Spss statistics for windows version 26

Parametric testing was carried out to compare continuous variables and normal distribution. Normal distribution was verified using the Shapiro–Wilk test. Homogeneity of the variables was checked with the Levene test. The 2-tailed Student’s t-test was applied for non-homoscedastic unpaired groups. The 2-tailed Mann–Whitney U test was used as a nonparametric test for unpaired groups.
To identify independent risk factors for infection and PJD, multivariate linear regression was performed after adjusting for potentially confounding factors such as chronological age, BMI, gender, comorbidity index and ASA score. p values < 0.05 were considered significant. All analyses were conducted with the Statistical Package for Social Science (IBM SPSS Statistics for Windows, Version 26.0; IBM Corp., Armonk, NY, USA).

To analyze the available data of 35 patients, a table with 11 parameters was compiled using the statistics software IBM SPSS Statistics for Windows, Version 26.0 (IBM Corporation). Each dataset was numerically encoded to allow statistical analysis. Patient data were evaluated anonymously. Descriptive data were represented by absolute and relative frequency, additionally in part by mean value, median, and standard deviation. Using the descriptive data, frequency tables and graphs were generated. To investigate the interrelations between two variables, cross tables were used and absolute and relative frequency for the individual subgroups were determined. Via chi‐square test two features were tested for independence.

Ordinal data of extremely preterm infants and very preterm infants were compared using a Mann–Whitney U Test and nominal data were compared using a Chi-Square (X²) test. No assumptions were violated when analyzing the groups using the Mann–Whitney U Test; ordinal data in two independent groups with independence of observations. Paired t-tests were used to compare BMD and BMC of extremely and very preterm infants over time. The dependent variables were continuous and normally distributed. Stepwise multiple regressions were carried out to evaluate the influence of macronutrient intake, gestational age, gender, ethnicity, birthweight, weight at TCA, weight at six months CA, necrotizing enterocolitis, bronchopulmonary dysplasia, and late onset sepsis on BMC and BMD. All variables were included in the analysis. However, only the significant models with corresponding variables were included in the results (Tables 5 and 6). The assumptions were met because of the linear between the dependent and independent variable, with the same variance of the residuals, homoscedasticity, and a normally distributed model. A p-value of <0.05 was considered statistically significant. All statistical analyses were conducted using the IBM^® SPSS^® Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA).

Methotrexate concentrations between courses following one or two FA doses (courses with rapid MTX clearance; S-MTX ≤ 0.2 μmol/L at 42 or 48 h) were compared to courses following three or more FA doses using the Student’s t test and proportions between groups using the standard normal deviate (SND) test. The associations between the use of proton pump inhibitors (PPI) and MTX excretion and between FA doses and toxicities were analyzed with the Fisher’s exact test. Statistical analyses were performed with IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp, Armonk, NY, USA) and StatsDirect Statistical Software, Version 3.2.8 (StatsDirect Ltd., England).

The Pearson correlation method is the most common method used for numerical variables; it assigns a value between −1 and 1, where 0 indicates no correlation, 1 a total positive correlation, and −1 a total negative correlation. In addition, a correlation value of 0.7 between two variables would indicate that a significant and positive relationship exists between the two. A positive correlation signifies that if variable A increases in value, then B will also increase, whereas if the correlation is negative, if A increases, B decreases [24 ]. The correlation between the levels of antibiotics and heavy metals present in broiler meat overall in Bangladesh and in different locations in Bangladesh was analyzed by using IBM SPSS Statistics (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp) [23 (link)]. The correlation results help an analyst to understand the relationships and their strength between variables, such as, in this study, the levels of antibiotics and heavy metals in broiler meat.