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Baes raised an open question in his monograph [17] as follows: The theory of self-regular functions has been created for linear programming by Jiming Peng, Cornelius Roos, and Tamás Terlaky [5].
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Venn diagrams were created for probesets with an absolute linear fold change > 1.5 and a P-value < 0.05 to find probesets commonly up- or downregulated in all contrasts.
Special software was created for calculation of energy balance of linear rasters.
Two models for linear multivariate regression analyses were created for each of the SF-36 summary scores with PCS (Table 2) and MCS (Table 3) as dependent variables.
The assumption of linearity for continuously valued factors was evaluated using Generalized Additive Models (GAM) [ 35], and relevant categorical variables were created for those that were significantly non-linear at the alpha = 0.05 level, and, for adiponectin, leptin and IGF-1 to minimize undue influence by extremely large values [ 35].
Moreover, white matter and ventricle reference time courses were created for each participant and added to the general linear model (GLM) along with the six motion correction parameters.
In multiple linear regression models, dummy variables were created for age, education and household income.
Even more, by using the accurate 3D non-linear reliability prediction models easy tools can be created for package designers.
VPC plots will be created in linear and semi-logarithmic scale for each of the different outputs available in the data.
Hence, a response surface model for each response was created including the linear, quadratic and interaction terms.
The mtk-QSBER model was created by using linear discriminant analysis (LDA) for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under many experimental conditions.
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