Data entry was done with MS Excel, and statistical analysis was performed using SPSS for Windows 10 (SPSS Inc., Chicago, IL, USA). Categorical variables were evaluated using the Chi-squared test and Pearson’s Chi-squared test to assess statistically significant differences in-between groups. Non-parametric test methods were assessed to investigate location shifts between groups (Mann–Whitney U-test). The Wilcoxon signed rank test for paired samples was used to recognize significant changes within both groups. Continuous variables were expressed as absolute mean concentrations ± SD (standard deviation) and the level of significance (α) was set at 5%.
Spss for windows 10
Manufactured by IBM
Sourced in United States
SPSS for Windows 10.0 is a software package designed for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is compatible with the Windows 10 operating system.
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47 protocols using spss for windows 10
1
Statistical Analysis of Categorical and Continuous Data
2
Frequency Comparison of Spontaneous and Induced
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The obtained data were indicated as arithmetic mean (x) ± standard error (SE). Comparison of the difference between the average spontaneous and induced frequencies was performed using Student's t-test. SPSS for windows 10.0 package program was used for the statistical analysis. All p values were two-tailed and accepted significance level was < 0.05.
3
Multivariate Analysis of Left Atrial Size
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Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) for Windows 10. Numerical variables with a normal distribution were presented as the mean ± SE, numerical variables with a skewed distribution as the median, and categorical variables as percentages. For numerical variables, an independent sample t-test and the Mann-Whitney U test were used for intergroup comparisons. The χ2 test and Fisher’s exact tests were used for comparisons of categorical variables. The association between variables was evaluated with the Pearson rank correlation. Subsequently, analysis was undertaken through multivariate logistic regression to assess a set of independent variable predictors of left atrial size. A two-tailed p < 0.05 was considered statistically significant for all analyses.
4
Musclin Levels Predict Atrial Fibrillation
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Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) for Windows 10. Numerical variables with a normal distribution were expressed as the mean ± SD, and numerical variables with a skewed distribution were expressed as the median and interquartile ranges (IQRs). Categorical variables were expressed as percentages. For numerical variables, an independent sample t test and Mann-Whitney U test were used for intergroup comparisons. Chi-square and Fisher’s exact tests were used to compare categorical variables. The correlations between musclin and other clinical parameters were analyzed with Spearman’s rank correlation test. To obtain the optimal cut-off points of musclin concentrations for predicting AF onset, receiver-operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC) was calculated. Those independent predictors derived from logistic regression were selected and incorporated in logistic regression models. A two-tailed P<0.05 was considered statistically significant for all analyses. For multiple comparisons, Bonferroni adjustment was used to correct P values to <0.025.
5
Quantitative and Qualitative Analysis of Survey Data
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Quantitative data from the questionnaires were analysed using the statistical package, SPSS for Windows 10. Non-parametric tests were used as appropriate. Qualitative data from the surveys were collated and analysed using thematic analysis [12 (link)]. Two researchers independently coded the responses and the initial review indicated that the researchers were in agreement on approximately 90% of initial codes. Divergence of initial codes for the remaining responses was resolved by further review and discussion with a third researcher. The three researchers then discussed and agreed meaningful themes, which were checked against the initial codes and the overall dataset, and amended where necessary. The final themes were subsequently organised into overarching domains.
6
Statistical Analysis of Experimental Data
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All analyses were done by using the Statistical Package for Social Sciences (SPSS) for Windows 10. Data has been expressed as mean ± standard deviation (M±SD). Statistical comparison between groups was performed using one way ANOVA and paired sample t test. The significant level was considered at p = 0.05.
7
Statistical Analysis of Non-Union Treatment
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Statistical analysis was performed utilizing SPSS for Windows 10 (SPSS Inc., Chicago, IL, USA). The Chi-square test was used to evaluate categorical and statistically significant differences between groups, location shifts between groups via non-parametric test methods (Mann–Whitney U-Test). Significant changes within both groups were analyzed via Wilcoxon signed-rank test for paired samples. Correlation analyses were conducted between all variables and the predictive performance of any logistic regression model was evaluated based on the area under the curve (AUC) of the respective ROC (receiver operating characteristic) curve. In particular, factors that were included into the regression analysis were the type of non-union, age, utilized type of BMP, method of osteosynthesis and diabetes. Continuous variables are expressed as absolute mean concentrations ± SD (standard deviation) and the level of significance (α) was set at 5%.
8
Statistical Analysis of Continuous Variables
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Statistical analyses were performed with SPSS for Windows 10.0. All continuous variable values were expressed as Mean±SD. Comparison of means between two groups was performed with student t tests. Comparisons of means among multiple groups were performed with one-way ANOVA followed by post hoc pairwise comparisons using Tukey’s tests. A two-tailed p < 0.05 was considered statistically significant in this study.
9
SPSS One-way ANOVA Analysis
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One-way analysis of variance was implemented in SPSS for Windows 10.0 (SPSS, Inc., Chicago, IL, USA).
10
Statistical Analysis of Experimental Data
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Statistical analyses were performed with SPSS for Windows 10.0 (SPSS Inc., Chicago, IL, USA). All data values were expressed as means±SD. Comparisons of means among multiple groups were performed with one-way ANOVA followed by post hoc pairwise comparisons using Tukey’s tests. A two-tailed p<0.05 was considered statistically significant in this study.
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