Rstudio desktop software

To describe the demographic data of the population sample, descriptive statistics were calculated separately by sex for the participant age, height, weight, and training sessions performed during the chosen week and for the running dynamics data. The data from the study variables were analyzed to check for extreme outlying values using Chauvenet’s criterion, because these may have represented abnormalities in the measurements, musculature, or nerve conduction of the participants.
After testing compliance with the assumptions of normality (Shapiro–Wilk test) and homogeneity of variances (Levene’s test), we decided to use non-parametric methods in our analyses. We used the Wilcoxon rank sum method (based on the Mann–Whitney U test) to compare the sex factor in the biomechanical patterns of pelvis use, muscular activation during the complete running cycle, and the mean activation between men and women at their self-selected speeds. G*Power software was used to calculate the sample size; to detect an effect size of 0.8 with a statistical power of 0.8, we calculated that we would require at least 21 participants in each group. We finally obtained data from 22 men and 16 women, and post-hoc calculations gave us a statistical power of 0.75. RStudio Desktop software (version 1.2.5 for macOS; RStudio Inc., Boston, MA, USA) was used for all of our statistical analyses.

Statistical analyses were performed in R Studio Desktop software (http://www.rstudio.com/). All data on plant responses were analyzed by factorial three‐way ANOVAs with the two‐level factors ‘endophyte’ (−, +), ‘RWW adults’ (−, +) and ‘RWW larvae’ (−, +). Leaf area consumed by RWW adults was analyzed by one‐way ANOVA with the two‐level factor ‘endophyte’. Endophyte root colonization was analyzed by two‐way ANOVA with the two‐level factors ‘RWW adults’ and ‘RWW larvae’. Data on larval performance were analyzed by two‐way ANOVA with the two‐level factors ‘endophyte’ and ‘RWW adults’. We checked the assumptions of ANOVA (using Shapiro and Levene tests), and data were transformed if necessary using the appropriate transformations, specifically log, arcsine and square‐root transformations. When transformation did not meet assumptions, or when sample sizes differed, we performed ANOVA using a generalized linear model with best‐fit family errors. P‐values between 0.10 and 0.05 were considered trends.

Statistical analyses were performed using the RStudio Desktop software (version 4.2.1). All data are presented as mean ± SEM (standard error of the mean) unless specified otherwise. Student t-tests were employed for analysis between groups; normality was not tested for. To test the significance between multiple groups, ANOVA and Tukey HSD post hoc tests were performed. The significance levels were set at ns $p\ge$ 0.05; * $p\le$ 0.05; ** $p\le$ 0.01; *** $p\le$ 0.001; **** $p\le$ 0.0001.