The association between the presence and relative abundance of H. fluviatilis and the climatic, water and soil variables was evaluated by logistic regression using the logit transformation as a link function and assuming a binomial distribution. Overdispersion of relative abundance data was accommodated by using a quasibinomial distribution. Logistic regression analyses were carried out with the glm function of the R package “Tidyverse” [25 (link)]. The percentage of H. fluviatilis reads was compared between autumn and spring by a mixed model using location as a random factor. Lesion length obtained in the inoculation test was compared between inoculated and non-inoculated seedlings across tree species with an ANOVA in JMP Pro (version 15.2.0; SAS Institute Inc., Cary, NC, USA). For each seedling, we averaged the lesion above and below the inoculation point. In order to meet the normality assumption, lesion length was log-transformed.
Jmp pro version
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JMP Pro is a statistical discovery software developed by SAS Institute. It provides advanced data analysis and visualization capabilities to aid in exploring, analyzing, and interpreting complex data sets.
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Lab products found in correlation
21 protocols using jmp pro version
1
Evaluating H. fluviatilis Abundance Drivers
2
Energy Malnutrition Risk Factors Assessment
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Data were represented as median (interquartile range (IQR)), range, or number. Values for differences between the energy malnutrition and non-energy malnutrition groups were analyzed by use of Wilcoxon rank-sum tests. In addition, logistic regression analysis was used to analyze the independent factors related to energy malnutrition. For the selection of candidates for logistic regression analysis, we used the single factor regression analysis (Spearman’s tests). Factors associated with energy malnutrition were profiled using decision tree analysis. A decision-tree algorithm is a data-mining technique that reveals a series of classification rules by identifying priorities and therefore allows clinicians to choose an option that maximizes the benefit for the patient [30 (link)]. Decision trees are a popular modeling technique in economics and clinical practice and have proved their usefulness in human medicine [31 (link),32 (link),33 (link)]. Finally, the Receiver Operating Characteristic (ROC) curve using the Youden index was used to determine the best cutoff value of independent factors to discriminate RQ < 0.85. All statistical analyses were conducted using Statistical Analysis Software (JMP Pro version 15.0; SAS Institute, Cary, NC, USA). The statistical significance level was set at p < 0.05.
3
Black Soldier Fly Life-History Traits
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A nested analysis was used to determine the impact of diet protein:carbohydrate and moisture content on life-history traits of the black soldier fly. Amount of diet provided, time (days) required for 40% prepupation, prepupal size, adult longevity, and egg production were compared across all diet-moisture treatments using analysis of variance followed by the Tukey–Kramer HSD test, and trial differences (significance set at p < 0.05) were tested using the paired t-test. Interactions between diet protein:carbohydrate, diet moisture, and trial on each life-history parameter were also examined. All analyses were done in JMP® Pro, Version 12.0.0 (SAS Institute Inc. 2015, Cary, NC, USA).
4
Maternal Folate Metabolism Dynamics
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The distributions of serum 5-MTHF, FA and total homocysteine levels used in the analysis were skewed, so continuous variables are shown as medians and interquartile ranges. The Wilcoxon signed-rank test was used to compare the folate metabolism-related substance levels in maternal serum between each blood sampling period (n = 113), and to compare the FA intake between early and late pregnancy (n = 118). Bonferroni correction was used to adjust for multiple comparisons (p < 0.0167). The difference between maternal blood and cord blood was tested using the Wilcoxon signed rank test (n = 114). Spearman’s rank correlation coefficient was used for the correlation between two variables. The significance level was p < 0.05 (two-tailed test). All statistical analyses were performed using JMP® Pro version 12.2.0 (SAS Institute Japan, Tokyo, Japan).
