From Technology and Social Change, 1987, edited by H. Russell Bernard and Pertti J. Pelto. 2nd ed.Prospect Heights, Illinois: Waveland Press. Pp. 359–368. TECHNOLOGY AND ANTHROPOLOGICAL THEORY Conclusion H. Russell Bernard and Pertti J. Pelto In putting together this collection of papers we have become aware of two things. First, each of the cases is historically unique and interesting – they can all stand on their own without need for further interpretation. Second, these cases
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VOLUME NO. 4 (2014), ISSUE N O. 10 (OCTOBER) ISSN 2231-5756 A Monthly Double-Blind Peer Reviewed (Refereed/Juried) Open Access International e-Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, ProQuest, U.S.A., EBSCO Publishing, U.S.A., Cabell’s Directories of Publishing Opportunities, U.S.A., Open J-Gage, India [link of the same is duly available at Inflibnet of University Grants Commission (U.G.C.)], Index Copernicus Publishers
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Abs tract: This study examines the strategic innovation management of the business creation process across the technology industry value chain in the global thin-film-transistor (TFT) liquid-crystal-display (LCD) industry based on an anonymous online survey of employees in the industry value chain and outside exp erts (universities, consultants, etc.). The study confirms that technology strategy formulation and a strategic center position are key industry concerns. It also affirms the utility of
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Linear Programming: Using Solver in Excel Linear Programming was conceptually developed before World War II by the outstanding Russian mathematician A.N.Kolmogorov and gained its popularity ever since the development of Simplex method by George B. Dantzig in 1947. Linear programming deals with problems of maximizing or minimizing a linear function in the presence of linear equality and/or inequality constraints. In these problems, we find the optimal, or most efficient way of using limited resources
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input variables for the classifiers. The approach is applied to predict bankruptcy of firms, and tested on a representative data set of Spanish firms. Results indicate that the approach may provide significant improvements in predictive accuracy, both in linear and nonlinear classifiers. Ó 2011 Elsevier B.V. All rights reserved. Article history: Received 9 February 2011 Received in revised form 2 October 2011 Accepted 3 November 2011 Available online 30 December 2011 Keywords: Bankruptcy prediction Classification
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Outline Global Company Profile: Tupperware What is Forecasting? Forecasting Time Horizons The Influence of Product Life Cycle Types of Forecasts The Strategic Importance of Forecasting Human Resources Capacity Supply-Chain Management Seven Steps in the Forecasting System 4-2 Outline - Continued Forecasting Approaches Overview of Qualitative Methods Overview of Quantitative Methods Time-Series Forecasting
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat
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Chapter 13 Chapter 13 • Forecasting Forecasting TRUE/FALSE 1. The repeated observations of demand for a product or service in their order of occurrence form a pattern known as a time series. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, repeated observations 2. One of the basic time series patterns is trend. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, trend 3. One of the basic time
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H E D I T I O N AN INTRODUCTION TO MANAGEMENT SCIENCE QUANTITATIVE APPROACHES TO DECISION MAKING David R. Anderson University of Cincinnati Dennis J. Sweeney University of Cincinnati Thomas A. Williams Rochester Institute of Technology Jeffrey D. Camm University of Cincinnati Kipp Martin University of Chicago Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States This is an electronic version of the print textbook. Due to
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of data technologies Develop new skills through trainings and in-depth tutorials Connect with an international community of thousands who work with data Job # 15420 2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King and Roger Magoulas 2014 Data Science Salary Survey by John King and Roger Magoulas The authors gratefully acknowledge the contribution of Owen S. Robbins and Benchmark Research Technologies, Inc., who
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