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Fluxomers and the FIA algorithm, as opposed to prior approaches, allow us to effectively reduce the number of system variables using a simple linear transformation on x.
gamma random variables using a simple recursive relation approach.
Initially, we examined these variables using a univariate analysis.
The authors suggest the selection of a smaller set of variables using a "double selection method".
We analyze the correlations between normalized versions of the variables using a series of linear regressions.
The Navier-Stokes equations are solved in primitive variables using a pseudospectral formulation.
The model was automatically generated depending on the optimization variables using a developed pre-processor script.
Various mathematical models have been formulated to evaluate important process variables using a multidisciplinary approach.
Aeration and agitation were selected as the independent variables using a 22 full factorial design.
More specifically, we start by estimating the probability of being migrants conditional on the X variables using a probit model.
Gaussian variables were compared using a t test and non-normally distributed variables using a Mann–Whitney test.
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