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For the hypothesis that the loss of groups of more distal isoforms cumulatively affected FSIQ a one way ANOVA was performed on the classification methods and amount of the variance accounted for by each model was quantified using an F statistic.

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The deviation of each test data set to its training model was quantified in terms of MAECV (cross-validation mean absolute error) and RMSECV (cross-validation root-mean-square error).

The mobility model was quantified with our device measurements for the best accuracy, and the characteristic fluctuation was validated with the experimentally measured DC baseband data from 15/20-nm 15/20-nm FinFET deviCMOSin our eandier work [24].

The quality of the model was quantified by R2.

Early detection performance of the model was quantified by the logarithmic scale of enrichment plot (logAUC) value.

16 The predictive ability of the final model was quantified using Hosmer Lemeshow statistical tests for goodness of fit.

The validity of the model was quantified using the C-Index and Hosmer-Lemeshow statistic for goodness of fit.

The impact of the overexpression of Nup44A on the disease model was quantified based on changes in the severity of an eye phenotype.

The efficacy of treatment in the pre-progression state of the model was quantified in terms of progression-free survival (PFS) and overall survival (OS).

The effect of varying cell length and position of the bleached region in the model was quantified and deemed minimal (Table 1).

The prognostic accuracy of the model was quantified by computing the concordance index (c-index) described by Harrell et al. [ 11].

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