Statistix 10

Categorical and continuous data were measured and compared using appropriate non-parametric and parametric methods to include one-way analysis of variance (ANOVA) with comparison of means, and Chi-square and/or Fisher exact tests. Regression models were developed using both best subset regression methods (univariate) and linear models. A p-value of <0.05 using a two-tailed hypothesis was utilized to determine significance. p-values above this cutoff were designated as “not significant (n.s.)” A power calculation was performed to determine the number of subjects that would identify a 20% difference in the G allele SNP frequency when the allele was present in 40% of the population. A sample size of 100 yielded a power of 0.973 with an alpha = 0.05 to detect this difference. Analyses were performed using Statistix 10.0 software (Analytical Software, Tallahassee, FL, USA).

The area under the Qy curve was calculated [38 (link)] and data were analyzed using the Kruskal–Wallis test (nonparametric one-way analysis of variance). Means were separated using Dunn’s test with a probability level of 0.05. Statistical analyses were performed using STATISTIX 10.0 software (Analytical Software, Tallahassee, FL, USA).
Proteomics and metabolomics statistical analyses were performed using three biological replicates per treatment and population. Multivariate analysis (principal component analysis (PCA)) was performed for both proteomics and metabolomics using the FactoMineR package in R v4.2.1 [51 (link)]. The non-parametric Kruskal–Wallis test was applied to determine statistically variable proteins and metabolites between treatments using the stats package in R [52 ]. Proteins and metabolites showing significant differences (p ≤ 0.05) that were up-accumulated under the different stress conditions were used for downstream analysis. Venn diagrams were generated using ggvenn function from R package ggven [53 ] to visualize unique and common proteins and metabolites between populations with the same treatment and between treatments for each population.

In this trial, an analysis of variance (ANOVA) of final values of the disease parameters DI, MS, M and AUDPC was performed. The mean values of analyzed parameters were compared using Fisher’s protected least significant difference test at p = 0.05.
By its side, the TE and MM parameters were analyzed using Kaplan-Meier survival analysis [52 (link)], in which the survival times were calculated as the week in which a plant died or showed disease symptoms for the first time. Comparisons were tested using the log-rank test at p = 0.05.
The Akaike’s information criterion and pseudo-R² parameter were used to evaluate the appropriateness of the logistic model to describe data.
Once the experiment was concluded, the combined analysis (Split-split-plot) of variance for values of AUDPC and MS revealed that the four analyzed factors (irrigation, fertilizer, soil-fertilizer interaction, and soil) were significant, with p values < 0.05. Significance of the interaction was due to use two different soils. For that, each soil was studied separately by new ANOVAs and Fisher’s test used for mean comparisons.
Statistix 10.0 software program (Analytical Software, Tallahasse, FL, USA) was used for all mentioned analyses.

All the samples (except those used to determine the essential oils compounds profile and those of the RSM experimental design) were analysed in triplicate, and data in tables represent mean values ± standard deviation (n = 3). Results were assessed for statistical significance using one-way ANOVA and Tukey’s HSD test as a post hoc test using Statistix 10.0 software (Analytical Software, Tallahassee, FL, USA). Differences were considered significant when p < 0.05. For the optimisation step, an experimental design based on RSM was planned and analysed using Minitab 16 software (Minitab, State Collage, PA, USA).

Data were statistically evaluated using Statistix 10.0 (Analytical Software, Tallahassee, FL). Continuous variables (heart rate, respiratory rate, pedometry data, manure production in kilograms, barium ball recovery (%), pharmacokinetic parameters, and plasma chemistry parameters) were assessed for normality using a Shapiro–Wilk test. Physiologic variables (heart rate and respiratory rate) were compared between treatment groups using a repeated measures ANOVA. Pedometry, plasma chemistry, and mentation and gait score data were compared using a Kruskal-Wallis test. Manure production and cumulative barium ball recovery by time and treatment were evaluated by factorial ANOVA.

Parametric and non-parametric comparisons were performed as appropriate to the data using Statistix 10.0. (Analytical Software, Tallahassee, FL). A p-value of 0.05 was considered significant. An unpaired Student’s t-test was used to determine significant differences in virus titers. Data are presented as means ± SEM. Comparisons of viral titers were performed by using a repeated measures two-way ANOVA (GraphPad Prism) with Bonferroni post-tests at each time point.

Statistical analyses were performed using STATISTIX 10.0 software (Analytical Software, Tallahassee, FL, USA). Anaphase I combinations were evaluated by an analysis of variance (ANOVA) as a completely randomized design. This analysis included a tangent transformation in the anaphase I combination where only one pole of the meiocytes showed H. chilense and H. vulgare signals. Tetrad combinations were analyzed by the Kruskal–Wallis test (nonparametric one-way analysis of variance).