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Jmp pro for windows

Manufactured by SAS Institute
Sourced in United States

JMP® Pro for Windows is a powerful data analysis and visualization software developed by SAS Institute. It provides advanced statistical and analytical capabilities for users to explore, analyze, and model complex data sets. JMP® Pro for Windows offers a range of tools and features to help users gain insights, make informed decisions, and communicate their findings effectively.

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4 protocols using jmp pro for windows

1

Statistical Analysis of Biological Data

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DNA damage and relative mtDNA copy number data were analyzed with Statview for Windows (Version 5.0.1, SAS Institute Inc., Cary, NC) and JMP Pro for Windows (Version 11.0.0, SAS Institute Inc., Cary, NC). One- or two-factor analysis of variance (ANOVA) was used and each chemical was analyzed as an independent experiment. A p-value of less than 0.05 was considered statistically significant.
Dopaminergic neurodegeneration data were analyzed using the statistical software R version 2.12.0 (R Foundation for Statistical Computing, Vienna, Austria), Statview or JMP. The nonparametric Kruskal-Wallis test was used to test for differences between dosage levels for each chemical at each time point. Due to sparseness in the cross-tabulations of dosage levels and scores, Fisher's exact test (FET) was used when testing independence of chemical dosage levels and scores at each time point. FET was also used to analyze laser axotomy data. A p-value of less than 0.05 was considered statistically significant.
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2

Statistical Analysis of Experimental Data

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Statistical testing was performed with Dunnet’s test and the Tukey–Kramer test using JMP® Pro for Windows (SAS Institute Inc., Cary, NC, USA). Values of P < 0.05 were considered significant.
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3

Comparative Analysis of Treatment Outcomes

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Results were given as mean ± SD. Statistical evaluations were determined using the Wilcoxon rank sum test, Dunnett’s test, or Student’s t-test. Statistical analyses were conducted using JMP® Pro for Windows (SAS Institute Inc., Cary, NC, USA. p-values of < 0.05 and < 0.01 were considered significant.
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4

Statistical Analysis of Morbidity Outcomes

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Statistical analyses were performed using JMP pro for windows (Version 14.0, SAS Institute, Cary, NC, USA) and included Fisher's exact test, Cox regression, and log-rank test. The cumulative incidence of the cure rate of mORN and the time of surgical intervention were calculated using the Kaplan-Meier method and analyzed by Cox regression analysis and the log-rank test. The factors associated with cure were tested using univariate regression analysis. The statistically significant factors were used in the multivariate regression analysis. Propensity scores were calculated from factors that were not significant in the univariate analysis and used as covariates in the multivariate analysis. A P value of <0.05 was considered statistically significant.
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