Spss 25 statistical software

The statistical analyses were conducted using SPSS statistical software (25.0, SPSS Inc., Chicago, IL, USA). To examine the differences in muscle activity between the three conditions, a repeated analysis of variance (ANOVA) for each muscle on both sides with Bonferroni post hoc tests was conducted. Repeated ANOVA was also used to examine differences in lifting time and 5RM loads in the three conditions. The data are presented as mean ± 95% confidence intervals and with Cohen’s d effect size (ES). An ES of < 0.2 was consider trivial, 0.2–0.5 small, 0.5–0.8 medium, and > 0.8 large (Cohen 1988 ). The significant level was set to > 0.05.

Statistical analyses were performed using SPSS statistical software (25.0). The Kruskal–Wallis test was used for continuous variable data, and the Chi-square test was used to analyze the correlation between the FBLN5 mRNA levels, protein levels, and clinicopathological characteristics of patients. The risk ratio (HR) and 95% confidence interval (CI) were estimated using the survival package of the Cox regression model. Survival analysis was performed using the Kaplan–Meier curve. A two-tailed p-value < 0.05 was considered to be statistically significant for all statistical analyses.

SPSS statistical software 25.0 was used for statistical analysis. Shapiro-Wilk test, histogram, and QQ graph were combined to detect data normality. Continuous variables were described as mean ± standard deviation (M ± SD), medians (inter-quartile ranges) (M (IQR)) and medians (5th percentile to 95th percentile) (P5-P95). Categorical variables were described as frequencies and percentages. Two-sample independent T-test,the chi-square test and Mann-Whitney test were used for comparisons between groups. Logistic regression was used to examine the differences in sleep problems between ASD and TD groups. Significance was presumed at p < 0.05.

Continuous values are shown as mean values and categorical variables are expressed as count and percentage values. The Shapiro–Wilk test was used to test for normality; for statistical significance of the mean values of two groups, the Student's t-test was used if equal variance was confirmed by the F test, whereas Welch's t-test was used if it followed normal distribution. The Mann–Whitney U test was used if it did not follow a normal distribution, and Spearman's correlation analysis was used for testing the relationship between two variables. Univariate and multivariate linear regression analyses were used to estimate the regression coefficients for adjusted SD HbA1c. Multivariate analysis was performed with adjusted SD HbA1c as the dependent variable, and age, BMI, and presence of hypoglycemia as independent variables. Dummy variables were created for gender and presence of hypoglycemia, and then used in multiple regression analysis. The cutoff value of adjusted SD HbA1c was examined based on the ROC curve. The calculated sample size in the ROC analysis was 30 patients in total, assuming an area under the curve (AUC) of 0.80, power 0.80, with significance level of 5%, and a non-hypoglycemic group versus the hypoglycemic group of 2:1. A p value < 0.05 was considered significant. All analyses were performed using SPSS Statistical Software 25.0 (SPSS Inc., Chicago, IL).

Artificial turtle nests were only considered predated if the quail eggs were damaged or missing from the nest [39 (link)]. To estimate the daily survival rates (DSR) of quail eggs, we used Mayfield’s method [40 ], which is based on the exposure time of nests (the cumulative number of days of artificial nests that survived) and the number of known predation events, and shows the probability that a given nest will remain intact for a day. The DSR values obtained at different locations were compared using the J-test, software developed by K. Halupka [41 (link),42 ]. The effect of turtle urine and the nest marking flag was evaluated using the chi-square test. All the statistical analyses were performed using SPSS statistical software 25.0 version of IBM SPSS Statistics, Armonk, NY, USA. For all applied statistical tests, p < 0.05 was the limit of significance.