Sentence examples for Each categorization from inspiring English sources

Each categorization is explained and example tools are given.

Furthermore, an original taxonomy of these protocols is introduced based upon the road scenarios with critical discussion on each categorization with respect to its strengths, weaknesses and important constraints.

The statistical fit of the model for each categorization was maintained in a separate database.

Analyzing the average number of tags used in each categorization, shown in Fig. 8, we observed the following facts.

The quality of the representation of problems was measured with the categories, their "frequencies" (number of problems accounted for), and the time required in each categorization task.

The performance of each categorization case was measured in terms of 1) overall model fit; 2) the statistical significance of individual coefficients; and 3) the consistency of coefficients with the order of levels of precursors (i.e. it is expected that "high" levels of precursors contribute more to crash risk than "low" level precursors).

They are associated to varying degrees with each disease categorization and with each other.

Using these features, we advance a nuanced understanding of those features that are common and those used to define each particular categorization.

For each comorbidity categorization, we calculated the standardized mortality risk ratio (SMR) across strata and divided this figure into the crude risk ratio (the unadjusted age/mortality association) to yield the relative risk due to confounding (RR c), which measures the direction and magnitude of risk ratio distortion due to confounding by comorbidity.

Next, each participant's categorization for each modality was compared to the reference standard, and the results were compared against the binomial distribution for categorical data.

The logistic curves were fit to each participant's categorization data in each condition (pre, post1, and post2) using SPSS software (version 19.0), which uses the following formula: E Yt) = (1 + β0 β1 t)−1.