All data (with exception of Fig. 4E ) are presented as mean ± standard deviation in at least three experiments in triplicate (n ≥ 9). Data sets were analyzed statistically using JMP Statistical Discovery™ software by SAS and tested for normality using the Shapiro-Wilks test (“normal distribution fit” tool -JMP software). Two-tailed significance tests were performed with p < 0.05 considered significant. Non-parametric analyses were done with the Mann-Whitney-U-Test (Wilcoxon rank-sum test), parametric with the t-test. Detailed statistical analysis of the data shown in Figs 1 and 2 is reported in Supplemental Statistical Tables 1 , 2 , 3 and 4 .
Jmp statistical discovery
Manufactured by SAS Institute
Sourced in Japan, United States
JMP Statistical Discovery is a statistical analysis software product developed by SAS Institute. It is designed to provide users with powerful statistical analysis tools and data visualization capabilities. The core function of JMP Statistical Discovery is to enable users to explore, analyze, and gain insights from their data through a user-friendly interface.
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Lab products found in correlation
14 protocols using jmp statistical discovery
1
Detailed Statistical Analysis of Experimental Data
2
Statistical Analysis of Diabetes Impact
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Statistical analysis by comparison between two groups (non-diabetes vs T2DM, non-diabetes with surgery vs T2DM with surgery, non-diabetes vs non-diabetes with surgery and T2DM vs T2DM with surgery) was conducted using EZR [36 (link)] and JMP Statistical Discovery™ (SAS Institute Inc, Japan). The data for the behavioral test and for the hippocampal NA and HbA1c levels were analyzed by the Mann–Whitney U test using EZR. Body weight, fasting blood glucose level and IPGTTs were subjected to two-way repeated measures analysis of variance (ANOVA) by using JMP. P values less than 0.05 were considered to indicate a significant difference. In the figures, the error bars of data are presented as medians ± quartile for the Mann–Whitney U test and mean ± SD for ANOVA.
3
Statistical Analysis of Variance
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Analysis of Variance (ANOVA) or Kruskal-Wallis tests were used with their corresponding contrasts, and previous variance homogeneity comparison. Values restricted to p < 0.05, were considered statistically significant [21 ]. JMP Statistical Discovery from SAS version 8.0 software was used.
4
Statistical Analysis of Fungicide Experiments
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All statistical analysis was performed using ‘JMP Statistical Discovery’ software package, version 10.0 (SAS institute). One-way ANOVA was used to assess the analysis of datasets from dual culture assay, compatibility of fungicides, pot, and field assay. Fisher’s least significant difference (LSD) means comparison (P = 0.05) was performed to determine statistical significance. The statistical analysis of association between variables was compared using the pairwise Pearson’s correlation coefficient.
5
Statistical Analysis of Research Data
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All of the statistical analyses were performed using JMP Statistical Discovery software (SAS Institute Inc., Cary, NC, USA).
6
Correlation Analysis of ESAS-Score
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The statistical analysis was based on descriptive statistical methods. Correlation analysis of the ESAS-score with the meningeal reaction and with the neuroscore was performed by calculation of the Spearman’s correlation coefficient. The statistical analysis was performed using the JMP statistical software (JMP Statistical Discovery™ From SAS Institute, Version 14.2.0).
7
Statistical Analysis of Research Variables
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We performed all statistical analyses using Microsoft Excel (version 10, Redmond, Washington, US) and JMP Statistical Discovery from SAS (version 13, Cary, North Carolina, US). We compared study variables using Student’s t-test for continuous variables expressed as the means with standard deviations, the Wilcoxon rank sum test for ordinal variables expressed as medians and interquartile ranges, and one-way analysis of variance (ANOVA) for pairwise comparisons. Significance was set as P value < 0.05. The power analysis estimated a total of 140 exposures were required to detect a 0.8 standard deviation effect size.
8
Gastric Emptying Dynamics in Cats
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The normality of data distribution was tested by the D'Agostino and Pearson omnibus normality test. With the exceptions of the subjects' age and BCS, all data were normally distributed. Unless otherwise stated, all data are reported as means and standard deviations. A mixed model ANOVA with cat as a random effect and intervention as a fixed effect was used to assess the difference in the amount of food consumed and time required for test meal consumption between drugs. A mixed model ANOVA with cat as a random effect and intervention as fixed effect was used to assess the difference in each fractional GE time, MI, CA and CF at each time point and MI AUC, CA AUC and CF AUC among groups. Post hoc pairwise comparisons were examined with the Tukey's test as appropriate. Two statistical software programs were used (JMP Statistical Discovery, SAS, Cary, NC, and GraphPad Prism, GraphPad Software Inc., La Jolla, CA) as appropriate, and a value of P < .05 was considered significant.
9
Comparative Analysis of Musculoskeletal Tissue
10
Comparative Analysis of Clinical Outcomes
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Data are expressed as number of cases and median (minimum-maximum). Comparisons between two groups were performed by the Wilcoxon test, and the difference was considered significant at p<0.05. All statistical analyses were conducted using the software program called JMP Statistical Discovery (SAS, Version 11, SAS Institute Japan, Tokyo, Japan).
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