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The first ground motion prediction equations (GMPEs) were, therefore, developed with as few as two principal predictor variables: magnitude and distance.
The principal predictor variables of the model are family sociodemographic characteristics, social support available to mother and child, HIV-related symptom distress in the mother, coping strategies of both mother and child, emotional distress of the mother, and quality of the parent child relationship.
Medical school that used UKCAT scores in borderline cases and as a factor had six independent and statistically significant principal predictor variables: sex, socioeconomic status, ethnicity, type of school attended, UKCAT score, and academic attainment.
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The primary predictor variable was M3.
The primary predictor variable was payer status.
Gender was the primary predictor variable.
The main predictor variable was "observed technique".
The main predictor variable was patient sex.
The idea in principal component analysis (PCA) is to transform the predictor variables into linearly independent variables or principal components—such that the first principal component has the largest variance, the second principal component has the second largest variance and is orthogonal to the first principal component, and so on.
Correlation among predictors was evaluated based on eigenvalues derived from principal component analysis of the predictor variables.
We additionally subjected predictor variables to factor analysis (principal components) to explore between-variable colinearity prior to building a multiple logistic regression model as described by Hosmer and Lemeshow.
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