...Applying ANOVA and Nonparametric Tests Simulation This week’s assignment was to take a simulation called Applying ANOVA and Nonparametric Tests. After carefully reviewing the simulation it became easier to answer the questions for the assignment. Researchers sometimes have difficult decisions to make. Applying the analysis of variance (ANOVA) helps businesses to recognize the challenges and opportunities of making a business decision. ANOVA testing is a statistical tool that test each population calculated with a normal distribution (University of Phoenix, 2011). The benefit of this test is it can narrow down the errors of an incorrect test method as long as there is statistical proof (University of Phoenix, 2011). On the other hand, other tests are required because sometimes there are inaccurate assumptions that come with the testing process and businesses than acquire the nonparametric test known as the Kruskal-Wallis test for further analysis (University of Phoenix, 2011). The three lessons learned related to the ANOVA and Nonparametric tests include how businesses can learn how to better monitor, measure and improve their business processes (University of Phoenix, 2011). A successful business is faced with many challenges daily. The goal is to provide quality products and excellent services to their customer’s, employees and shareholders. After reviewing the simulation, some concepts and analytic tools came to mine, which this would...
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...Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation RES 342 William Modey Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation ANOVA and Non Parametric tests can help in business endeavors wherever there is two or more variables or hypothesis. The ANOVA and Non Parametric Tests Simulation showed the various ways to do hypothesis testing with two or more hypothesis. Being able to do the various types of testing that come along with ANOVA and Non Parametric data sets is key to making the right decision when having two or more choices. The three lessons that I have learned after doing the ANOVA and Non Parametric Tests Simulation were to thoroughly analyze the presented problem before attempting to make a decision, enlist the help of others when making a decision or choosing a course of action, and to continually improve on decision making skills based on learning from past mistakes made. As a result of using this simulation the concepts and analytic tools that I would be able to use in my workplace are that I am now able to approach a decision making scenario with appropriate knowledge and testing procedures to help make the best decision. The skills that I learned in the simulation, such as the different hypothesis testing procedures, could be key to helping me improve my managerial skills. Based on my passed experiences and current knowledge, I would recommend that the key decision maker take his or her time when making...
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...Applying ANOVA and Nonparametric Tests Berdie Thompson RES/342 October 17th, 2011 Olivia Scott Applying ANOVA and Nonparametric Tests In the simulation regarding applying ANOVA and nonparametric tests, the problem being addressed is the farmer, Samuel, and his corn crop not yielding a good crop to harvest. Samuel needed to run tests to determine the reason why his neighbor’s crop grew and his did not. There are different factors that can contribute to Samuel not yielding a good corn crop such as: variety of corn, sunlight, moisture, soil type, and so on. Samuel needed to determine which test was appropriate to perform that would relay accurate results. In performing the ANOVA test, there are three important lessons that one must know. ANOVA always assumes that each population being studied has a normal distribution. The second lesson is that errors are random and independent of each other. The third lesson is that all populations have the same variance. In reviewing this simulation, it was interesting to see how these tools and concepts are intertwined with the everyday business world aspect. This simulation uses real-life situations and applies a statistical method to solving the problem. Personally, I understand things better when a real-life issue is incorporated into the problem. A tool that I learned about and plan on utilizing is called the Kruskal-Wallis test. “The Kruskal-Wallis test is used when it is difficult to meet all of the assumptions of ANOVA” (University...
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...Applying ANOVA and Nonparametric Tests Simulation In this week’s simulation, I chose the Kruskal-Wallis test. The three lessons learned relative to ANOVA and Nonparametric Tests were the errors are random and independent of each other, each population has a normal distribution, and all the populations have the same variance. From the lessons learned through the ANOVA and Nonparametric Tests Simulation, I will be able to apply the concepts and analytical tools learned at my workplace by applying ANOVA and various nonparametric tests to analyze the results of data for more efficient and effective operations within my organization. Some of the suggestions that I made in the simulation for month one were to conduct a Kruskal-Wallis Test, reject the null hypothesis, and provide training for increase competency, which will help increase productivity of the software engineers of Praxidike Systems. For month two, I selected the type of project and scope changes as factors for analysis. The correlation matrix showed that the factor “type of project” has a strong positive correlation with the productivity of software engineers. The factor “scope changes,” on the other hand, has a correlation coefficient of 0.6. It was difficult to make any definite conclusions from this number as it was more than 0.5, but less than 0.75. Thus, further investigation of the factor is appropriate. I also made suggestions to set competency levels for a project depending on the skill requirements...
