Spss 17.0 statistical

The results are reported as mean and standard deviation of at least triplicate. The statistically analysis was performed by variance analysis (ANOVA) using SPSS 17.0 statistical software (SPSS Inc., Chicago, IL, USA). Significant differences between the means were determined by Duncan test (P < 0.05). Pearson's correlation coefficients among parameters were also calculated using SPSS 17.0 statistical software.

Statistical analyses were performed using SPSS17.0 statistical software. The data are expressed as the mean ± standard deviation. Differences in continuous variables were compared using the Student's unpaired t-test, while differences in categorical variables were compared using the chi-square test. Then, a multiple logistic regression model yielding odds ratios (ORs) and 95% confidence intervals (CI) was used to identify predictors of poor prognosis (nonfunctional improvement based on the AIS grade) on discharge. Receiver operating characteristic (ROC) curves were drawn, and the area under the ROC curve along with its corresponding 95% CI provided a measure of overall validity. The cut-off value of the occupancy ratio for predicting the postoperative poor prognosis was determined using the ROC analysis and Youden's index [15 (link)]. A p value < 0.05 was considered to be statistically significant. All tests were two-tailed.

We used SPSS 17.0 statistical software for Windows for statistical analyses (SPSS, Chicago, IL, USA). Prism 5.0 software (GraphPad, San Diego, CA, USA) were used for the design of hSACIRC_004413 primers. analyses. The difference of data between control group and experimental group and different experimental groups was analyzed using with Student’s t-test or one-way analysis of variance by SPSS 17.0 statistical software. Data are expressed as the mean+−standard deviation (SD). The results were considered statistically significant at a value of P < 0.05.