I did this‚ Then I did this‚ But that didn’t work. So I tried this‚ And that didn’t work either‚ So in desperation I tried this last week‚
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group dynamics were such that we did not effectively utilize the resources we had‚ and consequently pooled a very limited amount of information. Rather than minimizing our risk‚ we increased it. I attribute much of our group’s failure at this simulation to process loss‚ which is defined as the problems that arise from lack of effective coordination among group members. A number of factors at play could explain the process loss which led to our counterintuitive results. First and foremost‚ one must
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LINKS Simulation Reference Notes Background: About LINKS: * LINKS is a supply chain management simulation * It details all the steps that go into efficiently running a supply chain and how they are interrelated including: analysis‚ planning‚ implementation‚ and evaluation. * Your team can view the results from lasts month’s decision on the LINKS website under the Excel Monthly Results. You should look at trends to determine how your decisions affect your performance. * Although
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“What factors contributed to your team’s performance‚ cohesion‚ and learning?” Summary The Everest Simulation is designed with the key factors relating to teamwork in mind. Things like effective communication; specific information sharing‚ process orientation and conflict management play a major role in determining the success of a team. Our team consisted of Ellena Berg who was the Team Leader‚ Gary Leduc the team’s Physician‚ AiPing Lee the Environmentalist‚ Ushita Shah the professional Photographer
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Use of Ranks in One-Criterion Variance Analysis Author(s): William H. Kruskal and W. Allen Wallis Source: Journal of the American Statistical Association‚ Vol. 47‚ No. 260 (Dec.‚ 1952)‚ pp. 583-621 Published by: Taylor & Francis‚ Ltd. on behalf of the American Statistical Association Stable URL: http://www.jstor.org/stable/2280779 Accessed: 05-03-2015 13:33 UTC Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use‚ available at http://www.jstor.org/page/info/about/policies/terms
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Statistical Arbitrage in the U.S. Equities Market Marco Avellaneda∗† and Jeong-Hyun Lee∗ First draft: July 11‚ 2008 This version: June 15‚ 2009 Abstract We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases‚ we consider the residuals‚ or idiosyncratic components of stock returns‚ and model them as mean-reverting processes. This leads naturally to “contrarian” trading
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