Sentence examples for linear regression approximation from inspiring English sources

To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation.

When the quality of data is high (dense marker maps, few experimental errors) the global optimization landscapes and the QTL mapping results for interval mapping and the linear regression approximation are very similar.

Though we have suggested using linear regression for approximation, we may take advantage of other machine learning methods.

Different chemometric tools such as stepwise multiple linear regression (MLR), genetic function approximation (GFA) and partial least squares (PLS) were employed in this study for development of the models.

Next, we show that in step (5) linear regression is a reasonable approximation of bit scores based on the matching HSP length.

If they vary, the estimation by linear regression can be a rather imprecise approximation.

The graph of the estimated regression equation for simple linear regression is a straight line approximation to the relationship between y and x.

Using linear regression, we obtain the following approximation of the prediction error, Figure 2 Error distribution as a function of delay and speed in the prediction.

Therefore, at least for the normal operation of the MCC at 150 mL/min and small deviations, a linear regression alignment is a suitable approximation.

Beaumont et al. (2002) have shown that a local linear regression (third step = estimation step) provides a better approximation of the posterior distribution.

Therefore, the proposed HSIC method is a better approximation to the linear regression when the univariate significance is lower.