Simple regression analysis between biological and environmental factors was carried out in STATISTICA v. 9 (StatSoft). A principal correspondence analysis was conducted using a statistical package—Canoco 4.5 for Windows v. (Ter Braak and Šmilauer 2002 ) and STATISTICA v. 9 (StatSoft).
Statistica v9
Manufactured by StatSoft
Sourced in United States, Poland
Statistica v9.0 is a statistical software package developed by StatSoft. It provides data analysis and visualization tools for researchers, scientists, and professionals. The software offers a range of functionalities, including data management, descriptive statistics, regression analysis, and advanced modeling techniques.
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39 protocols using statistica v9
1
Multivariate Analysis of Biological and Environmental Factors
2
Statistical Analysis of Phenotypic Data
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All phenotypic data show Eu frequency calculated from total individuals screened. Total sample size is illustrated on graphs. Significant differences were tested by Fisher's exact test. For the expression data we performed Kruskal–Wallis test. All statistical analyses were implemented in the program Statistica v. 9 (Statsoft).
3
Statistical Analysis of Research Data
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We performed the Wilcoxon matched pair test to compare variables at baseline and follow-up. We used the Mann–Whitney test for comparisons between two groups, and the Kruskal–Wallis test for comparisons between multiple groups. For categorical variables, the χ2 test was used. Correlations were calculated using Spearman's rank correlation. Statistical significance was set at the level of p<0.05. Statistical evaluation was performed by statistical software, STATISTICA V.9, StatSoft, USA.
4
Serum Lipids, TSH, and Osteocalcin
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The data are presented as means ± SD. Normality was assessed by the Shapiro-Wilk test. The independent sample t-test or the Mann- Whitney U test was used for comparison between groups, for normally and not normally distributed variables, respectively. Logarithmic (ln) transformation was applied to right-skewed data before correlations analysis. Multiple linear regression analysis was performed to identify relationships between serum lipid parameters and TSH and osteocalcin levels. Statistical analyses were conducted using Statistica v.9 software (StatSoft, USA). The level of significance was set at alpha = 0.05.
5
Statistical Analysis of Biological Data
6
Pharmacology and Behavioral Analysis Protocol
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Statistical analysis was performed with Graph-Pad Prism v4 (GraphPad, San Diego, CA) and Statistica v9 (StatSoft, Maisons-Alfort, France). In vitro pharmacology experiments were analyzed using a one-way ANOVA. Behavioral experiments were analyzed using a two-way ANOVA. Multiple comparisons were made using Newman-Keuls or Tukey tests for post hoc analysis. A paired t test was performed to verify that the apparatus used in the conditioned place preference test was unbiased. Place conditioning data were expressed as percentage of time spent in the drug-paired compartment. Four-way ANOVA was performed with gender, genotype and treatment as between-group factors and conditioning (pretest versus test session) as a within-group factor.
7
Evaluation of PAT Content in Samples
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All analyses were carried out in triplicate. The obtained results were evaluated with Statistica v.9 (StatSoft, Tulsa, OK, USA). The results are presented as the mean ± standard deviation (Table 2 ). In all of these analyses, we examined the outcome of the mean content of PAT using Student's t-test and p value ≤ 0.05 was considered as significant. In hierarchical clustering calculation, the Ward agglomeration procedure as a grouping method and Euclidean distance as a function of the distance was applied, and results are displayed graphically using a dendrogram (classification tree). Differences in profile among subclusters were verified with the nonparametric Mann–Whitney U test at p ≤ 0.05 as significant.
8
Statistical Analysis of Interferon Levels
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The levels of IFNs were calculated as a ratio from the standard curve. The distribution of values was not normal (D'Agostino-Pearson omnibus normality test), therefore respective non-parametric tests were used when calculating associations with levels of IFNs. We performed the Wilcoxon matched pair test to compare variables at baseline and follow-up. We used the Mann-Whitney U test for comparisons between the two groups. For categorical variables, the χ2 test was used. Correlations were calculated using Spearman's rank correlation. Statistical significance was set at the level of p<0.05. The statistical evaluation was performed using STATISTICA V.9, StatSoft, Tulsa, Oklahoma, USA.
9
Evaluating Treatment Effects via ANOVA
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The separate treatment effects were analysed using one-way ANOVA. Significant differences between the treatment means were calculated using the least significant difference (LSD) test at the p < 0.01, p < 0.05 and p < 0.1 levels. The results of the combined applications were evaluated using analysis of variance and regression analysis [28] . As the coefficient of determination (R 2 ) shows whether the model fits the data, only variables for which the Box-Wilson model gave R 2 values higher than 60% are discussed here, since this indicates that changes in these variables could be explained to at least a moderate extent by the model equation. The variance of these variables was determined using the F-test. Variability between the samples was determined by means of principal component analysis (PCA). Statistica v.9 (StatSoft Inc., Tulsa, OK, USA) software was used for the statistical evaluation.
10
Comparative Statistical Analysis of Experimental Outcomes
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Results observed in the four groups at the end of the experiment or % of changes were compared using ANOVA followed by HSD Tukey test. Data were transformed or Kruskal Wallis analysis was applied when necessary. Data from histological analysis were evaluated by Chi-Square test. An alpha value of 0.05 was adopted to reject the null hypothesis. Data analysis and graphs were carried out using the Statistica v.9 software (Statsoft Inc., Tulsa, USA).
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