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書名: Nonlinear Science Emergence & Dynamics of Coherent Structures 2/E 2003 (OXFORD) 0-19-85052-3 作者: A.SCOTT 版次: 2/E 出版日期: 2003年1月 ISBN: 9780198528524 出版社: OXFORD
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Nonlinear Science Emergence & Dynamics of Coherent Structures (2版)
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詳細資訊
書名: Nonlinear Science Emergence & Dynamics of Coherent Structures 2/E 2003 (OXFORD) 0-19-85052-3 作者: A.SCOTT 版次: 2/E 出版日期: 2003年1月 ISBN: 9780198528524 出版社: OXFORD
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【簡介】 The use of spreadsheets to obtain solutions to a diverse array of examples offers a reader-friendly way of addressing a topic (optimization) that can sometimes be viewed as intimidating. Many people are readily familiar with spreadsheets and how they work, yet are apt to be unaware of the incredible power of Excel for solving some rather complex optimization problems. A major goal of the book is to sell readers on why it is so important to understand optimization, and a large collection of examples for a wide range of business decision making areas (e.g., production planning and scheduling, workforce planning and scheduling, location and supply chain distribution, location of emergency services, assembly line balancing, vehicle routing, project scheduling, revenue management, advertising, product design, payout schedules, productivity measurement, investment portfolio management, sports league scheduling, ranking models, etc.) affords a practical mechanism for achieving that goal. Another important contribution of the book is that it provides coverage of the mechanics of some common yet sophisticated statistical methods (regression, logistic regression, discriminant analysis, factor analysis, and cluster analysis), which are often opaque to many users of such methods. 【目錄】 Introduction Optimization Examples in Prescriptive Analytics: Production Planning Workforce Planning Continuous Facility Location Discrete Facility Location Routing Problems Facility Layout Project Scheduling Marketing Finance Sports Optimization Examples for Multivariate Statistical Methods Used in Predictive and Descriptive Analytics: Regression Logistic Regression Linear Discriminant Analysis Factor Analysis Cluster Analysis
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