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The PVAD algorithm allows building a structural model of partial- and full-value variable associations in multiple layers that captures individual and interactive effects of multiple variables by learning from data.
RSM optimizes multiple variables by systematic variation of all variables in a well-designed experiment with a minimum number of experiments.
This is similar, if not equivalent, to maximizing a function of multiple variables by considering its dependence on all the variables rather than on a subset of them alone.
A scatter plot matrix presents multiple variables by pair-wise evaluation.
A Bayesian network is a directed graph representing conditional independence of multiple variables by a set of conditional probability distributions [ 19, 37].
Although likely missing at random, missing data were imputed using an iterative method that imputes multiple variables by using chained equations, a sequence of univariate imputation methods with fully conditional specification of prediction equations [ 63].
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Next Up defends the 7x multiple variable by referring to a two-year-old M&A deal.
The normalizing constant is the A-hypergeometric polynomial, which is a solution of a system of linear differential equations of multiple variables determined by a matrix A, called A-hypergeometric system.
The greatest advantage of these decision trees as regression methods is that they are able to accurately describe complex relationships among multiple variables, and by aggregating these decision trees, more accurate solutions are generated (Gleason and Im 2012).
Through PCA the original multiple variables were represented by seven principal components (PCs) and, hence, led to more than 99% reduction of input parameters in the ANN model.
Adjustment for multiple variables was performed by adding the covariates in a set of multiple logistic-regression models.
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