To make statistical inferences, a summary expression measure for each microRNA, possibly normalized between arrays, is needed.
The interpulse interval that results in a 50% reduction in the compound action potential component is then used as a standard measure when making statistical comparisons between groups.
A general linear model (GLM) for repeated measures was used in SAS for making statistical inferences of the dependent variables, namely: eosinophils, haptoglobin, SAA and fibrinogen.
PP made statistical analysis of the data.
PhD Ljuba Bromberg made statistical treatment.
The first one was a feature vector made up of four statistical measures of texture taken from the greyscale original image.
Baseline and exit preparedness comparisons were made using the same statistical measures and significance criteria as with interest (assessing proportions of overall preparedness and high-level preparedness for each skill).
The comparison was made according to the various statistical measures.
Numerous experimental trial runs of GATK HaplotypeCaller and UnifiedGenotyper were made, evaluating outputs by various statistical measures as well as by detailed hand examination (using the IGV genome browser [ 96]) of variant/genotype calls and aligned reads for a small random contig sampling.
A possible approach to overcome this issue is to make use of the statistical measure of partial correlation [9].
