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Nonparametric statistical inference/ Jean Dickinson Gibbons

By: Material type: TextTextPublication details: New York: McGraw-Hill, 1971Description: xiv, 306 pages; 22 cmSubject(s): DDC classification:
  • SA.12 G441
Contents:
Introduction and Fundamentals -- Order Statistics, Quantiles, and Coverages -- Tests of Randomness -- Tests of Goodness of Fit -- One-Sample and Paired-Sample Procedures -- The General Two-Sample Problem -- Linear Rank Statistics and the General Two-Sample Problem -- Linear Rank Tests for the Location Problem -- Linear Rank Tests for the Scale Problem -- Tests of the Equality of k Independent Samples -- Measures of Association for Bivariate Samples -- Measures of Association in Multiple Classifications -- Asymptotic Relative Efficiency -- Analysis of Count Data
Summary: This textbook provides in-depth coverage of widely used nonparametric procedures, emphasizing practical application over theoretical proofs. It includes real-world examples, exercises, and step-by-step guidance on implementing these methods using statistical software like R, SAS, MINITAB, and StatXact. The book also features an appendix with essential tables for solving data-oriented problems.
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3rd rev and expanded ed by J D Gibbons and Subhabrata Chakraborti. 1992 (Statistics : textbooks and monographs; 131)

Includes bibliography and index

Introduction and Fundamentals -- Order Statistics, Quantiles, and Coverages -- Tests of Randomness -- Tests of Goodness of Fit -- One-Sample and Paired-Sample Procedures -- The General Two-Sample Problem -- Linear Rank Statistics and the General Two-Sample Problem -- Linear Rank Tests for the Location Problem -- Linear Rank Tests for the Scale Problem -- Tests of the Equality of k Independent Samples -- Measures of Association for Bivariate Samples -- Measures of Association in Multiple Classifications -- Asymptotic Relative Efficiency -- Analysis of Count Data

This textbook provides in-depth coverage of widely used nonparametric procedures, emphasizing practical application over theoretical proofs. It includes real-world examples, exercises, and step-by-step guidance on implementing these methods using statistical software like R, SAS, MINITAB, and StatXact. The book also features an appendix with essential tables for solving data-oriented problems.

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