Response surface methodology (RS M was implemented for statistical design and analysis of experiments and process modeling.
Response surface methodology (RSM) was implemented for statistical design and analysis of experiments and process modelling.
SAS statistical software (The SAS institute, Cary, NC) was implemented for this statistical analysis (Table 2).
However, this study was too small to allow for statistical analysis, having been implemented in only several subjects, as opposed to the current study, which includes 15 adults.
statistic program for statistical analysis.
The R package DESeq, which implements negative binomial distribution statistics for RNA-seq data was used for statistical analysis [ 116].
For statistical analysis, Ct values were used as input to randomization tests implemented in Relative Expression Software Tool (REST - [ 64]).
For statistical analysis, Ct values were used as inputs for randomization tests implemented in the Relative Expression Software Tool (REST) [ 40], with P ≤ 0.05 considered as statistically significant.
Significance Analysis of Microarrays (SAM) [20 R., and Chu, G. 2001], implemented as the R package samr (version 1.20) (http://www-stat.stanford.edu/~tibs/SAM/Rdist/index.html), was used for statistical analysis.
For statistical analysis, categorical and numerical items are required, thus a high-granular data structure must be defined and implemented in the clinical setting.
