All results are expressed as means ± standard error of mean (SEM). All statistics were performed using Statistica v. 12 software (StatSoft, France). Normality of population was tested using the Shapiro-Wilk test. Adapted tests were then performed (Kruskal-Wallis, Mann and Whitney U test, two-way analyses of variance (ANOVA) and ANOVA for repeated measures). ANOVA were followed by a post-hoc test (HSD for n different). Significant differences (p < 0.05) between groups were mentioned by different letters (a, b, c…) in the tables and figures.
Statistica v 12
Manufactured by StatSoft
Sourced in United States, Poland, Germany, France, Sweden
Statistica v.12 is a comprehensive data analysis and visualization software suite developed by StatSoft. It provides a wide range of statistical and analytical tools for researchers, scientists, and professionals. The software enables users to perform advanced statistical analyses, model development, and data exploration across various industries and applications.
Automatically generated - may contain errors
Lab products found in correlation
Prism 5, by GraphPad (3 mentions)
Sas v9, by SAS Institute (2 mentions)
Prism v8, by GraphPad (2 mentions)
Medcalc v 20, by MedCalc (2 mentions)
Cfx96 amplifier, by Bio-Rad (1 mentions)
Taq polymerase, by Vector-Best (1 mentions)
Sigmaplot v12, by Grafiti LLC (1 mentions)
Spectrum quant software, by PerkinElmer (1 mentions)
Stata se v13, by StataCorp (1 mentions)
Tablecurve 2d v5, by Grafiti LLC (1 mentions)
Prism v6, by GraphPad (1 mentions)
Design expert v10, by Stat-Ease (1 mentions)
Mass profiler professional, by Agilent Technologies (1 mentions)
Simca 16, by Sartorius (1 mentions)
Spss statistics 25, by IBM (1 mentions)
Matlab, by MathWorks (1 mentions)
Simca p v 9, by Sartorius (1 mentions)
R version 3, by R Project for Statistical Computing (1 mentions)
Instat 3, by GraphPad (1 mentions)
Statistica, by StatSoft (1 mentions)
231 protocols using statistica v 12
1
Comparative Analysis of Cell Viability
2
Transcriptomic Analysis of mRNA Levels
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All results are given as means ± standard error of the mean (SEM). All statistics were performed using Statistica v.12 software (StatSoft, Paris, France). Normality of distributions was tested using the Shapiro-Wilk test. Adapted tests were then performed (Kruskal-Wallis, one-way analyses of variance (ANOVA), two-way ANOVA or ANOVA for repeated measures). Kruskal-Wallis, ANOVA and two-way ANOVA were followed by Mann and Whitney, Tukey and Bonferroni post-hoc tests, respectively. The significance threshold was set at p < 0.05 and differences between groups indicated on the figures by different letters (a and b) or by symbols.
A principal component analysis (PCA) was performed on levels of all mRNAs using R software and the FactoMineR package.
A principal component analysis (PCA) was performed on levels of all mRNAs using R software and the FactoMineR package.
3
Statistical Analysis of Survival Outcomes
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Statistica v.12 software (StatSoft, USA) was used for statistical analysis. The Kolmogorov-Smirnov test was used to verify the normality of distribution of continuous variables. Differences between the groups were assessed using Student’s t-test for normally distributed variables, which are expressed as mean ± standard deviation. A chi-square test, followed by Fisher’s exact test as appropriate, was used for comparisons of categorical data, which are expressed as counts and frequencies. 30-day survival was compared using the Kaplan-Meier method and the Mantel-Cox test was used to test survival differences between the study groups. P values < 0.05 were considered statistically significant.
4
Bacterial Survival Evaluation by PCA
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The mean values were compared using Tukey HSD’s ANOVA at a statistical significance set at p < 0.05 (Statgraphics Centurion XV, Statpoint Technologies Inc., USA). To assess the possibility of sample classification based on the survival of bacteria in samples during storage, Principal Component Analysis (PCA) was performed. The statistical analyses were conducted with Statistica v.12 software (StatSoft Inc., USA).
5
Multivariate Analysis of Salix Bioactives
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Principal component analysis (PCA) was performed with Statistica v. 12 software (Stat Soft Inc., Tulsa, OK, USA). The samples (bark and leaves of different Salix species) represented the cases, while the detected amounts of bioactive compounds were the variables. All data were standardized prior to calculation.
