Normality of distribution for continuous variables was tested by the Kolmogorov-Smirnov test. Normal distribution data were presented as mean ± SD, and the differences among groups were compared by unpaired Student’s t-test or analysis of variance (ANOVA). For non-normal distribution data, median (25th–75th interquartile range) was applied, and Mann-Whitney U or Kruskal-Wallis test were performed for comparison among groups. The multiple comparison was conducted by Dunnett-t test. The difference in genotype distribution as well as consistency of genotype distribution with Hardy-Weinberg equilibrium were tested using the chi-square test. Spearman rank correlation was used to calculated correlations between plasma leptin level and other biochemical variables. Logistic regression was preformed to evaluate the association of the genotypes and GDM risk. P < 0.05 was accepted as statistically significant. Cochran-Armitage trend test was used to calculate the gene-dose effect. Analyses were performed with SPSS Software, Version 18.0 for Windows (SPSS Inc., Chicago, IL, USA).
Spss software version 18.0 for windows
Manufactured by IBM
Sourced in United States
SPSS software version 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 reporting. The software is designed to handle a wide range of data types and offers a user-friendly interface for both novice and experienced users.
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Lab products found in correlation
39 protocols using spss software version 18.0 for windows
1
Statistical Analysis of Genetic Associations
2
Statistical Analyses of Cardiac Adverse Events
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Statistical analyses were performed using SPSS software version 18.0 for Windows (SPSS Inc., Chicago, Illinois, USA). Normal distributions of numeric data were analyzed by visual (histogram) and analytic (Kolmogorov-Smirnov) methods. Determinative analyses were expressed as mean ± standard deviation. The parameters not normally distributed among groups were assessed by the Mann-Whitney U test, and normally distributed parameters among groups were assessed by Student’s t-test. The frequencies of categorical variables such as demographic features were analyzed among groups by cross tables. Statistical differences of these frequencies among groups were analyzed by χ2 or Fisher tests. The Spearman test was used for determining correlation coefficients and statistical significance of variables not normally distributed, and Pearson’s test was used for determining correlation coefficients and statistical significance of normally distributed variables. To determine the independent risk factors for CAE, a forward stepwise logistic regression model was established. One-way ANOVA was used for determining the median PTH and vit-D levels of the 4 sub-groups of patients who had CAE because both of the parameters showed normal distribution. P-values lower than 0.05 were considered as statistically significant.
3
Salivary Biomarkers in Stress Response
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Numerical variables were presented as mean ± SEM (standard error of mean), while categorical variables were summarized as numbers (percentages). The Kolmogorov-Smirnov test was used to evaluate the distribution of quantitative variables. The variables were compared between two groups by Student’s t-test and the Chi-square/Fisher’s Exact Test. Salivary levels of amylase and IgA were compared using one-way ANOVA with post-hoc Tukey multiple comparisons test across the three studied groups. Association between quantitative variables was assessed using Pearson’s correlation coefficient (r). The statistical analyses were performed using the SPSS software version 18.0 for Windows (SPSS Inc., Chicago, IL). P-values <0.05 were considered statistically significant.
4
Comparative Analysis of Treatment Efficacy
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Values for continuous variables are reported as mean± standard deviation. Categorical variables are described as absolute frequencies and percentages. To compare two groups, t test was used for continuous variables and chi-square test was used for categorical variables. All statistical analyses were performed using SPSS software version 18.0 for Windows (SPSS Inc., Chicago, IL, USA). A p-value of less than 0.05 was considered statically significant.
5
Serum IgE Impact on Asthma Characteristics
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Values are presented as percentages and frequencies for qualitative data and mean ± standard deviation for quantitative data. Comparisons between asthma with high versus normal total serum IgE were analyzed with Student’s t-test. Categorical variables were contrasted through contingency tables and tested with chi-square, or Fisher’s test where appropriate. Pearson’s coefficient was employed for correlation analysis. The level of statistical significance was set as α = 0.05. Analysis was done with SPSS software version 18.0 for Windows (SPSS, Inc., Chicago, Il, USA).
