Sentence examples for basic regression model from inspiring English sources
In this section, we extend our basic regression model, Eq. (1), to include controls for local economic conditions.
The basic regression model is: ln {Y}_i=alpha +beta {mathrm{lnCoal}}_i+gamma {mathrm{lnControl}}_i+Dmathrm{dummy}+{varepsilon}_i.
The first econometric model that is used in this study is a basic regression model (Freedman 2005; Freedman et al. 2007).
The basic regression model, described above, was used, and associations between organochlorine compound concentrations and consumption of each food group (g/day) were analysed one at a time.
The outcome variable in the basic regression model is change in VO2peak; whereas predictors include study group, baseline VO2peak, as well as the interaction.
Determinants studied, using the basic regression model described above, were education level, smoking, alcohol consumption, country of birth, growing up in a fisherman/sportfisher family or along the Baltic Sea coast, and breast milk exposure during infancy.
The final basic regression models were decided with a backwards elimination procedure using a significance threshold of ≥0.1 for elimination of excessive independent variables.
We apply two basic regression models: First the linear probability model, that is, an OLS-regression of the variable indicating a mammography screening on the above-mentioned exogenous variables.K Second the probit regression model, which in contrast to the OLS-regression imposes the restriction that a predicted value lies inside the range of 0 and 1.
Birth outcomes for women from the same health facilities may be correlated, thereby violating the independence assumption of basic regression models.
The proposed MIMO RBF-ARX model has a basic regression-model structure that is analogous to the linear ARX model structure, and the elements of its regression matrices are composed of Gaussian radial basis function (RBF) neural networks that are dependent on the working-point state of the current system.
For the basic logistic regression model (10), we used very flat N 0,100) priors for the β parameters.
