Jmp 11

Manufactured by SAS Institute

Sourced in United States, Japan, Austria, Germany

JMP 11 is a statistical discovery software that provides visual data exploration, high-performance modeling, and discovery-driven analytics. It offers tools for data preparation, analysis, and reporting, allowing users to uncover insights from their data.

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Lab products found in correlation

Prism 6, by GraphPad (16 mentions) Prism 5, by GraphPad (11 mentions) R version 3, by R Project for Statistical Computing (6 mentions) Sas 9, by SAS Institute (5 mentions) Prism v6, by GraphPad (4 mentions) Prism 7, by GraphPad (4 mentions) Spss 20, by IBM (3 mentions) Prism 8, by GraphPad (3 mentions) Sas v9, by SAS Institute (2 mentions) Sas software, by SAS Institute (2 mentions) Spss 22, by IBM (2 mentions) Matlab, by MathWorks (2 mentions) Matlab 2014b, by MathWorks (2 mentions) Sas version 9, by SAS Institute (2 mentions) Statistical analysis system version 9, by SAS Institute (2 mentions) Spss statistics 22, by IBM (2 mentions) Spss amos, by IBM (1 mentions) Illustrator cs5, by Adobe (1 mentions) Simca p 11, by Sartorius (1 mentions) Spss 19, by IBM (1 mentions)

Close spelling variants of this product

JMP version 11 (334 citations) JMP software version 11 (88 citations) JMP Statistical Software, Version 11 (25 citations) JMP software for Windows, version 11.0 (10 citations) SAS JMP version 11 (9 citations) JMP 11.0 statistical software (7 citations) JMP 11 software program (7 citations) JMP version 11.0 for Windows (5 citations) JMP 11.0.0 statistical software (3 citations) JMP Pro 11 for Windows (3 citations)

758 protocols using jmp 11

Estrogen Responsive Genes Analysis

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Statistical analysis by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test was performed using JMP 11.0 (SAS, Cary, NC). Normality and equal variance of the data sets were analyzed by Shapiro-Wilk and O’Brien tests respectively, and those that did not pass both tests were transformed using log or arcsine methods as indicated in the figure legends. The dataset for the effects of estrogen on Vtg failed normality testing despite attempted transformations and, therefore, was rank transformed prior to ANOVA. Student’s standardized t-test was performed where specified using the online QuickCalcs calculator (GraphPad). See figure legends for details. Significance was set at P ≤ 0.05.
Statistical analysis by analysis of covariance (ANCOVA) was performed using JMP 11.0 (SAS) to determine the predictors of expression changes in estrogen responsive genes. Utilizing data from individual fish, we investigated the effect of the independent variables, ERα isoform expression (ERα_S or ERα_L), population (SC, NBH, NBH del), and their interactions on the dependent variable, expression of Vtg, AroB, or ERα. Variables were removed in a backwards, stepwise fashion if their effect was not significant (P < 0.05).

Cotter K.A., Nacci D., Champlin D., Chuprin J, & Callard G.V. (2014). Cloning of multiple ERα mRNA variants in killifish (Fundulus heteroclitus), and differential expression by tissue type, stage of reproduction, and estrogen exposure in fish from polluted and unpolluted environments. Aquatic toxicology (Amsterdam, Netherlands), 159, 184-197.

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Hygrosensory Behavior Assay Protocol

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Hygrosensory behavior was assayed as previously described (Knecht et al., 2016 (link)). Desiccation prior to analysis was performed using a modification of (Lin et al., 2014 (link)). Flies were sorted and placed in tubes containing a strip of filter paper soaked in 3% sucrose and let to dry. The vial stopper was pushed down below the vial lip, ~0.5 g Drierite spread over it, and Parafilm was placed over the top to seal the vial. Vials were kept in an incubator (25°C, 70% RH) for 6 hr before hygrosensory behavior was assessed as described above. Humidity preference data in Figure 3 did not conform to normal distributions (assessed by Shapiro-Wilk test, p<0.01) and were analyzed by Steel with control test using JMP11 (SAS). Humidity preference data in Figure 4 did conform to normal distributions and were analyzed using Tukey HSD using JMP11 (SAS).

Knecht Z.A., Silbering A.F., Cruz J., Yang L., Croset V., Benton R, & Garrity P.A. (2017). Ionotropic Receptor-dependent moist and dry cells control hygrosensation in Drosophila. eLife, 6, e26654.

