The results were statistically analyzed with SPSS for Windows 19.0 (SPSS Inc., Chicago, IL, USA). Descriptive data are expressed as the mean ± standard deviation or percentage. According to the Kolmogorov–Smirnov test, FEV1, MAC, and CC were normally distributed (P>0.20), but BMI and 6MWD were not normally distributed (P<0.05). Spearman’s correlation was used to evaluate the strength of the relationship between various anthropometric and pulmonary function indicators with the 6MWD. We used the sample size calculation for logistic regression with PASS 14 statistical software to retrospectively estimate the results of approximately 110 cases. The abilities of BMI, MAC, and CC in discriminating exercise capacity were analyzed with receiver operating characteristic (ROC) curves using the 6MWD (<350 m) as the reference standard. The areas under the curve (AUCs) of the ROC curves were determined. A larger AUC (maximum is 1) indicates better predictive ability. The association of BMI, MAC, and CC with the 6MWD was also analyzed with a multivariate logistic regression analysis. All models were controlled for possible confounders (age and FEV1). Statistical significance for all evaluations was set at α=0.05.
Spss 19 for windows
Manufactured by IBM
Sourced in United States
SPSS 19 for Windows is a statistical analysis software package developed by IBM. It provides tools for data management, analysis, and visualization. The software is designed to handle a wide range of data types and can be used for a variety of statistical techniques, including regression, correlation, and hypothesis testing.
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129 protocols using spss 19 for windows
1
Anthropometric Factors and Exercise Capacity
2
Statistical Analysis of Patient Data
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Statistical analysis was carried out using the SPSS for Windows 19.0 (SPSS Inc., Chicago, IL, United States) and Microsoft Office Excel 2016.
Two-sample T test and F test were used to analyze our patients’ data. Categorical variables were assessed using Pearson χ2 test and Fisher’s exact test.
As per standard pharmacovigilance practices, the values of the odds ratio (OR) were computed using 2 × 2 contingency table.
Significant differences were considered if p < 0.05.
Two-sample T test and F test were used to analyze our patients’ data. Categorical variables were assessed using Pearson χ2 test and Fisher’s exact test.
As per standard pharmacovigilance practices, the values of the odds ratio (OR) were computed using 2 × 2 contingency table.
Significant differences were considered if p < 0.05.
3
Correlates of Serum VEGF Levels
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Snellen VA was converted to the logarithm of minimum angle of resolution (logMAR) VA for the purpose of statistical analysis. Change in VA was calculated as the difference between VA at baseline and VA at follow-up.Statistical calculations were performed using SPSS for Windows 19.0 (SPSS, Chicago, IL, USA). As most data had a skewed distribution, numbers reported are median values unless indicated otherwise and non parametric test methods were used in statistical analyses. Continuous data were analyzed by Mann–Whitney U-test and categorical data by Fisher’s exact test. Factors that were significantly associated with s-VEGF after univariate analyses were then entered into multiple regression models to determine the independence of potential correlations and to estimate adjusted ORs (Exp(β)).
Genotype and haplotype analyses were performed with SNP Stats software. Comparisons between genotypes were adjusted using the Bonferroni multiple comparison correction and the reported p-values reflect this correction. The strength of the association between genotypes or alleles in each group was estimated by the calculation of the odds ratios (OR) and 95% confidence intervals (CI). Values of p < 0.05 were considered statistically significant.
Genotype and haplotype analyses were performed with SNP Stats software. Comparisons between genotypes were adjusted using the Bonferroni multiple comparison correction and the reported p-values reflect this correction. The strength of the association between genotypes or alleles in each group was estimated by the calculation of the odds ratios (OR) and 95% confidence intervals (CI). Values of p < 0.05 were considered statistically significant.
4
Patient Exposure Impacts over Time
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All the data are expressed as the mean ± SD (standard deviation), and the SPSS for Windows 19.0 was used for statistical analysis (SPSS, Inc., Chicago, IL, USA). Statistical analysis was performed using t tests for comparisons of different patient exposures on the first, third, and fifth days. The data of all groups obeyed normal distribution (P > 0.05). The difference was considered significant when P < 0.05.