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...ANOVA and Nonparametric Simulation For this assignment students are tasked with completing an online simulation and applying statistical research. To complete this paper we as students need to answer three basic questions. What are three lessons you learned relative to ANOVA and nonparametric tests? As a result of using this simulation, what concepts and analytic tools will you be able to use in your workplace (i.e., how do you expect to apply what you learned)? Based on your experience, what additional information would you recommend to the key decision maker in the simulation to solve the challenge given? For the first question relating to lessons learned; ANOVA is a test to compare the means of several populations. ANOVA doesn’t answer which population might have a larger or smaller mean: it only answers whether all the means are equal. This is done by evaluating the variances within the groups and between the groups being compared (Doane & Seward, 2007). ANOVA can be used to evaluate if different factors have a different effect on a variable. The factors are categorical variables with different levels. For instance, the context of the simulation is a software development company that wants to know if factors like project difficulty, experience, or requirement changes affect the productivity of the software engineers. Productivity is measured by the average number of lines of code written per day by a programmer. An ANOVA test can shed light as to whether productivity...
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...Applying ANOVA and Nonparametric Tests Simulation Many organizations use various tools to ensure quality assurance and management for their business. The challenge for them is to ensure that they provide the best quality of service to their clients in a time effective manner. As such, having a diversity of tool options in place helps the organization identify daily challenges and increase overall effectiveness practices in their decision making processes. Implicitly, identifying the problems is the first key component towards making a sound decision. Once the problems are identified organizations can use tests such as ANOVA, nonparametric test and Kruskal-Wallis test for operational research methods and total quality management. These methods will allow researchers to analyze significant data that will subsequently result in implementation of the found solutions. Accordingly, the Praxidike Systems Corporation has identified a problem with their turn-around time in delivering products to their clients. With that, using the ANOVA, nonparametric and Kruskal- Wallis test has taught me that utilizing these tools can assist in analyzing information in order to make the best possible decision for the organization. Furthermore, using these tools help make the process easier to control by breaking down the data into various groups in order to manage the information. When using these tools, it is easy to apply what was reviewed to everyday life especially...
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... | |Applying Analysis of Variance | |[Anova test simulation] | |Rochelle Kuebler | |[September 23, 2011] | Praxidike is a software company, which has concern defining why their assignments are not done on time. The set-up starts by handing out two nonparametric analysis methods that include ANOVA and Kruskal-Wallis. Nonparametric testing procedures need definite requests to make use of efficiently. The key norms of ANOVA testing consist of the following: the population consumes a standard distribution, mistakes are independent, and population consumes the same variance. The Kruskal-Wallis test, instead, does not involve the hypothesis of a common distribution and the facts need to be on an ordinal measure. This is characteristically a superior choice if the expectations of ANOVA will not be met. This setup runs three examples of how certain testing systems can be practical to real-world circumstances. The first part of the situation is to relate the Kruskal-Wallis test because the expectations of ANOVA may not be seen. After studying the facts, it was obvious...
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...Data Analysis in SPSS Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 February 21, 2004 If you wish to cite the contents of this document, the APA reference for them would be DeCoster, J. (2004). Data Analysis in SPSS. Retrieved from http://www.stat-help.com/notes.html Heather Claypool Department of Psychology Miami University of Ohio 136 Benton Hall Oxford, OH 45056 All rights to this document are reserved Table of Contents Introduction ...................................................................................................................................................................1 Interactive Mode versus Syntax Mode ..........................................................................................................................2 Descriptive Statistics .....................................................................................................................................................4 Transformations.............................................................................................................................................................5 Compute ....................................................................................................................................................................5 Recode ............................................................................................................................
