Jmp 5

Manufactured by SAS Institute

Sourced in United States

JMP 5.0.1 is a data analysis software developed by SAS Institute. It provides tools for data exploration, statistical modeling, and visualization. The software is designed to work with a variety of data formats and offers a user-friendly interface for analyzing data.

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Lab products found in correlation

Prism 6, by GraphPad (2 mentions) Unscrambler x 10, by CAMO Software (1 mentions) Spss statistics for window, by IBM (1 mentions) Stata version 16, by StataCorp (1 mentions) Abi prism 7500 sequence detection system software, by Thermo Fisher Scientific (1 mentions) Excel 2007, by SAS Institute (1 mentions) Prism 7, by GraphPad (1 mentions) Image pro plus 5, by Media Cybernetics (1 mentions) Ms excel, by SAS Institute (1 mentions)

Spelling variants of this product

JMP version 5 (37 citations) JMP software version 5.0 (28 citations) JMP IN 5.1 (8 citations) JMP Genomics 5.0 (6 citations) JMP 5.0.1a (5 citations) JMP Genomics version 5.0 (3 citations) JMP) software 5.0 (2 citations) JMP software Version 5.1 for Windows (2 citations) JMP-5 statistical software (2 citations) JMP software v. 5.0 (2 citations)

66 protocols using jmp 5

Sensory Analysis of Beer Fermentation

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Analysis of variance (ANOVA) and post-hoc significance tests were carried out using either the Minitab (Minitab Inc., Sydney, NSW) or JMP 5.0.1 (SAS, Cary, NC, USA) statistical software packages. For sensory analysis, the effects of yeast (Y), judge, fermentation replicate nested in yeast (FRep(Y)), yeast and fermentation replicate nested in tasting replicate (TRep(Y,FRep)), the interaction between judge and yeast (J*Y), and the interaction between judge and fermentation replicate nested in yeast were assessed (J*FRep(Y)). Principal component analysis (PCA) and partial least squares regression (PLS) were performed using the Unscrambler X10.3 (CAMO Software, Oslo, Norway). Data was scaled as the inverse of the standard deviation for multivariate analyses. All PCA and PLS analyses were performed with cross validation.

Bindon K.A., Kassara S., Solomon M., Bartel C., Smith P.A., Barker A, & Curtin C. (2019). Commercial Saccharomyces cerevisiae Yeast Strains Significantly Impact Shiraz Tannin and Polysaccharide Composition with Implications for Wine Colour and Astringency. Biomolecules, 9(9), 466.

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Moisture and Temperature Effects

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The effect of independent factors (moisture and temperature) and their interaction on the response variables was determined by ANOVA. Tukey's test was used for the comparison of the means. Statistical analyses were performed with the program JMP 5.0.1 (USA, SAS institute, Inc.). Values of p < 0.05 were accepted as statistically significant.

Salazar Lopez N.J., Loarca-Piña G., Campos-Vega R., Gaytán Martínez M., Morales Sánchez E., Esquerra-Brauer J.M., Gonzalez-Aguilar G.A, & Robles Sánchez M. (2016). The Extrusion Process as an Alternative for Improving the Biological Potential of Sorghum Bran: Phenolic Compounds and Antiradical and Anti-Inflammatory Capacity. Evidence-based Complementary and Alternative Medicine : eCAM, 2016, 8387975.

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Antioxidant and Sensory Evaluation of Pâté

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Descriptive statistics were used to present the antioxidant assays (n = 3). The data obtained from the consumer acceptance study were evaluated through a one-way analysis of variance (ANOVA) in order to assess the significance of the JSSE addition on all of the studied characteristics. Data from the color and lipid oxidation of the pâté samples were evaluated through a two-way analysis of variance (ANOVA). The data obtained from the Ames’ test and the microbiological and chemical analyses (n = 3) were subjected to the ANOVA analysis to establish differences that had resulted from the effects of the JSSE addition. A comparison of the means was performed by using the Tukey’s honestly significant test (p < 0.05). The data obtained from the sensory evaluation and the onion root tip assay were analyzed by using the non-parametric Kruskal–Wallis test. In all cases, the analyses were carried out by using SAS software (SAS Institute, Inc. JMP 5.0.1, Cary, NC, USA), and differences among treatments were considered significant with a confidence interval at p < 0.05 in all cases.

Chan-Higuera J.E., Ezquerra-Brauer J.M., Lipan L., Cano-Lamadrid M., Rizzitano R, & Carbonell-Barrachina A.A. (2019). Evaluation of Dosidicus gigas Skin Extract as An Antioxidant and Preservative in Tuna Pâté. Foods, 8(12), 693.

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Biomechanical Effects of Foot Orthoses

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After excluding any implausible data, missing markers or incomplete trials, excursions and ranges were finally calculated for 14 subjects by using JMP5.0.1, statistical discovery software (SAS Institute Inc.). Descriptive statistical analysis for outcome variables was made by calculating the mean ± standard deviation, standard error of the mean and 95% confidence intervals based on the averages of 50 right-footed stance phases out of 50 gait cycles per subject per condition. Group differences by condition were analyzed by one factor (foot orthoses) repeated measures ANOVA (α = 0.05).

