...70504 ABSTRACT Information Systems (IS) practitioners and educators have equal interest in the content of the Systems Analysis and Design Course (“SAD”). Previous research has examined instructors’ perceptions regarding the skills and topics that are most important in the teaching of the SAD course and the class time devoted to each. A similar assessment evaluated SAD course content from a practitioner perspective. Both studies used entropy calculations. A comparison of these studies is presented in this paper. For traditional topics, the group (either faculty or practitioner) with greater agreement believes the topic to be deserving of less class time. For structured and object-oriented topics, the group with the greater agreement also believes the topic to be of greater importance. This analysis demonstrates that practitioners and academics agree on approximately 40% of the SAD skills and knowledge areas. Keywords: Systems analysis and design, Structured analysis, Object-oriented analysis, Management Information Systems curricula, Entropy INTRODUCTION It is important that an education in Management Information Systems (MIS) is reflective of practices and techniques that are currently used in industry. Given the pace of technological innovation, there are ever-changing demands of technology workers [19] [30]. The content of each MIS course should be regularly compared to the skills that are required by employers, as an alignment must exist to ensure adequate preparation of...
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...Heisenberg’s Uncertainty Principle and its Implications for Financial Institutions Submitted in Partial Fulfilment of Master of Business Administration By KARTIK CHANDRA CHATURVEDI Batch (2013-2015) University Roll No: S133F0010 December 2014 Under the guidance of NIDHI KAICKER SCHOOL OF BUSINESS, PUBLIC POLICY AND SOCIAL ENTREPRENEURSHIP AMBEDKAR UNIVERSITY DELH PAGE 1 CERTIFICATE This is to certify that I have successfully completed the project titled Heisenberg’s Uncertainty Principle and its Implications for Financial Institutions submitted in partial fulfilment of the requirements for the Degree of Master of Business Administration at the School of Business, Public Policy and Social Entrepreneurship, Ambedkar University Delhi. It is further certified that the submitted report is based on original research work carried out by me. The material obtained from secondary sources is duly acknowledged. [Student Signature] KARTIK ……………………………………………………………………………..CHANDRA ……………………………………………………………………………..CHATURVEDI Roll No S133F0010 Batch: 2013-15 [Dean Signature] Dean SBPPSE [Faculty Signature] Faculty Advisor PAGE 2 ACKNOWLEDGEMENT I have taken efforts in this project. However, it would not have been possible without the kind support and help of many individuals in the organization and School of Business, Public Policy and Social Entrepreneurship, Ambedkar University, Delhi. I would like to extend my sincere thanks to all of them. I am highly...
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...JBR Paper June 27, 1999 DIVERSIFICATION AND MARKET ENTRY CHOICES IN THE CONTEXT OF FOREIGN DIRECT INVESTMENT Ram Mudambi University of Reading and Case Western Reserve University Susan McDowell Mudambi John Carroll University Address for correspondence: Dr. Susan McDowell Mudambi Department of Management, Marketing and Logistics Boler School of Business John Carroll University University Heights OH 44118 Phone: FAX: Email: (216) 397-3094 (216) 397-1728 smudambi@jcu.edu DIVERSIFICATION AND MARKET ENTRY CHOICES IN THE CONTEXT OF FOREIGN DIRECT INVESTMENT Abstract Multinational enterprises consider many factors when making decisions in the context of foreign direct investment (FDI). In deciding what to produce, the multinational enterprise (MNE) must decide whether to diversify or to concentration on its main line of business. This paper offers insights into influences on this choice, and identifies a number of conditions under which diversification is more likely to be chosen. Factors affecting the foreign entry mode decision are also analyzed. The international business literature has generally treated these strategic choices as independent. This paper introduces a more realistic selection model, in which the diversification choice and the entry mode choice are made sequentially and are therefore related. The model is tested using a data set of FDI into the United Kingdom by MNEs in engineering and related industries. The analysis indicates a strong relationship...
