Jmp 10.0 statistical software

Manufactured by SAS Institute

Sourced in United States

JMP 10.0 is a statistical software package developed by SAS Institute. It provides data analysis and visualization capabilities to users. The core function of JMP 10.0 is to assist in the exploration, analysis, and presentation of data using statistical methods.

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Lab products found in correlation

Spss statistics version 22.0 for windows, by IBM (1 mentions)

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JMP 10.0.0 statistical software JMP 10 statistical software JMP statistical software JMP software 10.0 JMP 10 JMP software

5 protocols using jmp 10.0 statistical software

Assessing Factors Influencing Graft Exposure

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Statistical assessment was done using IBM SPSS® Statistics version 22.0 for Windows. Level of significance was set to p < 0.05. Data presentation was performed by using JMP® 10.0 statistical software (SAS Institute, Cary, NC, USA). Grafting success, exposure rate and impact of such factors as Vacuum form splint, A-PRF® and flap design on the exposure rate.
For secondary outcome parameters possible risk factors (defect regions, defect and mesh sizes, smoking, tissue phenotype (thin and fragile phenotype, thick phenotype [25 (link)]), diabetes) for developing an exposure were defined. Statistical analyses were performed using Chi-Quadrat-Test and Fisher’s Exact-Test as appropriate for qualitative parameters, T-Test or Mann-Whitney-U-Test for quantitative parameters.

Hartmann A, & Seiler M. (2020). Minimizing risk of customized titanium mesh exposures – a retrospective analysis. BMC Oral Health, 20, 36.

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Tissue Microarray Data Analysis

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Tissue microarray data analysis was performed using JMP 10.0 statistical software (SAS Institute, Inc). The association between p140Cap expression and clinico-pathological parameters was evaluated using the Pearson chi-square test. For univariate and multivariate analysis, hazard ratios and 95% confidence intervals were obtained from the Cox proportional regression method. Differences in the growth rate of mouse tumours were analysed with Fisher’s Exact Test, or two-way ANOVA followed by Bonferroni multiple comparison post hoc test. Differences in acina area were evaluated using a Mann–Whitney non parametric t-test. For quantification, statistical significative differences were evaluated using unpaired t-tests. Error bar: s.e.m. using the Student’s t-test.

Grasso S., Chapelle J., Salemme V., Aramu S., Russo I., Vitale N., Verdun di Cantogno L., Dallaglio K., Castellano I., Amici A., Centonze G., Sharma N., Lunardi S., Cabodi S., Cavallo F., Lamolinara A., Stramucci L., Moiso E., Provero P., Albini A., Sapino A., Staaf J., Di Fiore P.P., Bertalot G., Pece S., Tosoni D., Confalonieri S., Iezzi M., Di Stefano P., Turco E, & Defilippi P. (2017). The scaffold protein p140Cap limits ERBB2-mediated breast cancer progression interfering with Rac GTPase-controlled circuitries. Nature Communications, 8, 14797.

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Statistical Methods for Biological Assays

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For radioactive assays, statistical analysis was performed using two-tailed Student’s t test with Excel when we compared two conditions. When more conditions were compared, Each Pair Student’s t test with JMP 10.0 statistical software (SAS Institute, Inc) was performed, which exploits the Fishers LSD test as a follow up to ANOVA.
Quantitation of the blots was performed with Photoshop. Statistical analysis was performed using two-tailed Student’s t test with Excel.
For migration assay, statistical analysis was performed using two-tailed Student’s t test with Excel.
For PMS and TEM, images were analyzed with ImageJ and statistical analysis was performed using GraphPad Prism. Two tailed paired Student’s t test was used to calculate statistical significance among different samples.

Pascolutti R., Algisi V., Conte A., Raimondi A., Pasham M., Upadhyayula S., Gaudin R., Maritzen T., Barbieri E., Caldieri G., Tordonato C., Confalonieri S., Freddi S., Malabarba M.G., Maspero E., Polo S., Tacchetti C., Haucke V., Kirchhausen T., Di Fiore P.P, & Sigismund S. (2019). Molecularly Distinct Clathrin-Coated Pits Differentially Impact EGFR Fate and Signaling. Cell Reports, 27(10), 3049-3061.e6.

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Quantitative Analysis of Protein Targets

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The data were analysed using Student T Test, Chi Square test or ANOVA test for multiple comparison in GraphPad Prism. All IF images are representative of at least 3 independent experiments. Western blots evaluations and IF evaluations are averages of at least 3 independent experiments. For TMA analysis differences between experimental groups were examined for statistical significance using the nonparametric comparisons for each pair using Wilcoxon Method (Figure 7B) or Pearson Chi Square test (Figure 7C). Where applicable, data are expressed as average ± SD. TMA data analysis were performed using JMP 10.0 statistical software (SAS Institute, Inc).

Baschieri F., Confalonieri S., Bertalot G., Di Fiore P.P., Dietmaier W., Leist M., Crespo P., Macara I.G, & Farhan H. (2014). Spatial control of Cdc42 signalling by a GM130-RasGRF complex regulates polarity and tumorigenesis. Nature communications, 5, 4839.

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Immunization Efficacy and Statistical Analyses

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Immunization studies show data from experiments that were repeated at least three times. The immune responses among groups of mice are presented as the mean ± standard error of the mean (SEM). The statistical significance of differences among groups was determined by one-way ANOVA followed by the Tukey’s test when the null model was rejected, using the GraphPad Prism (La Jolla, CA, USA) and JMP 10.0 statistical software (SAS, Institute Inc., Cary, NC, USA. A p value of less than 0.05 was considered significant. Prior to testing the null model, data were evaluated for normality with the Shapiro-Wilk’s test and equality of variance with the Brown-Forsythe tests. Data transformation was performed (either log10(x) or sqrt(x+3/8) to normalize the data when necessary. If test of equality of variance posterior to evaluating alternative transformation was still significant, the best transformation was used and the Welsh ANOVA test was used to allow for the standard deviation not being equal. However, posterior to transformation data there was little evidence of deviance from normality and groups were mainly homoscedastic. A series of evaluation of the influence of sample size on results were performed, this measure of elasticity (random change in samples on parameter estimates) showed that the results were robust (simulation not shown here).

Martínez O., Miranda E., Ramírez M., Santos S., Rivera C., Vázquez L., Sánchez T., Tremblay R.L., Ríos-Olivares E, & Otero M. (2015). Immunomodulator-Based Enhancement of Anti Smallpox Immune Responses. PLoS ONE, 10(4), e0123113.

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