TY  - BOOK
AU  - Huber,Peter J.
TI  - Data analysis: what can be learned from the past 50 years 
T2  - Wiley series in probability and statistics
SN  - 9781118018248
AV  - QA276.15 .H83 2011
U1  - 519.509 22
PY  - 2011///
CY  - Hoboken, N.J.
PB  - Wiley
KW  - Mathematical statistics
KW  - History
KW  - Philosophy
KW  - Numerical analysis
KW  - Methodology
KW  - MATHEMATICS
KW  - Probability & Statistics
KW  - General
KW  - bisacsh
KW  - Electronic books
N1  - Includes bibliographical references and index; What Is Data Analysis? -- Strategy Issues in Data Analysis -- Massive Data Sets -- Languages for Data Analysis -- Approximate Models -- Pitfalls -- Create Order in Data -- More Case Studies -- Wiley Series in Probability and Statistics
N2  - "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"--Provided by publisher; "This book explores the many provocative questions concerning the fundamentals of data analysis"--
UR  - http://dx.doi.org/10.1002/9781118018255
ER  -