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Prism 7.0 software for windows

Manufactured by GraphPad
Sourced in United States

GraphPad Prism 7.0 Software for Windows is a statistical and graph-building software. It allows users to analyze, visualize, and present scientific data.

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Lab products found in correlation

2 protocols using prism 7.0 software for windows

1

Comprehensive Statistical Analysis of CircRNA in Cervical Cancer

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All quantitative data were presented as mean ± standard deviation (SD) as indicated from at least three independent experiments. Statistical analyses were performed using GraphPad Prism 7.0 Software for Windows (GraphPad Software, La Jolla, CA, USA) and Service Solutions SPSS Software 19.0 (SPSS, Chicago, IL, USA). Between-group differences were tested for significance using one-way analysis of variance and Student’s t-test. The associations between circRNA expression and the clinicopathological parameters of CC were assessed by the Chi-square test. The receiver operating characteristic (ROC) curve was plotted to evaluate diagnostic values. Correlation analysis was performed using Spearman’s rank correlation coefficient. P < 0.05 was considered statistically significant.
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2

Predictive Factors of APIP in Patients

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We presented all the variables as mean ± standard deviation (SD) or median (range), as appropriate. Statistical analyses were conducted by GraphPad Prism 7.0 Software for Windows (GraphPad Software, La Jolla, CA, USA), Service Solutions SPSS Software 25.0 (SPSS, Chicago, IL, USA) and R statistical software (version 4.2.0; https://www.r-project.org/ ). Kolmogorov–Smirnov test was used for normality analysis of the study variables. Student’s t-test was applied to analyse normally distributed continuous variables, and the Mann-Whitney U test was utilized to analyse nonnormally distributed continuous variables. Univariate logistic regression analysis was performed to identify predictive factors of APIP. Predictive factors with p value less than 0.05 in univariate analysis were included in the multivariate analysis. Multivariate logistic regression analysis was conducted to identify independent predictive factors, and check the useful combination of factors that could predict APIP. All p values were two-sided, with statistical significance set at p values less than 0.05.
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