All statistical analyses in our study were performed using SPSS package for Windows, version 22.0 (Chicago, IL). Categorical variables were compared by Chi-square test or Fisher’s exact test. Survival parameters were calculated using Kaplan–Meier actuarial analysis and differences were compared using the log-rank test. Multivariate analyses were performed using the Cox proportional hazards regression model. All analyses were two-sided. The level of significance was set at P < 0.05.
Spss package for windows version 22
Manufactured by IBM
SPSS package for Windows, version 22.0 is a statistical software package developed by IBM. It provides tools for data manipulation, analysis, and presentation.
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Lab products found in correlation
5 protocols using spss package for windows version 22
1
Statistical Analyses in SPSS
2
Statistical Analysis of Nominal and Ordinal Data
3
Evaluating Survival Outcomes in Nasopharyngeal Carcinoma
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Statistical analyses were performed using SPSS package for Windows version 22.0 (Chicago, IL). Correlations between the different IC regimens and clinical characteristics of NPC were evaluated using the χ2 or Fisher’s exact test as appropriate. Kaplan–Meier survival curves were used to evaluate long-term survival; the survival rates were compared using log-rank test. A Cox proportional hazards model was used to perform multivariate analyses involving the following variables: age, sex, T stage, N stage, clinical stage, EBV DNA, and IC regimen. All analyses were two-sided. The level of significance was set at P < 0.05.
4
Fisher's Exact Test for Small Samples
5
Analyzing Motor Outcomes in Neonates
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Statistical analysis was performed using the SPSS package for Windows, version 22.0 (SPSS Inc, Chicago, IL). Intra-class correlation coefficient (ICC) statistics were applied to examine pairwise agreement of the motor optimality scores among the three scorers and an overall agreement among all scorers.
Fisher's exact test was applied to compare nominal data (e.g. neonatal complication × fidgety movements). The independent samples T-test was used to compare whether two groups (e.g. with absent or present fidgety movements) have different average values of birth weight and gestational age. The Mann–Whitney U test was applied to compare two groups with regard to neonatal complications on one dependent outcome variable (i.e. motor optimality score). Linear-by-linear association was applied to assess the relation between nominal variables (e.g. fidgety movements) and ordinal variables (i.e. categories of PDMS-2). Spearman's rank order correlation was applied to correlate ordinal variables (i.e. categories of PDMS-2) with metric scales (e.g. motor optimality score). To assess the relation between two metric variables (e.g. birth weight and motor optimality score), we applied Pearson's product–moment correlation coefficient.
Fisher's exact test was applied to compare nominal data (e.g. neonatal complication × fidgety movements). The independent samples T-test was used to compare whether two groups (e.g. with absent or present fidgety movements) have different average values of birth weight and gestational age. The Mann–Whitney U test was applied to compare two groups with regard to neonatal complications on one dependent outcome variable (i.e. motor optimality score). Linear-by-linear association was applied to assess the relation between nominal variables (e.g. fidgety movements) and ordinal variables (i.e. categories of PDMS-2). Spearman's rank order correlation was applied to correlate ordinal variables (i.e. categories of PDMS-2) with metric scales (e.g. motor optimality score). To assess the relation between two metric variables (e.g. birth weight and motor optimality score), we applied Pearson's product–moment correlation coefficient.
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