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...Statistical Methods in Credit Risk Modeling by Aijun Zhang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) in The University of Michigan 2009 Doctoral Committee: Professor Vijayan N. Nair, Co-Chair Agus Sudjianto, Co-Chair, Bank of America Professor Tailen Hsing Associate Professor Jionghua Jin Associate Professor Ji Zhu c Aijun Zhang 2009 All Rights Reserved To my elementary school, high school and university teachers ii ACKNOWLEDGEMENTS First of all, I would express my gratitude to my advisor Prof. Vijay Nair for guiding me during the entire PhD research. I appreciate his inspiration, encouragement and protection through these valuable years at the University of Michigan. I am thankful to Julian Faraway for his encouragement during the first years of my PhD journey. I would also like to thank Ji Zhu, Judy Jin and Tailen Hsing for serving on my doctoral committee and helpful discussions on this thesis and other research works. I am grateful to Dr. Agus Sudjianto, my co-advisor from Bank of America, for giving me the opportunity to work with him during the summers of 2006 and 2007 and for offering me a full-time position. I appreciate his guidance, active support and his many illuminating ideas. I would also like to thank Tony Nobili, Mike Bonn, Ruilong He, Shelly Ennis, Xuejun Zhou, Arun Pinto, and others I first met in 2006 at the Bank. They all persuaded me to jump into the...
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...& Summary Computer system users, administrators, and designers usually have a goal of highest performance at lowest cost. Modeling and simulation of system design trade off is good preparation for design and engineering decisions in real world jobs. In this Web site we study computer systems modeling and simulation. We need a proper knowledge of both the techniques of simulation modeling and the simulated systems themselves. The scenario described above is but one situation where computer simulation can be effectively used. In addition to its use as a tool to better understand and optimize performance and/or reliability of systems, simulation is also extensively used to verify the correctness of designs. Most if not all digital integrated circuits manufactured today are first extensively simulated before they are manufactured to identify and correct design errors. Simulation early in the design cycle is important because the cost to repair mistakes increases dramatically the later in the product life cycle that the error is detected. Another important application of simulation is in developing "virtual environments" , e.g., for training. Analogous to the holodeck in the popular science-fiction television program Star Trek, simulations generate dynamic environments with which users can interact "as if they were really there." Such simulations are used extensively today to train military personnel for battlefield situations, at a fraction of the cost of running exercises involving...
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...The Six Sigma Handbook Revised and Expanded A Complete Guide for Green Belts, Black Belts, and Managers at All Levels THOMAS PYZDEK McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2003 by The McGraw-HIll Companies, Inc. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-141596-3 The material in this eBook also appears in the print version of this title: 0-07-141015-5. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at george_hoare@mcgraw-hill.com or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licensors reserve all...
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...MASTER OF TECHNOLOGY ADVANCED ELECTIVES SELECTION For Semester II 2014/2015 ATA/SE-DIP/TS-11/V1.34 Master of Technology in Software /Knowledge Engineering and Enterprise Business Analytics Table of Contents. MTECH ADVANCED ELECTIVES 1. INTRODUCTION. 1.1 Overview. 1.2 Courses. 1.3 Assessment. 1.4 Elective Selection Process. 2 2 2 2 3 3 2. SCHEDULE FOR ADVANCED ELECTIVES OFFERED DURING SEMESTER II 2014/2015. 2.1 MTech SE and KE Students. 2.2 MTech EBAC Students. 5 5 9 3. CURRICULUM. 12 4. DESCRIPTION OF COURSES. 4.1 Department of Electrical & Computer Engineering. 4.2 School of Computing. 4.3 Institute of Systems Science. 4.4 Department of Industrial & Systems Engineering. 4.5 Division of Engineering & Technology Management. 12 15 23 31 32 34 ATA/SE-DIP/TS-11/V1.34 page 1 of 35 Master of Technology in Software /Knowledge Engineering and Enterprise Business Analytics MASTER OF TECHNOLOGY Advanced Electives 1. INTRODUCTION 1.1 Overview All students that expect to have passed four core courses and eight basic electives after completing the scheduled examinations in November, and also have or expect to pass their project/internship, will be entitled to commence their Advanced Electives in NUS Semester II 2014/2015, which starts on 12 January 2015. However, it should be noted that a student’s registration for the Advanced Electives will be withdrawn if they either: 1. 2. 3. 4. 5. Fail any elective examination in November. Do not successfully...
