Spss 26.0 program

Quantitative variables were expressed as mean ± standard deviation. The relationship between malnutrition diagnosis using different tools and mortality was estimated using the chi-square test, with Fisher’s correction when necessary. For the concordance between diagnostic techniques, the kappa coefficient was used. The variables that showed an association with mortality in the chi-square test were included in a multivariate logistic regression model to assess the association between mortality and malnutrition, controlling for confounding variables such as sex, age and Charlson Comorbidity Index. For calculations, significance was set at p < 0.05 for two tails. Data analysis was performed using the SPSS 26.0 program (SPSS Inc., Chicago, IL, USA).

Data were analyzed using SPSS 26.0 program (SPSS Inc., Chicago, IL, USA), and a mean difference of p < 0.05 was considered significant. Different letters in the actual figures stand for significant difference between various treatment groups at p < 0.05 by one-way ANOVA. Once significance was recognized, Tukey’s HSD test as a post hoc analysis was conducted to compare the difference between groups. Other methods and materials not described in this study were the same as recently reported [13 (link),14 (link),15 (link),16 (link),17 (link)].

The normal distribution of the data was examined using the Shapiro–Wilk test. The data was then analyzed utilizing the SPSS 26.0 program (SPSS, Inc., Chicago, IL, USA) through paired sample t-test and one-way ANOVA along with a Tukey post hoc test. Values of p < 0.05 were considered statistically significant.

All the experimental data were expressed as mean ± standard error (SE). The SPSS 26.0 Program (SPSS Inc., Chicago, IL, USA) was used for one-way ANOVA analysis at a significance level of p< 0.05. Origin 8 (OriginLab, Northampton, Massachusetts, USA) was used for chart processing.

This study used the SPSS 26.0 program for questionnaire analysis. First, an exploratory factor analysis (EFA) was conducted to analyze the reliability and validity of each item, and the reliability of each variable was analyzed. This study used the Varimax method for the rotation of the factors, and the number of factors was analyzed using a set with an eigenvalue of 1.0. Second, a non-hierarchical/hierarchical cluster analysis method was used to segment the market by deriving group characteristics with wellness tourism motivations. Cluster analysis is a technique that divides a sample into many groups by verifying homogeneity based on sub-characteristics [83 ]. Finally, to analyze differences in satisfaction, revisit intention, recommendation intention, and flow by group, a one-way ANOVA analysis was performed. Ex post analysis was carried out to derive detailed differences when the results of one-way ANOVA indicated that there were differences among the four groups. Among various ex post analysis methods, Scheffe’s ex post analysis was performed to analyze differences between the groups.

Semiquantitative analysis and biological activity testing were statistically analyzed via ANOVA (one-way analysis of the variance) hiring SPSS 26.0 program (SPSS Inc., Chicago, IL. USA) and Metaboanalyst 4.0 (http://www.metaboanalyst.ca/) which is a web-based tool for processing metabolomics data to construct hierarchical cluster analysis (HCA) heat maps.
In addition, SIMCA v 14 software (Umetrics, Sweden) was applied for the construction of Orthogonal Projections to Latent Structures-Discriminant Analysis model (OPLS-DA) followed by Orthogonal Projections to Latent Structures (OPLS) model that enabled the discrimination of different milk thistle organs extracts based on their chemical profile in addition to antiviral activity. OPLS-DA model enabled the identification of the phytoconstituents that generated such discrimination. Meanwhile, careful examination of the OPLS correlation coefficient plots enabled us to identify the metabolites strongly correlated to the investigated biological activity. Permutations plots were created to validate that the created models were not modelling the noise or over-fitted.