The correlation was further explored with a multiple linear regression analysis, and the covariance of the model R and the standardized correlation coefficient are presented.
The relationship between the various factors (age, gender, highest educational level achieved, group [SCD vs. AA], employment status) and depression was explored with a series of logistic regression models, and their relationship with loneliness was explored with multivariate linear regression.
Final harvest data were explored with linear and non-linear regression including logistic sigmoid and logistic with hormesis approaches (Stephenson et al. 2000), with the models selected being those with lowest variance of residuals.
Fig. 8 Random sampling with 10%% of 337 permutations being explored with the linear and the bagging tree regression methods and the aggregate regressor.
Factors affecting utility difference were explored with multiple linear regression analysis.
Possible associations between infant blood lead and the independent variables were separately explored with bivariate linear regression models.
The relation between IVF outcomes and quartiles of HCB, p, p´-DDE, and total DDT was explored with multivariable generalized linear regression models, with data structured to accommodate joint models for multiple outcomes and multiple cycles contributed per woman.
Covariates (treatment, age, sex, body mass index, and nonsteroidal anti-inflammatory drug [NSAID] use) were explored in a linear regression model for potential relationships with gallbladder volume.
The effect of slice location and WML load on FLAIR-derived thresholds was explored using a linear mixed model and expressed as means with 95% confidence intervals (CI).
Following significant standard regression models at α = 0.01, the relationships for the 6 sub-groups of patients for explanation by 5 measures of biochemical ratios were explored with backward, step-wise, linear regression models.
