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From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria.
Table 3 Test retest reliability with McNemar's coefficient for 2 × 2 tables, Bowker's coefficient for more than 2 classes variables and intraclass correlation for continuous variables Statistic P value McNemar's coefficient 0.11 3.57 0.74–0.06 Bowker's coefficient 0.00 10.07 0.03–1.00 Intraclass correlation 0.79 0.99 0.79 0.99.
(2) Extraction of 5 severity classes: Variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe.
Nonparametric tests were used to compare the distribution of the quality scores according to the value of a discrete variable (Mann–Whitney tests for dichotomic variables or Kruskal Wallis tests for multiple classes variables).
Non-parametric tests were used to compare the distribution of the quality scores according to the value of a discrete variable (Mann–Whitney tests for dichotomic variables and Kruskal Wallis tests for multiple classes variables).
The usual reason that you need to add a comment to a snippet of program code is because the names of the classes, variables, and subroutines don't tell the intent of the code.
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Expressions of PG,.P-ERK and PY-STAT3 were analyzed as a 3 classes variable: no/low expression, moderate expression and high expression as detailed in "Methods".
where is a latent class variable (or a topic) belonging to a set of class variables (topics).
The common practice (independent approach) is to devise an independent classifier for each class variable being predicted; yet this approach overlooks the dependencies among the class variables.
When a regression model includes class variables, the complexity of the model is affected by the number of levels of these class variables.
The class variables show a strong impact in the capital models (models 1 3).
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