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 ?
The ability of the SOM to handle categorical data (unlike the fuzzy K-means method) made it possible to directly use land cover, elevation, and slope as input parameters without the need to reclassify land cover.
61) Kendall [tau]b [+ or -] Standard Error (Mean Kendall [tau]b) Data Set Categorical Data Original 0.
Sion presented different schemes for numeric data and categorical data [4].
For categorical data (Exercise 1), sketches of each of the 10 leaf squares are made in the empty boxes provided on the data sheet (Figure 2).
hundreds to millions of documents or a billion numeric data points), provide visualization of numeric data, categorical data, genome sequences, chemical structures, and text documents - all in the same visual framework.
In this article, I employ categorical data analysis techniques to examine whether there are statistically significant associations among financial crises, liberalization, and government size.
Since the seasonal walk-away rates are classified into four season subclasses, logistic regression was applied to analyze the categorical data to check whether seasonality is significant to the walk-away rate.
Specifically, the third section, Models for Categorical Data, includes chapters on trends in categorical data analysis; ordinal regression models; latent class models; and discrete-time survival analysis.
Both the predictor and the criterion variables are categorical so measures of effect size must fit categorical data (Fleiss, 1994) instead of effect size of mean differences (parametric measures).
We shall show mathematically and empirically that PRIDIT analysis (using principal component analysis in conjunction with RIDIT scoring) achieves the above goals even though the technique does not presuppose the delineation of group membership prior to analysis (uses unsupervised learning) and uses rank-ordered categorical data that may not be interval level.
5 - that operate on continuous or categorical data following normal, k-means or static preprocessing; CHAID analyses the statistical significance of possible decisions while C4.