We performed statistical analysis using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland). Normality of variables was defined using the Shapiro–Wilk test, and homogeneity of variances was tested with Levene’s test. Depending on the results, we used Student’s t-test and the non-parametric U Mann–Whitney test to assess significant differences between subgroups divided according to age and the value of BMI, WHR, HOMA-IR or Quicki. The associations between oxidative stress parameters and lipids, glucose metabolism or hormone parameters were checked using Spearman’s rank-order correlation coefficient. The values were expressed as median, 1st quartile and 3rd quartile. In every analysis we considered the value of p < 0.05 as statistically significant.
Statistica software package version 13
Manufactured by StatSoft
Sourced in Poland
Statistica Software Package, version 13.3, is a comprehensive data analysis and statistical software solution. It provides a range of analytical tools and functionalities to support data management, visualization, and statistical modeling.
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Lab products found in correlation
11 protocols using statistica software package version 13
1
Oxidative Stress and Metabolic Parameters
2
Statistical Analysis of Multiple Sclerosis
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Values were expressed as mean and standard deviation (X ± SD) as well as median and 1st quartile and 3rd quartile. Normality of variables was tested using the Shapiro–Wilk test. Homogeneity of variance was assessed using Levene’s test. In case of a lack of normal distribution and variance uniformity, the differences between two groups (studied vs. control subgroups and male vs. female subgroups) were investigated using a non-parametric U Mann–Whitney test, while among five subgroups treated with specific DMT using Kruskal–Wallis one-way analysis of variance by ranks. Furthermore, post hoc analysis was performed to show a specific difference between groups.
For all individual parameters we calculated the receiver operating characteristic (ROC) curve analysis. Furthermore, Youden’s index is calculated using the following formula: Youden’s index = Sensitivity + Specificity − 1 to assess the ability of investigated parameters to discriminate between patients with RRMS (positive group) and the control group (negative group).
The correlation was determined using Spearman’s rank-order correlation coefficient. In all instances, p < 0.05 was considered statistically significant. Statistical analysis was performed using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland).
For all individual parameters we calculated the receiver operating characteristic (ROC) curve analysis. Furthermore, Youden’s index is calculated using the following formula: Youden’s index = Sensitivity + Specificity − 1 to assess the ability of investigated parameters to discriminate between patients with RRMS (positive group) and the control group (negative group).
The correlation was determined using Spearman’s rank-order correlation coefficient. In all instances, p < 0.05 was considered statistically significant. Statistical analysis was performed using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland).
3
Evaluating Hormonal and Lipid Profiles
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Statistical calculations were done using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland). Values were expressed as mean ± SD and median with interquartile range (IQR). The normality of the variables was tested using the Shapiro–Wilk test. The homogeneity of variance was assessed using Levene’s test. The significant difference between two groups was investigated using Student’s t-test (when the normality of distribution and equality of variance were satisfied) or the non-parametric Mann–Whitney U-test (when a lack of normal distribution and variance uniformity were revealed). Differences between four phenotype subgroups and between four subgroups divided according to BMI value were analyzed using Kruskal–Wallis one-way analysis of variance by ranks or one-way analysis of variance (ANOVA) only in cases of HDL-C concentration. Univariate and multiple regression analyses was performed to evaluate the association of hormone concentration with lipid profile parameters. The relationship between HDL-C or TG concentration and abnormal SHBG concentration or FAI value was determined by a binary logistic regression model. In all instances, p < 0.05 was considered statistically significant.
4
Body Composition and PCOS Phenotypes
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Statistical analysis was performed using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland). Values, except age (presented as mean ± SD) were expressed as median with interquartile range (IQR). Normality of variables was tested using the Shapiro-Wilk test. Homogeneity of variance was assessed using Levene’s test. Significant differences in hormones concentration and body composition parameters between groups of women with and without PCOS were investigated using non-parametric U Mann-Whitney test. Differences between four PCOS phenotype subgroups were analyzed using Kruskal-Wallis one-way analysis of variance by ranks. The correspondence analysis was performed to evaluate the potential association between quartile of body composition parameters and phenotypes of PCOS. Logistic regression was used to indicate which of the analyzed parameters increased the patient’s odds of belonging to the PCOS group. Furthermore, the ROC (receiver operating characteristic) analysis was performed to show which parameter best categorized women as suffering from PCOS. In all instances, p < 0.05 was considered statistically significant.
