Statistica software version 13

Results are given either as means ± SEM of the original values or as means of % of control±SEM. Results exceeding + /−2SDs were excluded. Statistical analysis was carried out either by two-way ANOVA or by repeated measures ANOVA as appropriate and indicated for each analysis, followed by post-hoc Fisher’s Least Significant Difference (LSD) test, using STATISTICA software version 13 (StatSoft, Tulsa, Oklahoma). The data met the assumptions of the tests, namely, normal distribution and the variance between the compared groups was similar. p ≤ 0.05 was considered statistically significant. As a rule of thumb, according to our years of experience, and as can be found in others’ recent reports in a high impact journal [52 (link), 53 (link)] a minimal number of animals or samples/group to achieve reproducibility is 5.

To assess the changes of fitness in the examined groups in relation to the examination terms, the analysis of variance (ANOVA) test was used with Tukey’s post hoc test. To compare the results of the questionnaire items in three examination terms, Friedman’s ANOVA test was used with post-hoc analysis between the terms. For testing the differences between the medians in the groups of boys and separately between the groups of girls in all three terms, the Mann–Whitney U test was used for the questionnaire items. The statistical significance was set at p < 0.05. Analyses were carried out with the use of STATISTICA software, version 13 (StatSoft, Krakow, Poland).

Data was tested for normality and for baseline differences between groups. Plasma NGAL, heart rate, plasma lactate, and WBC data were log-transformed to achieve normal distribution. Intergroup differences were assessed with repeated measures ANOVA type III for normally distributed data. If group difference was found, a post hoc test with unequal N test for groups Etx + NaCl vs. Hct + Etx, groups Etx + NaCl vs. Etx + Hct, and groups Etx + Hct vs. Hct + Etx was performed. Intergroup differences were assessed for non-normally distributed data with Kruskal-Wallis test. Change over time, for non-normally distributed data, was assessed with Friedman’s ANOVA. A Spearman Rank Order test was performed to analyze correlation between data. p < 0.05 was considered significant. We report values as means with standard deviation (SD) or medians with interquartile ranges (IQRs) when appropriate. Analyses were performed by using STATISTICA software, version 13 (StatSoft, Tulsa, OK).

All experiments were performed in triplicate and the results are mean ± SD. The amino acid content of WML-P analysis was performed in duplicate. Data were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s post-hoc test. Statistical differences were calculated at the significance level p < 0.05 and represented by superscript letters. The statistical analysis was performed using Statistica software, version 13 (StatSoft, Kraków, Poland).

Statistical analyses were carried out using STATISTICA software, version 13 (StatSoft, Tulsa, OH, USA). P-values < 0.05 were considered statistically significant. Variables are presented as mean ± SEM. Normality of variables was tested using the Kolmogorov–Smirnov and Lilliefors tests. Differences between 2 dependent values were tested using Wilcoxon Matched Pair test. Differences between 2 groups were tested using Mann-Whiney test. Differences between the three groups’ variables were tested using Kruskal–Wallis ANOVA and Median test. Differences within each group over the 2 years were tested using Friedman ANOVA. Correlations were tested using Spearman Rank Order Correlation. Logistic regressions were preformed to test which parameters affected primary outcome.

Statistical analysis was performed using the STATISTICA software version 13 (StatSoft Europe GmbH, Hamburg, Germany). For the analysis of the results variance, non-parametric statistics at a significance level of 0.050 were selected. Mann-Whitney tests were used to assess significant differences between datasets.
The Positive Matrix Factorization (PMF) model was used to identify the pollution sources contributing to PM levels [25 ]. PMF is a popular receptor model used for source apportionment studies, which decomposes the data matrix into two sub-data matrixes (factor profiles and factor contributions) without prior knowledge of the profiles of pollution sources [26 (link)]. PMF was applied to the datasets of PM sampled in “Quebedo” and “Mitrena”. Data below the limit of detection (LoD) were replaced by LoD/2 and the uncertainties were set to 5/6 of the LoD.