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This (random) variable allows each herd to have a distinct departure from the average response at Time = 0; a so-called herd-effect.
We thus have no reason to suspect that the observed average response at 0.06 ppm is inflated because of attributes of the target population or sampling error.
The average response at each IPD was treated as a vector and the goodness of IPD tuning was estimated by a synchronization coefficient (Goldberg and Brown, 1969; Kuwada et al., 1987).
The average response at the center point (observed value) is compared with the mean of the values obtained with the combinations of the factorial design (in this case 16).
It is simple to derive the average response at the time of the reward (t = N) in trial T, i.e., the average TD error δ T (N), from the TD learning rule with the simplified tapped delay-line time representation and a fixed learning rate α.
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Under these conditions, we could detect a partial recovery of these responses, the average responses at 24 h being 136% and 112% of the responses ex vivo for conjugates formed with or without SAg, respectively (n = 3 tumors in each case).
This indicates that the average responses at the departmental level are strongly correlated.
Therefore, average responses at time 0 consist of the average of responses from -50 to 50 ms after stimulus onset.
Given the sample data (eq 2), the sample average of the responses at w i across the n(w i ) replications follows as Denote 4as the I × 1 vector of sample average responses at the I distinct design points.
While the average response time at 37 °C was lower than that at 29 °C, the difference between the two averages was not statistically different due to the large error bars on the measurements (p = 0.1694, Supporting Information Table S2).
where RT ref is the desired average response time at Reader Tier and RT t) is the actual performance measurement at time t.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com