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...------------------------------------------------- ICT * * * * Stands for "Information and Communication Technologies." ICT refers to technologies that provide access to information through telecommunications. It is similar to Information Technology (IT), but focuses primarily on communication technologies. This includes the Internet, wireless networks, cell phones, and other communication mediums. In the past few decades, information and communication technologies have provided society with a vast array of new communication capabilities. For example, people can communicate in real-timewith others in different countries using technologies such as instant messaging, voice over IP (VoIP), and video-conferencing. Social networking websites like Facebook allow users from all over the world to remain in contact and communicate on a regular basis. Modern information and communication technologies have created a "global village," in which people can communicate with others across the world as if they were living next door. For this reason, ICT is often studied in the context of how modern communication technologies affect society. Tech Factor: 1 2 3 4 5 6 7 Updated: January 4, 2010 Webopedia.com Sign Up | Sign In ------------------------------------------------- Top of Form SEARCH Bottom of Form * Term of the Day * Recent Terms * Did You Know? * Quick Reference * All Categories * Resources * WeboBlog ...
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...* Definition of overconfidence In business or trading, an overestimation of one's abilities and of the precision of one's forecasts. Overconfident people set overly narrow confidence intervals in making predictions. They tend to overweigh their own forecasts relative to those of others. The self-serving attribution bias, under which individuals attribute past successes to their own skills and past failures to bad luck, can lead to overconfidence. In the context of financial markets, the confidence of a self-attributing investor increases when public information is in line with his or her forecast, but it does not decrease as much when public information contradicts his or her forecast. The investor therefore gains excessive confidence over time, after receiving different confirming and disconfirming public news. A similar cognitive bias, illusory superiority (or the above average effect), also causes people to overestimate their own abilities. Evidence of this bias is documented in studies that ask subjects to assess their abilities - indeed, a vast majority of people say they are above the average. * Hindsight bias Hindsight bias, also known as the knew-it-all-along effect or creeping determinism, is the inclination, after an event has occurred, to see the event as having been predictable, despite there having been little or no objective basis for predicting it, prior to its occurrence. It is a multifaceted phenomenon that can affect different stages of designs, processes...
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...Gini coefficient From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Gini_coefficient Graphical representation of the Gini coefficient The Gini coefficient is a measure of inequality of a distribution. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. It was developed by the Italian statistician Corrado Gini and published in his 1912 paper "Variabilità e mutabilità" ("Variability and Mutability"). The Gini index is the Gini coefficient expressed as a percentage, and is equal to the Gini coefficient multiplied by 100. (The Gini coefficient is equal to half of the relative mean difference.) The Gini coefficient is often used to measure income inequality. Here, 0 corresponds to perfect income equality (i.e. everyone has the same income) and 1 corresponds to perfect income inequality (i.e. one person has all the income, while everyone else has zero income). The Gini coefficient can also be used to measure wealth inequality. This use requires that no one has a negative net wealth. It is also commonly used for the measurement of discriminatory power of rating systems in the credit risk management. Calculation The Gini coefficient is defined as a ratio of the areas on the Lorenz curve diagram. If the area between the line of perfect equality and Lorenz curve is A, and the area under...
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...the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data.” — Ron Bekkerman Chief Data Officer at Carmel Ventures “A great book for business managers who lead or interact with data scientists, who wish to better understand the principals and algorithms available without the technical details of single-disciplinary books.” — Ronny Kohavi Partner Architect at Microsoft Online Services Division “Provost and Fawcett have distilled their mastery of both the art and science of real-world data analysis into an unrivalled introduction to the field.” —Geoff Webb Editor-in-Chief of Data Mining and Knowledge Discovery Journal “I would love it if everyone I had to work with had read this book.” — Claudia Perlich Chief Scientist of M6D (Media6Degrees) and Advertising Research Foundation Innovation Award Grand Winner (2013) www.it-ebooks.info “A foundational piece in the fast developing world of Data Science. A must read for anyone interested in the Big Data revolution." —Justin Gapper Business Unit Analytics Manager at Teledyne Scientific and Imaging “The authors, both renowned experts in data science before it had a name, have taken a complex topic and made it accessible to all levels, but mostly helpful to the budding data scientist. As far as I know, this is the first book of its kind—with a focus on data science concepts as applied to practical business problems. It is liberally...