5
Investigating Learning and Biochemical Markers
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Differences in learning, defined as the number of trials required to learn the visual cue, were compared using the Kaplan-Meier survival analysis with Cox regression to examine potential covariates that may influence learning [22 (link)]. Comparison of messenger RNA (mRNA) levels for BDNF; protein expression levels for BDNF, CREB, pCREB, and polySia-NCAM; body weight gain; and the level of plasma stress hormones between the Lf and control groups were carried out using Student’s t test. Data are expressed as mean ± SEM, and a significance level of 0.05 was used. All statistical analyses were completed with the use of SPSS for Windows 19.0 (SPSS, Inc., Chicago, IL, USA).
6
Maturation Status and Body Composition
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Normality assumptions were tested with the Shapiro-Wilk test. The independent samples t test was used to compare chronological age and bone age among the obese group. One way ANOVA was used to compare maturational status, body composition and physical performance. Multivariate analysis was use to verify the effect of maturational status on body composition and physical performance. Effect size was calculated by partial eta square (η2). All data were analyzed using SPSS for Windows 19.0 and α value was set at 0.05.
7
Porcine Milk Oligosaccharide Profiling
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The relative abundances of PMO were analyzed by a two-factor repeated measures analysis of variance model with 3 time points by using the Greenhouse-Geisser adjustment for asphericity. To investigate the different time trends for gilt and sow milk, the interaction between the dependent variable and grouping factor was determined. An overall comparison of relative abundances and different structures of PMOs between porcine milk of sow and gilt was obtained from the repeated measures analysis of the variance models, and comparisons at individual time points using t tests. All analyses were completed by SPSS for WINDOWS 19.0 (SPSS Inc, Chicago). Values were considered significant at P < 0.05.
8
Mortality and Outcomes Comparison across Hospitals
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The patient characteristics were analyzed between the medical center hospital and the nonmedical center hospital to determine differences in age, sex, CCI, and surgical clipping rate. After confirming that no differences in patient characteristics existed between the hospital levels, we further analyzed the outcomes regarding mortality, medical expenditure, and hospital LOS. The mortality of these patients was compared for differences in their demographic characteristics and hospital level by using bivariate analysis. Categorical variables were compared using the χ2 or a Mann-Whitney 2-independent-sample test. Unadjusted and adjusted logistic regression analyses were used to estimate the odds ratio (OR) and its 95% confidence interval (CI) between mortality and age, sex, CCI, hospital levels, and hospital and surgeon volume. A value of P < .05 was considered statistically significant. All statistical calculations were performed using the Statistical Package for Social Sciences for Windows (SPSS for Windows 19.0).
9
Physicochemical and Sensory Analysis of Cultivars
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SPSS for Windows, 19.0 (SPSS Inc. Chicago, IL, USA) was used to carry out an analysis of variance (ANOVA) of the mean values of physicochemical parameters, the area of volatile compounds, and sensory characteristics. Tukey’s honestly significant differences (HSD) test (p ≤ 0.05) was applied to separate means. Principal component analysis (PCA) was used to analyse the relationships among the parameters studied, using ‘ripening stage’ and ‘cultivar’ as classification variables.
10
Survival Analysis of Tumor Specimens
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Data were analyzed using SPSS for Windows 19.0. Due to missing normal distribution, the following parameter free statistic tests were used: Kruskall-Wallis test, Chi2-test, Mann-Whitney test, Kendall’s Tau (τ) and Spearman’s rank correlation analysis (rs). For survival analysis, the Kaplan-Meier method with a log-rank test was used. Only one representative specimen of each patient (primary tumors preferred) was considered for survival analysis (n = 64). All specimens (n = 121) were included into the Kendall’s Tau (τ) and Spearman’s rank correlation analyses. A p-value < 0.05 was considered statistically significant.
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