Data analysis was performed using Signal software (Cambridge Electronic Design, Cambridge, England). The magnitude of EPSC recorded after application of the peptide was expressed as percent of baseline and group means were calculated for drug condition. A One-way ANOVA was performed and Duncan post-hoc test used to determine significant differences between concentration or drug and peptide condition when justified; level of significance was set at 0.05 (Statistica V5.0, StatSoft).
Statistica v 5
Manufactured by StatSoft
Sourced in United States
Statistica v. 5.1 is a comprehensive data analysis and visualization software package. It provides a wide range of statistical and analytical tools for data processing, modeling, and reporting. The software is designed to handle large and complex datasets, offering capabilities for data exploration, hypothesis testing, and predictive modeling.
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Lab products found in correlation
15 protocols using statistica v 5
1
Peptide-Induced EPSC Magnitude Analysis
2
Statistical Analysis of Experimental Data
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Data analysis was performed using STATISTICA v 5.0 (StatSoft, Tula, USA). Data were checked for normality with the Shapiro–Wilk test (data are reported as mean ± standard deviation in case of normal distribution and as median and interquartile range in case of asymmetrical distribution); a t-test was used in the case of normally distributed data or a Mann–Whitney U test was used when data distribution was asymmetrical. The P-value of <0.05 was considered as a statistically significant difference.
3
Chicken Fillet Experiments: Statistical Analysis
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All experiments were carried out in triplicate with two independent batches of chicken fillets each. Significance was established at p < 0.05. Results were analyzed for statistical significance with analysis of variance (ANOVA). Duncan’s multiple range test was used to determine the significant differences among results [coefficients, ANOVA tables and significance (p < 0.05) were computed using Statistica v.5.0 (Statsoft Inc., Tulsa, OK, USA)].
4
Evaluating Antiplatelet Drug Responses
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Data analysis was performed using STATISTICA v 5.0 (StatSoft, Tula, USA). Data were checked for normality with the Shapiro–Wilk test (data are displayed as mean ± standard deviation in the case of normally distributed ones and as median and range in the case of asymmetrically distributed ones). Mean VASP-P values were compared statistically between individual drugs. Patients were classified as with or without HTPR, and differences between these 2 groups were analyzed with a chi-squared test. A p-value of <.05 was considered statistically significant.
5
Conditioned Place Preference Assay
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Preference score was determined by subtracting the time spent in the drug-paired compartment measured before the conditioning phase (Pre) to the time spent in the same compartment measured on the conditioning test day (Post). Preference score and locomotor activity (horizontal and stereotypic-like movements) measured during the conditioning test day were analyzed with a One-way analysis of variance (ANOVA). The Duncan's multiple range post-hoc tests was used for individual group comparisons. The accepted value for significance was set at 0.05 (Statistica V5.0, StatSoft).
6
Statistical Analysis of Biological Data
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Data analysis was performed using STATISTICA v 5.0 (StatSoft, Tula, USA). Data were checked for normality with the Shapiro–Wilk test (data are displayed as mean ± standard deviation in case of normally distributed ones and as median and range in case of asymmetrically distributed ones); a t‐test was used in the case of normally distributed data or a Mann–Whitney U test was used when data distribution was asymmetrical. The p‐value of <.05 was considered as a statistically significant difference.
7
Sediment Geochemical Characterization
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Dry sediment of ≤2 mm was analysed for pH, organic carbon (Corg), carbonate, granulometric parameters and TEs, Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, U and Zn as described in previous works (Trifuoggi et al. 2017 (link); Sarkar et al. 2017 (link); Mondal et al. 2018a (link)). For TEs, ~ 0.5 g was digested with 10 ml of a HCl-HNO3-H2O2 mixture, 6:3:1, and assisted by microwave (Mars-CEM, US). After cooling, the samples were filtered and taken to final volume of 50 ml with ultrapure water. Mineralized samples were analysed by ICP-MS (Aurora M90 Bruker, USA) in triplicate. The detection limit, LOD, and limit of quantification, LOQ, were calculated using the method of blank variability for each investigated element. The calculated average values of LOD and LOQ were 0.06 and 0.16 μg/kg, respectively. The accuracy, precision and recovery were evaluated using certified reference materials CR-CRM667, European Commission, Joint Research Centre, Belgium, and by participation to inter-laboratory circuits. The percentage recovery was 60–120% and the RSD, n=3, 2–15%. Statistical analysis consisted of skewness and kurtosis statistical tests (Zhang et al. 2009 (link)), Pearson’s correlation matrix, a principal component analysis (PCA) and a hierarchical cluster analysis (HCA) and was performed by Statistica v.5 (StatSoft Inc., Tulsa, OK, USA).
8
Assessing Demographic Stability in Populations
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Distributional properties of the experimental time series of adult populations were assessed using the mean, median, 5 th , 10 th , 25 th , 75 th , 90 th and 95 th percentiles in box plots (Zar 1999) . The constancy stability (Grimm and Wissel 1997) of the populations was measured as the fluctuation index (FI, Dey and Joshi 2006) which is given as:
where N is the mean population size over T generations and Nt+1 and Nt are the population sizes at the (t+1) th and t th generations, respectively. This formulation suggests that FI increases when the variance of the time series is increased, or the mean or the strength of autocorrelation of the time series is decreased.
In order to investigate the effects and interaction of larval and adult nutritional levels on constancy stability, the FI data were subjected to a two-way analysis of variance (ANOVA) with larval nutrition (fixed factor, two levels: Low and High) crossed with adult nutrition (fixed factor, two levels: Low and High). All statistical analyses were performed using STATISTICA ® v5 (StatSoft. Inc., Tulsa, Oklahoma). given generation, the model starts with the number of eggs (numegg), a fixed fraction (hatchability) of which hatch to form numlarva, i.e. number of larvae:
where N is the mean population size over T generations and Nt+1 and Nt are the population sizes at the (t+1) th and t th generations, respectively. This formulation suggests that FI increases when the variance of the time series is increased, or the mean or the strength of autocorrelation of the time series is decreased.
In order to investigate the effects and interaction of larval and adult nutritional levels on constancy stability, the FI data were subjected to a two-way analysis of variance (ANOVA) with larval nutrition (fixed factor, two levels: Low and High) crossed with adult nutrition (fixed factor, two levels: Low and High). All statistical analyses were performed using STATISTICA ® v5 (StatSoft. Inc., Tulsa, Oklahoma). given generation, the model starts with the number of eggs (numegg), a fixed fraction (hatchability) of which hatch to form numlarva, i.e. number of larvae:
9
Effects of Repeated Measures ANOVA
10
Statistical Analysis of Research Outcomes
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The results were analyzed with χ2 test, using STATISTICA v. 5.1 package (Statsoft, Tulsa, OK).
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