Results are presented as means ± SEM. Differences between means were analyzed using an unpaired Student’s t-test. Before unpaired Student’s t-test, all samples were checked for normal distribution by SPSS (Windows, Version 25; IBM). Comparisons involving more than 2 groups were analyzed by one-way ANOVA, Bonferroni correction for multiple comparisons were applied in one-way ANOVA (SPSS for Windows, Version 25; IBM). Differences were considered statistically significant at P < 0.05. Each experiment was repeated at least 3 times.
Spss windows version 25
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SPSS Windows version 25.0 is a statistical software package developed by IBM. It is designed to analyze and manipulate data, allowing users to perform a variety of statistical analyses, including regression, correlation, and hypothesis testing.
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Lab products found in correlation
15 protocols using spss windows version 25
1
Comparative Statistical Analysis of Experimental Data
2
Statistical Analysis of Experimental Data
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Data analyses were performed using IBM SPSS Windows version 25. The Shapiro–Wilk test was used to determine whether the data were normally distributed. The Wilcoxon signed-rank test was used to study matched samples. In order to correct for multiple comparisons, the Benjamini–Hochberg test was used. The false discovery rate (FDR) for the Benjamini–Hochberg test was set at 0.05. The quantitative parameters are presented as means ± standard deviation (SD) and the qualitative parameters as numbers and percentages per class. The significance level was set at p < 0.05.
3
Olfactory and Cognitive Impairment in Geriatric Syndromes
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We used the Mann–Whitney U‐test for the distribution of ordinal variables and χ2‐test (Fisher's exact test when required) for categorical variables to compare the two groups. Spearman's rank correlation analysis was used for determination of the link between two parameters. Multivariate logistic regression analysis was used to determine the associations of olfactory and cognitive impairment with common geriatric syndromes, and the associations of specific shrinkage and/or lesions of the brain with these geriatric syndromes, adjusted by age and sex. Data were analyzed by spss Windows (version 25.0; Chicago, IL, USA).
4
Cognitive Predictors of Rehospitalization and Mortality
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The variables are presented as means (SD). Cox regression model was applied to estimate hazard ratios (HRs) with 95% confidence interval (CI) for each cognitive test per 1 SD increase. Continuous standardized values of each cognitive test were entered as independent variables in separate models. Model 1 was adjusted for age and sex. On top of age and sex, Model 2 was adjusted for BMI, systolic blood pressure, New York Heart Association (NYHA) class at admission, diabetes, educational level, prevalent AF, smoking, and prior cardiovascular disease as independent variables. The time variable was calculated as the follow‐up time between the date of screening and date of first rehospitalization or death until the 31st of February 2020. Group differences in continuous variables between study participants and individuals with missing data were compared using one‐way ANOVA test, whereas categorical variables were compared using Pearson's χ2 test. Group differences in continuous variables between study participants with MoCA scores above and below 23 (and 26) were compared using one‐way ANOVA test, whereas categorical variables were compared using Pearson's χ2 test. All analyses were performed using SPSS Windows version 25.0, and a P value of <0.05 was considered statistically significant.
5
Ethnicity, Hypertension, and Obesity Effects on Cognition
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To test the separate impacts of hypertension and obesity and their interactions with ethnicity on cognition, two series of GLMs were performed in SPSS (Windows, Version 25.0). Each series included three GLMs that predicted Trails B/A, Digit Span Backward, and delayed verbal memory scores. In other words, a total of six GLMs which included the main effects of cardiovascular risk factor and ethnicity, the interaction between cardiovascular risk factor and ethnicity, and controlled for age and education were performed. Results presented above are F-statistics from GLMs with the exception of t-statistics from parameter estimates with robust standard errors in which there was a violation of homoskedasticity.
6
Factors Influencing Body Weight Perception
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All data were presented as means and SD and were analyzed using SPSS Windows version 25.0 (SPSS Inc., Chicago, IL, USA) and R software (version 3.6.1). Comparison of proportions between sex and other categorical measures was performed using chi-square test. The nature of the differences in psychological measures, according to the status of BWP (“underestimated,” “appropriate,” “overestimated”) and WCS (“dietary,” “exercise,” “WCS-m,” “never attempted”) were tested using ANOVA followed by Fisher’s least significant difference multiple comparison test. Correlations between variables were analyzed using Spearman’s correlation. Binary logistic regression was performed to determine the predictive factors of BWP and WCS by including sex, age, and actual BMI. Significance was defined as p < .05.
