Data Analysis What Can Be Learned From the Past 50 Years
Aus der Reihe
Wiley Series in Probability and Statistics
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:PDF
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116,99 €
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Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
27.04.2011
Verlag
Wiley-ScrivenerSeitenzahl
234 (Printausgabe)
Dateigröße
10032 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781118018248
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.
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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