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In the first stage, a linear regression model is postulated for the true underlying pattern of change of cartilage volume over time for each individual (for instance, initial cartilage volume and rate of change), and these patterns are allowed to differ for each individual.
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The first stage is a linear regression model trained on the source data using the latent feature space.
The second stage is also a linear regression model that is summed with the first stage.
Consider a linear regression model.
This amount is the minimal number of observations required to fit a linear regression model.
LPS concentrations were calculated from the standards using a linear regression model: y = A*x + B.
A linear regression model was used to determine the difference between conditions.
The r2 represents the fraction of the variation explained by a linear regression model.
A linear regression is obtained by plotting area(product)/area standard) against the calculated product concentration.
This fit accounts for 34% of the total variance using a linear regression model (Fig. 2b).
A linear regression was fitted to enable calculation of pA2 and conversion to Ki.
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