Values are presented as means ± SE. Analysis of variance (one-way ANOVA) and the Tukey–Kramer honest significant difference post hoc test were used to compare means. When comparing significant differences between only two groups, a Student’s t-test was applied. All data were tested for normal distribution prior to applying any statistical test. The significance level was P < 0.05 for all analyses, unless otherwise specified. Connecting letters report was used to display the results of the Tukey–Kramer test (P > 0.05). Results that share, or are connected by, the same letter do not differ statistically. Results that are not connected by a common letter do differ statistically. Notations of double letters, like ‘ab’, stand for two levels of significance ‘a’ and ‘b’. Asterisks were used for significant differences in the Student’s t-test. There is no special meaning for number of asterisks used. JMP 14 Pro software (SAS Institute, Cary, NC, USA) was used for the statistical analyses.
Jmp 14 pro software
Manufactured by SAS Institute
Sourced in United States
JMP 14 Pro software is a data analysis tool developed by SAS Institute. It provides advanced statistical and visual capabilities to explore, analyze, and model data. The software offers a range of features for data manipulation, statistical modeling, and reporting, enabling users to gain insights from their data.
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Lab products found in correlation
4 protocols using jmp 14 pro software
1
Antioxidant Activity Evaluation
2
Predicting Post-Hepatectomy Liver Failure
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Data were calculated as means, medians, frequencies, and percentages. We used the Mann–Whitney U test and the Kruskal–Wallis test to compare continuous variables. We used the χ2 test or Fisher’s exact test to compare categorical variables. The receiver operating characteristic (ROC) curves with the Youden’s index correction were estimated to determine the optimal cutoff values for analyzing the risk of PHLF [16 (link)]. Comparison of ROC curve analysis was performed by calculating the Standard Error of the area under the curve (AUC), and the differences between two AUCs [17 (link)]. Logistic regression analysis was used to perform univariate and multivariate analyses. Variables significant in univariate analyses were selected in the overall multivariate logistic regression model to identify PHLF predictive factors. All statistical tests were two-sided, and a p value of < 0.05 indicated significance. All analyses were performed with JMP14pro software (SAS Institute, Cary, NC, USA).
3
Curcumin's Neuroprotective Effects: A Comprehensive Analysis
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The statistical evaluation of data was done by B-W-Subject ANOVA, a Mixed Analysis of Variance Design for the difference in mean, the only probabilistic model correctly applicable to the type of experimental design to be evaluated: between groups (controls and untreated, curcumin); within observation periods (basal, 12 months). In the presence of significant interaction, treatment x time was applied. Tukey-Kramer Multiple Comparison Test for nonconfounded means to identify, among the observed mean, any differences within the planned observation periods and between the 2 groups to be compared. The variables analyzed with this procedure were MDS-UPDRS-III, COMPASS-31 score, H&Y score, NMSS score, skin sample rates, and adnexa rate. Multiple Regression, Prediction Profiler, and Spearman ρ were applied to study whether the clinical scales were influenced by confounders such as L-dopa dosage, age, sex, and disease duration. In particular, Spearman ρ for the correlative study between curcuminoid plasma analysis and clinical/demographic variables between basal and 12 months. All comparisons were conducted at p < 0.05 level. Statistical procedures were performed using the JMP14 Pro software from SAS Institute, Inc.
4
Triplicate Sample Preparation Analysis
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The processing trials were conducted in triplicate, using a fresh sample preparation each time; all analytical measurements were also conducted in triplicate. Data were analyzed using JMP 14 Pro software (SAS Institute Inc., Cary, NC). Statistical differences among means were determined using one-way ANOVA with a significance level α = 0.05.
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