For statistical analysis of non-normal distributed data obtained by immunohistochemistry the IBM “Statistic Package for Social Sciences” SPSS program for Windows (version 24) was used, employing a Mann-Whitney U-test for two independent samples. P-values of less than or equal to 0.05 were considered to show statistically significant differences between CDV groups. Graphs were designed with GraphPad Prism® (GraphPad Software, version 7.04).
Spss program for windows version 24
Manufactured by IBM
Sourced in United States
SPSS, or Statistical Package for the Social Sciences, is a widely used software program for statistical analysis. Version 24 is designed for Windows operating systems. The core function of SPSS is to provide users with advanced statistical analysis tools and techniques to help identify trends, test hypotheses, and make data-driven decisions.
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Lab products found in correlation
6 protocols using spss program for windows version 24
1
Nonparametric Statistical Analysis of Immunohistochemistry Data
2
Statistical Analysis of H. pylori CagA
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The data were analyzed using SPSS program for Windows version 24 (IBM Corp., NY, USA). Discrete variables were tested using a Chi-square test, whereas continuous variables were tested using the Mann–Whitney U- and t-tests [23 (link)]. A two-tailed p<0.05 was considered statistically significant [24 ]. Data for the CagA C-terminal structure patterning and SHP2 binding intensity were analyzed using non-parametric analysis.
3
Statistical Analysis of Hospital Outcomes
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Data were analyzed using a statistical package for social science (SPSS) program for Windows version 24 (SPSS Inc. Chicago, IL, USA). The mean and standard deviation were used for normally distributed data; otherwise, the median and interquartile range were used for non-normally distributed ones, while categorical data were expressed as numbers and percentages. A Chi-square (X2) was used to compare two groups of categorical data. The Mann–Whitney test was used as appropriate to compare two groups of quantitative variables. Linear/logistic regression analyses were used to predict risk factors for longer hospital stay and mortality, with a confidence interval of 95% (p < 0.05) to represent the statistical significance of the results. The multivariate regression model included univariate logistic regression variables with p values < 0.05 as predictors of hospital stay or mortality. All statistical analyses used two-sided hypothesis tests with a P < 0.05 significance level.
4
Statistical Analysis of Clinical Data
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The SPSS program for Windows version 24 (SPSS Inc., Chicago, IL, USA) was used for the statistical analyses. An unpaired Student’s t-test was used to compare continuous clinical data, and the chi-square test was used to compare discrete variables. For continuous variables, data were presented as mean SD, while, for categorical variables, the number and percentage of patients were used. In the study, the normality of data distribution was ascertained using the Shapiro–Wilk test. Continuous variables were compared using an unpaired Student’s t-test when normally distributed, and the Mann–Whitney U test when asymmetrically distributed. Continuous variables were reported as mean ± standard deviation when normally distributed, and as median values (interquartile range (IQR)) when asymmetrically distributed.
Additionally, a multiple logistic regression was used to incorporate all variables that by univariate analysis demonstrated significant differences. Statistical significance was set at p < 0.05.
Additionally, a multiple logistic regression was used to incorporate all variables that by univariate analysis demonstrated significant differences. Statistical significance was set at p < 0.05.
5
Quantitative and Qualitative Analysis of Data
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For the quantitative variables, the descriptive statistics used were the mean, standard deviation (SD), and the range (maximal and minimal values). These values were calculated in Excel tables and with the SPSS program version 24 for Windows (IBM Corp., Armonk, NY, USA) for 1989–2017. For the qualitative variables, the numbers and percentages of each category were used.
The Student’s t-test was used to compare the means between two groups for quantitative variables, and the chi2 (link) test for qualitative variables through a contingency table or Fisher’s exact test if the expected frequencies were small. P < 0.05 was considered significant. The analyses were performed with the statistical package SPSS, version 24.
The Student’s t-test was used to compare the means between two groups for quantitative variables, and the chi2 (link) test for qualitative variables through a contingency table or Fisher’s exact test if the expected frequencies were small. P < 0.05 was considered significant. The analyses were performed with the statistical package SPSS, version 24.
6
Statistical Analysis of Quantitative and Qualitative Data
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For the quantitative variables, the descriptive statistics used were the mean, standard deviation (SD), and the range (maximal and minimal values). These values were calculated in Excel tables and with the SPSS program version 24 for Windows (IBM Corp., Armonk, NY, USA) for 1989-2017. For the qualitative variables, the numbers and percentages of each category were used. The Student's t-test was used to compare the means between two groups for quantitative variables, and the chi 2 test for qualitative variables through a contingency table or Fisher's exact test if the expected frequencies were small. P < 0.05 was considered signi cant. The analyses were performed with the statistical package SPSS, version 24.
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