Spss package

Descriptive statistics were used for the analysis of the demographic and clinical features of the cohorts with data expressed as mean ± SD. Normality and equality of variances of the variables were tested. An independent sample t-test was used to compare the differences in continuous variables between the HCs and COPD cohort. χ², Fisher’s exact, and Mann–Whitney U tests were used as appropriate to compare differences in ordinal and nominal data between the groups. Pearson or Spearman rank coefficients were used to assess the correlation between aortic and pulmonary PWV and to look at the correlates of both of these with baseline demographic, spirometric, and MRI factors in the COPD cohort. Multiple linear regression analysis was performed with systemic pulse wave velocity (sPWV) and pulmonary pulse wave velocity (pPWV) entered separately as the dependent variables, with those factors which were (p<0.1) in single variable analysis entered as independent variables. All data were analyzed using SPSS package (Version 21.0; IBM Corporation, Armonk, NY, USA). Significance was assumed when p<0.05.

The analyses were completed using the SPSS package (version 24.0; IBM corporation, Armonk, New York, NY, United States), the MedCalc Statistical Software (version 14.8.1) and the GraphPad prism software (version 8.4.2, San Diego, California USA). Data were presented as mean ± SD for normally distributed values (at Kolmogorov–Smirnov test), median (IQ range) for variables with skewed distribution or frequency percentage. Differences between groups were determined by the unpaired T-test for normally distributed values, the Mann–Whitney U-test for non-parametric values and the chi-square followed by a Fisher’s exact test for frequency distributions. HD variations of retinal parameters were analyzed by a paired-T-test or by a Wilcoxon signed-rank test for non-parametric values.

The normal distribution as well as the skewness and kurtosis of the data was checked using the Kolmogorov–Smirnov test. Results are presented as the median or mean (± the standard deviation [SD]). The Mann–Whitney U test was used to check differences between groups since the data did not follow a parametric distribution at baseline. Differences on categorical variables were checked with the Chi-Squared test. To analyze the effect of the exercise programs we used the ANOVA mixed model with time as the within-group factor and the exercise group as the between-group factor. A per-protocol analysis was performed instead of an intention to treat analysis. The SPSS package, version 20.0 for Windows (IBM Corp., Armonk, NY) was used for all the statistical analyses, results were recognized as statistically significant at a threshold cut-off of p ≤ 0.05 for all the statistical analyses.

Unless specified otherwise, statistical analysis was performed using the SPSS package (IBM). Results for WT, DM and TM cells represent equal numbers of three independently derived cell lines for each PGRMC1 condition (e.g. n = 6 for WT includes 2x WT1, 2xWT2, and 2xWT3). For boxplot data depictions, whiskers represent quartiles 1 and 4 (with maximum and minimum values). Boxes represent quartiles 2 and 3, separated by the median, as generated by SPSS analyze data function. Datasets conforming with normal distribution were analyzed by ANOVA and post-hoc Bonferroni or Tukey HSD test (equal variance) or post-hoc Dunnett’s T3 Test (unequal variance). Statistical differences between divergent treatments of different cell lines were calculated using two way ANOVA and post-hoc pairwise comparisons. For non-parametric data sets Kruskal-Wallis or Kolmogorov-Smirnov tests were performed, as indicated in relevant figure legends.

To evaluate GP-reported diagnostic practices as well as reasons for (not) testing and intentions towards pertussis testing among GPs, we analysed the data using descriptive statistics, paired t-tests and chi-squared tests where appropriate. Analyses were performed using SPSS package version 21.0 (IBM Inc., Somers, New York, USA).

SPSS^® Package (IBM, New York, NY, USA) was used to verify the statistical significance of the measurements after use compared with the measurements before use. Significance was confirmed when the significance probability was <5% (p < 0.05) in the 95% confidence interval. Normality was verified using the Shapiro–Wilk method. Comparison of before and after use was performed using the paired t-test, which is a parametric method, and by the Wilcoxon signed rank test, which is a non-parametric method. In parametric cases, ANOVA was used to analyze repetitive data three or more times from the same subjects, and a post hoc comparison was performed using the Bonferroni correction. In non-parametric cases, the Friedman test was used for analysis, and paired comparisons were performed using the Wilcoxon signed rank test, followed by revision using the Bonferroni correction method. Comparisons between the two groups at each point were performed by repeated-measures ANOVA when homogeneity was satisfied and by ranked ANCOVA when homogeneity was not achieved. Comparisons between more than three groups at each point were performed by one-way ANOVA, then revised by the Bonferroni correction when homogeneity was satisfied, and by the Kruskal–Wallis test followed by revision with the Mann–Whitney test when homogeneity was not achieved.