5
Comparative Analysis of Perception in IM and GS
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All statistical analyses were completed using the JMP Pro version 9.0.0 statistical package (© SAS Institute, Inc., Cary, NC). Perceptions of the IM and GS programs were compared with Student t-tests. Group comparisons were performed using ANOVA or Kruskal-Wallis testing as appropriate. Directionality of statistically significant differences was determined using Tukey-Kramer pairwise testing or non-parametric pairwise methods as appropriate. To assess whether each numeric response question was significantly different from a “neutral” score of 50, two-tailed t-tests were utilized. Descriptive statistics were calculated to characterize responses to multiple choice questions. Because of the small sample sizes of each subgroup being compared, we used Wilcoxon Rank Sum and Kruskal-Wallis tests to determine absolute differences between undergraduate training groups and non-parametric pairwise comparisons to determine directionality. To correct for multiple comparisons, a significance threshold of 0.01 was set before beginning statistical analysis.
6
Toxicity Assessment of Chemical Compounds
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Data are presented as means ± 95% confidence intervals (CI). All parameters were compared across treatments with one-way analysis of variance (ANOVA, n = 3, p < 0.05) using the JMP software (JMP® Pro version 13.1, SAS Institute, USA). Multiple comparison tests based on the least significant difference (LSD) were then carried out to find significant differences (p < 0.05) from controls and between treatments. The effective concentration at which 50% inhibition occurs (EC50) was estimated by the linear interpolation method using ToxCalc 5.0 (Tidepool Science, USA). The coefficient of variation (CV), the standard deviation expressed as a percentage of the mean, was calculated to estimate the precision of test values.
7
Predicting First-Pass Success in CTI
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Statistical analysis was performed using JMP Pro version 13.0 for Macintosh (SAS Institute, Cary, NC, USA). Results are expressed as median (interquartile range). Qualitative data are presented as numbers and percentages. The nonparametric Mann–Whitney U test was used to test for differences between the two groups. Pearson’s chi-square test was applied for categorical variables. The receiver operating curve (ROC) was used to determine the best cut-off value of CTI depth for first-pass success. The best cut-off value was determined according to maximum Youden’s index. A p value < 0.05 was considered statistically significant.
8
Predictors of Successful CTI Ablation
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Statistical analysis was performed using JMP Pro version 16.0 for Macintosh (SAS Institute, Cary, NC). Results are expressed as median and interquartile range (IQR). Qualitative data are presented as number and percentage. The nonparametric Mann-Whitney U test was used to test for differences between the 2 groups. Pearson’s chi-square test was applied for categorical variables. Univariate and multivariate logistic regression analyses were performed to determine independent predictors of first-pass success of CTI linear ablation. The receiver-operating characteristic (ROC) curve was used to determine the best cutoff value of local voltage parameters for the conduction gap. The best cutoff value was determined according to maximum Youden index. A P value <.05 was considered to be statistically significant.
9
Prostate Cancer Survival Analysis
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The statistical analyses were carried out using the JMP® Pro, Version 11.0.0 software package (SAS Institute, Inc., Cary, NC, USA). The PSA failure-free rate was determined according to the Kaplan–Meier method, and the significance of clinicopathological parameters associated with PSA failure was assessed using the Cox proportional hazards regression model. The log-rank test and Kruskal–Wallis test were used to determine differences between the risk groups and groups of each number of risk factors. P < 0.05 was considered to indicate a statistically significant difference.
10
Evaluating Intervention Impacts Across Subpopulations
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All statistical analyses were performed using JMP® Pro version 13.1.0 (SAS, USA); differences with p < 0.05 were considered to be statistically significant, excluding participants with missing data.
The Kruskal-Wallis test and the chi-squared test, adjusted by Bonferroni correction (p < 0.0167), were used to compare participant characteristics. The Wilcoxon signed-rank test was used to perform within-group comparisons of pre-and post-intervention measurements. Regression analysis was performed using the above covariates as predictors. For each intervention group, subgroup analysis was performed between the different subpopulations defined by the baseline characteristics of each participant to investigate whether the impact of the intervention was different across these patient characteristics.
The Kruskal-Wallis test and the chi-squared test, adjusted by Bonferroni correction (p < 0.0167), were used to compare participant characteristics. The Wilcoxon signed-rank test was used to perform within-group comparisons of pre-and post-intervention measurements. Regression analysis was performed using the above covariates as predictors. For each intervention group, subgroup analysis was performed between the different subpopulations defined by the baseline characteristics of each participant to investigate whether the impact of the intervention was different across these patient characteristics.
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