Sentence examples for weak regression from inspiring English sources

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Height was associated with RB1 methylation, although this association was relatively weak (regression coefficient = 0.007; 95 % CI 0.000, 0.014; p = 0.049; r = 0.0116).

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For the simulated data, the results of simulation model 2 (6 weak, correlated regression coefficients in two clusters) are presented since it is the more challenging model.

This experiment was performed to confirm the method for estimating the slip ratio and slip angle during turning maneuver: whether conventional slip ratio estimation (Eq. 27) can be applied on the weak slope, and regression function for slip angle estimation (Eq. 29).

A relationship involving a HADS score as independent variable is therefore considered weak if the regression coefficient is below 2.5 (5/2), and a relationship involving a HADS score as dependent variable is considered weak if the regression coefficient is below 0.1 (1/10.1

In addition, the degree of dependency between attrition and the follow-up variable had only weak effects on regression estimates and their 95% coverages.

The number of returning hatchery adults in the parental generations (2 years before smolt outmigration, 4 years before offspring return) may also affect the of proportions of hatchery and hybrid origin smolts; though, the relationship was weak (very small regression coefficients).

In addition, a pairwise comparison through the computation of Bayes factors reveals a substantial to strong (logistic regression) or weak to substantial (neural network) support for the multiregional origin over the western origin model (see Supplementary Note 1, Supplementary Table 14 and Supplementary Fig. 5b).

Moreover, an ~20% increase in CPA dose eliminated the partial tumor regression and weak innate immune cell recruitment seen in a subset of the every 6-day treated tumors.

His research has chiefly been in the areas of the use of high-dimensional statistical methods in economic applications, estimation of panel data models, inference using clustered standard errors, quantile regression, and weak instruments.

Assuming, in each boosting iteration stage, that there are K possible local patches, which are represented by SURF feature (mathbb {F}), each stage is a boosting training procedure with logistic regression as weak classifiers.

However, the effect of local variations in exposure (measured by household parasite rates) was weak in the multiple regression model though there was evidence of spatial clustering of clinical malaria attacks.

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