Because several linear regressions were calculated, a Bonferroni correction was applied to adjust the level of significance accordingly.
It was noteworthy that since several traits (e.g. SDI) were repeatedly used in several linear regressions, the Type I errors of this set of analyses were inevitably inflated.
Several linear (multiple linear regression, principal component regression, partial least squares regression) and nonlinear (multivariate polynomial regression, artificial neural networks, support vector machines) regression models are now available, which have the ability to relate the input and output variables (Singh et al. 2012).
In this paper, the problem of assessing any contrasts of several simple linear regression models by using simultaneous confidence bands is considered.
If they believed that they drive faster than average speed, their answer was recoded as 1; otherwise, it was recoded as 2. Several log linear regression models were developed in order to explain exceeding speed limit for all types of roads using variables from the set of independent variables described above.
We conducted several multivariable linear regression models for each exposure-outcome association.
Although the groups differed in several parameters, linear regression confirmed our findings even if adjusted for possible confounders.
We then fitted several multiple linear regression models based on alternative sets of underlying causal relationships represented conceptually by directed acyclic graphs (Greenland et al. 1999).
Several simple linear regression analyses with duration of gestation as the dependent variable and each of the possible determinants as independent variables were performed.
Results from several multiple linear regression analyses explaining the variance in selected HoNOSCA scales from similar ASEBA scales completed by parents (CBCL), teachers (TRF), and patients 11-17 YSRrs (YSR), controlled for sex and age (continuous variable).
