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To calculate "attractiveness," a latent construct, the correlation matrix of a previous study [12] was used in a principal component analysis [24], where a linear combination of three indicators of attractiveness was identified to maximize the explained variance.
Contrary to covariance-based SEM which minimizes the difference in covariance between the actual data and the values predicted by the model, PLS-SEM attempts to maximize the explained variance within latent constructs iteratively using a series of OLS regressions (Hair et al. 2011; Monecke and Leisch 2012).
Variable inclusion in the biplots was adjusted to maximize the explained variance.
All covariates were tested by stepwise inclusion into or exclusion from the model to maximize the explained variance of the dependent variable by the model (R-squared test).
RRR determines factors from food intake data that maximize the explained variation in an intermediate marker that is hypothesized to be related to the health outcome.
However, the purpose of health risk adjustment for expenditures is not to maximize the explained R in spending, but instead to capture the components of spending that are the consequence of poor health.
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The first Principal Component of the standardized variables (parameters) maximizes the explained variance and therefore, its eigenvector furnishes the weights that maximize Cronbach's Alpha.
Using RRR, we identified a food pattern maximizing the explained variation in PAI-1 and fibrinogen.
In simpler terms, RRR extracts food pattern scores while concurrently maximizing the explained variation in a set of response variables.
Using reduced rank regression, we identified a food pattern maximizing the explained variation in PAI-1 and fibrinogen.
PLS regression reduces the original set of predictor variables to a smaller number of latent variables that maximizes the explained variance in the dependent variable.
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