Sentence examples for common regression from inspiring English sources
Given their data-hungry nature, common regression tree − based modeling approaches may be inadequate for reliably predicting rangeland indicators with the smaller sample sizes of AIM data than typically used for remote sensing studies.
To do the first, a common regression was run to tune the cubic EOS to PVT experimental data.
The massive enrichment of the right tail of the BMI distribution implies that the data cannot be analysed with BMI or BMI-associated outcomes as response variables in common regression models.
Thus, the massive enrichment of the right tail of the BMI distribution implies that the data cannot be analysed with BMI or BMI-associated outcomes as response variables in common regression models.
Adjusted means, based on a common regression slope, were calculated using ANCOVA following log-log transformation.
A pooled within-sex regression of shape on centroid size was then used to obtain a common regression vector.
Our choice of log-binomial versus the more common logistic regression was based on the fact that odds ratios obtained from logistic regression tend to overestimate the true effect size when the outcome under study is common (>20%) and because prevalence ratios are more readily interpretable compared to odds ratios in cross-sectional studies [ 46, 47].
We investigate the D-optimal design problem in the common trigonometric regression model, where the design space is a partial circle.
The problem of determining optimal designs for least squares estimation is considered in the common linear regression model with correlated observations.
Decision trees and support vector models are common, but regression, a mainstay of traditional statistics, also plays a role, after being adjusted to handle large numbers of explanatory variables.
It is shown with the application problem considered that the Kriging interpolation models are efficient surrogate models for structural reliability problems and can provide significantly more accurate failure probability predictions as compared with the most common polynomial regression models.
