Sentence examples for model regression coefficient from inspiring English sources

Logistic regression model; regression coefficient, standard error of estimate (SE), Wald test, odds ratio and 95% confidence interval (CI) (n = 2105).

To check the possibility of a chance correlation, 10 times shuffled activities were correlated to the variables, and the produced regression coefficients were compared with the developed model regression coefficient.

The test statistic used to assess any benefit of the ITD relative to the sham device will be computed as the generalized linear model regression coefficient divided by the estimated "robust" standard error based on the Huber- White sandwich estimator[reference] in order to account for within group variability which might depart from the classical assumptions.

Adjusting for between school differences, daily periodicity in indoor AH, and auto-regressive lags within the model, outdoor AH was associated with indoor AH over the course of the winter school period (ARCH model regression coefficient 0.51; confidence intervals [CI] 0.48, 0.54; p < 0.001; Table 2: Model A).

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).

These ratios were obtained by exponentiating the model regression coefficients.

In fact, within each model, regression coefficients tended to be very similar across training datasets that included the line itself.

In addition, the PLS model regression coefficients resembled cell wall spectral peaks (Additional file 9) indicating that the model was based on cell wall chemistry.

This risk index was defined as a linear combination of gene-expression values weighted by their estimated univariate Cox model regression coefficients.

To efficiently apply the model, regression coefficients for covariates that are not of major interest and serve as confounding effects may be assumed to be independent of c or as having no interactions with the previous disease status.

The model regression coefficients indicate that the predictors with p-values < 0.05 are PM10 (p = 0.00003), the interaction between PM10 and wind (p = 0.00005), the interaction between PM10 and temperature (p = 0.0006), and soil moisture (p = 0.006).