Spss statistical 22

We express all experimental data as the mean value ± standard deviations in triplicate. We analyzed all variance in the data (one-way ANOVA) using the IBM SPSS statistical 22.0 software (IBM, Chicago, IL, USA). We defined p values < 0.05 as statistically significant.

Clinical, laboratory, and meteorology data were recorded in a Microsoft Excel database (Microsoft, Richmond, US). For continuous variables, non-normally distributed variables were expressed as medians and ranges; for categorical variables, data were presented as percentages. The differences between the two groups were determined by using the Wilcoxon Manne-Whitney U test for quantitative data and Fisher’s exact test for qualitative data, respectively. A P < 0.05 was considered statistically significant. All statistical analyses were performed by using SPSS statistical 22.0 software (IBM, Armonk, NY, USA).

Clinical, laboratory and meteorology data were recorded in a Microsoft Excel database (Microsoft, Richmond, US). For continuous variables, normally distributed continuous data were presented as means ± standard deviations (SDs), and non-normally distributed variables were expressed as medians and interquartile ranges (IQRs); for categorical variables, data were presented as percentages. The comparison between iNTS and non-iNTS infections was calculated by logistic regression analysis; however, when zero observation was contained for a particular variable of one group, the P value was calculated with a two-tailed Fisher’s exact test instead. The Mann-Whitney test was used for the comparison of median age (non-normally distributed data) between iNTS and non-iNTS infections. Inter-annual linear trends of drug resistance were assessed by linear regression analysis. The numbers of NTS infection between every two seasons were compared using the chi-square test (categorical variables). The number of NTS infection cases and rainfall and temperature were examined by Spearman’s rank coefficient test. P <0.05 was considered statistically significant. All statistical analyses were performed using SPSS statistical 22.0 software (IBM, Armonk, NY, USA).

All the data are presented as the means ± standard errors (SEs). The data were analyzed by independent sample t-test using the IBM SPSS statistical 22.0 software (SPSS Inc., Chicago, IL, USA). Significant differences in the means between groups at the same time point were compared. Asterisks (* and **) were used to represent significant differences at p < 0.05 and p < 0.01, respectively.

The data were analysed and evaluated by a similarity evaluation system for the chromatographic fingerprint of TCM (2012, China), which was recommended by SFDA [31 ]. The similarity among different chromatograms was quantified by calculating the correlative coefficient. The similarity between the samples was acquired by computing the correlation coefficients of different chromatograms. R language conducts HCA according to the similarity degree of each component among different samples. IBM SPSS Statistical 22.0 software (IBM, New York, USA) applies the square Euclidean distance computing of the content of each component in the sample to perform HCA. HCA based on R language and SPSS distinguish herbal species. In order to verify the feasibility of QAMS, the other five active components in dandelion samples were determined by applying cichoric acid as an internal reference.