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Differential Gene Expression Analysis

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The contigs were processed for read alignment and abundance estimation with Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) and RSEM (RNA-Seq by Expectation Maximization, http://deweylab.github.io/RSEM/). The expression level of each contig was calculated using the fragments per kilo base of exon per million mapped fragments (FPKM) method, which excluded sequencing discrepancies in the calculation of gene expression and the influence of different gene lengths. The FPKM values of all unigene contigs were measured using the RSEM package. For identification of differentially expressed genes (DEGs) among the three time courses using the log₂FPKM value, a one-way ANOVA was performed at P<0.05 using JMP 11 (SAS Institute, Cary, NC, USA). After the first round of DEG identification, FDR of < 0.05 and the absolute value of log₂ (fold change) >1 was used for thresholds to determine significantly different gene expression for the final DEG set. Based on expression patterns, DEGs were categorized using JMP 11(SAS Institute, Cary, NC, USA). The comparison of mRNA expression levels between RNA-Seq and qRT-PCR was evaluated using Pearson correlations computed by SAS (SAS Institute, Cary, NC, USA).

Nah G., Lee M., Kim D.S., Rayburn A.L., Voigt T, & Lee D.K. (2016). Transcriptome Analysis of Spartina pectinata in Response to Freezing Stress. PLoS ONE, 11(3), e0152294.

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Survival Analysis of Animal Model

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Data are presented as the means ± SEM. The log-rank test was used to detect differences in animal survival (Kaplan-Meier survival curves). All other comparisons were performed with the two-tailed Student's t-test. For performing multiple comparisons, all the P-values were adjusted by the Holm's method using the Microsoft Office Excel 2015 (BellCurve, Tokyo, Japan). Other statistical analyses were conducted using JMP11 (SAS Institute Japan, Tokyo, Japan). Other statistical analyses were conducted using JMP11 (SAS Institute Japan, Tokyo, Japan). A value of P<0.05 was considered to indicate a statistically significant difference.

Yoshida M., Taguchi A., Kawana K., Ogishima J., Adachi K., Kawata A., Nakamura H., Sato M., Fujimoto A., Inoue T., Tomio K., Mori M., Nagamatsu T., Arimoto T., Koga K., Hiraike O.W., Oda K., Kiyono T., Osuga Y, & Fujii T. (2018). Intraperitoneal neutrophils activated by KRAS-induced ovarian cancer exert antitumor effects by modulating adaptive immunity. International Journal of Oncology, 53(4), 1580-1590.

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Evaluating Seasonal Condition Changes

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For each of the four condition indices, a mixed model restricted maximum likelihood analysis was undertaken, with population type (vulnerable or not vulnerable), year (first year = moult 2007 and breeding 2008; second year = moult 2008 and breeding 2009) and season (breeding vs. moult) designated as fixed factors, and species designated as a random factor to account for general differences in measures among species (SAS institute JMP11.1.1). This ensured that the model was testing for differences in seasonal condition changes among predicted population types. Student's paired t-tests were used to identify where differences lay (SAS Institute, JMP11.1.1).

Maute K., French K., Legge S., Astheimer L, & Garnett S. (2015). Condition index monitoring supports conservation priorities for the protection of threatened grass-finch populations. Conservation Physiology, 3(1), cov025.

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Airway Tone and Asthma Pathophysiology

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Data analysis was performed using JMP 11.0.0 software (SAS Institute). Unpaired t-tests were used to compare the two participant groups in terms of the two primary outcomes, airway tone and airway tone heterogeneity, as well as in terms of spirometric outcomes, lung volumes, airway diameter, and airway wall thickness measurements. To compare changes in pulmonary function after maximum bronchodilation with albuterol and changes in airway dimensions and airway wall thickness after maximum bronchodilation within and between the healthy and asthma groups, a mixed within-between groups ANOVA was used. Simple linear regressions were performed to measure the correlation between airway tone or airway tone heterogeneity and Mch reactivity (PC₂₀), spirometric or lung volume outcomes, and outcomes of airway morphology. Logistic regressions were performed to examine the independent effects of airway tone and airway tone heterogeneity on the clinical phenotype (asthma and healthy status). Significance was accepted at P ≤ 0.05.

