Spss statistics for macos

Standard statistical analyses were performed by SPSS statistics (IBM SPSS Statistics for MacOS, Version 26.0; IBM Corp., Armonk, NY, USA). Nominal variables were denoted as frequency and percentage. Continuous variables were reported as mean and standard deviation or as median and 25th percentile (p25) and 75th percentile (p75) for non-normally distributed data. Perioperative characteristics were compared between surgeons using the chi-squared test or Fisher’s exact test (in case of frequencies less than 5) for categoric variables, the Kruskal–Wallis test for non-normal distributed continuous variables, and the one-way analysis of variance test for normally distributed continuous variables. A P-value of less than 0.05 was considered statistically significant. Missing data were reported as such.

The distribution of the quantitative data was evaluated by using the Kolmogorov–Smirnov test. Quantitative variables are reported as mean and standard deviation (SD) or median and interquartile range within squared brackets [IQR]. Qualitative variables are presented as numbers and percentages. Associations between categorical variables were assessed by using Chi-squared and Fisher’s exact tests. Risk is reported as the odd ratio (OR) with confidence interval (CI) at 95% within squared brackets. Mann and Whitney U and Kruskal–Wallis tests were used to analyze nonparametric quantitative data. Correlations analyses were carried out by using Spearman’s test. The level of statistical significance has been set at 0.05 for all the statistics; for Chi-squared and Fisher’s exact tests, a two-tailed significance was used, while, for Spearman’s test, a one-tailed one was used. All tests were performed using SPSS (IBM Corp. Released 2019. IBM SPSS Statistics for MacOS, Version 26.0. Armonk, NY: IBM Corp). The statistical power analysis was performed by using G*Power Software for MacOS v 3.1 [15 (link)].

Data of herbivore bioassay were analysed with Student’s t-test. Analyses on the contents of Bxs and phytohormones were performed using one-way analysis of variance (ANOVA) and significance was determined by post hoc test (P<0.05). Two-way ANOVA was performed to analyse the effect of time of post-pretreatment resting and priming on Bx contents, and time of resting (3, 7, and 12 d) and priming (pretreatment or not of W+OS on third leaves) were treated as two independent variables. Student’s t-test and one-way and two-way ANOVA were performed using SPSS Statistics for Mac OS (IBM Corp., USA; Version 26.0). Principal component analysis was conducted and plotted using the plotPCA in the DESeq2 of the R package (Love et al., 2014 (link)). A violin plot was made using the ggplot2 package in R software (Wickham, 2016 ).

Statistical analysis was performed with SPSS (version 27.0, IBM SPSS Statistics for MacOS, IBM Corp.) and Microsoft Excel (2021). A p-value of 0.05 was set as threshold for statistical significance.
Significant difference between aSNR and aCNR of synthetic and true T2-w fs images from the ten representative datasets was evaluated using the Wilcoxon signed-rank test.
Image and fat saturation quality grading of synthetic and true T2-w fs was analyzed using descriptive statistics. Significant differences between image and fat saturation quality grading of synthetic and true T2-w fs were evaluated using the Wilcoxon signed-rank test.
The Turing test was analyzed using descriptive statistics. Significant difference real condition versus expert grading between true and synthetic T2-w fs images was evaluated using McNemar’s test.
To evaluate the intermethod agreement of pathology assessment based on the synthetic versus the original protocol, Cohen’s kappa (ĸ) coefficients were calculated [34 (link)]. Also, the interrater agreement for pathology grading was calculated using Cohen’s ĸ coefficients. Significant differences between Cohen’s ĸ coefficients were evaluated using the Wilcoxon signed-rank test.
For comparison with the gold standard, accuracy of grading was calculated and corresponding significance was evaluated using a McNemar’s test.

Statistical analyses were first conducted using R Core Team (2020). First, basic descriptive statistics (means, standard deviations, and frequencies) were calculated for all variables. After verifying assumptions of ordinarily and monotonicity (Wissler, 1905 (link)), Spearman's rank correlation coefficients (Savicky, 2014 ) were calculated to explore the direction and strength of potential relationships between emotional affect, FTP, life satisfaction, social relationships, and career effects. Then, after verifying that our observations were independent and have non-perfect separations, but are not normal, linear, or homoscedastic (Stoltzfus, 2011 (link)), we employed single and multivariate logistic regression analyses (R Stats Package, 2020 ; Wickham et al., 2021 ) to assess the relationship among loneliness, FTP, life satisfaction, and social relationships, controlling for potential demographic confounding variables such as race, socioeconomic status, and education level. Finally, to assess potential common variance bias, we used Harman's Single Factor Test in SPSS Version 28 (IBM Corp, 2021) (SPSS Statistics for MacOS, 2021 ).