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Spss 25.0 statistical software for windows

Manufactured by IBM
Sourced in United States

SPSS 25.0 is a statistical software package for Windows that provides data analysis, visualization, and predictive modeling capabilities. It offers a wide range of statistical techniques, including regression analysis, factor analysis, and time series analysis, among others. The software is designed to help users analyze and interpret data effectively, supporting decision-making processes.

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Lab products found in correlation

3 protocols using spss 25.0 statistical software for windows

1

MRI Findings and Disease Duration in JIA

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The Kolmogorov–Smirnov test showed a not normal distribution of the variables; therefore, a non-parametric approach was adopted. The Chi2 test was applied referring to the categorical variables to compare JIA and control groups. The relationship analysis between the MRI findings and the mean duration of disease was performed through the Chi2 test. With reference to numerical variables, the Mann–Whitney test was applied to compare the groups. In order to assess any significant interdependence between the mean disease duration and VAS vs. the other clinical parameters, the Spearman correlation test was used.
All numerical variables are reported as median and interquartile range (IQR), whereas the categorical data are presented as numbers and percentages. Statistical analyses were performed using SPSS 25.0 statistical software for Windows (SPSS Inc., Chicago, IL, USA). p-values < 0.05 were considered statistically significant.
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2

Survival Analysis of Treatment Regimens

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Data were analyzed using SPSS 25.0 statistical software for windows (SPSS Inc., Chicago, IL). Clinical variables are presented as numbers and relative frequencies or medians and interquartile ranges (IQRs). A t test or analysis of variance was used to compare normally distributed continuous variables, and the Mann–Whitney U test or Kolmogorov–Smirnov test was used to compare nonnormally distributed continuous variables. The Kaplan-Meier (K-M) method was used to compare differences in the prognoses of patients receiving different treatment regimens. Factors potentially affecting prognosis were subjected to univariate Cox regression analysis, and those with a P value <0.2 were included in the multivariate Cox regression model. The binary logistic regression model included factors with a P value <0.2 were included in the multivariate logistic regression analysis. P values <0.05 were considered statistically significant.
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3

Estimating Cancer Risks in Chinese Population

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Data were analyzed using SPSS 25.0 statistical software for Windows (SPSS, Chicago, IL, USA). The Kaplan-Meier method and the Cox proportional hazards model were used to estimate the cumulative cancer risks. The relative cancer risk was calculated using Poisson regression analysis. The age, sex, and tumor-specific incidence of the general Chinese population from 1989 to 2015 were derived from the National Cancer Center, Beijing, China (18 (link)–25 (link)).
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