We first examined the frequency distributions of socio-demographic and behavioral characteristics of the study participants. Characteristics were summarized using means (±standard deviation) for continuous variables and counts and percentages for categorical variables. Mean (±standard deviation) of MEQ and ESS scores were calculated across socio-demographic and behavioral characteristics and the associations were tested using a one-way ANOVA for multi-level characteristics and a two-sample t-test for two-level characteristics. Multivariable linear regression models were also fitted to evaluate the associations. We also calculated the distribution of morningness-eveningness chronotype across demographic and behavioral characteristics and Chi-square tests were used to determine bivariate differences. Additionally, we calculated the distribution of daytime sleepiness and evening chronotype across energy drink consumption status. Multivariable logistic regression models were used to calculate odds ratios (ORs) and 95 % confidence intervals (95 % CIs) for the associations. All analyses were performed using SPSS Statistical Software for Windows (IBM SPSS, version 20, Chicago, IL, USA). All reported p-values are two-sided and deemed statistically significant at a 0.05 level.
Spss statistical software for windows
Manufactured by IBM
Sourced in United States, United Kingdom
SPSS is a statistical software package for Windows that provides advanced analytical capabilities. It offers a range of statistical techniques for data analysis, including descriptive statistics, bivariate analysis, and multivariate analysis. SPSS is widely used in academic, research, and business settings for data management, analysis, and reporting.
Automatically generated - may contain errors
Lab products found in correlation
Sas statistical software for windows version 9, by SAS Institute (2 mentions)
Prism software, by GraphPad (2 mentions)
Spss statistics for window, by IBM (1 mentions)
Excel 2007, by IBM (1 mentions)
Statistical software for windows, by MedCalc (1 mentions)
Geforce rtx 3090 graphics card, by NVIDIA (1 mentions)
133 protocols using spss statistical software for windows
1
Chronotype and Daytime Sleepiness Patterns
2
Analyzing Normality and Comparisons in Research
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The Kolmogorov–Smirnov test was used to verify the normality of data distribution. For quantitative comparisons between groups, we used the Student t-test for independent samples in parametric variables and the independent Mann-Whitney U test for the non-parametric variables. Pearson correlation coefficients were calculated to assess the relationship between variables. Binary logistic regression is calculated to assess the influence of VF and RNFL changes on the diagnosis of glaucoma or intracranial tumor. Statistical analyses were performed using SPSS statistical software for Windows (version 20.0, IBM-SPSS, Chicago, IL). The level of statistical significance was set at p<0.05.
3
Normality Testing and Statistical Analysis
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The Kolmogorov–Smirnov test was used to verify the normality of data distribution. For quantitative comparisons between groups, we used the Student’s t-test for independent samples in parametric variables and independent Mann–Whitney U test for the non-parametric variables. Pearson correlation coefficients were calculated to assess the relation between variables. Binary logistic regression is calculated to assess the influence. Statistical analyses were performed using SPSS statistical software for Windows (version 20.0, IBM-SPSS, Chicago, IL, USA). The level of statistical significance was set at p < 0.05. Power analysis was calculated at the website “http://powerandsamplesize.com/ ”.
4
Statistical Analysis Methodology Protocol
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All statistical analyses were performed using SPSS statistical software for Windows (version 26.0; IBM Corp., Armonk, NY, USA). The variables were investigated using analytical methods (Kolmogorov–Smirnov/Shapiro–Wilk tests) to determine the normality of their distributions. Continuous variables are expressed as means ± standard deviations (SDs) for parametric data and medians with interquartile ranges (IQRs) for non-parametric data, whereas categorical variables are presented as crude numbers and percentages. Continuous variables were compared using Student’s t-tests. Mann–Whitney U tests were performed for the non-normally distributed variables. Kruskal–Wallis tests were performed as nonparametric tests to test for significant differences among continuous dependent variables with categorical independent variables with two or more groups. Chi-square tests or Fisher’s exact tests were used for categorical variables. Kaplan–Meier analyses were conducted to demonstrate freedom from mortality and freedom from secondary interventions. The differences between groups were compared using Mantel–Cox log-rank tests. A p-value < 0.05 was considered statistically significant.
