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A slightly more rigorous analysis incorporating multiple regression provides similar results.
Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment, and is the subject the next paper in this series.
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Multiple regression analysis provides a predictive equation (1).
In addition to these solution spaces, the multiple regression analysis provides further evidence for how the factors included in this study influence SEV.
Multiple regression analysis provides one approach, which despite significant limitations, can be successful when very large data sets are available and only annual estimates are required.
Thus, multiple regression analysis provides a means of assessing the degree and the character of the relationship between the independent variables and the dependent variable; the regression coefficients indicate the relative importance of each of the independent variables in the prediction of the dependent variable (Sekaran and Bougie 2013).
Although multiple regression analysis provides an effect estimate for each pair of exposure and outcome variables, it is prone to multiple testing problems and chance findings.
The multiple regression test provided a significant result.
Linear, nonlinear, and Box-Cox multiple regression models provided accurate results.
The multiple regression analysis provided the prediction equations of the soft tissue profile changes from the related dento-skeletal and other factors.
Multiple regression analyses provide evidence that such patterns could be related to the different availability, composition and size spectra of food particles in the sediments.
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