Spss statistics base

Quantitative variables are presented as median and interquartile range (IQR) according to their distribution, and the categorical variables as number and proportions. Kolmogorov-Smirnov test was used to explore the data distribution. For the bivariate comparisons between groups, the Chi square test was used for categorical variables and the U Mann-Whitney test or the Kruskal-Wallis test for quantitative variables. Odds ratio and 95% Confidence Interval (CI) were also calculated. Statistical tests were two-sided and significance was considered with a probability of null hypothesis ≤5%. A multiple logistic regression model was performed to predict mortality using the forward conditional elimination method. The probability for entering the model was set at 0.10 and for elimination at 0.20. Variables that reached a significance <0.10 in the univariate analysis were included. The statistical package IBM SPSS Statistics Base version 22 NY, USA, was used for data processing and for statistical analysis.

IBM SPSS Statistics Base was used in data analysis as a factorial experiment. It was a completely randomized blocks design (CRBD) with 7 replications for each concentration and treatment at p=0.05.

Statistical analyses of samples were performed using IBM SPSS Statistics Base (V22, IBM, Armonk, NY, USA.) and GraphPad Prism (V8, GraphPad Software, San Diego, CA, USA). Each experiment was performed at least three times. The data are expressed as the mean ± SD. Statistical significance was determined using a one-way analysis of variance (ANOVA) test, unless otherwise stated, p < 0.05 was considered to be significant.

Statistical analysis was conducted with a commercial statistical software package (SPSS Statistics Base, International Business Machines, Armonk, NY, USA). Because the Kolmogorov–Smirnov test confirmed the normal distribution of data, a 2-way analysis of variance (ANOVA) for flexural properties and shear bond strengths and 1-way ANOVA for marginal adaptation, polymerization shrinkage, and shrinkage stress, with Tukey’s post-hoc honest significant difference test (significance level of 0.05), were used for data analysis. Correlations among different indicator values were analyzed by linear regression (significance level of 0.05, adjusted by Bonferroni correction to 0.001).

All investigations were conducted in this study in triplicate (n = 3). Data were reported as mean ± SD values. Analysis of variant (ANOVA) was used to evaluate the differences among the treatments by Duncan’s multiple range tests (DMRTs) with p value of 0.05 using SPSS Statistics Base version 19 program for Windows (IBM Corp, Chicago, IL, USA).