Analyses were performed using the SPSS statistical software program for Windows (SPSS Inc., Chicago, IL). Categorical data were analyzed using χ2 tests. Within-group correlations of continuous and ordinal variables were assessed using Pearson's correlation coefficient or Spearman's rank correlation coefficient when appropriate. The Kaplan-Meier method was used to estimate survival rates, and the log-rank and the Wilcoxon rank sum tests were used to assess survival duration differences among groups. In all of the tests, a two-sided p < 0.05 was considered statistically significant.
Spss statistical software program for windows
Manufactured by IBM
SPSS is a statistical software program for Windows that provides advanced analytical capabilities. It is designed to analyze complex data, identify trends, and support data-driven decision-making. The software offers a range of statistical techniques, data management tools, and visualization options to help users extract insights from their data.
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Lab products found in correlation
2 protocols using spss statistical software program for windows
1
Statistical Analysis of Survival Outcomes
2
Statistical Analysis of PPI Response
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Statistical analysis was performed with the SPSS statistical software program for Windows (version 20.1). The analysis of the differences between groups in the event of normal data distribution was performed by means of one-way analysis of variance (ANOVA). In the event that the verification of normal data distribution was negative, the Kruskall–Wallis test was used. The study of differences between dichotomous qualitative groups and variables was carried out using Pearson’s chi-square test. Data were expressed as mean ± standard deviation (DS) for qualitative variables and as a percentage of the total for quantitative ones. All p values were two-tailed with statistical significance indicated by a value of p < 0.05. The Spearman correlation test was used to analyze the association between PGI levels and response to PPI in terms of G17 elevation. All variables with a p > 0.10 were considered for inclusion through a manual forward stepwise variable selection process in a multivariable logistic regression analysis.
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