Jmp 13

Manufactured by SAS Institute

Sourced in United States, Japan, Germany, Australia, United Kingdom, China

JMP 13 is a data analysis software tool developed by SAS Institute. It provides interactive and visual data exploration, statistical modeling, and reporting capabilities. The core function of JMP 13 is to enable users to analyze, visualize, and gain insights from their data.

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Lab products found in correlation

Sas 9, by SAS Institute (26 mentions) Prism 8, by GraphPad (9 mentions) Prism 6, by GraphPad (7 mentions) Prism 7, by GraphPad (6 mentions) Prism v7, by GraphPad (4 mentions) Originpro 8, by OriginLab (3 mentions) Sas version 9, by SAS Institute (3 mentions) Matlab script, by MathWorks (3 mentions) Statview software, by Abacus (3 mentions) Prism software, by GraphPad (2 mentions) Spss 23, by IBM (2 mentions) Matlab statistics toolbox, by MathWorks (2 mentions) Prism ver 8.4.2 for windows, by GraphPad (2 mentions) Prism v 8.4.2 for mac, by GraphPad (2 mentions) Matlab, by MathWorks (2 mentions) Labview, by Wavemetrics (2 mentions) Igor pro, by Wavemetrics (2 mentions) R version 3, by R Project for Statistical Computing (2 mentions) Prism 5, by GraphPad (2 mentions) Statview 5, by SAS Institute (2 mentions)

Spelling variants of this product

JMP Pro version 13 (111 citations) JMP ver.13 (20 citations) JMP software program, version 13.2 (5 citations) JMP 13 for Windows (4 citations) JMP Pro (Ver. 13, (4 citations) JMP 13.2.1 statistical software (3 citations) JMP Pro 13 statistical package (2 citations) JMP Statistics, version 13.2.1 (2 citations) JMP Pro 13.0 for Mac (2 citations) JMP ver 13.0 for Mac (2 citations)

781 protocols using jmp 13

Evaluating Treatment Effects Using ANOVA

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Statistical analyses were performed using JMP 13 software (SAS Institute Inc., Cary, NC, U.S.A.) [26 (link), 27 (link)]. The
data were compared using a one-way analysis of variance (ANOVA) and pooled t-tests fitted in JMP 13 (SAS Institute Inc.). P value was adjusted through
comparisons of all pairs using Tukey-Kramer HSD test. Dunnett’s multiple comparisons procedure was used to compare the MSW and HSW data at the end of the experiment (9 month) against day 0
(pretreatment control). The values are presented as the mean ± standard error (SE). All differences were considered significant when P<0.05.

GHANEM M., ZEINELDIN M., EISSA A., EL EBISSY E., MOHAMMED R, & ABDELRAOF Y. (2018). The effects of saline water consumption on the ultrasonographic and histopathological appearance of the kidney and liver in Barki sheep. The Journal of Veterinary Medical Science, 80(5), 741-748.

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Hierarchical clustering and environmental correlates

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For the hierarchical clustering, we generated a progressive alignment of 262 SNPs for the MOT1 gene tree and 37,574 SNPs for the whole-genome tree from 74 plants from Busoms et al. (2018) . Pairwise genetic distance between individuals and between demes was calculated using the Maximum Likelihood statistical method and Jukes and Cantor substitution model using JMP 13.0 (SAS, 2016 ). Mean-standardized values (−1< value >1) of elemental contents of soil and leaf material were used to represent the radar plots and compare between MOT1 allelic variants (Supporting Information Dataset S2). One-way or multivariate ANOVA was used to test for significant differences between means of fitness, elemental contents of soil and leaf material, gene expression and geostatistical variables. To test for correlations between two variables a Bivariate Fit was conducted. To perform multiple comparisons of group means we used Tukey’s HSD (Supporting Information Dataset S5) using JMP 13.0 (SAS, 2016 ).

Busoms S., Terés J., Yant L., Poschenrieder C, & Salt D.E. (2021). Adaptation to coastal soils through pleiotropic boosting of ion and stress hormone levels in wild Arabidopsis thaliana. The New phytologist, 232(1), 208-220.