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...CONTENTS 1 2 3 4 5 6 Abbreviations/Definitions Code of Conduct and Ethics for Students Important Academic Rules Scheme of Studies Important Notes Detailed Syllabus 1 2 3 17 19 20 Lingaya’s University, Faridabad ABBREVIATIONS/DEFINITIONS "AC" means, Academic Council of the University. "BOM" means, the Board of Management of the University. "BOS" means, the Board of Studies of the Department. “CAU/AUC-option” CAU/AUC means change from Credit to Audit option / change from Audit to Credit option "Class/Course Committee" means, the Class/Course Committee of a class/course. "Course" means, a specific subject usually identified by its course-number and course-title, with a specified syllabus / course-description, a set of references, taught by some teacher(s) / course- instructor(s) to a specific class (group of students) during a specific academic-semester / semester. “Course Instructor" means, the teacher or the Course Instructor of a Course. "Curriculum" means the set of Course-Structure and Course-Contents. "DAA" means, the Dean of Academic Affairs. “DAAB” means Departmental Academic Appeals Board. “DEC/PEC” means Dissertation Evaluation Committee / Project Evaluation committee. “Department” means a group in the University devoted to a specific discipline also called a School. Department and School are used interchangeably. "DSA" means, Dean Student Affairs. “ESE” means End-Semester Examination “EYE” means End-Year Examination. "Faculty Advisor/Class Counsellor”...
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...Astrophysics: the study of the physics of the universe Bacteriology: the study of bacteria in relation to disease Biochemistry: the study of the organic chemistry of compounds and processes occurring in organisms Biophysics: the application of theories and methods of the physical sciences to questions of biology Biology: the science that studies living organisms Botany: the scientific study of plant life Chemical Engineering: the application of science, mathematics, and economics to the process of converting raw materials or chemicals into more useful or valuable forms Chemistry: the science of matter and its interactions with energy and itself Climatology: the study of climates and investigations of its phenomena and causes Computer Science: the systematic study of computing systems and computation Ecology: the study of how organisms interact with each other and their environment Electronics: science and technology of electronic phenomena Engineering: the practical application of science to commerce or industry Entomology: the study of insects Environmental Science: the science of the interactions between the physical, chemical, and biological components of the environment Forestry: the science of studying and managing forests and plantations, and related natural resources Genetics: the science of genes, heredity, and the variation of organisms Geology: the science of the Earth, its structure, and history ...
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...mining techniques P. Ravisankar a, V. Ravi a,⁎, G. Raghava Rao a, I. Bose b a b Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, AP, India School of Business, The University of Hong Kong, Pokfulam Road, Hong Kong a r t i c l e i n f o a b s t r a c t Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved. Article history: Received 20 November 2009 Received in revised form 14 June 2010 Accepted 3 November 2010 Available online 12 November 2010 Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP 1. Introduction Financial fraud is a serious problem worldwide and more so in fast growing countries like China. Traditionally, auditors are responsible for detecting financial statement fraud...
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... * DQ#1: What is the importance of statistics in business decision making? Describe a business situation where statistics was used in making a decision. 1. Using statistics to evaluate the performance of your business. Taking all factors into account, determine whether you are making or losing money. In addition, determine the trend of your business. For example, determine whether, over time, you are making more or less profit (or loss). Track the share of the market that your business holds, and how this changes over time. Evaluate all these factors for each product type, even each model, in your company's line. The statistics to use here are simple line and bar graphs of data. These data can alert you to where you need to change aspects of your business, and how quickly you have to make that change. Furthermore, statistics are an excellent way to determine how you should allocate your resources to increase sales. Sales in a given market are a result of a number of factors, such as pricing, the size of the sales force, and the type and number of advertisements placed. However, these factors will not be equally important for every product. You would use multiple regressions to determine which of these factors are most important, and how much changing your asset allocation (for example, the size of the sales force) will change sales. 2. Statistics: The science of collecting, organizing, and analyzing data. Descriptive Statistics - Finding The Skew 16,15,18,118 ...
