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The model was produced by using the stepwise multiple linear regression technique on a database that consists of 67 recently discovered 1,3,4-oxadiazole substituted naphthyridine derivatives.
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.
Significant factors were selected by using the stepwise selection method.
The main effect model is built by using the stepwise forward selection and backward elimination technique.
A final multivariable model was chosen by using the stepwise selection procedure.
We constructed models by using the stepwise procedure without excluding and after excluding bone mineral density.
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The final logistic regression model was constructed by using the backward stepwise elimination procedure, removing predictors with p>0.05.
To evaluate the risk factors related to the presence of drug-drug interactions in prescription mixes, a multiple logistic regression analysis by using the backward stepwise method was performed; the correlation terms and interactions among selected variables were also explored, and goodness of fit test was assessed for the best model.
The relationship between the dependent and the independent variables were analyzed by a multiple regression analysis using the stepwise method.
In total, 41 unique multilocus genotypes were identified (AZK: 14, COQ: 27) by using the Assign Clones function and the Stepwise Mutation Model for calculating the interindividual distances in GenoDive.
For the multivariable analysis, logistic regression with backward stepwise elimination by using the likelihood test statistic was used to assess potential predictors of development of chronic pain in survivors of critical illness.
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