categorical data


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Related to categorical data: Numerical data

categorical data

A term used in the context of a clinical trial for data evaluated by sorting values—e.g., severe, moderate and mild—into various categories.

categorical data

data relating to category such as qualitative data, e.g. dog, cat, female. It may be nominal when a name is used, e.g. location, breed, or ordinal when a range of categories is used, e.g. calf, yearling, cow.
References in periodicals archive ?
Among the topics are the environment of the SPSS statistics software package, exploring assumptions, regression, comparing two means, analysis of covariance, repeated-measure designs, non-parametric tests, and categorical data.
After introducing statistical graphs and ODS graphics, they discuss graphs for displaying the characteristics of univariate data, displaying cross-classified categorical data, applying t-tests and analyses of variance, linear regression and the scatterplot, logistic regression, longitudinal data, and survival data.
Emerging applications include bioinformatics, categorical data and clinical trials, econometrics, longitudinal data analysis, and microarray data analysis, among others.
com/research/a75459/applied_categorica) has announced the addition of John Wiley and Sons Ltd's new report "Applied Categorical Data Analysis and Translational Research, 2nd Edition" to their offering.
Most textbooks cover either nonparametric statistics or categorical data analysis, he says, but with this textbook his is able to deal with both during a one-semester course that also sets out a framework for choosing the best statistical technique.
This is the third war data handbook of the Correlates of War project, which seeks to gather comprehensive quantifiable and categorical data on all types of wars.
A concluding chapter on statistical assessment explains modern standards of the Comparative Method as well as combinatorial explanation, categorical data analysis and probabilistic relationships.
The treatment of the common mean of univariate normal populations, tests of homogeneity, one-way random effects model, categorical data, recovery of inter-block information, and combination of polls is entirely new.
Using SPSS examples extensively, they cover the basics of planning analysis, creating data sets, describing and examining data, comparing one or two samples in the t-test, performing correlation and regression, analyzing categorical data, analyzing variance and covariance, and working through logistic regression.
The text includes newly added and updated results on convergence, and new discussion of categorical data, numerical differentiation, and variants of the EM algorithm.
Niewiadomska-Bugaj's areas of interest include general statistical methodology, nonparametric statistics, classification, and categorical data analysis.