Spss statistic for windows version 25

Data were analyzed using SPSS Statistic for Windows, version 25.0 (IBM Corp., Armonk, N.Y., USA). Data normality was evaluated using one-sample Kolmogorov-Smirnov test and was presented as mean ± standard deviation (SD) for normally distributed data, median [interquartile range (IQR)] for skewed data, and frequency (percentage) for nominal data. To identify the risk factors for depression, anxiety, and stress, two steps logistic regression analysis was used. First, univariate regression was used to identify each sociodemographic variable that was associated with depression, anxiety, and stress. Variables with p-value < 0.25 [38 (link)] were then subjected to multivariate regression using backward selection method. Variables with p-value < 0.05 from the multivariate regression analysis were considered as independent risk factors. During the logistic regression analysis, variables with missing data of more than 20% were excluded, and depression, anxiety, and stress variables were re-categorized as dichotomous (normal or not) variables with the cut-off scores as follows: 9 for depression, 6 for anxiety, and 10 for stress [36 ].

Statistical analysis was performed using GraphPad Prism 6.01 Software (San Diego, CA, USA). One-way analysis of variance (ANOVA with a Tukey’s HSD post hoc test) was used to compare the existence of significant differences amongst the chemical profiles. Concerning the cellular assays, the level of significance between different treatment groups relative to control was determined by one-way analysis of variance (ANOVA), followed by Bonferroni test. Values of p ≤ 0.05 were considered statistically significant.
Principal component analysis (PCA) was carried out using IBM SPSS Statistic for Windows version 25.0 (Armonk, NY, USA) and was applied to data obtained from three replicates, to inspect if the ordination of species could be related to fatty acid composition. PCA is a method that indicates (indirect) gradients by producing a smaller set of variables (principal components) that explain the variability of a larger set of variables [47 (link)]. PCA was applied to fatty acid composition (μg mg⁻¹ dry extract): C12:0, C13:0, C14:1n-5c, C14:0, C15:1n-5, C15:0, C16:1n-7, C16:0, C17:1n-7, C17:0, C18:3n-6c, C18:2n-6c, C18:1n-9c, C18:1n-9t, C18:0, C20:4n-6, C20:5n-3, C20:3n-6, C20:2n-6, C18:3n-3c, C20:0, C21:0, C22:6n-3, C18:2n-6t, C22:1n-9, C22:0 and n-6/n-3.

Acquired data was analysed using SPSS Statistic for Windows, version 25.0 (IBM Corp., Armonk, N.Y., USA). Data analysis was conducted in two phases. In the first phase, univariate logistic regression was used to identify independent variables that were associated with stunting or underweight. Variables with p < 0.1 were included in the next phase. In second phase, multivariate logistic regression using backward selection was used. Variables with p <0.05 from multivariate analysis were considered as the determinants.