Spss for windows version 23

Descriptive statistics were used to describe the participants in terms of stroke and demographic information, upper extremity measures and independence in daily living. Pearson correlations were used to assess the associations between the TLT to the upper extremity and other measures. Correlations ranging from 0.25 to 0.49 were considered fair, and values of 0.5 to 0.75 were considered moderate to good relationships [45 ]. Differences between groups were assessed using Independent-samples t-test for continuous variables and Chi square for dichotomous variables.
Cohen’s d Effect size (95% Confidence Interval) for Independent-samples t-test was based on calculating the mean difference between the two groups, and then dividing the result by the pooled standard deviation. Cohen’s d = 0.2 is considered a ‘small’ effect size, 0.5 represents a ‘medium’ effect size and 0.8 or larger is considered a ‘large’ effect size [46 ]. Bonferroni correction of significance level for multiple comparisons was used, therefore statistical significance was set at (α/n) 0.05/29 = p = 0.0017. All analyses were conducted using SPSS for Windows version 23.0 (SPSS Inc, Chicago, IL).

All statistical analyses were performed using SPSS for windows version 23.0 (SPSS Inc., Chicago, IL, USA). Student’s t-test was used to compare continuous variables, and the chi-square or Fisher’s exact test was used for categorical variables. Overall survival (OS) was computed from the day treatment began until the most recent follow-up or death. Survival time and rate were estimated by the Kaplan-Meier method, and differences between groups were assessed using the log-rank test. A P value < 0.05 was considered statistically significant.
The CS differences between groups were compared by calculating standardized differences (d), which were used in terms of effect size. Standardized differences were calculated as follows: $d=\frac{\left(Pp-Pe\right)}{\sqrt{\left[Pp\left(1-Pp\right)+Pe\left(1-Pe\right)\right]}/2}$ where P_p and P_e denote the proportion of a binary baseline variable in two groups [42 (link)]. d values less than 0.1 indicate very small differences between groups; d values between 0.1 and 0.3 indicate small differences; d values between 0.3 and 0.5 indicate moderate differences; and d values greater than 0.5 indicate considerable differences.

Statistical analyses were performed using SPSS for Windows, Version 23.0 (SPSS Inc., Chicago, IL, USA). Student's t-test was used for continuous variables. Chi-square and Fisher's exact tests were used for categorical variables. Statistical significance was set at P < 0.05. Kaplan-Meier survival curves and log-rank statistics were employed to evaluate time to tumor recurrence and overall survival. Multivariate regression analysis was performed using the Cox proportional hazards model.

Statistical analysis of the demographic and clinical data was carried out using the SPSS for Windows (version23.0; SPSS, Chicago, IL, United States). Mann–Whitney U test and χ² tests were conducted according to the characteristics of data.
The analyses of imaging data were performed using SPM8² software. One-sample t-tests were conducted on the individual z-maps of the two groups separately, producing four t-maps corresponding to FC patterns of left and right NAc ROIs in both groups. Then two-sample t-tests were done for the two ROIs separately to investigate group differences in FC between EOS patients and HCs within the whole-brain mask with age, gender, and mean FD regressed out as covariates. For both one- and two-sample t-tests, the Gaussian random-field (GRF) method was applied for multiple comparison correction. A corrected threshold of p < 0.05 was considered the criterion for significance with a voxel level threshold of p < 0.005 and cluster size > 50 voxels.
Finally, to explore the relationship between clinical, demographic features and the strength of FC, Pearson’s correlation coefficients were calculated between the mean z-values of each cluster showing significant group difference and the total score as well as subscale scores of PANSS and age of the patients. A two-tailed p level of 0.05 was used as the criterion of statistical significance.

The sample size (n = 250) was obtained to determine differences over 10 mg/dL of triglyceride concentrations with 90% power and 5% significance. Statistical evaluation was performed using SPSS for Windows, version 23.0 software package (SPSS Inc. Chicago, IL, USA). We performed the analysis with a dominant genetic model (TT vs. TC + CC). Descriptive statistics are presented as mean ± standard deviation for continuous variables and as a percentage for categorical variables. Two-tailed Student’s t-test was used to analyze continuous variables with normal distribution. Chi-square test, with Yates correction as necessary, was used to analyze categorical variables. In order to reduce Type I errors in the association analysis, the Bonferroni test was applied for multiple testing. The statistical analysis to evaluate the interaction between the gene and the dietary intervention was performed using ANCOVA (covariance analysis) adjusted by age, sex, and BMI modeling the dependent variable with the starting values. Hardy–Weinberg equilibrium was determined with Chi-square test to compare our expected and observed data.