Normally distributed data are presented as the means ± SDs, and nonnormally distributed data are presented as the medians and interquartile ranges (IQRs). Groups of data with a normal distribution were compared using one-way analysis of variance followed by a post hoc Tukey–Kramer analysis. For data that were not normally distributed, the Kruskal–Wallis test followed by a post hoc Steel-Dwass analysis was used. Significance was set at p < 0.05. All statistical analyses were performed with JMP Pro 14 software (Statistical Analysis System Inc. Cary, NC, USA).
Jmp pro 14
Manufactured by SAS Institute
Sourced in United States, Japan, Germany
JMP Pro 14 is a statistical discovery software designed for data analysis and visualization. It provides a range of tools for exploring, modeling, and interpreting data. JMP Pro 14 supports various data types and offers features for regression analysis, clustering, and predictive modeling.
Automatically generated - may contain errors
Lab products found in correlation
Prism 8, by GraphPad (23 mentions)
Sas 9, by SAS Institute (14 mentions)
Prism 7, by GraphPad (11 mentions)
Sas version 9, by SAS Institute (5 mentions)
R version 3, by R Project for Statistical Computing (4 mentions)
Calcusyn software, by Biosoft (4 mentions)
Prism 9, by GraphPad (3 mentions)
Spss statistics base 22, by IBM (3 mentions)
Matlab r2015a, by MathWorks (3 mentions)
Prism 6, by GraphPad (2 mentions)
Prism 5, by GraphPad (2 mentions)
Spss statistics 25, by IBM (2 mentions)
Expert statview, by SAS Institute (2 mentions)
Spad 502, by Konica Minolta (2 mentions)
Spss statistics 24, by IBM (2 mentions)
Originpro 2018, by OriginLab (2 mentions)
Sas software, by SAS Institute (2 mentions)
Sas v9, by SAS Institute (2 mentions)
Originpro 9, by OriginLab (1 mentions)
Geneseek, by Neogen (1 mentions)
834 protocols using jmp pro 14
1
Statistical Analysis of Experimental Data
2
Pomegranate Pest Analysis: Sex Ratio and Population Dynamics
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Data on the 2016 pomegranate trial was not statistically analyzed, because ovisacs were only found on two plants. In 2019, one-way analysis of variance (ANOVA) was used to analyze the number of male pupae and gravid females found on 14 pomegranate cultivars. Sex ratio (male to female ratio) was calculated using the mean number of male pupae divided by gravid females for each plant species and cultivars. Log transformed of the original data (No. of CMBS +1) was performed to improve normality of data distribution. Data for the number of male pupae and gravid females on seven genera from June to December 2019 were analyzed using multivariate analysis of variance (MANOVA) for repeated measures of all infested plant species and cultivars. Univariate split-plot approach ANOVA was also conducted for the numbers of male pupae and gravid females recorded on all infested plant species and cultivars, and multiple means were compared using All Pair, Tukey HSD with JMP software (JMP Pro14, Statistical Analysis System, Cary, NC, USA).
3
Genotypic Variance Analysis for Disease Severity
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Analysis of variance (ANOVA) was performed using JMP® Pro 14.1 (SAS Institute Inc., Cary, NC) for the separate experiments with the effect of genotype and replication considered random. Further, an ANOVA of the combined experiments considered the random effects for genotype, replication, experiment and the interaction between genotype and experiment. BLUPs for each genotype, adjusted by the grand mean, was used for QTL mapping. Broad-sense heritability (H2) was calculated from the ANOVA table as Vg / [Vg + Vg×expt/expt + Ve/rep × expt], where Vg = genotypic variance, Vg×expt = genotype by experiment variance, Ve = error (residual) variance, rep = number of replications, and expt = number of experiments (Holland et al. 2003 ). The distributions of disease severity within each experiment and in the joined data were tested for deviations from normality with Shapiro–Wilk tests (Shapiro and Wilk 1965 ). Correlation between experiments was assessed using pairwise Pearson correlations (r) calculated in JMP® Pro 14.1 (SAS Institute Inc., Cary, NC).
4
Semaglutide Impacts on Eating Behavior
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The data are presented as the mean ± standard deviation, and were analyzed using commercial software (JMP Pro 14.1; SAS Institute, Cary, NC, USA). The time-course change for each parameter was statistically evaluated using the one-way ANOVA test. We examined the data to confirm the normality of the data distribution using the Shapiro–Wilk test prior to the ANOVA test (JMP Pro 14.1; SAS Institute, Cary, NC, USA). p < 0.05 was considered statistically significant. Next, we calculated the correlation coefficients among changes in BW, FBS, HbA1c and eating behavior 3 and 6 months after semaglutide treatment by simple regression analyses. Finally, multiple regression analyses were conducted for changes in HbA1c as a dependent variable, and changes in eating behaviors as independent variables.
5
Salmonella Recovery and HDP Expression
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Salmonella recovery levels (cfu/g of cecal contents, n ¼ 6 per treatment) were analyzed by ANOVA using the JMP Pro 14 software (SAS Institute, Cary, NC, USA). The model included the main effect of log10 cfu, sorted by day. Significant differences (p < 0.05) were further separated using Tukey's test. Chi-Square analysis was performed to determine significant differences between groups in S. typhimurium recovery levels and ceca colonization. Changes in expression of HDP (n ¼ 5 per treatment) were analyzed by ANOVA in the JMP Pro 14 software (SAS Institute). The pooled standard error was obtained from ANOVA. The model included the main effect of treatment, sorted by tissue and genes. Significant differences (p < 0.05) were further separated using Tukey's test.