5
Statistical Analysis of Serum and Gene Expression
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The data of serum parameters, and expression level of genes were analyzed by one-way analysis of variance (ANOVA) with the Duncan post hoc test for multiple comparisons, using IBM SPSS statistical software for Windows (Version 26.0, IBM Corp., Armonk, NT, USA). Results are expressed as treatment means with their pooled standard error of the mean. A probability value of P < 0.05 was considered statistically significant.
6
Analyzing Gestational Diabetes Risk Factors
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All analyses were performed using IBM's SPSS Statistical Software for Windows (IBM SPSS Version 24, Chicago, Illinois, USA). Socio-demographic characteristics were evaluated using proportions (%) for categorical variables and means (± standard deviations) for continuous variables. Adjusting for covariates of interest, we used bivariate logistic regression analysis to calculate odds ratios and 95% confidence intervals (95% CI) to estimate associations of variables and GDM. All reported p-values are deemed statistically significant at < 0.05.
7
Statistical Analysis of Experimental Data
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All data were analyzed using GraphPad Prism (GraphPad Software, La Jolla, CA, USA) or SPSS Statistical Software for Windows (IBM, Armonk, NY, USA). The Kolmogorov–Smirnov test was used to determine whether the data were parametric or nonparametric. Significant differences between the control and treatment groups were statistically analyzed using the t-test for parametric distributions and the Mann–Whitney U-test for nonparametric distributions, as well as analysis of variance using Tukey’s multiple comparison test and two-way analysis of variance using the Bonferroni post hoc test. A value of p < 0.05 was considered statistically significant, and significance is noted as *p < 0.05; **p < 0.01; and ***p < 0.001.
8
Evaluating Cellular Stress Response
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All data are presented as the mean ± SD. All statistical analyses were performed using SPSS statistical software for Windows (version 20.0; IBM Corp). All experiments were repeated at least 3 times. One-way ANOVA followed by the Tukey's test and unpaired t-tests were used to evaluate the significance of the differences between the groups. P<0.05 was considered to indicate a statistically significant difference.
9
AI Mortality Prediction for COVID-19
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Continuous variables are reported as means ± standard deviations or medians and interquartile ranges, and categorical variables are presented as percentages and frequencies. Comparisons between groups were performed using the independent sample t-test or chi-square test. The performance of the AI model was measured using the AUROC to predict the dataset accuracy, recall (sensitivity), specificity, and F1 score. Recall is the ratio of correctly predicted positive observations to the total observations, while the F1 score (balanced F-score) is the harmonic mean of the precision and recall. In addition, to predict mortality in the admission of COVID-19 patients, we performed a Cox proportional-hazards model regression analysis. For all variables, p < 0·05 was considered statistically significant. Statistical analyses were performed using SPSS statistical software for Windows (version 21.0; IBM, Armonk, New York, United States).
10
Firth Logistic Regression for Rare Events
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Statistical analyses were computed using statistical package R (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria) and SPSS statistical software for Windows (version 21.0, IBM Corp., Armonk, NY, USA). Continuous variables are presented as the mean ± standard deviation. Probability values of p < 0.05 were considered statistically significant. Data were analyzed using independent t-test and Fisher’s exact test for the baseline demographics. Univariate and multivariate logistic regression analyses were performed to evaluate the factors associated with implant exposure. Using standard maximum likelihood logistic regression with highly unbalanced dependent variables can underestimate the probability and bias standard errors. The analysis of rare events, such as implant exposure in this study, requires penalized likelihood models. Firth logistic regression (FLR) is one such method for logistic regression. It uses Firth’s bias reduction, an ideal way to handle separation in logistic regression to reduce bias. Therefore, FLRs were conducted in R using the “logistf” package, with the results interpreted in the same manner as traditional logistic regressions.
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