{"product_id":"data-analysis-what-can-be-learned-from-the-past-50-years-hardback-9781118010648","title":"Data Analysis; What Can Be Learned From the Past 50 Years (Hardback) 9781118010648","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eWhat Can Be Learned From the Past 50 Years\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePeter J. Huber (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118010648, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 May 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e234 pages, Tables: 50 B\u0026amp;W, 0 Color; Graphs: 25 B\u0026amp;W, 0 Color\u003cbr\u003e24.6 x 16 x 1.8 cm, 0.494 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface.  \u003cp\u003e\u003cb\u003e1 What is Data Analysis?\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Tukey's 1962 paper.\u003c\/p\u003e \u003cp\u003e1.2 The Path of Statistics.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Strategy Issues in Data Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Strategy in Data Analysis.\u003c\/p\u003e \u003cp\u003e2.2 Philosophical issues.\u003c\/p\u003e \u003cp\u003e2.3 Issues of size.\u003c\/p\u003e \u003cp\u003e2.4 Strategic planning.\u003c\/p\u003e \u003cp\u003e2.5 The stages of data analysis.\u003c\/p\u003e \u003cp\u003e2.6 Tools required for strategy reasons.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Massive Data Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Disclosure: Personal experiences.\u003c\/p\u003e \u003cp\u003e3.3 What is i massive? A classification of size.\u003c\/p\u003e \u003cp\u003e3.4 Obstacles to scaling.\u003c\/p\u003e \u003cp\u003e3.5 On the structure of large data sets.\u003c\/p\u003e \u003cp\u003e3.6 Data base management and related issues.\u003c\/p\u003e \u003cp\u003e3.7 The stages of a data analysis.\u003c\/p\u003e \u003cp\u003e3.8 Examples and some thoughts on strategy.\u003c\/p\u003e \u003cp\u003e3.9 Volume reduction.\u003c\/p\u003e \u003cp\u003e3.10 Supercomputers and software challenges.\u003c\/p\u003e \u003cp\u003e3.11 Summary of conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Languages for Data Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Goals and purposes.\u003c\/p\u003e \u003cp\u003e4.2 Natural languages and computing languages.\u003c\/p\u003e \u003cp\u003e4.3 Interface issues.\u003c\/p\u003e \u003cp\u003e4.4 Miscellaneous issues.\u003c\/p\u003e \u003cp\u003e4.5 Requirements for a general purpose immediate language.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Approximate Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Models.\u003c\/p\u003e \u003cp\u003e5.2 Bayesian modeling.\u003c\/p\u003e \u003cp\u003e5.3 Mathematical statistics and approximate models.\u003c\/p\u003e \u003cp\u003e5.4 Statistical significance and physical relevance.\u003c\/p\u003e \u003cp\u003e5.5 Judicious use of a wrong model.\u003c\/p\u003e \u003cp\u003e5.6 Composite models.\u003c\/p\u003e \u003cp\u003e5.7 Modeling the length of day.\u003c\/p\u003e \u003cp\u003e5.8 The role of simulation.\u003c\/p\u003e \u003cp\u003e5.9 Summary of conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Pitfalls.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Simpson's paradox.\u003c\/p\u003e \u003cp\u003e6.2 Missing data.\u003c\/p\u003e \u003cp\u003e6.3 Regression of \u003ci\u003eY\u003c\/i\u003e on \u003ci\u003eX\u003c\/i\u003e or of \u003ci\u003eX\u003c\/i\u003e on \u003ci\u003eY\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Create order in data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 General considerations.\u003c\/p\u003e \u003cp\u003e7.2 Principal component methods.\u003c\/p\u003e \u003cp\u003e7.3 Multidimensional scaling.\u003c\/p\u003e \u003cp\u003e7.4 Correspondence analysis.\u003c\/p\u003e \u003cp\u003e7.5 Multidimensional scaling vs. Correspondence analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 More case studies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 A nutshell example.\u003c\/p\u003e \u003cp\u003e8.2 Shape invariant modeling.\u003c\/p\u003e \u003cp\u003e8.3 Comparison of point configurations.\u003c\/p\u003e \u003cp\u003e8.4 Notes on numerical optimization.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52417745912088,"sku":"9781118010648","price":96.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118010648.jpg?v=1784506177","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-analysis-what-can-be-learned-from-the-past-50-years-hardback-9781118010648","provider":"Freshly Printed Books","version":"1.0","type":"link"}