6
One-Way ANOVA and Correlation Analysis
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Statistical analysis (one-way analysis of variance for a completely randomized design) was performed using JMP Pro 14 (SAS Institute, Cary, NC, USA). Means were separated using Tukey's honestly signi cant difference (HSD) test (α = 0.05). Correlations between cell expansion rate and EXP gene transcript abundance in fruit were determined using pairwise correlations using JMP Pro 14 (SAS Institute, Cary, NC, USA).
7
Evaluating Insecticide Impact on Bee Foraging
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Once bees had been adequately trained to both the feeders and feeding time, video recordings using Go Pro Hero 3+ Black Edition cameras (GoPro, San Mateo, CA) were begun on day 0 to record the bees that were at the feeder and those that were actively feeding. Recordings were analyzed at 5-min intervals during the peak recruitment and feeding period (50 min) for bees that were actively feeding. The number of actively feeding bees relative to time was recorded as bees in 'attendance'. Chambers were randomly assigned treatments and their feeders were dosed with 84 mg ai/liter of PQZ in treated sucrose solutions during the exposure times in days 1-4. The control chambers were only given the 50:50 water: sucrose solution. Data from days 0 through 4 were analyzed as above where a multivariate analysis of variance was used to model a repeated measures ANOVA (JMP Pro 14, SAS Institute). After the repeated measure analysis, each day was analyzed using an ANOVA (JMP Pro 14, SAS Institute). All 5 d of the experiment constituted one experimental run. The 2015 field season allowed for two such runs to be conducted with a total of three control nucs and three PQZ-treated nucs. In 2016, one run with two control nucs and two PQZ-treated nucs was carried out.
8
Fruit EXP Gene Expression Analysis
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Statistical analysis (one-way analysis of variance for a completely randomized design) was performed using JMP Pro 14 (SAS Institute, Cary, NC, USA). Means were separated using Tukey’s honestly significant difference (HSD) test (α = 0.05). Correlations between cell expansion rate and EXP gene transcript abundance in fruit were determined using pairwise correlations using JMP Pro 14 (SAS Institute, Cary, NC, USA).
9
Evaluating Live Performance and Blood Physiology in Animals
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Data were analyzed using JMP Pro 14 (“SAS Institute,” 2009 ). Live performance and blood physiology data were analyzed as a two-way ANOVA by day with means separated with Tukey's adjustment for multiple comparisons test (P < 0.05). Pen was considered the individual experimental unit for live performance parameters, while individual blood draw was considered the experimental unit (n = 20) for blood physiology parameters. Analysis of 42 d health status scores were analyzed as independent variables with similar dependent variables as above and the GLM procedure of SAS. The PROC GLIMMIX procedure of JMP Pro 14 (“SAS Institute,” 2009 ) was used to analyze BF, FH, Thymus, data shown as % detectable.
Blood physiology data were analyzed as a three-way ANOVA with means separates with Tukey's adjustment for multiple comparisons test (P < 0.05) using strain, sex, and day of age as independent variables; data are presented as main effect means for brevity, with interaction model p-values indicated and described fully in text. Finally, linear and quadratic regression models were used to explore the influence of biomarker over age, data presented as regression coefficients.
Blood physiology data were analyzed as a three-way ANOVA with means separates with Tukey's adjustment for multiple comparisons test (P < 0.05) using strain, sex, and day of age as independent variables; data are presented as main effect means for brevity, with interaction model p-values indicated and described fully in text. Finally, linear and quadratic regression models were used to explore the influence of biomarker over age, data presented as regression coefficients.
10
Gastric Lipolysis Kinetics by Droplet Size
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The change of the volume-weighted mean droplet size along gastric digestion was evaluated. For this, the statistical software JMP (JMP pro14, SAS Institute Inc., Cary, NC, USA) was used to carry out one-way ANOVA and Tukey HSD comparison analyses to determine significant differences at a 5% significance level (P < 0.05).
Single-response modeling was performed to evaluate the overall kinetic behavior of gastric lipolysis as affected by droplet size. The software utilized was JMP (JMP pro14, SAS Institute Inc., Cary, NC, USA).
The response selected for modeling was the % of TAG digested during the gastric phase with respect to the initial emulsion. For this purpose, an empirical, fractional conversion model was chosen to model the data and compare the digestion behavior as affected by droplet size. The parameters to be estimated are indicated in equation ( 5), where C (%) represents the predicted response at time t during the gastric phase; Cf (%) is the maximum or plateau value of the parameter;; and k (min -1 ) is the reaction rate constant of the evaluated process.
The estimated kinetic parameters (Cf (%) and k (min -1 )) were compared by calculating confidence intervals (95%).
Single-response modeling was performed to evaluate the overall kinetic behavior of gastric lipolysis as affected by droplet size. The software utilized was JMP (JMP pro14, SAS Institute Inc., Cary, NC, USA).
The response selected for modeling was the % of TAG digested during the gastric phase with respect to the initial emulsion. For this purpose, an empirical, fractional conversion model was chosen to model the data and compare the digestion behavior as affected by droplet size. The parameters to be estimated are indicated in equation ( 5), where C (%) represents the predicted response at time t during the gastric phase; Cf (%) is the maximum or plateau value of the parameter;; and k (min -1 ) is the reaction rate constant of the evaluated process.
The estimated kinetic parameters (Cf (%) and k (min -1 )) were compared by calculating confidence intervals (95%).
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