Sentence examples for a basic regression from inspiring English sources

The first econometric model that is used in this study is a basic regression model (Freedman 2005; Freedman et al. 2007).

Specifications B1 to B4 focus on the population of native workers under indefinite-term contracts, while specifications C1 to C4 study the population of native workers under fixed-term contracts.17 For panels B and C, I first implement a basic regression that is similar to specification A1.

It may also be of value to quantify the role of age in tumour growth (although age at diagnosis was found not to be associated with tumour size, among postmenopausal women, in a basic regression analysis).

As noted in a basic regression text [ 16], the slope parameter represents the average increase in the dependent variable for a unit increase in the predictor variable, while the intercept parameter represents the expected value of the outcome measure when all the predictors are zero.

We analyse the following logistic models, M0: η i = w ′ i γ M1: η i = w ′ i γ + f1 age) + f2 los) + f3 ct) M2: η i = w ′ i γ + f1 age) + f2 los) + f3 ct) + v i + u i + h i M3: η i = w ′ i γ + f1 age) + f2 los) + f3 trend) + f4 season) + v i + u i + h i Model M0 is a basic regression model of fixed covariates only (Table 1).

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.

The starting point is a very basic regression model with dummy variables for the four age groups under 31 as well as a series of fundamental socio-demographic controls.

BAYESCAN directly estimates the posterior probability that a locus is under selection (see below) in contrast to an earlier method build on the same basic regression model, which provided an approximated p-value [ 57].

Basic regression approaches assume a linear association between exposure and the log odds of outcome, but there exist straightforward methods to model non-linear relationships.

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.

In order to analyze the relationship between PNC (Y) use and individual and contextual characteristics (X), we first used a basic logistic regression model.