Predicted genome sizes, calculated using the linear models with estimated beta parameters for these five enzymes (table 2), matched actual observed genome size values extremely well (supplementary fig. S3, Supplementary Material online).
After estimation of the model, for each subject the beta parameter for the 1 4 positions was aggregated as a linear "repetition effect" using a [−3, −1, +1, +3] contrast over sequence positions 1 4.
For the categorical variable "type of breast tissue" the beta parameter for the dense or very dense tissue can be interpreted as the average higher pain score in this category compared with women with complete or partly completed involution, corrected for biopsy depth and number of biopsies.
For each participant, the magnitude of activation for the different conditions in each ROI was determined by averaging the relevant beta parameter from each participant's fitted beta estimates from the general linear model.
Because the number of completed training runs varied slightly across participants, i.e. participants completed 19 , 20 17, 17, 12, 19, or 22 training runs, the beta parameter estimates for each participant were grouped and averaged into 12 bins (=minimum number of training runs that all participants completed) in a nearest neighbor fashion.
Means and coefficients of variation, together with maximum and minimum values and correlation coefficients of prices and costs, are read in via a spreadsheet and used to estimate beta distribution parameters for each data cell.
The Beta distribution parameters for each activity were selected using Monte Carlo sampling based on the characteristic range of values for these parameters published in AbouRizk and Halpin (1990) for earthmoving operations, as detailed in Eiris (2016) and summarized in Fig. 7.
Based on the confidence intervals of the beta parameters, survival for size states 1 and 2 did not differ from each other, and survival of size states 2, 3, and 4 did not differ (Table 5).
Beta parameter estimates for effects of isochrony versus random timing were extracted for such peak voxels.
Specifically, participants showed a significant increase in beta parameter estimates for the differential feedback signal associated with training (Fig. 3; differential signal linear regression: r = 0.83, F 1,10) = 47.39, p < 0.01).