5
Comparison of Organic and Commercial Goat Milk
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The data were analyzed using one-way analysis of variance (ANOVA) to identify significant differences in the contents of fatty acids, minerals, and folates between organic and commercial goat’s milk and fermented goat’s milk drinks. Statistically different results were assessed using the Duncan’s test at a significance level of p < 0.05. The Statistica software package version 13.3 (StatSoft, Kraków, Poland, 2016) was used [58 ].
6
Statistical Analysis of Experimental Data
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Statistical analysis was performed using the Statistica Software Package, version 13.3 (Polish version; StatSoft, Kraków, Poland). Values were expressed as the median, 1st quartile and 3rd quartile. Normality of the variables was tested using the Shapiro–Wilk test. Homogeneity of variance was assessed using Levene’s test. Differences between two groups were investigated using the non-parametric Mann–Whitney U test, while differences between the five subgroups were investigated using the Kruskal–Wallis one-way analysis of variance on ranks. Correlations were checked using Spearman’s rank correlation coefficient. In all instances, p < 0.05 was considered statistically significant.
7
Lifespan Assay for Drosophila
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All experimental flies were collected within 6 h after eclosion, and sorted into mixed groups consisting of 20 flies (10 males and 10 females) per vial. Flies were transferred to fresh food vials almost every second day. Survivorship was scored at the time of transfer until all flies died. All vials were maintained with foam stoppers and placed on their side to prevent flies from getting stuck in the medium. Statistica software package, version 13.0 (Statsoft. Inc., Tulsa, USA) was used to generate the Kaplan–Meier survivorship curves that were compared using Mantel–Cox log-rank tests for estimation of the life-span values. Experiments were done in a special environment room, with a 12/12-h light/dark cycle, at 24 ± 1 °C and 60% relative humidity.
8
Statistical Analysis of Experimental Data
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Statistical calculations were done using the Statistica Software Package, version 13.0 (Polish version: StatSoft, Krakow, Poland). The normality of the distributions of data was assessed by the Shapiro–Wilk W test. To test the differences between the two groups, the parametric Student’s t-test (normal distribution) or nonparametric U Mann–Whitney test for continuous variables was used. The homogeneity of variance was tested with Levene’s test. The differences between the examined groups were assessed using a one-way Analysis of Variance (ANOVA) with Duncan’s multiple comparison test (normal distribution) or the Kruskal–Wallis test (lack of a normal distribution). In all instances, p < 0.05 was considered statistically significant.
9
Analyzing Acute Pancreatitis Dynamics
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The data were presented as median values. The normality of the variables was tested using the Shapiro-Wilk W test. Differences between the groups were tested using the Student's t‑test (normal distribution) or nonparametric U Mann-Whitney test (lack of normal distribution) for continuous variables, and χ2tests for categorical variables. To assess the dynamics of changes of the examined parameters during hospitalization of the patients with AP, the Wilcoxon's test was used. In order to verify correlations between the examined parameters, the multiple linear regression models were performed. In all instances, p<0.05 was considered statistically significant. Statistical calculations were done using the Statistica Software Package, version 13.1 (Polish version: StatSoft, Krakow, Poland).
10
Chromium Supplementation Statistical Analysis
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The Statistica software package version 13.1 (Statsoft Inc., 2016) was used to determine whether variables differed between treatment groups. Values below the limit of quantification (LOQ) in the statistical analysis were set as “0”. The comparison of control group vs all other groups was performed by planned contrast analysis. Two-way ANOVA was performed to assess the effects of the chromium supplementation levels, the source of chromium, and the interaction between the level and source (level x source). When the ANOVA indicated significant treatment effects, means were separated using Tukey’s multiple range test. The results are presented in the tables as means with pooled standard errors. The residuals were checked for normality using Shapiro-Wilk test prior to the statistical analysis. Differences were considered significant at P ≤ 0.05.
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