The data were statistically analyzed using ANOVA. A p-value <0.05 was considered significant. All statistical analyses were performed using PASW software (Version 18.0; IBM Co., Armonk, NY, USA).
Pasw software
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PASW software is a statistical analysis tool developed by IBM. It provides capabilities for data management, advanced statistics, and visualization. The software allows users to analyze data, model relationships, and generate reports.
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Lab products found in correlation
60 protocols using pasw software
1
Statistical Analysis of Experimental Data
2
Associations of Anxiety and Pain Levels
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Sample size calculation was done before the study. A total of 116 participants were required to detect an effect size of 0.2 with a multiple linear regression including 18 variables (all variables listed in Table 5 ), and a significant level of 0.05 and a power of 0.8.
Patient characteristics were reported as mean and standard deviation, or number and percentage. Pearson coefficients were calculated to quantify the associations between SAI and TAI scores with that of the C-IDAF-4C. Five levels of correlation were defined according to the Pearson correlation coefficient (γ), including very weak (γ < 0.2), weak (0.2 ≤ γ < 0.4), moderate (0.4 ≤ γ < 0.6), strong (0.6 ≤ γ < 0.8), and very strong (γ ≥ 0.8). The linear regression model was used to analyze associations between dependent variables and SAI, TAI, C-IDAF-4C or postoperative pain levels. Parameters with P ≤.1 revealed by univariate analysis were then put into a multivariable regression model, and results of the final model were determined after a backward or forward selection procedure. The level of significance for all tests was set to 5%. All statistical analyses were 2-sided, and performed using PASW software (version 21.0, IBM Corp., Armonk, NY).
Patient characteristics were reported as mean and standard deviation, or number and percentage. Pearson coefficients were calculated to quantify the associations between SAI and TAI scores with that of the C-IDAF-4C. Five levels of correlation were defined according to the Pearson correlation coefficient (γ), including very weak (γ < 0.2), weak (0.2 ≤ γ < 0.4), moderate (0.4 ≤ γ < 0.6), strong (0.6 ≤ γ < 0.8), and very strong (γ ≥ 0.8). The linear regression model was used to analyze associations between dependent variables and SAI, TAI, C-IDAF-4C or postoperative pain levels. Parameters with P ≤.1 revealed by univariate analysis were then put into a multivariable regression model, and results of the final model were determined after a backward or forward selection procedure. The level of significance for all tests was set to 5%. All statistical analyses were 2-sided, and performed using PASW software (version 21.0, IBM Corp., Armonk, NY).
3
Analyzing Cognitive Effects of Exercise
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The PASW software (Release 22.0; IBM, Armonk, NY, USA) was used for statistical analyses. Prior to main statistical analyses, the Shapiro–Wilk test was conducted to confirm the normal distribution of the outcome variables. The data on cognitive function and psychological responses were analyzed using a two-way analysis of variance with two within-factors of condition (N, M, and S), and time (baseline; pre-exercise; and 10, 30, and 60 min after exercise). Significant interactions were further analyzed using a one-way repeated-measure test and/or paired test when necessary. The effect size of partial eta squared (η2) was calculated to determine the main and interaction effects. The effect size was considered small if η2 < 0.06 and large if η2 > 0.14 [48 (link)]. Cohen’s d values were used to access the effect sizes for the difference between the exercise trial and the control, and scores of 0.2, 0.5, and >0.8 were classified as small, moderate, and large, respectively [49 ]. All data are expressed as the mean ± standard deviation (SD), and p < 0.05 was taken as the level of statistical significance.
4
Statistical analysis
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Data were analysed with the chi‐square test using PASW software (IBM). P‐values < .05 were considered to indicate statistical significance.
