Descriptive statistics were performed to investigate sample characteristics; mean and median interval interquartile (IQR) and standard deviation was chosen to summarize continuous variables. The assumption of normality for continuous variables was verified statistically using the Kolmogorov-Smirnov test. The Student t-test and the one-way analysis of variance (ANOVA) are used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups. The Kruskal–Wallis test by ranks was used in between-group comparisons for variables not normally distributed. The threshold for statistical significance was set at p < 0.05. IBM SPSS Statistics 23 for Windows (SPSS, Chicago, IL) were used for statistical analyses.
Spss statistics 23 for windows
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SPSS Statistics 23 for Windows is a comprehensive software package for statistical analysis. It provides a wide range of data management, analysis, and visualization tools to help users interpret and understand their data. The software is designed to be user-friendly and offers a variety of analytical techniques, including regression, correlation, and hypothesis testing.
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83 protocols using spss statistics 23 for windows
1
Statistical Analysis of Sample Characteristics
2
Fatigue and Social Support in Patients
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Descriptive statistics were used to describe demographic and treatment characteristics. Categorical variables are presented as counts with percentages. The continuous variables are presented with mean and standard deviation (SD) or with median and minimum and maximum values. The various demographic and treatment-related variables were dichotomised before further analyses. Univariate regression analysis was performed to find possible association between fatigue and social support, and a possible association between social support and demographic and treatment-related variables. As the variables living arrangement and marital status were strongly correlated with each other (p ≤ 0.05), only living arrangement was included in the regression analysis to avoid multicollinearity. The variables that were statistically significant in univariate analyses were further included in the multivariate regression analysis. p values < 0.05 were considered statistically significant. The statistics programme SPSS Statistics 23 for Windows was used to perform the analyses [28 ].
3
Sling-Based Plyometric and Sprint Training Effects
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The Kolmogorov–Smirnov test was used to determine any obvious effects and estimate the distribution of the data. Homogeneity of variance was tested using the Levene’s test. A mixed model (factorial) analysis of variance (ANOVA) with repeated measures was conducted (2: sling-based plyometric/sprint training group * 2: pre- and post-test) to investigate the impact of the training modality on shooting performance of the three different shots, the peak rotational velocity at each load in the core strength test and the calculated 1RM. In addition, Pearson correlation coefficients were calculated to investigate whether the change in shooting performance was related to changes in core strength. The test-retest reliability (three repeats per condition measured during the pretest), as indicated by intra-class correlations (ICC), was ≥ 0.95 for ball shooting velocity with the different shots and ≥ 0.86 for the core strength tests with different loads. The effect size used and reported in this study was partial eta squared (η2), where 0.01 ≤ η2 < 0.06 constituted a small effect, 0.06 ≤ η2 < 0.14 constituted a medium effect, and η2 < 0.14 constituted a large effect (Cohen, 1988 ). The data were analyzed in SPSS Statistics 23 for Windows (SPSS Inc., Chicago, IL, USA), with the alpha (α) for all statistical tests set at p ≤ 0.05 to determine statistical significance.
4
Statistical Analysis of Functional Data
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Descriptive statistics were performed to investigate the sample characteristics; mean ± standard deviation, or median and interquartile interval (IQR) were chosen to summarize continuous variables, while absolute and relative frequencies (n, %) were used for categorical variables. Differences among time points were analyzed using repeated measures analysis of variance (ANOVA) for functional data or Friedman test (corrected for missing values) for immunohistochemical data. When repeated measures ANOVA was statistically significant, the post hoc tests with Bonferroni correction were performed. The Friedman test applied for morphologic data multiple comparison was followed by the Wilcoxon rank test for comparison between time points (T0, T1, T2, T12). Paired data Student’s t-test was applied to compare days of work lost before and after BT. Data analysis was performed using the Stat View SE Graphics program (Abacus Concepts Inc., Berkeley, CA, USA) and IBM SPSS Statistics 23 for Windows (SPSS, Chicago, IL). A p value <0.05 was considered as statistically significant.
5
Corneal Nerve Density and Glucose Metabolism
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Statistical analysis was performed in SPSS Statistics 23 for Windows; SPSS, Chicago, IL. Differences between group characteristics were tested using one‐way analysis of variance (anova ) with post hoc testing by the least significant difference (LSD) method for continuous variables and chi‐square tests for categorical variables. Multivariable linear regression was used to analyse the association between glucose metabolism status (prediabetes and DM2; determinant) and CNFL (outcome). We combined the categories impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) into prediabetes, because analyses did not show differences between IFG and IGT (data not shown). First, a crude analysis was performed. Next, associations were adjusted for age and sex. The results were expressed as regression coefficients (β), representing the mean difference in CNFL as compared with NGM, with their 95% confidence intervals (95% CIs) and p‐values. The Wilcoxon–Mann–Whitney 2‐tailed test was used for statistical power calculation to compare the CNFL in individuals with prediabetes versus individuals with NGM. Due to insufficient group size, the statistical power of 80% was not achieved.
6
Respiratory Rate Monitoring Validation
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No power analysis was performed due to the observational nature of this study.
