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Linear regression and multivariate normal regression models were used.
Our imputation method filled in missing values for continuous variables using multivariate normal regression.
For steps 2 and 3 the multivariate normal regression model was used as described.
STATA's method of multivariate normal regression was used to impute ratings for these cases.
In the imputation process, we created M = 100 data sets with multivariate normal regression.
The mean difference in weight change between older and middle-aged groups was obtained using multivariate normal regression.
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Univariate and multivariate log-normal regression models were used to examine the relationship between various factors and their impact on total healthcare costs.
All other covariables used in this multivariate censored-normal regression model have non-significant coefficients, suggesting a weak explanatory power on median OS in the presence of the highly significant TDE regressor (P < 0.0001).
Multivariate censored-normal regression was used to censor the use of glucose-lowering medications and to assess the linear association of fasting glucose with WHR and physical activity independently and jointly.
The simulations comprised 20,000 random draws of all model coefficients from multivariate normal distributions using the estimated regression coefficients and their associated variance-covariance matrix.
We present a general introduction to the topic followed by overviews of how to calculate tolerance intervals for some continuous and discrete distributions, nonparametric tolerance limits, tolerance intervals for regression settings, and multivariate normal tolerance regions.
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