5
Effects of Hypoxia on Cognitive Performance
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The PASW software (Release 22.0; IBM, New York, NY, USA) was used for statistical analyses. Before the main analyses, the Shapiro-Wilk test was conducted to confirm that the outcome variables were normally distributed. Independent samples t-tests were conducted to evaluate demographic differences across sex (Table 1 ). In order to observe the real alternations between NOR and HYP, changes in reaction time (Δ RT) and accuracy (Δ Accuracy) were calculated using the pre- and post-values obtained in the NOR or HYP trial minus the corresponding pre- and post-values obtained in the CON trial. Two-factor repeated measures ANOVA was performed to determine the main effects of exercise (pre- and post-HIE) and oxygen level (FIO2: 0.209 and 0.154), and interaction effects (exercise × condition) on cognitive task performance and physiological parameters (i.e., HR, RPE and SpO2). Tukey’s post hoc test was used to compare the group differences when there was a significant interaction effect. The effect size of partial eta squared (η2) was calculated to determine the effect sizes of the main and interaction effects. The effect size was considered small if η2 < 0.06 and large if η2 > 0.14. All data were expressed as mean ± standard deviation (SD), and p < 0.05 was taken as the level of statistical significance.
6
Comparative Statistical Analysis of Surgical Outcomes
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All statistical data were analyzed with the PASW software package (ver. 18.0; IBM Corp., Armonk, NY, USA). A p-value less than 0.05 (p<0.05) was set as the level of statistical significance. In each group, the preoperative and postoperative data were assessed using a paired t-test. Comparison of data between the two groups were performed with an independent t-test.
7
Statistical Analysis of Experimental Data
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All results were expressed as the mean ± standard error. Statistical significance was determined by analysis of variance tests followed by Dunnett's t-tests. All statistical analyses were carried out using PASW software (version 20.0; IBM Co., Armonk, NY, USA), and P values < 0.05 were considered to indicate statistical significance.
8
Comparative Analysis of Treatment Efficacy
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The data were analyzed using PASW software, version 22 (IBM Corporation, Armonk, NY). The graphs were plotted using GraphPad Prism, version 8.00 (GraphPad Software, Inc., San Diego, CA). Disease duration was compared between groups using the Mann–Whitney U test. Independent t-tests were used to compare the patients’ ages, ADSI scores, and T-cell counts. The chi-squared test and Fisher’s exact were used to determine statistical differences between the groups in relation to sex, the anatomical site treated, the effective rate of treatment, and the recurrence rate. A two-tailed P value < 0.05 was considered statistically significant. Unless otherwise specified, data are presented as mean ± SD.
9
Factors Influencing TB Treatment Outcomes
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All data were analyzed using PASW software (version 22; IBM Corp, Armonk, NY). Categorical variables were reported as number and percentage, and TB treatment outcomes were categorized as successful outcomes (cured or completed treatment) or unsuccessful outcomes (treatment failure, stopped treatment, transferred to another facility, and death) [3 (link)]. Univariate logistic regression analyses were used to evaluate the associations of the patients’ characteristics with unsuccessful treatment outcomes and prolonged treatment durations. Multivariate logistic regression analyses were used to evaluate the characteristics’ independent associations with these outcomes. The results were reported as odds ratios (OR), 95% confidence intervals (CIs), and standard error. Differences with a p-value of <0.05 were considered statistically significant.
10
Effectiveness of Interval Training on Fitness
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Data analysis was conducted using the PASW software (Release 22.0; IBM, New York, USA). Before the main statistical analyses, Kolmogorov–Smirnov tests were conducted to confirm a normal distribution of all outcome variables. Levene's test was used to assess homogeneity of variance. A two-way ANOVA (time × condition) with repeated measures was used to examine the main effects (time) and interaction effects (time × group) on the outcome variables. Significant interaction effects were further analysed using the Tukey post hoc test. One-way ANOVA test was computed to detect the changes of training workload, duration, mean power output and percentage of maximal HR during the 4 intervention weeks. Regarding effect size (ES) measures of the main and interaction effects, partial η2 was considered small if η2 < 0.01 and large if η2 > 0.14. Cohen's d values were also used to access the effect sizes for the difference between variables, which was considered small when d was between 0.20 and 0.49, medium when d was around 0.50–0.79 and large when d > 0.80.33 (link) All data were presented as mean ± standard deviation, and p < 0.05 was considered as significant statistical difference.
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