Rstudio statistical software version 1

One-way ANOVA was computed to compare a continuous variable with categorical explanatory variables. All values were expressed as mean and standard deviation (±SD). Tukey's post hoc test with a 95% confidence interval (CI) was performed to evaluate the association between different variables in pairs. The Kaplan–Meier method was applied for survival analysis, and the log-rank test calculated the differences. The Cox proportional hazard model was employed for the univariate and multivariate survival analysis. In all statistical analyses, p < 0.05 was considered significant. The RStudio statistical software version 1.2.5033 was used for statistical analysis.

All experiments and analysis were conducted in triplicate. Means and standard deviations (SD) of test results were reported. A two-sample student’s t-test was performed to determine the significant differences between cell counts of LP before and after drying. A two-way analysis of Variance (ANOVA) (UHPH level and drying methods), and post hoc Tukey’s studentized range tests (α = 0.05) were employed to determine the statistical significance of observed differences among means using statistical software (RStudio statistical software version 1.2.5033, RStudio, Inc. Boston, MA USA). Pearson’s bivariate correlation analysis was used to (1) evaluate the correlations between UHPH pressure levels (MPa) and particle size distributions values (D₁₀, D₅₀, D₉₀, and SPAN) of LP powders, (2) study the relationship between particle size distribution of powders and cell survival of LP.

The data were entered by Epidata 3.1 onto two separate files. We checked for errors by comparing the two files and the original questionnaires. The data were analyzed using RStudio Statistical Software Version 1.1.463 Mac OS X 10_12_6.
We used the χ² test and t‐test to compare various characteristics of the nail workers and the office workers. Multiple logistic regression analysis was used to analyze the results of health symptoms among nail workers who either exceeded or did not exceed the VOEL for acetone. Odds ratios (ORs) were adjusted for the potential confounding effects of age, smoking, alcohol consumption, exercise frequency, work experience, and marital status. The same methods were used to compare the results between office workers and nail workers.
We used the Akaike information criterion to check the distribution of the concentration of target VOCs and acetone before placing them in a Gamma family attached to the log‐link function of the generalized linear model. We used the Bayesian Model Averaging package for choosing the predicted factors for the concentration of target VOCs and acetone because of the small sample size with other chemicals.