Sentence examples for model variables selected from inspiring English sources

Table 1 Model variables selected for each subject by using the stepwise method based on AIC Selecteded variables AIC Subject A ((V_y, O_z, F_z)) 16.310 Subject B ((M_x, V_y, F_z)) 16.292 Subject C ((M_y, V_y, F_z)) 20.171 Subject D ((M_y, V_y, F_z)) 18.983.

In comparison to the model variables selected from the imputed data, fewer variables were selected for the model on the complete case data.

In a second stage, for the final multivariate model: variables selected in the previous stage were introduced into the final multivariate model (including variables from all groups) and those with a P value ≤ 0.05 remained in the final model.

NPMR was compared with logistic regression (LR) by building reduced models from variables selected as best by NPMR and full models from variables identified as significant with a forward stepwise process and further manual testing.

2) tailoring a score model with the variables selected for the Bayesian model to our data set.

Starting with an initial model including the variables selected from step 2. Forward selection was performed, with significance level for entry at 0.2, to consider for inclusion the variables initially not selected at step 1. Starting with the model selected from step 3. Finetuning was done by stepwise selection- entry/staying at 0.05.

3. Starting with an initial model including the variables selected from step 2. Forward selection was performed, with significance level for entry (Slentry) = 0.2, to consider for inclusion the variables initially not selected at step 1. 4. Starting with the model selected from step 3. Finetuning was done by stepwise selection- Slentry/Slstay 0.05.

estimated coefficients, standard error for the coefficient, reference category Standard deviation in mixing distribution = 1.054, Standard error = 0.099 Starting from the full model with seven variables selected by univariate analyses, the best model was constructed on the basis of AIC.

The final model included all variables selected in the previous steps; no reduction of the model was attempted at this stage.

We constructed an LAD model using the variables selected in the support-set, with the purpose of classifying new observations.

In the multivariate regression model, adjusting for variables selected from the univariate analyses, joint pain severity and prior use of tamoxifen were statistically significant independent predictors of premature discontinuation (Table  2).