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...Classical Techniques: Statistics, Neighborhoods and Clustering Next Generation Techniques: Trees, Networks and Rules Each section will describe a number of data mining algorithms at a high level, focusing on the "big picture" so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques. Overall, six broad classes of data mining algorithms are covered. Although there are a number of other algorithms and many variations of the techniques described, one of the algorithms from this group of six is almost always used in real world deployments of data mining systems. I. Classical Techniques: Statistics, Neighborhoods and Clustering 1.1. The Classics These two sections have been broken up based on when the data mining technique was developed and when it became technically mature enough to be used for business, especially for aiding in the optimization of customer relationship management systems. Thus this section contains descriptions of techniques that have classically been used for decades the next section represents techniques that have only been widely used since the early 1980s. This section should help the user to understand the rough differences in the techniques and at least enough information to be dangerous and well armed enough to not be baffled by the vendors of different data mining tools. The main techniques that we will discuss here are the ones that are used 99.9% of the time on existing business...
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...Subscriptions: http://hum.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav Downloaded from http://hum.sagepub.com at University of Leicester Library on July 22, 2008 © 2001 The Tavistock Institute. All rights reserved. Not for commercial use or unauthorized distribution. Human Relations [0018-7267(200104)54:4] Volume 54(4): 469–494: 016604 Copyright © 2001 The Tavistock Institute ® SAGE Publications London, Thousand Oaks CA, New Delhi The nature of leadership Richard A. Barker A B S T R AC T Trait/characteristic theories and empirical approaches to the study of leadership have been supported by mounds of data, graphic models, and regression statistics. While there has been criticism of these mainstream approaches, there has been little in the way of metaphysical support developed for either side of the argument. This paper attempts to address the ‘science’ of leadership study at its most fundamental level. KEYWORDS ethics leadership social evolution transformative systems Leadership studies in the past few decades have come under increasing criticism for maintaining outmoded constructs and for bearing less than scholastic integrity (Barker, 1997; Burns, 1978; Foster, 1986; Gemmill & Oakley, 1992; Rost, 1991). At a recent leadership conference, faculty members of internationally known leadership education programs...
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...I39 We thank Eliane Ralison and Lalaina Randrianarison for their assistance in collecting and cleaning the data. Funding for this project was provided by USAID and Cornell University. y Department of Economics, University of Oxford, Manor Road, Oxford OX1 3UQ. Email: . Fax: +44(0)1865-281447. Tel: +44(0)1865-281446. z Cornell Food and Nutrition Policy Program, Cornell University, Ithaca NY. Email: 1. Introduction There has long been a suspicion that poverty favors criminal activity, but hard evidence of this relationship is di¢ cult to come by. There are several reasons for this state of a¤airs, all having to do with the joint causality between poverty and crime (Ehrlich 1973). First, the prevalence of crime in an area discourages business, hence contributing to poverty. Secondly, high crime areas may also attract criminals because they …nd it easier to elude detection or because...
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...CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. SEE PROFILE Available from: Hossain Amran Retrieved on: 12 April 2016 USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION Fabio Pagnotta Mat:-093579 Mohammad Amran Hossain Mat:-093192 Department of Computer science, University of Camerino Advanced Database In this project, we use a data set about Portuguese student on two courses ( Mathematics and Portuguese ) which was collected and analysed by Paulo Cortez and Alice Silva, University of Minho,Portugal. Our work intends to approach student addiction on alcohol in secondary level using business intelligence (BI) and Data Mining (DM) techniques. The result shows that a good predictive accuracy can be achieved, provided that addiction of alcohol can impact to the student performance. In addition,the result also provides the correlation between alcohol usage and the social, gender and study time attributes for each student. As a direct outcome of our project, more efficient prediction tools can be developed in order to pay more attention to the student and share how the alcohol impact so badly in his life. Introduction Alcohol had lots of bad impact in our life. Drinking too much on a single occasion or over time can take a serious toll on our health. If who drinks alcohol it’s likely...
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