Spss for windows 13

Statistical analyses were performed with SPSS for Windows 13.0. All continuous variable values were expressed as Mean ± SD (n = 3). Comparison of means between two groups was performed with student t tests. Comparisons of means among multiple groups were performed with one-way ANOVA followed by LSD test (homogeneity of variance) and Games—Howell test (heterogeneity of variance). P (probability) < 0.05 was accepted as statistically significant.

All statistical analyses were performed by using SPSS for Windows 13.0 (SPSS Inc, Chicago, IL, USA). Continuous variables were presented as mean ± SD and categorical variables were shown in absolute numbers or percentages. Differences between NGT, IGR, and diabetes groups were assessed by Chi-square test for categorical and ANOVA for continuous variables. Correlation between Gensini score and continuous variables was determined by Pearson correlation coefficients. Stepwise adjustments included: 1) age and gender, 2) smoking status, family history of CHD, history of atrial fibrillation, history of hypertension, BMI, WHR, SBP, and DBP, 3) lipid profile and creatinine. Multiple linear regressions were performed to evaluate the associations between Gensini score and glycemic variables. Confounders adjusted in linear regressions included age, gender, smoking status, family history of CHD, history of atrial fibrillation, history of hypertension, BMI, WHR, SBP, DBP, lipid profile, creatinine, and duration of diabetes. Logistic regressions were also performed to evaluate the associations between the presence of coronary heart disease and glycemic variables. In all analysis, P < 0.05 was considered statistically significant.

Multiple linear regression analysis (MLRA) is usually used to model the best combination of two or more independent variables (x_i) to predict or estimate the dependent variable (Y) by fitting a linear equation. It shows the contribution of each independent variable to the dependent variable in the following form: $$Y=b_0+\sum _{i=1}^n{b}_i{x}_i(n=1,2,3,4\dots )$$ where Y is the estimated value and represents the dependent variable and x_i are the uncorrelated variables; b₀ represents the estimated constant, and b_i is called the regression coefficients. In this study, MLRA was introduced to combine data from the chemical fingerprints (peak area of each component) and biothermal activity (main quantitative thermo-kinetic parameters) of S. miltiorrhizae samples on P. aeruginosa. This allowed us to establish the fingerprint-activity relationships using SPSS statistical software (SPSS for Windows 13.0, SPSS Inc., USA), to further explore the active components of S. miltiorrhizae that primarily contributed largely to the anti-P. aeruginosa activity.