All data in this study are the means ± SE of more than three biological replications (n > 3). The data were analyzed using Duncan’s multiple comparison range test at p = 0.05 with the SAS statistical software 8.2 or unpaired two-tailed Mann–Whitney nonparametric test using GraphPad Prism 8.0.2 for the significant differences. The interaction between the DIF and light intensity (LI) treatments on the specific investigated traits were determined using two-way ANOVA ‘Linear models’ of F-test according to Fisher’s least significant difference (LSD) test (ns, p > 0.05; *, 0.01 < p < 0.05; **, p < 0.01; and ***, p < 0.001) with the SAS statistical software 8.2. Bar graphs were plotted using GraphPad Prism 8.2 software. The correlation heat map was generated and PCA (principal component analysis) was performed using the Origin 2022 program.
Sas statistical software 8
Manufactured by SAS Institute
Sourced in United States
SAS statistical software (version 8.2) is a comprehensive platform for data analysis, management, and visualization. It provides a wide range of statistical and analytical capabilities for users to explore, model, and interpret data. The software is designed to handle large and complex datasets, offering efficient data processing and advanced statistical techniques. SAS statistical software (8.2) caters to the needs of researchers, analysts, and decision-makers across various industries and domains.
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Lab products found in correlation
5 protocols using sas statistical software 8
1
Statistical Analysis of Biological Replicates
2
Postoperative Mortality Predictors
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Results were expressed as total numbers (percentage) for categorical variables and mean ± standard deviation or median (25th to 75th percentiles) as appropriate for continuous variables. Continuous variables were compared by the unpaired Student t-test or nonparametric (Mann-Whitney) test as appropriate. Categorical variables were compared by the chi-square test or Fisher’s exact test as appropriate. Risk factors found to be predictive of postoperative mortality in bivariate analysis P-values <0.1 were entered into a multivariate logistic regression analysis using backward selection on a criterion of P <0.05. A P-value <0.05 was considered significant. Calibration was assessed using the Hosmer and Lemeshow goodness-of-fit test. Analysis was performed using SAS statistical software (8.2, Cary, NC, USA).
3
Risk Factors for Ventilator-Associated Pneumonia
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Results were expressed as number (percentage) for categorical variables and as median [25 th -75 th percentiles] for continuous variables. Categorical variables were compared using the chi-square test, the Kruskal-Wallis test, or Fisher's exact test, as appropriate. Continuous variables were compared using the nonparametric Mann-Whitney test.
Risk factors associated with a rst episode of SM-VAP with a p <0.05 in univariate analysis were entered into the multivariate logistic regression model. In cases of collinearity, only the most clinically relevant factors were entered in the multivariate model. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-t test. The signi cance level was set at p<0.05. Statistical analyses were performed using SAS statistical software (8.2, Cary, NC, USA).
Risk factors associated with a rst episode of SM-VAP with a p <0.05 in univariate analysis were entered into the multivariate logistic regression model. In cases of collinearity, only the most clinically relevant factors were entered in the multivariate model. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-t test. The signi cance level was set at p<0.05. Statistical analyses were performed using SAS statistical software (8.2, Cary, NC, USA).
4
Experimental Design for Data Analysis
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All data are presented as means ± Standard error of the mean (SEM). The experimental data were prepared by Excel 2016 software. The data were subjected to one-way ANOVA analysis in SAS statistical software 8.1 (SAS Institute, Inc. Cary, NC, USA), according to a completely randomized one-factorial design. Duncan’s test was performed to identify differences among groups. Significance was set at p < 0.05.
5
Rigorous Data Management and Analysis
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A structural data management system using Microsoft FoxPro® 2000 was designed for the study. All data were double-entered. Dual checks and error checks were performed on all batches of data to assure validity, integrity, and confidentiality of data. Data were analyzed with SAS statistical software 8.1 (SAS Institute Inc., Cary, NC, USA).
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