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The cover of “Understanding Robust and Exploratory Data Analysis” - Open Library.

"Understanding Robust and Exploratory Data Analysis" was published by Wiley in 1983 - New York, it has 447 pages and the language of the book is English.


“Understanding Robust and Exploratory Data Analysis” Metadata:

  • Title: ➤  Understanding Robust and Exploratory Data Analysis
  • Authors:
  • Language: English
  • Number of Pages: 447
  • Publisher: Wiley
  • Publish Date:
  • Publish Location: New York

“Understanding Robust and Exploratory Data Analysis” Subjects and Themes:

Edition Specifications:

  • Format: Paperback; Hardcover
  • Weight: 1.62 pounds
  • Dimensions: 9.33 x 6.38 x 1.09 inches
  • Pagination: xvi, 447 p. :

Edition Identifiers:

AI-generated Review of “Understanding Robust and Exploratory Data Analysis”:


"Understanding Robust and Exploratory Data Analysis" Table Of Contents:

  • 1- -Stem-and-leaf displays / John D. Emerson and David C. Hoaglin --
  • 2- -Letter values: a set of selected order statistics / David C. Hoaglin --
  • 3- -Boxplots and batch comparison / John D. Emerson and Judith Strenio --
  • 4- -Transforming data / John D. Emerson and Michael A. Stoto --
  • 5- -Resistant lines for y versus x / John D. Emerson and David C. Hoaglin --
  • 6- -Analysis of two-way tables by medians / John D. Emerson and David C. Hoaglin --
  • 7- -Examining residuals / Colin Goodall --
  • 8- -Mathematical aspects of transformation / John D. Emerson --
  • 9- -Introduction to more refined estimators / David C. Hoaglin, Frederick Mosteller, and John W. Tukey --
  • 10- -Comparing location estimators: trimmed means, medians, and trimean / James L. Rosenberger and Miriam Gasko --
  • 11- -M-estimators of location: an outline of the theory / Colin Goodall --
  • 12- -Robust scale estimators and confidence intervals for location / Boris Iglewicz.

"Understanding Robust and Exploratory Data Analysis" Description:

The Open Library:

Provides conceptual, logical, and mathematical support for fundamental exploratory data analysis and robust and resistant methods. Discusses the attitudes and philosophy underlying these methods and examines the connections between exploratory techniques, conventional techniques, and classical statistical theory. Features a unified treatment of transformations. Also explains robust and resistant techniques of estimation through examples and accessible mathematics, emphasizing estimation of (symmetric) location and estimation of scale.

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