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Contingency Table Analysis [electronic resource] : Methods and Implementation Using R / by Maria Kateri.

By: Contributor(s): Series: Statistics for Industry and TechnologyPublisher: New York, NY : Springer New York : Imprint: Birkhäuser, 2014Description: XVII, 304 p. 21 illus., 8 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780817648114
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 519.5 23
LOC classification:
  • QA276-280
Online resources:
Contents:
Preface -- Introduction -- Analysis of Two-Way Tables -- Analysis of Multi-Way Tables -- Log-Linear Models -- Generalized Linear Models and Extensions -- Association Models -- More on Association Models and Related Methods -- Response Variable Analysis in Contingency Tables -- Analysis of Square Tables -- Further Topics.
In: Springer eBooksSummary: Combining theory and applications, this book presents models and methods for the analysis of two‐ and multi‐dimensional contingency tables. The author uses a threefold approach: fundamental models and related inferences are presented, their interpretational aspects are highlighted, and their practical usefulness is demonstrated. Throughout, practical guidance for using R is provided along with a comprehensive R-functions web-appendix.   Contingency tables arise in diverse fields, including the life, pedagogic, social and political sciences. They also play a prominent role in market research and opinion surveys. The analysis of contingency tables can provide insight into essential structures, relevant quantities and their interactions, and thus leads to improved decision-making.   Special features include:   ·         A motivating example for each topic ·         Applications and implementations in R for all models discussed ·         Emphasis on association and symmetry models ·         Extensive bibliography ·         Up-to-date supplementary material available on the author’s website   An excellent reference for graduate students, researchers, and practitioners in statistics as well as in the biosciences and social sciences, Contingency Table Analysis may also be used as a supplementary textbook for courses on categorical data analysis with emphasis on special models for ordinal data. Prerequisites include basic background knowledge of statistical inference.
Item type: eBooks
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Preface -- Introduction -- Analysis of Two-Way Tables -- Analysis of Multi-Way Tables -- Log-Linear Models -- Generalized Linear Models and Extensions -- Association Models -- More on Association Models and Related Methods -- Response Variable Analysis in Contingency Tables -- Analysis of Square Tables -- Further Topics.

Combining theory and applications, this book presents models and methods for the analysis of two‐ and multi‐dimensional contingency tables. The author uses a threefold approach: fundamental models and related inferences are presented, their interpretational aspects are highlighted, and their practical usefulness is demonstrated. Throughout, practical guidance for using R is provided along with a comprehensive R-functions web-appendix.   Contingency tables arise in diverse fields, including the life, pedagogic, social and political sciences. They also play a prominent role in market research and opinion surveys. The analysis of contingency tables can provide insight into essential structures, relevant quantities and their interactions, and thus leads to improved decision-making.   Special features include:   ·         A motivating example for each topic ·         Applications and implementations in R for all models discussed ·         Emphasis on association and symmetry models ·         Extensive bibliography ·         Up-to-date supplementary material available on the author’s website   An excellent reference for graduate students, researchers, and practitioners in statistics as well as in the biosciences and social sciences, Contingency Table Analysis may also be used as a supplementary textbook for courses on categorical data analysis with emphasis on special models for ordinal data. Prerequisites include basic background knowledge of statistical inference.

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