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Contingency Table Analysis : Methods and Implementation Using R / by Maria Kateri

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Statistics for Industry and Technology | SpringerLink Bücher | Springer eBook Collection Mathematics and StatisticsPublisher: New York, NY : Birkhäuser, 2014Description: Online-Ressource (XVII, 304 p. 21 illus., 8 illus. in color, online resource)ISBN:
  • 9780817648114
Subject(s): Additional physical formats: 9780817648107 | Erscheint auch als: 978-081-764-810-7 Druck-AusgabeDDC classification:
  • 519.5
MSC: MSC: *62-01 | 62H17 | 62P25 | 62-04 | 62F15LOC classification:
  • QA276-280
DOI: DOI: 10.1007/978-0-8176-4811-4Online resources: Summary: 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 inferencePPN: PPN: 1657957632Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
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