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...Ethical and Professional Standards The candidate should be able to demonstrate a thorough knowledge of the CFA Institute Code of Ethics and Standards of Professional Conduct, familiarity with the Global Investment Performance Standards, and familiarity with corporate governance issues and risks affecting companies. Study Session 1 Ethical and Professional Standards Reading Assignments 1.* “Code of Ethics and Standards of Professional Conduct” Standards of Practice Handbook, 9th edition (CFA Institute, 2005) * 2. “Guidance” for Standards I – VII, Standards of Practice Handbook, 9th edition (CFA Institute, 2005) * 3. Introduction to the Global Investment Performance Standards (GIPS®) Global Investment Performance Standards (GIPS®), pp. i–iii and 1–9, (CFA Institute, 4.* 2005) A. Preface: Background of the GIPS Standards B. I. Introduction C. II.0. Provisions of the Global Investment Performance Standards – Fundamentals of Compliance * 5. The Corporate Governance of Listed Companies: A Manual for Investors (CFA Institute, 2005) Learning Outcomes 1. “Code of Ethics and Standards of Professional Conduct” The Code of Ethics establishes the framework for ethical decision making in the investment profession. The candidate should be able to state the six components of the Code of Ethics. The Standards of Professional Conduct are organized into seven standards: I. Professionalism II. Integrity of Capital Markets III. Duties to Clients and Prospective...
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...e eBook Collection RESEARCH METHODS FOR BUSINESS A Skill-Building Approach Fourth Edition Uma Sekaran Southern Illinois University at Carbondale John Wiley & Sons, Inc. http://www.wiley.com/college ACQUISITIONS EDITOR MARKETING MANAGER SENIOR PRODUCTION EDITOR SENIOR DESIGNER PRODUCTION MANAGEMENT SERVICES COVER IMAGE Jeff Marshall Ilse Wolfe Patricia McFadden Harry Nolan Hermitage Publishing Services José Ortega/Stock Illustration Source This book was set in 10/12 Garamond by Hermitage Publishing Services and printed and bound by Malloy Lithographing, Inc. The cover was printed by Von Hoffmann Press, Inc. This book is printed on acid-free paper. Copyright 2003 © John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008, E-Mail: PERMREQ@WILEY.COM. To order books please...
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...Mostly Harmless Econometrics: An Empiricist’ Companion s Joshua D. Angrist Massachusetts Institute of Technology Jörn-Ste¤en Pischke The London School of Economics March 2008 ii Contents Preface Acknowledgments Organization of this Book xi xiii xv I Introduction 1 3 9 10 12 16 1 Questions about Questions 2 The Experimental Ideal 2.1 2.2 2.3 The Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random Assignment Solves the Selection Problem . . . . . . . . . . . . . . . . . . . . . . . . Regression Analysis of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II The Core 19 21 22 23 26 30 36 38 38 44 47 51 51 3 Making Regression Make Sense 3.1 Regression Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 3.1.2 3.1.3 3.1.4 3.2 Economic Relationships and the Conditional Expectation Function . . . . . . . . . . . Linear Regression and the CEF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic OLS Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saturated Models, Main E¤ects, and Other Regression Talk . . . . . . . . . . . . . . . Regression and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 3.2.2 3.2.3 The Conditional Independence Assumption . . . . . . . . . . . . . . . . . . . . . . . . The Omitted Variables Bias Formula . ....
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