Spss statistics 20 for windows

To eliminate environmental effects, the best linear unbiased prediction (BLUP) values across three repetitions were conducted using the MIXED procedure in SAS [50 ]. The BLUP values for each trait were used to determine descriptive statistics, for ANOVA testing, and to obtain the H′ and Pearson correlation coefficients, using IBM SPSS Statistics for Windows 20.0 (IBM Corp., Chicago, IL, USA). The H² was calculated using the formula H² = V_G/(V_G + V_E/r), where V_G is the genotypic variance, V_E is the environment variance, and r is the number of replications [51 ]. To screen for P-deficiency-tolerance, we used a weighting method to acquire the S value of each landrace genotype. The S value was calculated using the following $S={\sum }_{i=1}^k\mathit{\text{riYi}}/{\sum }_{i=1}^k\mathit{ri}$ [26 ].

All data were analyzed with IBM SPSS Statistics for Windows 20.0 (IBM Corp., Armonk, NY, USA). Numerical data determined to be normally distributed based on the results of Kolmogorov–Smirnov tests are given as mean ± standard deviation (SD) values, while non-normally distributed variables are given as median (25th–75th quartile) values. For comparisons between groups, Student t-test and Mann–Whitney U test were used in line with the normality of the considered distribution. Categorical variables are given as numbers and percentages, and inter-group comparisons were conducted with Chi-square and Fisher exact tests. Spearman correlation analyses were applied to evaluate the relationships between numerical variables. Receiver operating characteristic (ROC) curve analysis was applied to assess diagnostic performance. Threshold values were determined by the Youden index method. Comparison of the areas under the curves (AUCs) was performed with a nonparametric approach using the theory on generalized U-statistics to generate an estimated covariance matrix, as previously reported by DeLong et al. [17 (link)]. Significance was accepted at p < 0.05 (*) for all statistical analyses.

The independent t-test was used to compare continuous variables between participants with and without anxiety or aggression propensity. The chi-square test was used for categorical variables, and data were expressed as percentages. A multivariate analysis of variance (MANOVA) was conducted to examine differences in the subdomains of aggression propensity (physical aggression, verbal aggression, anger, hostility, and total), since each of the subdomains of aggression was correlated with at least one other subdomain. To analyze the relationship between anxiety and aggression propensity, Pearson’s correlation coefficient was used. The area under the receiver operator characteristics (AUROC) curve was calculated for the cut-off of aggression scores. Multivariable logistic regression analysis was performed, using the backward stepwise method. The anxiety and control groups were classified according to the RCMAS (control group: RCMAS < 25, anxiety group: RCMAS ≥25). Odds ratio (OR) and adjusted OR (AOR) were calculated with 95% confidence interval (CI). The model fit of the prediction model was assessed by an analysis of the AUROC. P value of less than 0.05 was considered statistically significant. Statistical analysis was conducted using SPSS Statistics for Windows 20.0 (IBM Cop., Armonk, NY).