Experimental Errors and Uncertainty No physical quantity can be measured with perfect certainty; there are always errors in any measurement. This means that if we measure some quantity and‚ then‚ repeat the measurement‚ we will almost certainly measure a different value the second time. How‚ then‚ can we know the “true” value of a physical quantity? The short answer is that we can’t. However‚ as we take greater care in our measurements and apply ever more refined experimental methods‚ we can reduce
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Number: (443)956-6067 E-mail Address: surban1@jhu.edu Teaching Assistant Ben Brock: bbrock1@jhu.edu Office Hours Saturdays‚ 10:00am – 12:30pm at DC Center (room 201) or by appointment Required Text and Learning Materials: Stochastic Simulation and Applications in Finance with MATLAB Programs‚ (2008)‚ Huu Tue Huynh‚ Van Son Lai‚ Issouf Soumare (HLS) [this book may be available as an e-book for students] MATLAB: An Introduction with Applications 4th Edition (2010) or earlier Editions
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PSCAD simulation model is shown below figure‚ Definition of Transmission line AB Definition of Transmission line BC Definition of transmission line BD Comparison of voltages in each substation with the results got from Bewley Lattice Diagram and simulation results are shown in below‚ Substation A Substation B Substation C Substation D When we compare simulation results with the graphs we got using Bewley lattice
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Table of Contents 1.0 Introduction 3 2.0 Current Simulation Model 3 2.1 Clock Options 3 2.2 The warm-up period 3 2.3 Results collection period 4 2.4 The number of trials used 4 2.5 Results analysis 4 3.0 Pooling Resources 5 3.1 The impact of pooling resources 5 3.2 Comparison between initial model and pooled model 6 4.0 Usefulness of Simulation Model in Business Context 6 4.1 Simulation and decision making 6 4.2 Researcher Recommendation
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ERRORS IN MEASUREMENT Errors in Measurement Structure 2.1 Introduction Objectives 2.2 Classification of Errors 2.2.1 Gross Errors 2.2.2 Systematic Errors 2.2.3 Random Errors 2.3 Accuracy and Precision 2.4 Calibration of the Instrument 2.5 Analysis of the Errors 2.5.1 Error Analysis on Common Sense Basis 2.5.2 Statistical Analysis of Experimental Data 2.6 Summary 2.7 Key Words 2.8 Answers to SAQs 2.1 INTRODUCTION The
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Kenji L Logie Modeling Simulation Final Project Simulating a Worst Case Social Security Model System of Interest For the purpose of this simulation a simplified worst case social security model was created for a developing country’s social security program. The program simulates how long it would take this new social security program to go bankrupt‚ if it earns no interest on its capital‚ and its only source of paying out benefits is members’ monthly contributions and its initial capital of
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EM 408 Advanced Human Relations in the Organization and the Filipino Personality Reporter: Dolores R. Dela Cruz Ph.D. EM Student Topic: Human Behavior and Training Experiential Method‚ Instructional Method‚ Simulation Method I. Introduction Methods are the means or ways that we use to teach material to our students. Our choice of methods depends on what we want to teach (content)‚ who we are teaching‚ and the level of competence expected. A training method is
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Notes I reviewed the following products that developed Monte Carlo analysis package: Monte Carlo Simulation within Microsoft Excel Data Analysis and Business Palisade ’s @RiskModeling Oracle ’s Crystal Ball‚ RiskDecision ’s Predict! Risk Controller I really found two of the four solutions excellent. 1. Monte Carlo Simulation within Mocrosoft Excel I really was amazed by by Monte Carlo Simulation that is available within the Excel Software. The cost of the book Microsoft Excel Data Analysis
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Contents Product Annual Demand Daily Demand1 Weekly demand2 Level 0 Level 1 Level 2 Hub 1 2‚100 8.4 42 Sleeve (1) 2‚100 8.4 42 Mount (4) 8‚400 33.6 168 Bracket (2) 16‚800 67.2 336 Bolt (2) 16‚800 67.2 336 Hub 2 1700 6.8 34 Sleeve (1) 1‚700 6.8 34 Mount (5) 8‚500 34 170 Bracket (2) 17‚000 68 340 Bolt (2) 17‚000 68 340 Hub 3 2‚000 8 40 Sleeve (1) 2‚000 8 40 Mount (4) 8‚000 32 160
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Lecture 01 Introduction to Modelling and Simulation Peer-Olaf Siebers pos@cs.nott.ac.uk Container Terminal of Novorossiysk G54SIM 2 Module Mission Statement • This module will explain the main systems simulation methods in detail so that students will be competent in choosing and implementing the right method for their particular problem. • Students will learn the general principles and techniques used in modelling and simulation and will gain some practical experience
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