Brown R.H, & Togias A. (2016). Measurement of intraindividual airway tone heterogeneity and its importance in asthma. Journal of Applied Physiology, 121(1), 223-232.

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Survival Analysis of Tumor Recurrence

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Differences in clinicopathological data were compared between cases with and without recurrence employing the Chi-square test. The Chi square test was utilized when investigating associations between the TIL distribution and clinicopathological features in each group. The Kaplan-Meier method was used to estimate survival duration from the time-point of recurrence detection. Differences between overall survival curves were determined with the log-rank test. For both univariate and multivariate analyses, Cox regression was used to evaluate the influences of the variables on survival time. All of the data were analyzed employing JMP 11.0.0 (SAS Institute Inc., Cary, NC, USA) statistical software. A value of P<0.05 was considered to indicate a statistically significant difference.

Miyoshi Y., Shien T., Ogiya A., Ishida N., Yamazaki K., Horii R., Horimoto Y., Masuda N., Yasojima H., Inao T., Osako T., Takahashi M., Tomioka N., Wanifuchi-Endo Y., Hosoda M., Doihara H, & Yamashita H. (2018). Associations in tumor infiltrating lymphocytes between clinicopathological factors and clinical outcomes in estrogen receptor-positive/human epidermal growth factor receptor type 2 negative breast cancer. Oncology Letters, 17(2), 2177-2186.

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Comprehensive Statistical Analysis of Risk Factors

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Statistical analyses were performed using JMP® 11.0.0 statistical software (2013, SAS Institute Inc.). Trends for means were tested by linear contrast in one-way ANOVA and trends for percentage by Cochran Armitage trend test of proportions. If necessary, Bonferroni correction for the adjustment of p values was applied. Three-way ANOVA and logistic regression were used to determine a possible influence of sex, age groups and the year(s) of examination on risk factors.

Cífková R., Bruthans J., Wohlfahrt P., Krajčoviechová A., Šulc P., Jozífová M., Eremiášová L., Pudil J., Linhart A., Widimský J J.r., Filipovský J., Mayer O J.r., Škodová Z., Poledne R., Stávek P, & Lánská V. (2020). 30-year trends in major cardiovascular risk factors in the Czech population, Czech MONICA and Czech post-MONICA, 1985 – 2016/17. PLoS ONE, 15(5), e0232845.

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Statistical Analysis of Experimental Data

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Statistical analyses were conducted using JMP 11.00 (SAS Institute, Cary, NC, USA). To assess statistical significance, we performed 1-way or 2-way analysis of variance (ANOVA); results obtained by standard least square fits are shown. For multiple comparison tests, ANOVA analyses were followed by a post hoc Tukey’s honestly significant difference (HSD) test. The Steel-Dwass test, Pearson’s chi-square test or Fisher’s exact test was applied for non-parametric multiple comparisons. The threshold P values for statistical significance were <0.05 (*), <0.01 (**), and <0.001 (***).

Yoneshima Y., Abolhassani N., Iyama T., Sakumi K., Shiomi N., Mori M., Shiomi T., Noda T., Tsuchimoto D, & Nakabeppu Y. (2016). Deoxyinosine triphosphate induces MLH1/PMS2- and p53-dependent cell growth arrest and DNA instability in mammalian cells. Scientific Reports, 6, 32849.

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Prognostic Factors in Survival Analysis

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We selected the median of continuous variables as the cutoff value when analyzing the prognostic value of each clinical factor. To evaluate the effect of clinical variables on OS and PFS, univariate and multivariate Cox proportional hazard models were analyzed. Hazard ratios and 95% confidence intervals were derived.
We analyzed survival outcomes (OS and PFS) using the Kaplan–Meier method and performed a log-rank test to compare these estimates. The association between Hs-mGPS ≥ 1 and other clinical variables was tested using the chi-squared test and Wilcoxon’s signed-rank test. All tests were 2-sided, and a p < 0.05 was considered significant. We used the JMP statistical software for statistical analysis; JMP 11.0.0 (SAS Institute, Cary, NC, USA).

Ando K., Sakamoto S., Saito S., Maimaiti M., Imamura Y., Sazuka T., Sato N., Komiya A., Anzai N, & Ichikawa T. (2021). Prognostic Value of High-Sensitivity Modified Glasgow Prognostic Score in Castration-Resistant Prostate Cancer Patients Who Received Docetaxel. Cancers, 13(4), 773.

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