Sentence examples for multiple regression adjustment from inspiring English sources

Results between the cohorts were not statistically significant after multiple regression adjustment for differences in patient characteristics (OR: 0.72 for GX [95% CI: 0.47, 1.09] and 1.20 for LL [95% CI: 0.98, 1.46]).

The association between CL and each of these four factors was slightly attenuated after multiple regression adjustment, and reporting sand fly bites was no longer significantly associated with increased CL.

In order to determine the relationship between the input and output parameters, the GEP model was developed and the results were compared with non-linear multiple regression (NLMR) method.

There were no significant differences (P>0.05) between the DE and DCP values estimated by the substitution or by the multiple regression (MR) methods for wheat bran (WB), wheat, full-fat soya and sunflower meal.

Then, a methodology based on the application of several kinds of multiple regression (MLR), including Brandon's method (which provides a mathematical model using priority of selected variables) is applied.

This was done for multiple regression (MR) and change score analysis.

Validation of the multiple regression ADMA model was performed using studentized residuals.

Hierarchical Multiple Regression (HMR) analysis was conducted to test the incremental variance by any given set of independent variables.

3 The main analyses of the current study consisted of two Hierarchical Multiple Regression (HMR) analyses with the IMS and number of diary intrusions as dependent variables.

Partial Least Squares multiple regression (PLSR) was applied to model the relationships between polymorphisms allele frequencies found in the 31 sheep breeds genotyped and a matrix describing environmental factors (14 geographical and climatic variables) as in Fumagalli et al. [ 51].

The algorithm proposed in this study also bears similarities to Stepwise Multiple Regression (SMR) where the contributions of independent variables to one dependant variable are considered and the independent variables that do not contribute significantly are eliminated.