All data represent the mean ± standard error of the mean. The differences between the groups were evaluated with one-way analyses of variance, which was followed by Tukey's posthoc tests or Student's t-test. The differences in the water contents between the ipsilateral and contralateral sides of the brain were compared with paired t-tests. A two-sided probability value (P) less than 0.05 was considered statistically significant. All measurements were taken by observers who were blinded to the individual treatments. All statistical analyses were performed with PASW 18.0 for Windows (IBM Corporation, Armonk, NY, USA).
Pasw 18.0 for windows
Manufactured by IBM
Sourced in United States
PASW 18.0 for Windows is a statistical analysis software package developed by IBM. It provides a comprehensive set of tools for data management, analysis, and visualization. The software is designed to help users extract insights from their data and make informed decisions.
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Lab products found in correlation
6 protocols using pasw 18.0 for windows
1
Brain Water Content Analysis
2
Statistical Analysis of Research Data
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Descriptive analysis was conducted to determine the central tendency (means and medians) and standard deviation (SD) for quantitative variables; proportions were used for categorical variables. Categorical variables were compared using the Chi-square test. Normal distribution was investigated using the Kolmogorov-Smirnov test. A t-test was used for comparing data with parametric distribution. Data that were not normally distributed were compared using the Mann-Whitney U test.
Statistical analysis was carried out using the Predictive Analytics Software (PASW) 18.0 for Windows (IBM Corp., Armonk, NY, USA) and the Microsoft Office Excel software (Microsoft Corp., Redmond, WA, USA).
All statistical tests were performed at a significance level α of 0.05 (p<0.05). Data were expressed as mean ± SD.
Statistical analysis was carried out using the Predictive Analytics Software (PASW) 18.0 for Windows (IBM Corp., Armonk, NY, USA) and the Microsoft Office Excel software (Microsoft Corp., Redmond, WA, USA).
All statistical tests were performed at a significance level α of 0.05 (p<0.05). Data were expressed as mean ± SD.
3
Predictors of Sexual Function in Menopause
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All data were processed using PASW 18.0 for Windows (SPSS, Chicago, IL). Intergroup differences of continuous variables were analyzed by ANOVA. Intergroup differences of categorical variables were compared by chi-square tests. Post-hoc tests should be performed and results of pairwise comparisons. Ordinal logistic regression was used to identify the predictors of SXscore. The effects of early menopause on SXscore were investigated in predetermined subgroups: patients with different types of menopause (natural, hysterectomy or oophorectomy). A 2-sided value of P <.05 was considered significantly different.
4
Evaluating Glycemic Control Interventions
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Statistical analyses were performed using PASW 18.0 for Windows (SPSS, Quarry Bay, HK). Variables not normally distributed were as follows: total daily insulin, percentage of values below the target and within the target, absolute and percentage difference of HbA1c between follow-up and baseline visit, and mean postprandial (lunch and dinner) glucose. A 2-step rank transformation was performed to normalize these variables before applying parametric tests. The t test for unpaired data was used to compare means between two groups, and the chi-square test was used to compare percentages. The t test for paired data was used to compare variables measured at baseline and follow-up visit within each group.
5
Neuroprotective Effects of Antioxidant Treatment
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All data are expressed as mean ± standard error of the mean (SEM). A Student’s t test or the Mann–Whitney-U test was used to compare the results of TTC staining between the aLA and control groups. Continuous variables were compared between the aLA, control, and sham groups by the Kruskal-Wallis test. Statistical comparisons were performed using ANOVA with repeated measures to assess behavioral performance. Survival analysis was used to compare mortality between the aLA and control groups. A P value of <0.05 was considered statistically significant. All measurements were taken by observers blind to the individual treatments. All statistical analyses were performed using PASW 18.0 for Windows (SPSS, Inc., Somers, NY, USA).
6
Predictors of Nursing Exam Success
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The collected data were analyzed using PASW 18.0 for Windows (SPSS Inc., Chicago, IL, USA). Univariate statistics were used to determine the frequencies and percentages of the categorical variables and means and standard deviations (SDs) of the continuous variables. The association between a selected independent variable (demographic and academic characteristics) and the outcome in the RN exam (pass or fail) was tested using independent t tests and χ2 tests. Binary logistic regression analysis was conducted to identify predictors of success in the RN licensure exam. When the 95% confidence intervals of the relative risk of a given factor did not include 1, the risk was considered statistically significant.
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