Spss 21 for windows

The data were analysed using analysis of variance (ANOVA) with wheat straw and GM in rice and tillage and rice management in subsequent wheat as sub-plot in the split plot design at p < 0.05 statistically significant using IBM SPSS for Windows 21.0. The correlations matrix between the variables was assessed by determining Pearson’s correlation coefficients (r) and probabilities. The partial K budget for a RWS was estimated from the difference in cumulative (2011–18) total K input and the K uptake by crops.

Firstly, IBM SPSS for Windows 21.0 software (SPSS, Inc., Chicago, IL, USA) was used to processs the data. The categorical data were reported as numbers with proportions and the quantitative data were reported as means with standard deviation. Risk factors for TFI were identified with odds ratios (ORs) and their 95% confidence intervals (CIs) using a binary logistic regression model. The backward likelihood ratio method was employed to create a final multivariate model and analyze the independent risk factors. Second, the nomogram and its calibration curve were displayed using the package of Regression Modeling Strategies package (package = “rms”; http://CRAN.R-project.org/) in R (version 3.3.3, http://www.R-project.org/), its calibration plot was set at 200 repetitions (B value as 200). Third, a receiver operating characteristics (ROC) curve was constructed using MedCalc Statistical Software (version 17.4; MedCalc Software bvba, Ostend, Belgium; 2017) based on the probability of the final multivariable model and the occurrence of TFI. AUC, and optimal cutoff point (including its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (PPV)) were evaluated using the subject’s ROC curve. A two-sided p < 0.05 was considered statistically significant.

The statistical analysis of our study was performed using the SPSS for Windows 21.0 program (IBM, Armonk, NY, USA). The quantitative variables of groups were compared using student’s (t) test, and were presented as the arithmetic mean and standard error of the mean value (SE). Qualitative determinants were expressed as a percentage; their interdependence was assessed using Chi-square test (χ²). Independent two-sample t-test was used, and u-test was used for small samples. T-test for pair samples was used to compare repetitive measurements. The treatment effect after 6 months was assessed using analysis of covariance (ANCOVA). In the ANCOVA, the data after 6 months were used as dependent variables, the variables before the operation as a covariate, and the patients group as a factor. For the comparison of two samples, dispersive analysis was used. Verification of statistical hypotheses selected statistical confidence level of p < 0.05 (95% statistical confidence).

Data were reported as mean ± standard deviation and analyzed using SPSS for Windows 21.0 (IBM, Armonk, NY, USA). Differences between groups were assessed for significance using the independent-samples t test or Kruskal-Wallis test. Differences associated with P < 0.05 were considered significant.

The SPSS for Windows 21.0 (IBM) software package was used in the statistical analysis of the study. In the present study, the continuous variables were expressed with mean value, standard deviation, maximum and minimum values. The Shapiro-Wilk test was used for the normality test of continuous variables. Normally distributed continuous variables were compared by means of variables were evaluated with samples t test, nonparametric variables between the two groups were made by Mann–Whitney U test, categorical variables were compared by Chi square test.