6
Comparative Analysis of Treatment Outcomes
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The results were analysed using the SPSS software Version 18.0 for Windows (SPSS Inc., Chicago, IL, USA). After conducting descriptive analysis of the variables (means and standard deviations), we performed baseline comparisons of the two treatment groups to determine whether they were equivalent on the measured variables. Next, within-group comparisons of pre- and postintervention scores were performed using t-tests, and between-group comparisons of change scores on all outcome measures were performed using t-tests, with a confidence interval of 95% (P = 0.05).
7
Obstetric Factors Influencing Preterm Delivery
8
Association of Platelet-Lymphocyte Ratio with Mortality
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Data were analyzed with SPSS software, version 18.0 for Windows (SPSS Inc, Chicago, Illinois, USA). The Kolmogorov- Smirnov test was used to verify the normality of the distribution of continuous variables. Continuous variables were defined as means±standard deviation; categorical variables were given as percentages. Comparison among multiple groups was performed by Kruskal-Wallis test or one-way analysis of variance (ANOVA) test, and the chi-square Fisher exact test was carried out for categorical variables as appropriate. For the post-hoc analysis, either the Scheffe or Mann-Whitney U test was performed. Statistical significance was defined as p<0.05. Variables for which the p value was <0.05 in the univariate analysis were assessed by multiple logistic regression analysis to evaluate the independent predictors of in-hospital mortality. All variables found to be significant in the univariate analysis were included in the logistic regression model, and the results are shown as odds ratio (OR) with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) curve analysis was used to determine the optimum cut-off levels of the PLR in association with in-hospital mortality.
9
Statistical Analysis of Continuous and Categorical Variables
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Data were analyzed with the SPSS software version 18.0 for Windows (SPSS, Inc., Chicago, IL). The Kolmogorov–Smirnov test was used to verify the normality of distribution of continuous variables. Continuous variables were defined as mean ± standard deviation; categorical variables were given as percentages. According to the distribution pattern, the independent samples t test or Mann–Whitney U test was used for the continuous variables between two groups and the chi-square test for categorical variables. Comparison between 3 groups was performed by one-way analysis of variance (ANOVA) or Kruskal–Wallis test. Post hoc analyses of the pair wise comparisons were conducted with Tukey's post hoc test in one-way ANOVA. Mann–Whitney U test with Bonferroni correction was used to conduct the pair wise comparisons when there is a significant difference between 3 groups in Kruskal–Wallis test. A p value <0.017 (0.05/3) was considered as the cut-off point to reject H0 hypothesis during the pair wise comparisons. Pearson or Spearman correlation test was used for correlation analysis. With the help of an online program, the needed sample size for the study was calculated as 30 subjects per group in order to achieve the power of test above 80%. Statistical significance was defined as p < 0.05.
10
Predictors of 5-Year Mortality Analysis
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Data were analyzed with SPSS software, version 18.0 for Windows (SPSS Inc, Chicago, Illinois, USA). The Kolmogorov-Smirnov test was used to verify the normality of the distribution of continuous variables. Continuous variables were defined as mean ± standard deviation or median (interquartile range); categorical variables were given as percentages. Comparison among multiple groups was performed by one-way analysis of variance (ANOVA) test or Kruskal-Wallis test, and the χ2 or Fisher exact test was carried out for categorical variables as appropriate. For the post-hoc analysis, either the Scheffe or Mann-Whitney U test was performed. Statistical significance was defined as p < 0.05. Variables for which the p-value was < 0.25 in the univariate analysis were assessed by Cox regression analysis to evaluate the independent predictors of 5-year mortality. All variables found to be significant in the univariate analysis were included in the Cox regression model, and the results are shown as the odds ratio (OR) with 95% confidence intervals (CIs). Survival rates between PLR tertiles were calculated by the Kaplan-Meier method and compared by the log-rank test. A value of p < 0.05 (2-sided) was considered as statistically significant.
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