categorical data


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Related to categorical data: Numerical data

categorical data

[‚kad·ə¦gör·i·kəl ′dad·ə]
(statistics)
Data separable into categories that are mutually exclusive, for example, age groups.
References in periodicals archive ?
Categorical data like gender and colonoscopic findings were analyzed by their frequencies and percentages.
However, the majority of data found in practice are categorical, and this may require an IRT model that allows the analysis of latent means for categorical data.
Therefore, multiple correspondence analyses were suitable choices to determine detailed results of complex categorical data in migraine risk factors.
The proportional odds (PO) model, which is also called cumulative odds model (Agresti, 1996, 2002 ; Armstrong & Sloan, 1989; Long, 1997, Long & Freese, 2006; McCullagh, 1980; McCullagh & Nelder, 1989; Powers & Xie, 2000; O'Connell, 2006), is one of the most commonly used models for the analysis of ordinal categorical data and comes from the class of generalized linear models.
Kvam and Vidakovic (industrial and systems engineering and statistics, respectively, Georgia Institute of Technology) focus on the needs of graduate students in engineering, engineering researchers and practitioners who wish to develop their understanding of order statistics, methods of categorical data analysis, nonparametric regression and curve fitting methods along with nonparametric procedures that are becoming increasingly relevant.
Nominal datas a type of categorical data which is classified and not ordered.
It illustrates what information graphical displays can reveal in data and discusses the literature on graphics for data analysis and statistics, alternative software to R, and information on the internet, then examines continuous variables, displaying categorical data, looking for structure, investigating multivariate continuous data, studying multivariate categorical data, getting an overview, graphics and data quality, comparisons, graphics for time series, ensemble graphics, and using R for graphics.
of Florida-Gainesville) incorporates the new models for analyzing categorical data that continue to be developed apace, and describes methods that were new in earlier editions.
Through 190 or so pages, the tutor instructs her in data types, understanding numerical and categorical data, using histograms to simplify data, measures of central tendency and variability, standardization, probability, correlations and hypothesis tests.