Exact(5)
It was synthesized by the authors of TID2008 to test metric performance for artificial images.
Using the traditional keyboard-based test metric, a significant difference in learning was not observed between the low and high embodied groups.
The Mann–Whitney independent sample test was used to make the above determination by performing a rank sum test against the errors of each model (delta algorithm versus ARIMA algorithm), resulting in a test metric W = 2,270,619 and p value ≤ 0.0001.
These means were minimally normalized by dividing by the median intensity for all probes for each soil type, and the ratio of the normalized means was used as the test metric.
The wards were compared by using the nonparametric Kruskal Wallis rank sum test (metric variables) for analysis of secondary outcomes because patients were unequally distributed among the wards, with some table cells being unacceptably small for an analysis of variance.
Similar(55)
Both metric versions demonstrate high improvement of performances over standard IQA metrics and over tested metric dedicated to synthesis-related artifacts.
Proposed metrics achieve significantly higher correlation with human judgment compared to the state-of-the-art image quality metrics and compared to the tested metric dedicated to synthesis-related artifacts.
They have better performances than tested metric dedicated to synthesis-related artifacts also.
Functions of data transformation encapsulated by components (i.e. their behaviour) are analysed according to the Observability testing metric.
These initial results show that the digital alloy sample is comparable to or better than the reference sample in each tested metric, and thus is worthy of further investigation.
Secondly, we tested metric invariance by constraining factor loadings (measurement weights), scalar invariance by constraining measurement weights and intercepts, and full uniqueness measurement invariance by constraining measurement residuals (all parameters constant across groups; Arbuckle, 2013).
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