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Table 6 presents the multivariate regression for university-educated CE and FE immigrant women that corresponds to Table 5 for men.

Quantitative analyses included use of exploratory factor analysis for construct validity; Pearson's correlations for test retest reliability; Cronbach's alpha for internal reliability; and multivariate regression for investigating relationships between feeding practices and toddler diet and anthropometry.

Table 2 shows the explanatory power for the full multivariate regression for each cost type in predicting transcript levels.

Last observation carried forwards (figures from 2010) were used in multivariate regression for this variable.

Final models using multivariate regression, for distance and MEE separately, were chosen based on likely and relevant confounders after examination of the findings from univariate analysis.

Since we are interested in identifying the disease-sensitive and QT-relevant biomarkers, we consider performing both logistic regression for classifying disease status and multivariate regression for predicting cognitive memory scores simultaneously (Wang et al., 2011).

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To analyze expenditure I present tobit estimates for the amounts spent on (1) other housing, (2) cars, and (3) other big ticket items, and multivariate regressions for the amount spent on (4) food and other non durables (see Table 11a).

Table 4 presents the univariate and multivariate regressions for each of the factors on the adjusted claim rate.

Table 5 summarizes the genetic effects obtained from multivariate regressions for concentrations of Hg biomarkers, adjusted for fish intake, gender, and age.

Multivariate regression models for cognitive measures included control for potential confounders including age, sex, education, income and ancestry admixture.

The residuals from multivariate regression analyses for all outcome domains were checked for normality.

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