Anthropometric and CRF characteristics of the study sample are presented as means and SD. Normality of selected variables was verified using histograms and Q‐Q plots. Differences were analyzed by two‐way analysis of variance (ANOVA) or Chi‐square test (X2) to explore sex and age differences. Smoothed and specific curves for each age were obtained via a penalized maximum likelihood with the following abbreviations: (1) L (Box‐Cox transformation), (2) M (median), and (3) S (coefficient of variation) (Cole & Green, 1992 ). The LMS method assumes that the outcome variable has a normal distribution after a Box‐Cox power transformation is applied using the LMS method implemented in the LMSChartMaker Pro Version 2.54, (Medical Research Council, London, UK, http://www.healthforallchildren.com/shop-base/software/lmschartmaker-light /). The appropriate number of degrees of freedom was selected on the basis of deviance, Q‐tests and worm plots, following the suggestions of Royston & Wright (Royston & Wright, 2000 ). The 3rd, 10th, 25th, 50th, 75th, 90th, and 97th smoothing percentiles were chosen by sex and age for reference values. We used SPSS V. 21.0 software for Windows (SPSS, Chicago, Illinois) for all but the LMS method calculations. Statistical significance was set at P < .05
Spss v 21.0 software for windows
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SPSS V. 21.0 software for Windows is a statistical analysis software that provides tools for data management, analysis, and visualization. It offers a range of analytical techniques to help users explore, model, and predict data.
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Lab products found in correlation
6 protocols using spss v 21.0 software for windows
1
Anthropometric and Fitness Characteristics
2
Genetic Analysis of Alopecia Areata
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For genetic analysis, the sample size was calculated based on 2% worldwide alopecia areata incidence,11 assuming a power of 99.0% (a Z value of 2.33), a minimum number of 43 subjects is sufficient.
Data analysis was performed with the SPSS v21.0 software for windows (SPSS, Inc., Chicago, IL, USA) and the Epi-INFO TM 7 statistical program (Stone. Mountain, GA, USD Inc). Hardy–Weinberg equilibrium for the alleles was obtained using a goodness-of-fit test and the genotypic dependence between patients and controls was determined with a χ2 test; OR was calculated from 2 × 2 contingency tables (p < 0.05).
Data analysis was performed with the SPSS v21.0 software for windows (SPSS, Inc., Chicago, IL, USA) and the Epi-INFO TM 7 statistical program (Stone. Mountain, GA, USD Inc). Hardy–Weinberg equilibrium for the alleles was obtained using a goodness-of-fit test and the genotypic dependence between patients and controls was determined with a χ2 test; OR was calculated from 2 × 2 contingency tables (p < 0.05).
3
Statistical Analysis of Experimental Data
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Numerical data were expressed as means ± standard errors of the means (SEM) and were analyzed using GraphPad Prism software (version 9.4; GraphPad Software Inc.). Differences between groups were analyzed by one-way analysis of variance (ANOVA). SPSS v21.0 software for windows (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. A P value of <0.05 was considered statistically significant (indicated by * in the figures), and a P value of <0.01 was considered extremely significant (indicated by ** in the figures).
4
Multivariate Analysis of Hypertension
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For the build-up of the database and the necessary analysis, the SPSS® v.21.0 software for Windows was used.
Descriptive statistics was used to calculate measures of central tendency and dispersion of the quantitative variables studied, as well as the frequencies of categorical variables. Shapiro–Wilk and Kolmogorov–Smirnov tests were used to check the type of distribution of the variables studied. The Chi-square test was used to compare proportions and the Student’s t-test was used to compare means for variables with a Gaussian distribution.
To perform multivariate logistic regression, the backward stepwise method was used. The selection of variables to be included in the regression was based on the following criteria: p < 0.10 when comparing the hypertension (HPTN) group and the RH group and biological plausibility.
The level of significance adopted in the statistical tests was 5% (p < 0.05).
Descriptive statistics was used to calculate measures of central tendency and dispersion of the quantitative variables studied, as well as the frequencies of categorical variables. Shapiro–Wilk and Kolmogorov–Smirnov tests were used to check the type of distribution of the variables studied. The Chi-square test was used to compare proportions and the Student’s t-test was used to compare means for variables with a Gaussian distribution.
To perform multivariate logistic regression, the backward stepwise method was used. The selection of variables to be included in the regression was based on the following criteria: p < 0.10 when comparing the hypertension (HPTN) group and the RH group and biological plausibility.
The level of significance adopted in the statistical tests was 5% (p < 0.05).
5
Obesity Indices and Reaction Time
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Descriptive statistics were run on all variables. Data were screened for problems of skew, kurtosis, and outliers. Initial Pearson product–moment correlations were conducted on relationship between RT tests and age, SES and PA score. Any variable exhibiting a significant correlation with the dependent variable (RT) was included as a covariate/confounder in the analyses. For finding the relationship between the RT tests and the obesity indices hierarchical regression analysis was conducted in 2 steps. In the first step confounding variables (age, SES and PA) were entered, and in the second step obesity indices were entered, separately. All calculations were performed using SPSS v.21.0 software for Windows (SPSS Inc, Chicago, IL, USA). The significance level was set at p < 0.05.
6
Anthropometric and VJ Reference Values
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Anthropometric and VJ characteristics from the study sample are presented as the mean with SD. Normality for selected variables was verified using histograms and Q-Q plots. Data were then split by sex; a 2-way analysis of variance (ANOVA) with post hoc tests (Bonferroni) was used to identify differences between age groups within sexes. The LMS method assumes that the outcome variable has a normal distribution after a Box-Cox power transformation is applied, using the LMS method implemented in LMSChart-Maker Pro Version 2.54 (Medical Research Council, London, United Kingdom, http://www.healthforallchildren.com/ shop-base/software/lmschartmaker-light/). Smoothed and specific curves for each age were obtained via a penalized maximum likelihood with the following abbreviations: M (median), L (Box-Cox transformation), and S (coefficient of variation) (5) . The appropriate number of degrees of freedom was selected on the basis of the deviance, Q tests, and worm plots, following the suggestions of Royston and Wright (25) . The 3rd, 10th, 25th, 50th, 75th, 90th, and 97th smoothing centiles were chosen as age-and genderspecific reference values. We used SPSS V. 21.0 software for Windows (SPSS, Chicago, IL, USA) for all but the LMS method calculations. Statistical significance was set at p # 0.05.
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