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Evaluating Calf Gut Microbiome and Growth

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The Shapiro-Wilks test in SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used to check normality of body measurements at birth and during the preweaning period. The UNIVARIATE procedure in SAS 9.4 was used for body measurements between L-eff and M-eff groups at birth, cumulative DMI and cumulative BWG. The MIXED procedure in SAS 9.4 was used for repeated measures analysis of body measurements, daily DMI and ADG at d 14, 28 and 42 of age. Both RFI groups and time (day or week) were considered as fixed factors in the model, and the random effect was calf. Comparison of bacterial alpha diversity indices in hindgut microbial communities between L-eff and M-eff groups at birth was performed with the nonparametric Mann-Whitney unpaired t-test with JMP 13.2 (SAS Institute Inc., Cary, NC, USA). Permutational multivariate analysis of variance (PERMANOVA) utilizing a Bray-Curtis dissimilarity index, a non-parametric multivariate analysis of variance was run in JMP 13.2 (SAS Institute Inc., Cary, NC, USA) to determine differences in bacterial alpha diversity indices between RFI groups at d 14, 28 and 42. Linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify the differential genera between L-eff and M-eff groups. Significance was determined at P ≤ 0.05.

Elolimy A., Alharthi A., Zeineldin M., Parys C, & Loor J.J. (2020). Residual feed intake divergence during the preweaning period is associated with unique hindgut microbiome and metabolome profiles in neonatal Holstein heifer calves. Journal of Animal Science and Biotechnology, 11, 13.

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Multivariate Analysis of Metabolomics Data

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Principal component analysis was performed on autoscaled peak areas using the software Latentix 2.12 (Latentix Aps, Frederiksberg, Denmark). Analysis of variance was performed using JMP 13.0.0 (SAS Institute Inc., Cary, NC, USA) and least square means were compared by Student’s t-test (p ≤ 0.05) (JMP 13.0.0, SAS Institute Inc.).

Bak K.H., Petersen M.A., Lametsch R., Hansen E.T, & Ruiz-Carrascal J. (2018). Development of Volatile Compounds during Hydrolysis of Porcine Hemoglobin with Papain. Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry, 23(2), 357.

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Salinity Effects on Sperm Quality

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JMP 13 software (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis. JMP 13 was used to perform a one-way analysis of variance (ANOVA) and pooled t-tests on the data (SAS Institute Inc.). The P value was adjusted by comparing all pairs using the Tukey-Kramer HSD test. The MSW and HSW data at the end of the experiment (9 months) were compared to day 0 using Dunnett’s multiple comparisons procedure (pretreatment control). The correlations between the water salinity and sperm viability, morphology and sperm cell concentration were done using Spearman’s test. The values are shown as the mean standard error (SE). When the p-value was < 0.05, all differences were considered significant.

Mohamed R.S., Mohamed R.H., Wehrend A., Tahoun E.A, & Hussein H.A. (2024). The effects of saline water consumption on sperm parameters, testicular histopathology, hormonal and antioxidants concentrations in Barki Rams. BMC Veterinary Research, 20, 219.

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Comprehensive Signaling and Gene Analysis

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Data are presented as mean ± SEM. Scatterplots, correlations, heatmaps, and hierarchical clustering were performed using JMP 13 software (SAS Institute, Cary, NC). Signaling pathway analysis was performed using Ingenuity Pathways Analysis Software (Ingenuity Systems, Redwood City, CA), and gene ontology analysis was performed using the Gene Ontology Consortium web interface [44 (link),45 (link)]. ANOVA analyses were performed in SigmaPlot 11. One-way ANOVA was used when measuring the effect of a single factor, such as effect of Igf1 on proliferation. Two-way ANOVA was used when the effect of more than one factor was being assessed, such as when comparing the effect of age on gene expression between tibia and phalanx. p-Values were corrected for multiple comparisons, whenever applicable, using the Holm-Sidak method.

Lui J.C., Jee Y.H., Garrison P., Iben J.R., Yue S., Ad M., Nguyen Q., Kikani B., Wakabayashi Y, & Baron J. (2018). Differential aging of growth plate cartilage underlies differences in bone length and thus helps determine skeletal proportions. PLoS Biology, 16(7), e2005263.

