Sentence examples for empty regression from inspiring English sources

First, an empty regression was run with only the log of the effect estimate of pooled studies under the additive model to determine the baseline value for τ2, an estimate of between-study variation (baseline τ2 = 0.025).

The continuous variable age was tested for linearity of its association to the logit of the outcome probability using Box-Tidwell tests in both fitted and empty regression models yielding no violation of the linearity assumption.

Based on an empty logistic regression model, the intraclass correlation coefficient (ICC) was 2.85%, indicating that 2.85% of the total variance in positive emotional well-being was accounted for by the area level.

MORs from empty multilevel logistic regressions showed a twofold geographic variation in diabetes prevalence and detection across China.

Logistic regression analysis for empty cell numbers showed a significant effect due to surgical approach (p < 0.001, for model regression coefficient), but no evidence of statistical significance for anterior or posterior location within the head (p = 0.319) or for an interaction between position and group (p = 0.282).

The variation in prevalence between practices was quantified for each indicator by an empty mixed effects two level logistic regression model with patients nested within practices (using the xtmelogit command in Stata).

(The location of animals could not be included in the model because many regression classes would have been empty).

For prediction of anxiety and depressive symptoms, hierarchical linear regression was chosen because of unequal and empty cell distributions.

Parameter estimates from the regression model suggest that the odds ratio for empty nuclei was 1.71 (95% confidence interval (CI) 1.59 to 1.83) for the posterior approach group versus the non-posterior group; i.e. the data indicates that there were significantly more empty cells in the posterior approach group than the non-posterior group.

The extent to which the signals observed in the combined analysis were independent of one another was next examined using stepwise logistic regression analysis wherein variables were iteratively added into an empty model.

Furthermore, stepwise multiple regression analysis revealed that combining performance on duration discrimination with filled and empty intervals in the range of milliseconds accounted for 22% of the total variance of general fluid intelligence.