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The relative influence of each factor on the springback was determined using the ANOVA method.
Variance components were estimated using the anova method to compute a coefficient of determination R for each ecotype.
Characteristics were compared across the four groups according to goal achievement status using the ANOVA method for continuous variables and χ tests for categorical variables.
Results were compared to MOCK samples (reference sample), which had been stimulated with EGF, and the significance of the impact on pRB phosphorylation was tested using the ANOVA method.
Statistical significance of protein expression changes and pRB phosphorylation due to knockdowns via RNA interference were calculated using the ANOVA method: protein expression ~ knockdown effect + biological replicate factor + error A multiple testing correction was performed using Benjamini-Hochberg's method [ 40] with a false discovery rate (FDR) significance cut off of 1%.
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Through the use of the ANOVA method a value of F (17.27) was obtained, which is greater than the critical values of 0.05 (4.225) and 0.025 5.6588).
All seed quality data collected from each line were analyzed with the Minitab Statistical Software Suite Release 12 (Minitab, State College, PA 16801 3008) using the Anova-Fisher's LSD method (p ≤ 0.05) and Tukey's method of pairwise comparisons of all transgenic lines to the non-transformed Quantum control.
In addition, pairwise comparisons using the functional ANOVA method and a bootstrap distance based test are carried out to discern which factor levels provide different ways of degradation.
The ANOVA method was used when comparing a continuous outcome variable with several categorical explanatory variables.
To confirm the apparent difference between the macrophage populations, we analysed the data using an ANOVA method.
Change from baseline in FPG, body weight, prandial increments, PG, and HRQoL were analyzed using an ANOVA method similar to that applied to the primary end point.
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