Wahmkow G., Cassel M., Mayer F, & Baur H. (2017). Effects of different medial arch support heights on rearfoot kinematics. PLoS ONE, 12(3), e0172334.

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Statistical Analysis of Experimental Data

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Statistical analysis was performed using JMP^® 5.0.1 (SAS Institute Inc., Cary, NC, USA), Student’s t-test, χ² test, Kaplan-Meier method and log-rank test. P<0.05 was considered to indicate a statistically significant difference.

OMOTO I., MATSUMOTO M., OKUMURA H., UCHIKADO Y., SETOYAMA T., KITA Y., OWAKI T., KIJIMA Y., SHINCHI H., ISHIGAMI S., UENO S, & NATSUGOE S. (2014). Expression of vascular endothelial growth factor-C and vascular endothelial growth factor receptor-3 in esophageal squamous cell carcinoma. Oncology Letters, 7(4), 1027-1032.

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Statistical Analysis of Biological Data

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Statistical analysis was performed using JMP 5.0.1 software (SAS Institute, Cary, NC). The unpaired t test, paired t test, Mann-Whitney U test, and Wilcoxon signed-rank test were used, as appropriate. Values of P < .05 were considered significant.

Sakaura H., Ohnishi A., Yamagishi A, & Ohwada T. (2018). Differences in Postoperative Changes of Cervical Sagittal Alignment and Balance After Laminoplasty Between Cervical Spondylotic Myelopathy and Cervical Ossification of the Posterior Longitudinal Ligament. Global Spine Journal, 9(3), 266-271.

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X-Ray Attenuation Coefficient Characterization

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The x-ray system energy weighting ratio c (function of x-ray source and detector) was determined from equation (6) using distilled water as a standard for both DR and CT at two energy levels for each sample run. The value of c was determined before every experimental sample run using distilled water to ensure standardization. The x-ray linear attenuation values (μ) for distilled water determined at various tube voltages, the known density of water (ρ) and β (from Fig.1) were substituted in equation (6) to obtain the values of

c = {(\frac{E_{2}}{E_{1}})}^{l}

for the particular pair of tube voltages. Thus a direct relationship between apparent density of the material (ρ) and x-ray linear attenuation coefficients (μ) described in equation (7) was obtained.
Statistical software JMP 5.0.1 (SAS Institute, Cary, NC) was used to conduct analysis of variance data. Least significant differences were calculated to compare mean values, with significance defined at p < 0.05.

Kelkar S., Boushey C.J, & Okos M. (2015). A Method to Determine the Density of Foods using X-ray Imaging. Journal of food engineering, 159, 36-41.

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Comparative Analysis of Experimental Conditions

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The data obtained were analyzed using analysis of variance (ANOVA) and when significant differences were found, comparisons among means were carried out by using Tukey test (p < 0.05) by JMP 5.0.1 (SAS Institute, Inc., Cary, NC, USA), Sokal and Rohlf [47 ]. Linear regression analysis was calculated from Microsoft EXCEL, 2003.

Kočar D., Köse S., Koral S., Tufan B., Ščavničar A, & Pompe M. (2021). Analysis of Biogenic Amines Using Immunoassays, HPLC, and a Newly Developed IC-MS/MS Technique in Fish Products—A Comparative Study. Molecules, 26(20), 6156.

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Survival Analysis of Prostate Cancer

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Statistical analyses were accomplished using JMP 5.0.1 software (SAS Institute Inc., Cary, NC, USA) performing Pearson’s chi-square test for contingency tables. Analysis of Variances (ANOVA) was used to test the association of Ki67 labeling index (LI) and SSTR2 staining, Kolmogorov-Smirnov test for determination of normal distribution and subsequent unpaired t-test or Mann-Whitney-U test calculations were performed for in silico cDNA expression analysis using PRISM 6 (Graphpad Software, Inc, La Jolla, CA, USA). Survival curves were calculated by Kaplan–Meier analysis and compared with log rank test. Multivariate analysis utilized the Cox regression model to identify independence of clinical parameters and SSTR2 immunohistochemistry to predict PSA-recurrence, cancer-specific survival and time to onset of metastatic disease after radical prostatectomy.

Hennigs J.K., Müller J., Adam M., Spin J.M., Riedel E., Graefen M., Bokemeyer C., Sauter G., Huland H., Schlomm T, & Minner S. (2014). Loss of Somatostatin Receptor Subtype 2 in Prostate Cancer Is Linked to an Aggressive Cancer Phenotype, High Tumor Cell Proliferation and Predicts Early Metastatic and Biochemical Relapse. PLoS ONE, 9(7), e100469.

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Non-Parametric Statistical Analysis

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The Mann-Whitney U test and Spearman rank correlation were used for statistical analysis with JMP 5.0.1 software (SAS Institute, Cary, North Carolina, United States), as appropriate. Values of p < 0.05 were considered significant.

Sakaura H., Miwa T., Yamashita T., Kuroda Y, & Ohwada T. (2015). Lifestyle-Related Diseases Affect Surgical Outcomes after Posterior Lumbar Interbody Fusion. Global Spine Journal, 6(1), 2-6.

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