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These analyses were used to test for population differences, using an error term that includes the variation among vials; temperature, location and their interaction were tested against the between populations variance.
The data were analyzed as a hierarchical ANOVA using an error term that included the variation between replicate lines of the same type (two replicate bottles were set up for each line).
The general form of the statistical analysis used in the present study was to test for grand mean line type (selected and control lines) differences, and treatment differences, using an error term that included the variation between replicate lines of the same type.
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In our control algorithm, we use an error term that corresponds to how far off the robot was from the center of the line, and a differential term that corresponds to the difference between the current error and the error of the last reading, providing an effective perpendicular velocity to the line.
The primary focus will be on the group × time interaction, for which the cluster × time interaction will be used as an error term for testing significance.
One may argue that if heterogeneity is detected, then the effect for heterogeneity may be used as an error term for testing inconsistency because heterogeneity effects are nested within the effects for inconsistency.
The residual error was accounted for using an additive error term.
This comparison, using a pooled error term, revealed a significant difference between control and VLPFC monkeys, t7 = −2.35, P=0.025 (one-tailed), and between monkeys with VLPFC and orbital lesions, t6 = −2.00, P=0.043 (one-tailed).
Residual variability was modeled using a proportional error term for PK (non log-transformed TAK-875 plasma concentration) and a log-normal error term (additive on log-transformed scale) for PK-FPG and HbA1c analyses.
For each effect size, we calculated an error term (SELOR) using the following equation: The weights (wLOR) associated with each effect size estimate were then calculated using: We used ESLOR as the dependent variable in linear models, including wLOR to weight each effect size based on its sample size.
Besides the simple error term used in validation we incorporate also an error term that insures consistency among the initial network states used in simulations.
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