All data were analyzed with SPSS Statistics 23 for Windows (SPSS Inc., IBM Business Analytics Software, Armonk, NY, USA). The statistical significance level was set at p < 0.05. The Kolmogorov–Smirnov test confirmed the non-normal distribution of the data. Therefore, Spearman’s rho correlation coefficients were presented for the time points “arrival at the PACU” and “discharge from the PACU” separately, in order to consider intra-individual dependencies within one dataset.
The Bland–Altman plot (Figure 3 ) compares the BR obtained with IRT, and the ground truth (GT, which is the BR derived from body surface ECG). For subgroup analysis, patients were assigned to category A (BR < 12 bpm), category B (BR: 12–15 bpm), or category C (BR > 15 bpm). Since the number of resulting datasets was relatively low, correlation analysis was performed over both time points.
All data were analyzed with SPSS Statistics 23 for Windows (SPSS Inc., IBM Business Analytics Software, Armonk, NY, USA). The statistical significance level was set at p < 0.05. The Kolmogorov–Smirnov test confirmed the non-normal distribution of the data. Therefore, Spearman’s rho correlation coefficients were presented for the time points “arrival at the PACU” and “discharge from the PACU” separately, in order to consider intra-individual dependencies within one dataset.
The Bland–Altman plot (
7
Predictors of Double Dropping at Festivals
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Bivariable and multivariable logistic regressions determined unique predictors of reported double dropping at the last festival attended. Candidate variables in multivariable regression model included demographic or drug use variables with bivariable tests p<.25 (21) . When running a complete case analysis, 34% (n=261) of the cases were missing. Almost all missing cases (98%, n=257) came from one variable 'frequency of general ecstasy use'. Missing data for this variable were deemed as missing at random given they were most likely related to respondents not realising they had to scroll right when completing the survey via mobile phone. The multiple imputation (MI) function within SPSS was used to generate 34 imputed datasets, corresponding to the percentage of missing cases (22) . All analyses were conducted using SPSS Statistics 23 for Windows (23) .
8
Soil Chemistry, Plant Uptake, and PCA Analysis
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The data obtained were analysed with the statistical program IBM-SPSS Statistics 23 for Windows. Data were checked for normality (Shapiro–Wilk test) and homogeneity of variances (Levene test) and, when possible, a simple ANOVA and Tukey test (p < 0.05) was applied. Data not satisfying these assumptions were analysed using a non-parametric analysis of Kruskal–Wallis test (p < 0.05) and the Man-Whitney U Test for comparison among areas. Principal component analysis (PCA) was applied to the data set for identifying the possible relations among chemical properties of the soils, multielemental concentrations in roots and shoots and in the available fraction of the soil, and multielemental concentrations in shoots and physiological parameters. For statistical purposes, the results below the detection limit were assumed as half of the detection limit.
9
Statistical Analysis of Sample Differences
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Tukey’s
honestly significant
difference test (p < 0.05) was performed with
the IBM-SPSS Statistics 23 for Windows software package to evaluate
the significance of the differences observed among samples. Results
of the statistical analysis are included in the tables shown in theSupporting Information .
honestly significant
difference test (p < 0.05) was performed with
the IBM-SPSS Statistics 23 for Windows software package to evaluate
the significance of the differences observed among samples. Results
of the statistical analysis are included in the tables shown in the
10
Sucking Frequency in Preterm Infants
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Statistical analyses were performed using SAS software, version 9.4 of the SAS System for Windows (SAS Institute, Cary, NC) and IBM SPSS Statistics 23 for Windows (IBM Corp, Somers, NY).
According to the intervention’s objectives, the primary outcome of the trial was the number of sucks/min while pauses/min, feeding time, heart rate, respiratory rate, oxygen saturation, volume of milk intake, and data from the questionnaire were secondary outcomes.
Sample size calculation was performed under the assumption of a mean number of 70 sucks/min and a standard deviation of 9 sucks/min [16 (link)]. Differences in the primary outcome variable (sucking frequency) were considered relevant if they were in the order of a magnitude of at least 10%. Based on this information and a significance level of 5%, the necessary sample size comprised 29 evaluable cases per group to detect relevant differences in the two-sided Mann-Whitney U test with 80% statistical power.
The data were described for categorical variables by absolute and relative frequencies and for continuous variables by mean, standard deviation, median, and range. Categorical variables were compared between groups by Fisher’s exact test and for continuous variables using the Mann-Whitney U test. P values <.05 were considered to be statistically significant. All p values reported were two-sided.
According to the intervention’s objectives, the primary outcome of the trial was the number of sucks/min while pauses/min, feeding time, heart rate, respiratory rate, oxygen saturation, volume of milk intake, and data from the questionnaire were secondary outcomes.
Sample size calculation was performed under the assumption of a mean number of 70 sucks/min and a standard deviation of 9 sucks/min [16 (link)]. Differences in the primary outcome variable (sucking frequency) were considered relevant if they were in the order of a magnitude of at least 10%. Based on this information and a significance level of 5%, the necessary sample size comprised 29 evaluable cases per group to detect relevant differences in the two-sided Mann-Whitney U test with 80% statistical power.
The data were described for categorical variables by absolute and relative frequencies and for continuous variables by mean, standard deviation, median, and range. Categorical variables were compared between groups by Fisher’s exact test and for continuous variables using the Mann-Whitney U test. P values <.05 were considered to be statistically significant. All p values reported were two-sided.
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