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An introduction to nonparametric statistics / John E. Kolassa.

By: Material type: TextTextSeries: Chapman & Hall/CRC texts in statistical science seriesPublisher: Boca Raton, Florida : CRC Press, 2021Edition: First editionDescription: ix, 212 pages ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780367194840
Subject(s): Additional physical formats: Online version:: An introduction to nonparametric statisticsDDC classification:
  • 519.5 K830i 23
LOC classification:
  • QA278.8 .K65 2020
Contents:
Background -- One-sample nonparametric inference -- Two-sample testing -- Methods for three or more groups -- Group difference with blocking -- Bivariate methods -- Multivariate analysis -- Density estimation -- Regression function estimates -- Resampling techniques -- Appendix A. Analysis using the SAS system -- Appendix B. Construction of heuristic tables and figures using R.
Summary: "This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students"-- Provided by publisher.
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Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books Main Library Graduate School Library GRD 519.5 K830i 2021 (Browse shelf(Opens below)) 1-1 Available 030161

Includes bibliographical references and index.

Background -- One-sample nonparametric inference -- Two-sample testing -- Methods for three or more groups -- Group difference with blocking -- Bivariate methods -- Multivariate analysis -- Density estimation -- Regression function estimates -- Resampling techniques -- Appendix A. Analysis using the SAS system -- Appendix B. Construction of heuristic tables and figures using R.

"This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students"-- Provided by publisher.

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