Spss software version 27

Statistical data analysis was performed with SPSS software version 27. First, overall scores and descriptive statistics were calculated for the scales: PSQI, knowledge of sleep hygiene, and knowledge of diet and supplementation. At this stage, detailed scores for the 6 subscales of the PSQI were also calculated, and an analysis of the distribution normality was performed using the Kolmogorov–Smirnov test. A correlation analysis was then conducted using Pearson’s R coefficient between all variables in the study. The cut-off points used for the correlation coefficient were as follows: <0.40 as low, 0.40–0.69 as moderate, and ≥0.70 as high correlation. A p-value below 0.05 was considered significant. In the next step, a linear regression analysis for general sleep quality (PSQI) was conducted by including predictors such as knowledge of sleep hygiene, knowledge of diet and supplementation, use of medications, and use of supplements. The same relationships were tested for the overall PSQI by respondents’ education and place of residence. Moreover, additional detailed analyses were conducted for the particular component of sleep quality.

We conducted an analysis using data from NFHS-5, focusing on participants aged 18 years and older, and incorporated individual sampling weights. Our study explored the relationships between several dependent variables: ever-measured blood pressure, prehypertension prevalence, and raised blood pressure, in relation to various determinants. Our approach involved determining sample sizes N), estimating prevalence rates with 95% confidence intervals (CIs), and calculating adjusted odds ratios (AORs) with their respective 95% CIs.
We went beyond estimating proportions and visualized the data on a color-coded map of India, categorizing it into ranges based on prevalence distributions across all districts. This visualization enabled straightforward geographical comparisons. We employed multivariate logistic regression analysis at both state and district levels and set statistical significance at p < 0.05. Our results were presented in tabular form, highlighting factors associated with either “Higher odds” H) or “Lower odds” L) based on the odds ratios. For data analysis, we utilized SPSS software version 27 and employed a data wrapper for visualization purposes.

Statistical processing of the data obtained was carried out by analysis of variance (ANOVA) and mean separation using Duncan’s multiple range test with reference to the probability level of 0.05, using the SPSS software version 27. The data expressed in percentage were subjected to angular transformation before processing.

The Allelic data set was codified by numeric scores, distinguished from 0 (absence of any allele), 1 (heterozygosity), 2 (homozygosity). The matrix generated from the following annotations was used for the sub-mentioned statistical analysis and is available in the H2020 BRESOV repository on the Zenodo database and is also present in the Supplementary data in Table S1. The Statistical analysis was performed using the SPSS software version 27. Data were transformed using the percentage rank of the analyzed matrix and normalized using the angular coefficient (DEGRES(ASIN(RACINE(x/100))). Pearson’s correlation was performed to identify the allelic variants involved in the size of inflorescence. The alleles that showed the highest correlation with the morphometric traits were selected. Moreover, the principal component analysis (PCA), as a powerful tool for clustering and dimension reduction, was also performed to discriminate the accessions studied and explain the variability among genotypes by the main components reducing the size of data by the factorial analysis regression method.

Descriptive statistics were used to analyze the characteristics of the sample. Chi-square tests were used to evaluate differences between samples. ANOVA test was used to assess differences in means across rounds. A logistic regression analysis was used to predict willingness to comply with health regulations without compensation for lost wages for the entire dataset, with data collection rounds serving as one of the independent factors introduced into the analysis. The dependent variable was re-categorized into two categories: Yes versus No (excluding responses of not sure and do not know). Variables were introduced into the regression analysis if they were associated with the dependent variable in the univariate analysis. The regression analysis was done both unadjusted and adjusted. All statistical analyses were performed using SPSS software version 27. P values lower than 0.05 were considered to be statistically significant.