Variables selected more than 5 times out of 10 replications were included in the multivariate model after multiple imputations.
Multivariate models used multiple logistic regression.
When we consider multivariate models containing multiple brain regions the normalization approach does not seem to be that important.
They then adjusted all multivariate models for multiple vascular risk factors, to increase the likelihood of observing apoE-dependent effects.
Cohen continued to publish ecological analyses on radon and lung cancer, using multivariate models with multiple factors in an attempt to control confounding [ 22].
Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset.
Multivariate models translate multiple effects in one number, which is the interpretation of the risk of harboring an event from 0 to 100%%.
Once we had identified variables for further evaluation in the multivariate model, we used multiple imputation for variables with missing observations.
However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories.
The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model.
