All statistical analyses were performed using the IBM SPSS Statistic software (v.24). One-way and factorial ANOVAs, included repeated measures (rm) ANOVAs were used where appropriate. The Huynh-Feldt correction was applied to violations of the sphericity assumption for rmANOVAs. With a statistically significant interaction between main factors, simple main effects were calculated to provide clarification of statistical significance between-genotype and within-genotype differences. Multiple comparisons were Bonferroni adjusted. Tukey’s HSD or the Games-Howell method was used as post hoc tests. Probability value for all analyses was p<.05. Data from each of the five cohorts were first analyzed independently across cohorts. Where cohorts showed differences in significance meta-analysis were applied to summarize across studies (Holeboard total pokes outcome variable and fear conditioning freezing in males).
Spss statistic software v 24
Manufactured by IBM
Sourced in United States
SPSS Statistics (v.24) is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is designed to handle a wide range of data types and offers a user-friendly interface for conducting statistical tests, generating reports, and visualizing data.
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Lab products found in correlation
4 protocols using spss statistic software v 24
1
Multivariate Analysis of Behavioral Outcomes
2
Statistical Analysis of Experimental Data
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Results were expressed as the mean and standard error of the mean (SEM) from at least five independent experiments. Data were analyzed with IBM® SPSS® Statistic software v24 (IBM, Armonk, NY, USA), using the paired Wilcoxon test. Given that data were not normally distributed, Bonferroni’s correction were applied. p-values ≤ 0.05 were considered statically significant.
3
Optimizing Hydrogel Strength: A Central Composite Design Approach
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A statistical central composite design was carried out to evaluate the influence of the carrageenan concentration (C) and the concentration of two different added salts (KCl and CaCl 2 ) (S KCl and S CaCl2 ) in the hydrogel strength (HS). The considered ranges for each variable in this model, which were determined empirically through preliminary tests, are shown in Table 1. According to the central composite design, 13 runs for each combination of carrageenan type and type of salt were carried out. The matrix designs and the graphical analysis were performed using IBM SPSS Statistic software (v.24) (IBM corp., USA) and Matlab (Mathworks, Inc., USA), respectively.
The response values were predicted by the quadratic polynomial equation as follows:
where γ corresponds to the predicted response: β 0 , β i , β ii and β ij were the constant regression coefficients of the model; X i and X j represent the independent variables. The results obtained were analysed by ANOVA to investigate the significance and fitness of the model, as well as the effect of significant individual terms and the interactions on the responses. The optimization of the response was obtained using a response surface methodology.
The response values were predicted by the quadratic polynomial equation as follows:
where γ corresponds to the predicted response: β 0 , β i , β ii and β ij were the constant regression coefficients of the model; X i and X j represent the independent variables. The results obtained were analysed by ANOVA to investigate the significance and fitness of the model, as well as the effect of significant individual terms and the interactions on the responses. The optimization of the response was obtained using a response surface methodology.
4
Optimizing Emulsion Gel Strength through Design
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A statistical central composite design was carried out to evaluate the influence of the carrageenan concentration (C), salt concentration (KCl) and oil:water volume ratio (OWR) in the emulsion gel strength (GS). The considered ranges for each variable in this model are shown in Table 1 and were established based on preliminary experiments, ensuring that emulsion gels with good homogeneity and sufficient mechanical integrity to be manipulated could be obtained. According to the central composite design, 15 runs for each carrageenan type were carried out. The matrix designs and the graphical analysis were performed using IBM SPSS Statistic software (v.24) (IBM corp., USA) and Matlab (Mathworks, Inc., USA), respectively.
The response values were predicted by the quadratic polynomial equation as follows:
where γ correspond to the predicted response: β0, βi, βii and βij were the constant regression coefficients of the model; Xi and Xj represent the independent variables. The results obtained were analyzed by ANOVA to investigate the significance and fitness of the model, as well as the effect of significant individual terms and the interactions on the responses. The optimization of the response was obtained using a response surface methodology.
The response values were predicted by the quadratic polynomial equation as follows:
where γ correspond to the predicted response: β0, βi, βii and βij were the constant regression coefficients of the model; Xi and Xj represent the independent variables. The results obtained were analyzed by ANOVA to investigate the significance and fitness of the model, as well as the effect of significant individual terms and the interactions on the responses. The optimization of the response was obtained using a response surface methodology.
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