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Statistical Analysis of Experimental Data

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Data analysis, statistics and graphs were performed from the experimental data via Microsoft^® Excel^® (Microsoft Office Professional Plus 2013, Microsoft Excel 15.0.5023.100, Microsoft Corporation, Redmond, WA, USA), OriginPro^® 8.6 software (OriginLab^® Corporation, Northampton, MA, USA) and JMP^® 13 Software (SAS Institute, Cary, NC, USA). The significance of the difference between a pair of investigated formulation groups was calculated via a one-way analysis of variance (ANOVA) with post-hoc Tukey test in Minitab^® 17.1.0 software (Minitab, LLC, State College, Pennsylvania, PA, USA) with p < 0.05 as a minimal level of significance. Results were expressed as the mean value ± standard error. Three independently prepared parallel samples were made and studied in the investigations.

Németh Z., Pallagi E., Dobó D.G., Kozma G., Kónya Z, & Csóka I. (2021). An Updated Risk Assessment as Part of the QbD-Based Liposome Design and Development. Pharmaceutics, 13(7), 1071.

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Statistical Analysis of Continuous and Categorical Data

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Continuous data were expressed as mean ± standard deviation and compared using the Student t test. Categorical data were given as a percentage and compared with a chi-square test. Variables with a P value < 0.20 as tested in univariate analysis were incorporated into the multivariate models as continuous or dichotomous variables. A P value < 0.05 was considered statistically significant. Statistical analyses were performed with JMP 13 software (SAS Institute Inc., Cary, NC).

Khaled S., Hachicha Z, & Elkhateeb O. (2020). Left Ventricular Thrombus in Myocardial Infarction After Successful Primary Percutaneous Coronary Intervention: Prevalence and Predictors—A Middle Eastern Single-Centre Experience. CJC Open, 2(3), 104-110.

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Prognostic Factors Analysis in Oncology

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All analyses were carried out using JMP13 software (SAS Institute, Cary, NC, USA). Continuous and categorical data were analyzed by Wilcoxon rank sum and Pearson’s chi squared tests, respectively. Survival analyses were conducted by the Kaplan‐Meier method and log‐rank test. A Cox proportional hazards model was used to estimate hazard ratios (HR). The differential prognostic value of clinicopathological parameters was investigated through interaction tests. All P values were two‐sided; P < .05 was considered significant.

Shiota M., Terada N., Saito T., Yokomizo A., Kohei N., Goto T., Kawamura S., Hashimoto Y., Takahashi A., Kimura T., Tabata K., Tomida R., Hashimoto K., Sakurai T., Shimazui T., Sakamoto S., Kamiyama M., Tanaka N., Mitsuzuka K., Kato T., Narita S., Yasumoto H., Teraoka S., Kato M., Osawa T., Nagumo Y., Matsumoto H., Enokida H., Sugiyama T., Kuroiwa K., Inoue T., Mizowaki T., Kamoto T., Kojima T., Kitamura H., Sugimoto M., Nishiyama H, & Eto M. (2021). Differential prognostic factors in low‐ and high‐burden de novo metastatic hormone‐sensitive prostate cancer patients. Cancer Science, 112(4), 1524-1533.

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Statistical Analysis of Cell Fractions

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We used JMP 13 software (SAS, Cary, NC, USA) to carry out statistical analysis, to determine means and percentiles, and to perform ordinal logistic regressions. We determined whether the fractions of cells missing in two or more than two groups were equal using Fisher’s Exact test and Pearson’s chi-square test, respectively. We determined whether the survival functions of different groups were equal using the Peto-Prentice Wilcoxon test. p-values were calculated for control and experimental animals examined at the same time. We used the Bonferroni correction to adjust p-values when performing multiple comparisons.

Apfeld J, & Fontana W. (2017). Age-Dependence and Aging-Dependence: Neuronal Loss and Lifespan in a C. elegans Model of Parkinson’s Disease. Biology, 7(1), 1.

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