Ms excel

Experiments were performed at least in duplicates, and shown data points correspond to the mean of independent experiments. Statistical analysis was performed by using the MS Excel and the Statistical Package for Social Sciences (IBM SPSS; version 19.0 for Windows); significance was evaluated using one‐way analysis of variance (ANOVA). Error bars indicate standard deviation (SD); significance at P < .05 or P < .01 is indicated in graphs or heatmaps by one or two asterisks, respectively. Significance for the phosphoproteomic and cytokine/chemokine secretion set of experiments was estimated as a combination of median fluorescence intensity (MFI) value above 600 and fold change (FC) value above 0.3 when compared to control samples.
Additional methods are available in Supporting Information.

Experiments were performed in duplicates or triplicates as indicated in Figure legends. For statistical analyses MS Excel and the Statistical Package for Social Sciences (IBM SPSS; version 19.0 for Windows, Armonk, NY, USA) were used. Statistical significance was evaluated using one-way analysis of variance (ANOVA). Data points correspond to the mean of the independent experiments and error bars denote standard deviation (SD).

Data obtained from the survey were analyzed using MS Excel and SPSS software. The cross-sectional study of this research involved both descriptive and inferential methods. The descriptive analysis consisted of frequency, percentage, cumulative percentage, etc. The Cross-Tabulation technique was employed to display a breakdown of the data. A chi-square test for independence also called Pearson’s chi-square test of association was used to determine whether there was a significant relationship between two categorical variables (Franke et al. 2012 (link); Rana 2015 (link)). The chi-square test was calculated by using the formula,
$${\left(\chi \right)}^n=\sum \left(\frac{{\left(O_i-E_i\right)}^2}{E_i\frac{}{}}\right)$$
With a degree of freedom (DF) = (r-1) × (c-1), where c denotes the number of levels for one categorical variable, and r denotes the number of levels for the other categorical variable. Likewise, O_i represents observed frequency and E_i represents the expected frequency under the null hypothesis which was computed separately for each level of one categorical variable at each level of the other categorical variable. Based on the test statistics and the DF, the P-values were also determined at a 0.05 level of significance.