Spss for windows v 22

Homeostatic model assessment of insulin resistance (HOMA-IR) was calculated.35 (link) Data were analyzed using SPSS for Windows V.22 (SPSS, Chertsey, UK). All data are presented as means±SEM unless otherwise stated. Areas under the curve (AUCs) were calculated by the trapezoid method. AUCs have been divided by the relevant time period to give time-averaged values. All data sets were tested for normality according to the Shapiro-Wilk test. Comparisons between the placebo and omega-3 groups were made using an independent t-test or Mann Whitney U tests for nonparametric data. For comparisons within the groups before and after supplementation were made using a students paired t-test or the nonparametric equivalent. Postprandial data were compared using repeated measures analysis of variance, with time and treatment as factors to investigate the change within the omega-3 FA group over time for specific metabolites. Bonferroni posthoc analysis was performed where appropriate to adjust for multiple comparisons. Associations between variables were carried out using Spearman’s rank correlation coefficient. Statistical significance was set at p<0.05.
For in vitro cell studies, data were analyzed using GraphPad Prism 7 software using an independent t-test or Mann Whitney U test for nonparametric data (GraphPad software, La Jolla, USA).

All statistical calculations were performed using the software program SPSS for Windows (v. 22). After testing for ANOVA assumptions (homogeneity of variances with the Levene’s mean test, and normality with the Kolmogorov-Smirnov test), statistical differences were evaluated by one-way analysis of variance (ANOVA), followed by the post hoc Tukey’s test (p < 0.05). For statistical analysis of RWC, TBI, RABI and RBBI arcsine transformation was performed in percentage data.

The descriptive statistical method was applied to gain insight into the descriptive statistics indicators and parameters of the form (normality) of the distribution for the seven basic personality traits measured with the BF+2. Data visualization was graphically applied to show the differences between groups on the dimensions of the BF+2 questionnaire. Differences between the groups, in all analyzes, were examined using one-way multivariate analysis of variance (One-way MANOVA). The reliability of the VP+2 questionnaire dimensions was examined using Cronbach’s α coefficient [38 (link)]. All of the analyzes were performed in SPSS for Windows v22 [39 (link)].

Analyses were run separately for each of the clinical outcome measures: YSR, RATS, and their respective subscales. In the first instance, correlations were conducted to ascertain bivariate relationships between adolescent symptom scores and both socio-demographic and parent clinical variables. Further analyses were undertaken with variables found to be significant at the p < 0.05 level.
Pearson product-moment and Spearman’s rank correlations were employed for parametric and non-parametric data, respectively. T-tests and analyses of variance were conducted to determine group differences for symptom measures. Mann–Whitney U tests and Kruskal–Wallis tests were applied to non-normally distributed dimensional data. Chi Square tests were employed for bivariate analyses of categorical variables.
Multiple linear and hierarchal regression analyses were conducted to determine socio-demographic and clinical predictors of symptom scales. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) for Windows v.22. An alpha of 95% was applied throughout. False discovery rate (FDR) post hoc tests were applied to multiple comparison analyses.