All of the data are presented as the mean ± standard error (SE). The statistical analyses were performed using JMP11-0 statistical software (SAS Institute, Inc., Cary, NC). Pearson's chi-square test was used for the comparison of categorical variables. We compared data among the three groups using the analysis of variance (ANOVA) tests for continuous variables and Tukey's honestly significant difference (HSD) test. We compared variables between two groups using Student's t-test. A P value < 0.05 was considered to be statistically significant.
Jmp 11.0 statistical software
Manufactured by SAS Institute
Sourced in United States
JMP 11.0 is a statistical software application developed by SAS Institute. It provides data analysis and visualization capabilities for researchers and professionals. The software offers tools for exploring, modeling, and analyzing data to support decision-making processes.
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Lab products found in correlation
7 protocols using jmp 11.0 statistical software
1
Comparative Analysis of Experimental Groups
2
Comparing IVIG Therapy Outcomes
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Categorical data are reported as numbers and percentages, and continuous data are summarized with the median and range (i.e., the minimum and maximum values) unless otherwise indicated. To compare categorical variables or outcomes, such as the occurrence of infection or in-hospital death, between the IVIG group (n = 28 patients) and the control group, which included 29 patients who did not receive IVIG therapy, the chi-squared test was used. For inter-group comparisons of continuous data, the two-tailed two-sample t test was generally performed. If the original data exhibited a log-normal distribution, e.g., ICU length of stay (LOS), then raw data were log-transformed prior to analysis with the t test. A p value < 0.05 was assumed to indicate a statistically significant difference between the groups. The data analysis was performed using JMP® 11.0 statistical software (SAS Institute Inc., Cary, NC, USA).
3
Statistical Analysis of Experimental Data
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Samples were compared primarily using one-way ANOVA. In the case of a statistically significant one-way ANOVA result, Tukey’s honestly significant difference (HSD) test was performed. Test results yielding a P value less than 0.05 were assumed to indicate statistical significance. This part of the analysis was done with the JMP® 11.0 statistical software (SAS Institute, Cary, NC, USA).
4
Coronary Bifurcation Angle Analysis
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Continuous variables were expressed as mean ± standard deviation (SD). The 95% confidence interval (CI) was calculated as ± 1.96 SDs from the mean. Two-group comparisons were performed with unpaired Student’s t tests for means if the data were normally distributed or Mann–Whitney U-test if the data were not normally distributed. Correlation between continuous variables was performed using the Spearman correlation test. Multivariable linear regression analysis was performed to examine the independent correlations between FFRCT and baseline parameters. A hierarchical cluster analysis (Ward’s method) was performed to classify parameters related to bifurcation angles. Receiver operating characteristics curves were generated to determine the cut-off value that the highest diagnostic performance of an FFRCT ≤ 0.80. All statistical analyses were performed using JMP 11.0 statistical software (SAS Institute).
5
Burnout Risk Factors: Multivariate Analysis
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JMP 11.0 statistical software (SAS Institute, Inc., Cary, NC, USA) was used for data processing and analysis. A bivariate analysis was conducted using chi-square tests to identify the variables relevant to the multivariate analysis. Subsequently, a series of logistic models was generated, fitted by age, gender, and length of time on the job. On the basis of this, the odds ratios were obtained, which were transformed into prevalence ratios. The different dimensions of burnout were treated as dependent variables, and working conditions and stressful situations were treated as independent variables. The assumptions of the models were tested in all cases.
Finally, a cluster analysis, which is a multivariate method used to group people with the same variance together to determine whether the variables studied are associated with the likelihood of developing a particular disease, was performed. A hierarchical cluster analysis was carried out, and the Ward's minimum variance method with squared Euclidean distance was used to determine the number of clusters in a dendrogram. A correspondence analysis was performed using a chi-square test to analyze whether the clusters were associated with the different dimensions of burnout.
Finally, a cluster analysis, which is a multivariate method used to group people with the same variance together to determine whether the variables studied are associated with the likelihood of developing a particular disease, was performed. A hierarchical cluster analysis was carried out, and the Ward's minimum variance method with squared Euclidean distance was used to determine the number of clusters in a dendrogram. A correspondence analysis was performed using a chi-square test to analyze whether the clusters were associated with the different dimensions of burnout.
6
Brachypodium Genotypes Root System Analysis
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Twelve seeds of each of the Brachypodium spp. genotypes were sown in each of the two culture media and distributed in three replicates (4 seedlings per replicate). To compare the means of the RSA and W variables, the least-squares difference test (LSD) or t-Student was used, and the principal component analysis (PCA) of the RSA variables was done using StatGraphics plus v.5.1 software. Two-way hierarchical cluster analysis was performed from the genetic data (SSR markers) and phenotypic (RSA variables) using the JMP ® 11.0 statistical software (SAS Institute Inc., Cary, NC, USA) with Component analysis procedure [41].
7
Suboptimal TTE Study Analysis in ACHD
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Continuous values are expressed as mean AE SD or as proportions (%). Frequencies of suboptimal TTE study in each group were compared using x 2 [ 1 _ T D $ D I F F ] test and Fisher's exact test when appropriate.
Improvement score in each complexity of ACHD group [20] were compared with Kruskal-Wallis followed Dunn tests. A value of p < 0.05 was set as the threshold for significance. All statistical analyses were performed using JMP 11.0 statistical software (SAS Institute Inc., Cary, NC, USA).
Improvement score in each complexity of ACHD group [20] were compared with Kruskal-Wallis followed Dunn tests. A value of p < 0.05 was set as the threshold for significance. All statistical analyses were performed using JMP 11.0 statistical software (SAS Institute Inc., Cary, NC, USA).
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