Excel 2016

Experiments were performed at least twice, and measurements were made in triplicate. Data are expressed as mean ± standard deviation (SD). Regression analyses and other statistical analyses were performed using Microsoft Excel 2016 and the SPSS program, version 17.0. Means were compared by Duncan's new multiple range test. Statistically significant differences were set at p < 0.05.

The data collected were duly recorded in a pre-coded database for input in Excel 2016 software, and then analyzed using the (SPSS, IBM, Armonk, New York, United States), version 21.0. For the analysis, the relative and absolute frequencies of the classes of each qualitative variable were calculated. For the quantitative variables, means and medians were used to summarize the information, in addition to standard deviations, minimum and maximum values, to indicate data variability. The significance level set was 5%. The statistical analyses were performed using SPSS software, version 21.0.

Microsoft Excel 2016 and SPSS 22.0 were used for data compilation and analysis. Noncompliance and compliance were considered the dependent variables. Year, county/city, summer vacation grouping, weekdays/weekends, types of stores, and age verification were treated as the test variables. Proportion of male adolescents, the smoking rate among adolescents, divorce rate, exposure rate to secondhand smoke at home, and juvenile offender rate were the independent variables. Curve estimation was used to analyze the time trend of compliance rate from 2009 to 2019. Chi-squared test and independent sample t-test were used to analyze the differences of test variables, independent variables and dependent variables. If the variable was significant in the difference analysis, it was adjusted in the final regression analysis. Finally, we used logistic regression analysis to examine the influencing factors of dichotomous dependent variables, with adjustment for the test variables and independent variables (except juvenile offender rate). We also showed the Nagelkerke’s R squares for goodness-of-fit results, β and standard errors, Wald statistic, odds ratio (OR), and 95% confidence interval. A p-value < 0.05 was considered statistically significant.

The research data were processed and analyzed using Microsoft Excel 2016 and SPSS version 16.0. The data were examined for normality and homogeneity prior to the analysis of variance. Analysis of variance (ANOVA) at a 95% confidence interval was used to determine the significant difference between the test parameter treatments. If the results are significantly different, Duncan's further test is used to determine the significant difference between treatments.

Excel 2016 and SPSS 24.0 software were used for data analysis. A statistical description was completed for the results of this questionnaire survey. Qualitative data were expressed as frequency (percentage), quantitative data were expressed as (⁻x ± s), data that did not follow a normal distribution were expressed through a median (M) and inter-quartile range (Q1, Q3). For bivariate analysis, the Chi-square test was used for qualitative data, an ANOVA or t-test was used for quantitative data, and the rank-sum test was used when data did not fulfill the conditions for parametric tests. A difference of P < 0.05 was considered to be statistically significant. Differences that were statistically significant (P < 0.05) in the bivariate analysis were included in the multivariate unconditional logistic regression.
Type 2 diabetes prevention and treatment knowledge mastery was classified based on the number of correct answers. A score higher than the mean was considered to be a passing score; a score lower than the mean was considered to be a failing score [13 ].