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Jasp software v 0

Manufactured by JASP Stats

JASP is a free and open-source statistical software package, currently in version 0.18. It provides a user-friendly interface for conducting a wide range of statistical analyses, including t-tests, ANOVA, regression, and more. JASP is designed to be an intuitive and accessible tool for researchers, students, and data analysts.

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8 protocols using jasp software v 0

1

Muscle Fiber Composition Analysis

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The Shapiro–Wilk test was used to assess the normality, and Lavene’s test was used for the homogeneity of the data. No violations of normality or homogeneity in the distributions were found (p > 0.05). Descriptive statistics were used for statistical analysis: mean ± standard deviation. Correlations between variables were examined with the Pearson r correlation coefficient, including the coefficient of determination (Pearson’s r squared, R2). The interpretation of the correlations was performed according to Hopkins’ ranking (0.3–0.5 was considered moderate, 0.51–0.70 was considered large, 0.71–0.90 was considered very large, and >0.91 was considered almost perfect) [21 (link)]. Partial correlations were also calculated to exclude the effects of muscle mass and strength on the correlations between echo intensity and fiber type composition. Differences between groups were analyzed with the Student T-Test. Cohen’s d effect size was also calculated (small: 0.2, medium: 0.5, large: 0.8). The level of significance was set at p ≤ 0.05. Reliability for all measurements was assessed using a two-way random-effect intra-class correlation coefficient (ICC). Statistical analysis was performed with JASP software v. 0.18 (University of Amsterdam, The Netherlands).
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2

Preterm Premature Rupture of Membranes Outcomes

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Data are expressed as absolute numbers and mean or medians where appropriate based on normality of the data as determined by the Shapiro Wilk-test. Statistical analysis was carried out using unpaired Student's t test or Mann Whitney U test when appropriate. This analysis was utilized for comparison of those with and without the combined outcome of BPD/death as well as the comparison of those who required and did not require intubation. Statistical analysis using the Chi-Squared test was utilized to determine if there was an association between the cause of PPROM (spontaneous vs. procedural) and the combined outcome of BPD/death.
A correlation analysis of length of stay with GA at rupture of membranes (ROM), GA at delivery and lowest AFI level during the pregnancy was performed. The correlation coefficient is expressed as Pearsons's coefficient or Spearman's coefficient where appropriate based on the normality of the data as determined by the Shapiro Wilktest. For all statistical tests, statistical significance was accepted at a p-value less than 0.05. Study data was analyzed using JASP software (v 0.18). [11]
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3

Effectiveness of Exercise Training on Cardiometabolic Outcomes

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Descriptive statistics were used for the statistical analysis: mean ± standard deviation. The normality of the data was assessed with the Shapiro–Wilk test, and no violations in normality were observed. The differences before and after the training period were analyzed with Student’s paired-sample t-Test. The Cohen’s d effect sizes were calculated. The correlation between the variables was examined with Pearson’s correlation coefficient. The level of significance was set at p ≤ 0.05. The reliability of all the measurements was tested using a two-way random effect intra-class correlation coefficient (ICC). The statistical analysis was performed with JASP software v. 0.18 (University of Amsterdam, The Netherlands).
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4

Optimizing Cellular Assays and Analytics

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Data analysis of SAR, cellular viability process by MTT, and MHT technique efficiency by BLI and flow cytometry were presented as the mean and standard deviation. The MHT efficacy evaluation was compared by the ANOVA test, being considered statistical significance for p < 0.05. All statistical analysis was performed with the JASP software v.0.14.1 (http://www.jasp-stats.org; Accessed on 10 may 2019).
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5

Statistical Analysis of SPION Uptake in BM-MNC

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Data were presented as the mean and standard deviation in each analysis. For the in vitro study, the cellular viability and iron load quantification of the SPIONNIRF-Rh concentrations of young and old BM-MNC were compared by the ANOVA test, following post hoc tests corrected by Bonferroni or Student’s t-test for two samples. Previously, we analyzed the normal distribution and homoscedasticity of the data of each group, and we considered the significative level p < 0.05. All statistical analysis was performed with the JASP software v.0.14.1 (http://www.jasp-stats.org; Accessed on 1 January 2020).
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6

Comparative Behavioral Analysis Across Groups

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In each analysis, the data were reported as the mean and standard deviation. To determine variance equality, Levene’s tests were used. Behavioral analysis across groups was compared using the ANOVA test, followed by Bonferroni-corrected post hoc testing, and all analysis was considered at the 0.05 level of significance. JASP software v0.14.1 (http://www.jasp-stats.org, accessed in November 2021) was used for all statistical analysis.
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7

Genetic Ancestry Inference and Statistical Analysis

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Inference of genetic ancestry based on the AIM panel was done with Structure software v.2.3.4 [17 (link)]. R language [18 ] was used to assess Hardy-Weinberg Equilibrium (HWE) of genotype distribution. JASP software v. 0.9.2.0 [19 ] was used to perform all other statistical analyses (Student’s t test, chi-squared test, logistic regression). P-values ≤0.05 were considered statistically significant.
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8

Multimodal Approach to Attention Prediction

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Bayes factor analyses with default priors (r = 0.707) were performed on fNIRS and EEG data. Bayesian analysis was conducted using JASP software v. 0.9.2.0 to test the null hypothesis (BF10 < 0.333: substantial evidence for the null hypothesis; BF10 >1: support for H1 over H0).
ROC analysis was performed for the GLM binomial model. Each model was used to predict the attention performance based on EEG, ΔHbO, and NVC metrics for each miniblock at a single subject level. We calculated the false positive rate (FPR, 1-specificity) and true positive rate (TPR, sensitivity) and generated the ROC curve by varying the p-value threshold (0 to 1). The AUC of the ROC can be calculated to quantify the overall performance of each model. A higher AUC indicates that the model performs better in predicting the behavior. To compare the performance of different models, a one-way repeated ANOVA test was employed to evaluate the difference in these metrics among the models based on EEG, ΔHbO, and NVC metrics.
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