Data Analysis: What Can Be Learned From the Past 50 Years
John Wiley & Sons, 2012/01/09 - 240 ページ
This 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.
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ALGOL algorithms Applications approach approximate batch Bayesian Brownian motion bytes calculated checking Clausewitz clausulae command language command line comparison computing languages correspondence analysis Cyclodecane data collection data mining data sets derived sets dimension reduction distribution eponyms errors estimated example experience goodness-of-fit goodness-of-fit tests graphics Huber human immediate languages In(v interactive interface interpretation Johnson km/h large data Larsen least squares linear Lunar Six Mascons massive data sets matrix measurements menu missing values Moonset multidimensional scaling natural languages nonlinear observations operations optimization P-values parameters particular periodogram preprocessing principal component problem procedures programming languages projection pursuit purpose questions range raw data Regression robustness sample Second Edition Section simulation singular value decomposition SMALLTALK spectrum Springer Science+Business Media standard Statistical Methods statisticians Stochastic strategy structure subset supercomputers Survival Analysis syntax tasks Theory Third Edition Tukey Tukey’s typical UNIX variables visual