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We assessed correlations between parameters using the Spearman Rho's correlation test.
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Correlations between individual continuous parameters were sought using the Spearman correlation and between dichotomous parameters using the Kruskall-Wallis or Mann Whitney U test, when appropriate.
For each scenario, we first simulated five test data sets and checked convergence of all treatment effect parameters and between-trial variance parameters using the Gelman Rubin convergence diagnostics.
To investigate the association between corneal biomechanical parameters using the Ocular Response Analyzer (ORA) and glaucoma severity.
Up to 23 wt.% conversion, no substantial differences were found between the obtained parameters using the copolymer compositions determined in terms of both integration methods.
We made a quantitative assessment of the relationships between the various circulatory parameters, using the simple conventional measurement method of treating acceleration plethysmograms (the second derivatives of digital plethysmograms) as indicators of peripheral circulation kinetics.
[7] statistically modeled the relationship between energy consumption and machining parameters using the Response Surface Method.
Parameter optimization was achieved by floating all parameters and minimizing the sum-of-squares differences between the data and free parameters using the solver module of an Excel spreadsheet.
The main goal is to minimize the objective function formulated as the difference between the desired and generated functional parameters using the Generalized Reduced Gradient (GRG) method.
We hypothesized that as our combined PET/MRI parameters using the ratio between metabolo-volumetric parameter and ADC reflects metabolic activity, tumor burden and cellularity, they could show better prognostic value than each PET or MRI parameter.
In spite of extensive research on noise modeling and sensor characterization, there still exists a need to bridge the gap between parameters used in sensor noise models, and quantities used to characterize commercially available sensors.
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