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Data was analyzed using standard statistical methods.
Significant correlations were evaluated using standard statistical methods.
Here, we quantify the degree to which the simple model represented the data using standard statistical methods (see, e.g., Press et al. 1992).
Using standard statistical methods, no significant differences of individual parameters were apparent between groups despite PCLF showing a tendency to perform better than the others.
Statistical analysis of these data was performed using standard statistical methods: Shapiro-Wilk tests for normal distribution of quantitative data and chi-square testing to determine whether statistically significant association existed between the choice of drug and several independent variables.
This approach would enable the detection of genetic risks for secondary traits while accounting for any stratification, confounding, or association between traits and other variables using standard statistical methods.
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We used standard statistical methods to generate descriptive statistics.
Analysis used standard statistical methods and the SAS software procedures (SAS Institute Inc., Cary, NC, USA).
We used standard statistical methods appropriate for paired comparisons to reanalyze FEV1 responses after exposure to 0.06 ppm O3 relative to filtered air.
These limited studies use standard statistical methods such as the classical and generalized linear models that do not appropriately allow for such adjustments.
The second stage is to use standard statistical methods to fit the model of interest to each of the imputed datasets.
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