Statistical inference on categorical variables.

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Abstract

Categorical data are data that capture a characteristic of an experimental unit (such as a tissue specimen) rather than a numerical value. In this chapter, we first describe types of categorical data (nominal and ordinal) and how these types of data are distributed (binomial, multinomial, and independent multinomial). Next, methods for estimation and making statistical inferences for categorical data in commonly seen situations are presented. This includes approximation of the binomial distribution with a normal distribution, estimation and inference for one and two binomial samples, inference for 2 x 2 and R x C contingency tables, and estimation of sample size. Relevant data examples, along with discussions of which study designs generated the data, are presented throughout the chapter.

Original languageEnglish
Pages (from-to)73-88
Number of pages16
JournalMethods in molecular biology (Clifton, N.J.)
Volume404
StatePublished - 2007

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Binomial Distribution
Normal Distribution
Sample Size

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Statistical inference on categorical variables. / Perkins, Susan.

In: Methods in molecular biology (Clifton, N.J.), Vol. 404, 2007, p. 73-88.

Research output: Contribution to journalArticle

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