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When ASE was considered as a binary variable, comparisons of proportions between cases and controls were performed using a likelihood ratio test derived from logistic regression adjusting for population source.
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Relaxation had a strong tendency to be slower and was certainly more variable (comparison of variances P < 0.001, comparison of means with Welch's correction for unequal variances, P = 0.058).
For continuous dependent variables, comparisons of the means were analyzed by 2-tailed t-test (2 groups) or one-way ANOVA followed by Scheffe's multiple comparisons test, and the data are presented as mean ± SD.
Error Mean 198.16 Estimation Method Bootstrap results are based on 1000 bootstrap samples Table 2 Comparison of ASK, RPK and load factor of EM and EF flights Variables Comparison of flights Mean Difference Std.
Statistical analysis: χ2 test for categorical variables; comparison of proADM levels based upon etiologic agent, site of infection and presence of renal failure were performed by using repeated measures analysis of variance (ANOVA).
Due to all these complex variables, comparison of inoculation treatments (that is, a stated seed A with a stated inoculant A vs another seed B with an inoculant B combination) is frequently misleading.
Effect of the intervention on secondary outcome variables: comparison of the proportion the percentage of EBF or PBF at 1, 3 and 12 months using the Chi-squared test and the calculation of confidence intervals.
The lack of validated, consistent definitions and outcome variables makes comparisons of the different tests difficult.
Kruskal-Wallis test was used for continuous variables in comparisons of several groups.
For all variables, pairwise comparisons of the two doses of the test product compared with the placebo were performed.
Standard diagnostic tests were conducted (e.g. correlations between independent variables and comparisons of residuals and predicted values).
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