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The data was evaluated by chi-square analysis using standard statistical software with a P value of < 0.05 being statistically significant.
It is established that such negative variance components in generalized linear mixed models can occur in practice and that they can be estimated using standard statistical software.
The resulting strategy allows exploration of a wide range of the potential model space, can reveal several plausible models for the data if they exist, and can be completed using standard statistical software.
This stepwise method can be implemented using standard statistical software.
Statistical analysis was performed using standard statistical software.
A two-stage approach can provide quick and approximate inferences using standard statistical software.
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Statistical analyses were conducted using standard statistical computer software (IBM SPSS Statistics 20.0).
The analysis was conducted using standard statistical programming software available in STATA 12.1 [ 21].
These analyses are performed using standard statistical analysis methods in Matlab software and include: 1. Selection of statistically different levels of expression using the Student's t-test with the commonly accepted significance threshold of P < 0.05.
This was conducted using standard statistical analysis methods in Matlab software, as described[ 83, 84].
For recurrence and survival analysis, differentially expressed miRNAs and mRNAs were identified using standard statistical methods employed by the software.
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using standard statistical t-test
using standard proprietary software
using standard statistical probability
using standard available software
using standard statistical performance
using standard commercial software
using standard statistical programming
using standard crystallographic software
using standard statistical theory
using standard statistical modelling
using GenSTAT statistical software
using standard confocal software
using standard numerical software
using standard statistical analysis
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