7
Statistical Analysis of Diabetic Retinopathy Data
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SPSS Windows Version 25.0 (Statistical Package for the Social Sciences, SPSS Inc., Chicago, IL, USA) was used to analyze the data collected. The missing data were listwise deleted. Non-parametric data are presented as median (interquartile range) and parametric data as mean ± SD. P-values are results of Mann–Whitney test for continuous data and Fisher's exact test for categorical data.. Chi-squared and Fisher’s exact test were used to assess the differences between the categorical variables. Unpaired t test was used for calculating significant differences between continuous variables.
Group with DR and without DR were compared using Chi-Square tests for categorical variables and independent-t test for continuous variables. A p value < 0.05 is considered significant. Among those with DR, 13 had mild disease, 11 had moderate and only 3 had severe retinopathy. Thus, we were not able to further categorise the sample based on severity of DR. Correlation between two continuous variables were analysed using Spearmans rho test for non-parametric data. Cohen’s (1988) cut-off points were used for interpretation of the strength of correlation. Accordingly, a value of r ≥ 0.5 showed a strong correlation whereas 0.3 – 0.5 showed a moderate correlation. Weak and insubstantial correlation was indicated by r < 0.3 – 0.1 and < 0.1, respectively.
Group with DR and without DR were compared using Chi-Square tests for categorical variables and independent-t test for continuous variables. A p value < 0.05 is considered significant. Among those with DR, 13 had mild disease, 11 had moderate and only 3 had severe retinopathy. Thus, we were not able to further categorise the sample based on severity of DR. Correlation between two continuous variables were analysed using Spearmans rho test for non-parametric data. Cohen’s (1988) cut-off points were used for interpretation of the strength of correlation. Accordingly, a value of r ≥ 0.5 showed a strong correlation whereas 0.3 – 0.5 showed a moderate correlation. Weak and insubstantial correlation was indicated by r < 0.3 – 0.1 and < 0.1, respectively.
8
Validating AIRDS System Efficacy
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Linear regression analysis and coefficient of variation statistics were computed to determine the validity and variation of the AIRDS system. A power analysis, using G-Power software, was conducted to assess the sample size requirement ( 8), based on our pilot study which demonstrated effect size (Eta squared, 1.02) and power ( 0.8). Significance was assigned at a P of less than 0.05 for all analyses. Statistical analyses were performed with SPSS/Windows (version 25.0).
9
Prognostic Value of Neutrophil-Lymphocyte Ratio in COVID-19
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Data were entered into Microsoft Excel 2013 (Microsoft Corp.) and analyzed using the Statistical Product and Service Solutions (SPSS) Windows version 25.0 (IBM, United States). Descriptive statistics, including the mean-standard deviation (SD), median-interquartile range (IQR), and number with percentage, were used as appropriate according to the data distribution of variables. Mean-SD and median-IQR were used for normally and non-normally distributed quantitative data, respectively. The normality of data distribution of variables was determine using Kolmogorov–Smirnov test. Normally and non-normally distributed quantitative variables were compared using the t-test and Mann–Whitney tests, respectively. Meanwhile, the X2 test or Fisher's exact test was applied as appropriate for categorical variables with a p-value less than 0.05.
ROC analysis was performed to determine the cut-off value (COV) of NLR between COVID-10 with and without proven SPBI on D1 and D3. The magnitude of sensitivity, specificity, and prevalence ratio31 based on the COV of NLR was obtained and evaluated. ROC analysis was performed using MedCalc version 5.2 (MedCalc Software Ltd, Belgium).
ROC analysis was performed to determine the cut-off value (COV) of NLR between COVID-10 with and without proven SPBI on D1 and D3. The magnitude of sensitivity, specificity, and prevalence ratio31 based on the COV of NLR was obtained and evaluated. ROC analysis was performed using MedCalc version 5.2 (MedCalc Software Ltd, Belgium).
10
Statistical Analysis of Clinical Data
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IBM SPSS Windows version 25.0 and IBM Modeller version 18.0 were used for data analysis. Clinical data were analysed with paired t-tests, chi-square, and odds ratios.
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