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The Seven Pillars of Statistical Wisdom

Stephen M. Stigler
Format
Book
Published
Cambridge, Massachusetts : Harvard University Press, 2016.
Language
English
Variant Title
7 pillars of statistical wisdom
Statistical wisdom
ISBN
9780674088917, 0674088913
Summary
"A summary of the seven most consequential ideas in the history of statistics, ideas that have proven their importance over a century or more and yet still define the basis of statistical science in the present day. Separately each was a radical idea when introduced, and most remain radical today when they are extended to new territory. Together they define statistics as a scientific field in a way that differentiates it from mathematics and computer science, fields which partner with statistics today but also maintain their separate identities. These "pillars" are presented in their historical context, and some flavor of their development and variety of forms is also given in historical context. The framework of these seven is quite different from the usual ways statistical ideas are arranged, such as in most courses on the subject, and thus they give a new way to think about statistics."--Provided by publisher.
Contents
  • Aggregation : from tables and means to least squares
  • Information : its measurement and rate of change
  • Likelihood : calibration on a probability scale
  • Intercomparison : within-sample variation as a standard
  • Regression : multivariate analysis, Bayesian inference, and causal inference
  • Design : experimental planning and the role of randomization
  • Residual : scientific logic, model comparison, and diagnostic display.
Description
230 pages : illustrations ; 18 cm
Notes
Includes bibliographical references (pages 211-223) and index.
Technical Details
  • Access in Virgo Classic
  • Staff View

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    a| Aggregation : from tables and means to least squares -- Information : its measurement and rate of change -- Likelihood : calibration on a probability scale -- Intercomparison : within-sample variation as a standard -- Regression : multivariate analysis, Bayesian inference, and causal inference -- Design : experimental planning and the role of randomization -- Residual : scientific logic, model comparison, and diagnostic display.
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