Group comparisons of categorical variables were evaluated using the linear-by-linear association. The means were compared using the independent sample’s t-test. Values are presented as the mean ± SEM. P-values <0.05 were considered statistically significant. Analyses were performed using SPSS Version 22.0 statistical software (SPSS Inc., USA).
Spss version 22.0 statistical software
Manufactured by IBM
Sourced in United States
SPSS version 22.0 is a statistical software package designed for data analysis and management. It provides a comprehensive set of tools for data manipulation, statistical modeling, and visualization. The software is capable of handling a wide range of data types and offers a user-friendly interface for conducting various statistical analyses.
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54 protocols using spss version 22.0 statistical software
1
Comparative Statistical Analysis Methods
2
Statistical Analysis of Research Data
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We presented the categorical variables as counts and percentages then compared by Pearson chi-square analysis or Fisher’s exact test. We tested the normal distribution of our continuous data by Kolmogorov-Smirnov test. We presented continuous and normally distributed data as mean ± SD and were compared by unpaired t test. Analysis of variance (ANOVA) test was used to compare differences between more than two groups. The inter-observer agreement was calculated with weighted Kappa statistics. Correlations were done by Spearman correlation coefficient test. All P values are two-tailed, and statistical significance was defined if P < 0.05. Our analyses were performed with SPSS version 22.0 statistical software (SPSS Inc., Chicago, IL, USA).
3
Ki-67 Threshold for Neoadjuvant Chemotherapy Response
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All analyses were carried out using SPSS version 22.0 statistical software (SPSS Inc, Chicago, IL, USA). Differences in the distribution of characteristics between the parameters of patients with pCR or pPR and patients with pNR were evaluated using two-sided Fisher’s Exact Test. Two-sided Mann-Whitney-Wilcoxon test was used to define age distributions in pCR vs. pNR and vs. pPR. The optimal cut-off value for Ki-67 percentage to discriminate response to treatment was assessed by receiver operating characteristic (ROC) curve analysis. To identify the optimal Ki-67 threshold for NAC, only pCR and pNR cases were involved in ROC analyses, because pPR status is considered as a soft endpoint. Kaplan-Meier curve estimation, log-rank test and Cox regression analysis were carried out to analyze the association between Ki-67 categories and DMFS or OS. P values ≤0.05 were considered to be statistically significant.
4
Comparing Dental Material Thickness
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A pilot study was performed on 8 teeth (4 teeth for each group) to calculate the sample size and power, assuming means of 109.4 µm for APC Flash Free group, 214.3 µm for APC Plus group and a common SD of ±100.6 µm, a sample size of 20 per group was found to be sufficient to obtain a type I error rate of 5% and a power higher than 90%. All Statistical analyses were conducted using SPSS version 22.0 statistical software (SPSS Inc., Chicago, IL, USA). The mean, standard deviation, median, and range were calculated for the descriptive analysis. Statistical significances were measured using non-parametric Mann-Whitney U test with P values of less than 0.05 considered statistically significant.
5
Quantitative Analysis of Cell Viability
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All experiments were performed in triplicate. All computations were carried out using the SPSS version 22.0 statistical software (SPSS Inc). Data were expressed as mean ± standard deviation. Student t test was used to compare the significant difference of 2 groups, while 1-way analysis of variance with least significant difference was applied when analyzing more than 2 groups. Differences were considered statistically when P was less than .05.
6
Strength Changes in Intervention Trial
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Changes in body weight, bench press, squat, and a composite strength score (combined bench press and squat) from pre-test to post-test were analyzed using separate 2 (group) × 2 (time) repeated measures analysis of variance (RM ANOVA) due to differences in the number of participants who completed each measure. Differences were considered significant at p < .05. Effect sizes were calculated using partial eta squared (ŋp2) for the RM ANOVA. All statistical analyses were performed using SPSS version 22.0 statistical software (SPSS Inc., Chicago, Ill, USA).
7
Factors Associated with GHQ-28 Scores
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In this study, quantitative variables were initially tested for normality using the Kolmogorov–Smirnov criterion. The same variables were expressed as mean (SD = Standard Deviation) or median (interquantile range), absolute and relative frequencies. Student’s t-tests were computed for the comparison of mean values. Multiple linear regression analysis was used with the dependentGHQ-28 scores. The regression equation included terms for participants’ demographics, work-related characteristics and the occurrence of an error during their work. Adjusted regression coefficients (β) with standard errors (SE) were computed from the results of the linear regression analyses. In this study, the p-values were two-tailed. Statistical significance was set at p < 0.05. The analysis was accomplished using SPSS-version 22.0 statistical software.
8
Kaplan-Meier Survival Analysis for OS
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We used the Kaplan-Meier method to estimate survival and compared between risk groups using log-rank testing. Estimates of the predictive effect of GA for OS were expressed as hazard ratios (HRs). A value of P ≤ 0.05 was considered to be significant differences. The statistical analysis was performed using Statistical Product and Service Solutions (SPSS) version 22.0 statistical software (SPSS Inc., Chicago, IL, USA).
9
Statistical Analysis of Experimental Data
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Statistical Product and Service Solutions (SPSS) version 22.0 statistical software (SPSS Inc., Chicago, IL, USA) and R software (http://www.R-project.org ) were used for statistical analysis. The data that conformed to the normal distribution were described by mean ± standard deviation (SD). Counting data are described as percentages. The stepwise method was used in this study. P < 0.05 was considered statistically significant.
10
Analyzing Antifungal Susceptibility Using SPSS
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The SPSS version 22.0 statistical software (SPSS Inc., Chicago, Il, USA) was used to compute the present study’s statistical analyses. The data were found not to be normally distributed by applying the Shapiro-Wilk test for normality assumption and hence non-parametric tests were conducted. Statistical differences in the data obtained from the treated and untreated groups were analyzed using Friedman’s test; two way analysis of variance by ranks. Moreover, pairwise comparisons (using Friedman’s test) were computed, which treat one group as a control (unexposed to antifungal agent) and compare the other groups (exposed to antifungal sub-MICs) against it. A p-value of less than 0.05 was considered statistically significant.
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