The predictor variables' error terms, denoted e1 and e2, may be correlated (r) if an important variable influencing both predictors is omitted, i.e. in the case of bias in exposure measures.
The relative influences of predictors in the classifiers of NT1/NT2.
Peer and supervisory influence as predictors of technology adoptions are both culturally bounded.
Summarizing the conceptual model: multilevel factors including person, community, and state levels of influence are predictors of the odds of cancer diagnosed at an advanced stage.
The proximal influences involved predictors outlined in most extant models of successful aging (e.g., Rowe & Kahn, 1998 [Rowe, J. W., & Kahn, R. L. (1998). Successful aging. New York: Pantheon Books.]).]
When appropriate, the influence of predictors was also evaluated in multivariate analyses, using logistic regression (dependent variables: responders or MOH) or linear regression (dependent variables: headache days/month, HI/month, or medication days/month).
First, the influence of predictors on outcome variables was assessed with univariate logistic regression models.
These variables are latent and can be influenced by predictors and other covariates.
Multivariate logistic regression was then fitted to determine the joint influence of predictors on the outcome variable.
Multivariable linear regression analysis will be performed to evaluate the potential influence of predictors of primary outcomes.
