or a linear regression in case of quantitative dependent variables.
Non-parametric tests (Mann–Whitney or Kruskal-Wallis), in case of categorical variables, or linear regression model, in case of continuous variables, were performed to assess potential differences between the sociodemographic variables regarding the three SMILE scores (IOS, IOW and IOWS).
To test the convergent and criterion validity of the RPQ total score and the RA and PA scores, bivariate linear regression analyses (in case of continuous dependent variables) and bivariate logistic regression analyses (in case of categorical dependent variables) were performed.
Through linear regression: in this case, a and b are occurring after applying linear regression.
In addition to discussing the method for determining the load model parameters, using exhaustive search and multiple linear regression, a real case study with data obtained with a power quality meter exemplifies the model application in an electronic load.
To examine the determinants of transnational electoral and non-electoral political engagement, we estimate Poisson models for our count dependent variables because linear regression in this case can lead to inconsistent and biased estimates (Guarnizo et al., 2003; Long & Freese, 2006).
However, due to the skewed nature of response variables, linear regression in this case is not suitable and the problem with the Poisson regression model lies in its basic assumption of discreteness of response variables, which may not be applicable in real situations.
The association of ancestry with confounders was assessed using linear regression in the case of age, and the continuous anthropometric and reproductive variables, and Kruskal-Wallis and Wilcoxon rank-sum tests in the case of categorical variables.
When comparing individual differences between morphological groups, we adjusted for age at diagnosis of endometrial cancer and study, using linear regression in the case of continuous variables and a 'modified' Poisson approach with robust standard errors [ 21] for binary variables.
We accomplished this refinement by running a linear regression on weekly case counts for each disease at each geographic resolution and refitting the resulting residuals to a trend line with a slope of 0 and an intercept set to the most recent fitted value.
