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Finite order linear regression source class.
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The average velocity V and the velocity dispersion σ were determined by a linear regression for these sources.
We predict that lines of isotopic enrichment will be formed for each experimental run, with lines of best fit from linear regression passing through the source water.
Each model's R was calculated for 2 time windows (as above) and 6 regions separately using multiple linear regression between the mean source activity in the time window and the 3 models (HMaxC1, HMaxC1 and HMaxC2, and HMax and semantic features).
These steps include using linear regression to remove potential sources of noise, including estimated subject motion parameters (3 translation components and 3 rotation components), BOLD signals in white matter and CSF areas.
The first stage is a linear regression model trained on the source data using the latent feature space.
We use robust linear regression to measure transmissibility and source of SDRMs, which does not require dense sampling.
After adjustment, the multivariate linear regression model suggested that funding source remained independent and significant predictors of overall quality.
As we will describe in detail later, this objective was pursued here applying multiple linear regression in combination with distributed source estimation to MEG responses evoked by spoken picture naming.
The mean time series of each region was then acquired by averaging the time series of all voxels within that region, followed by a multiple linear regression analysis to remove several sources of spurious variances from estimated head-motion profiles and global brain signal [13].
Using secondary data from several sources, eight multiple linear regression models were created and analyzed.
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