Spss spss v 21

SAS (SAS 9.4; SAS Institute, Cary, NC) software was used to clean the original data of the 2020 Chongqing Tobacco Survey. SPSS (SPSS V.21.0, Inc., Chicago, Illinois) software was used to perform essential weighting, non-response correction, and post-stratification correction on the cleaned data. Then, the complex sampling module was used for data analysis. Frequencies between groups were compared statistically using the Rao-Scott χ² test. Logistic regression was used to analyze the factors affecting smoking attempts and behavior. Variables with statistical significance in the univariate logistic regression analysis were subjected to multivariate logistic regression analysis, including: age (25–44, 45–64, and ≥65 years); residence (urban, rural), whether or not had seen the media campaign for tobacco control publicity (yes/no); education level (elementary and lower, junior, senior, college and higher); occupation (government staff, service staff, teacher, medical staff, student, military, unemployed, retirees, or other); and whether or not there was secondhand smoke exposure (yes/no). An inspection level α=0.05 and a difference with p<0.05 were considered statistically significant.

Statistical analysis was performed by SPSS (SPSS V21.0) and Prism 9 (GraphPad Software Inc., San Diego, CA, USA). Sample calculation power was calculated for the primary outcome and venous plasma antioxidant capacity (TAC) using Statmate version 2.0 (GraphPad Software Inc.). Taking an α = 0.01, a sample size of 14 subjects allows for detection of a difference of 0.21 mmol TAC/L between groups, calculated from an expected SD of between-meal group differences of 0.21 mmol/L. Results are presented as means ± SD, and significance was at p < or = 0.05. Before any statistical analysis, all variables were tested for normal distribution. Repeated measures ANOVA (Analysis of Variance) and Bonferroni post-tests were performed on differences between plasma and serum samples at 30 min, 1.5 and 3 h, for each meal group and on change from baseline data for venous plasma TAC and serum biomarkers. Differences between the two treatment groups at any time and time period from baseline were also tested by paired t-tests. An incremental Area Under Curve (iAUC) was calculated using the trapezoidal method and ignoring the area below baseline. Differences between iAUCs for each treatment were tested by Repeated ANOVA. The variables followed a normal distribution.