6
Diabetes Impact on Spirometry Parameters
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Statistical analysis was performed with the use of Statistica v12 software (StatSoft, Inc., Tulsa, OK, USA). The normality of distribution was assessed using a Shapiro-Wilk test.
Due to the lack of normal distribution, further analyses were based on the Mann-Whitney U or Kruskall-Wallis test. Chi-square test was used for qualitative data. Qualitative variables were expressed as percentage (%). Logistic regression models were used to determine odds ratios and confidence intervals. Three individual models for diabetes were used to characterize the relationships between diabetes and the spirometry parameters FVC, FEV1.0, and PEF. Three subsequent models showing the relationship between diabetes and spirometry parameters were adjusted for gender, age, BMI, and cigarette smoking. Discriminant analysis was used. The significance level was set at p ≤ 0.05.
Due to the lack of normal distribution, further analyses were based on the Mann-Whitney U or Kruskall-Wallis test. Chi-square test was used for qualitative data. Qualitative variables were expressed as percentage (%). Logistic regression models were used to determine odds ratios and confidence intervals. Three individual models for diabetes were used to characterize the relationships between diabetes and the spirometry parameters FVC, FEV1.0, and PEF. Three subsequent models showing the relationship between diabetes and spirometry parameters were adjusted for gender, age, BMI, and cigarette smoking. Discriminant analysis was used. The significance level was set at p ≤ 0.05.
7
Longitudinal Health Changes After Disaster
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Continuous variables were evaluated using analysis of variance ANOVA and Tukey's honest significant difference test to identify differences due to ageing. Significance was defined as a p value <0.05. Statistica V.12 software (StatSoft, Tulsa, Oklahoma, USA) was used.
As the health data from 2008 to 2013 included the same participants repeatedly, the sample was considered non-independent and generalised estimating equation modelling was used.19 (link) The predisaster time period (2008–2010) was categorised for comparison to the postdisaster 2012 and 2013. The presence or absence of lifestyle-related disease was treated as dependent, with medical examination consultation year, age at time of visit and gender independent, with adjustment for both age and gender. An unstructured correlation structure was used to account for correlation among repeated measures on the same participants. The least-squares means and OR, adjusted for age and gender, were used for repeated measure logistic regression analysis. Significance was set to a level of 0.05. SAS V.9.3 (SAS Institute , Cary, North Carolina, USA) was used for analysis.
As the health data from 2008 to 2013 included the same participants repeatedly, the sample was considered non-independent and generalised estimating equation modelling was used.19 (link) The predisaster time period (2008–2010) was categorised for comparison to the postdisaster 2012 and 2013. The presence or absence of lifestyle-related disease was treated as dependent, with medical examination consultation year, age at time of visit and gender independent, with adjustment for both age and gender. An unstructured correlation structure was used to account for correlation among repeated measures on the same participants. The least-squares means and OR, adjusted for age and gender, were used for repeated measure logistic regression analysis. Significance was set to a level of 0.05. SAS V.9.3 (SAS Institute , Cary, North Carolina, USA) was used for analysis.
8
Statistical Analysis of Experimental Data
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Quantitative data were compared by Student’s t-test or Mann–Whitney U test. For multiple comparisons, ANOVA or ANOVA Kruskal–Wallis were used; p < 0.05 was considered significant. All calculations were performed with Statistica v. 12 software (StatSoft Polska, Cracow, Poland).
9
Triplicate Experimental Analysis
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The whole experiment was carried out in three replications. The analyses were performed in triplicates. The data were subjected to analysis of variance (ANOVA) using Statistica v12 software (StatSoft, Tulsa, USA). The differences between the mean values were evaluated using the Duncan test at the significance level of P < 0.05.
10
Correlating miRNA and mRNA Expression
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Correlations between miRNA fold-change values and mRNA-expression values were determined using the R statistical software 3.11.1 package in Bioconductor (Release 3.0). The Student's t-test was used to determine statistical differences between pairs of groups. Two-way analysis of variance (ANOVA) was used to evaluate the statistical significance for comparisons within groups. P < 0.05 (two sided) was considered as statistically significant. P-values obtained were adjusted for false discovery rate due to multiple testing correction (Benjamini and Hochberg 1995 ). For generation of correlation graphs and statistical analysis, Statistica v12 software (StatSoft, Inc.) and GraphPad Prism software package (version 6.0) were used.
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