It is therefore necessary to rely on a limited amount of empirical and theoretical work to identify possible variables for inclusion in a more formal, multivariate regression model.
This simple analysis was then extended with a more complex multivariate regression model with the infant mortality ratio as the dependent variable and all four factors as independent ones, taking into consideration the presence of the correlated data (time-repeated measurements of 7 years of observation).
For detailed program planning purposes, when high accuracy is more important, the bivariate and multivariate regression models provide slightly more accurate estimates on average in our analysis their average and maximum error values were smaller.
Note the diagnostic eras were, on multivariate regression, more evenly distributed than the univariate assessment indicated; the HR for each succeeding era changed by a factor of 0.90.
In this table, we present the independent factors associated with co-enrollment of one patient into two or more studies identified by multivariate regression analysis, presented using odds ratios (OR) and 95% confidence intervals (95% CI).
In the best fitting multivariate regression model, more residential stability was associated with lower work injury rates for young people.
We note, however, that much of the literature on imputation has focused on the use of imputation to improve more complex parameter estimation (eg, multivariate regression coefficients).
The bivariate and multivariate regression models, although more accurate for a greater number of countries, are also more complex; they were created using regression techniques following a natural log transformation of both the logistics (AMD or AQD and DHSS prevalence data for the method.
Logistic regression was used to perform both univariate (regression model with one independent variable) and multivariate (regression model with more than one independent variable) analyses.
Finally, future studies should favor the use of more powerful statistical analyses, such as multivariate regression analyses or multivariate analysis of variance (MANOVA), and add variables that significantly differ between respondents and non-respondents as covariates in statistical models [ 52].
