Next, condition (post-intervention and post-control; model 2), the interaction between condition and SDC (model 3) and the interaction between condition and observation moment (model 4) were entered as possible predictors.
Structures of the resulting regression models were compared between conditions and word-form types with Meng's Z-test31 that compares correlated correlation coefficients in order to find out if there were significant differences in how well the model predicted the neural response changes to different word-form types in different conditions.
To compare source activity between conditions, the model was applied to individual ERPs for the color, angle, and dual conditions and a spatial component containing the individual blink topography was added to correct for eyeblink artifacts.
This is done in order to avoid different boundary conditions between sample and model.
For instance, if a parameter shouldn't change between conditions, the models should probably be constrained so it can't change (it's kind of cheating to let the models fit better by changing those kinds of params).
First, a regression model was calculated for the association between training conditions and intention to leave (Model 1).
Indeed, in a retrospective study Hueston et al. investigated how to best distinguish between acute bronchitis and common cold and concluded: "…there was considerable overlap between the 2 conditions, and the logistic model explained only 37%% of the variation between the diagnoses.
This study exemplifies the magnitude of the difference between projections made by a model with different initial conditions and between different models with the same initial conditions.
Then, using the bound (20) on the error between the terminal conditions of function and model, we have biglVert nabla f_{k_{i}}^{T}h_{f_{k_{i}}}-g_{k_{i}}^{T}h_{m_{k_{i}}} bigrVert leqslantkappa_{2} alpha_{k_{i}}Delta _{k_{i}} leqslantkappa_{2} iotaalpha_{k_{i}} biglVert g_{k_{i}}^{T}h_{m_{k_{i}}} bigrVert leqslant kappa_{2} iota biglVert g_{k_{i}}^{T}h_{m_{k_{i}}} bigrVert.
This flexibility enables the generation of experimental information on the relationship between process conditions and product quality for different model compound systems using one crystallizer.
