Sentence examples for regression I from inspiring English sources

By multiple logistic regression: (i) checkpoint alcohol dispensers for glove-on hand rubbing between zones of risk, and (ii) fever screening at the fever screen station outside the emergency department, were the significant methods effectively minimising nosocomial SARS infection of HCWs (P < 0.05).

Before the correlation analysis, several sources of spurious variance were also removed from the data through linear regression: (i) six parameters obtained by rigid body correction of head motion, (ii) the whole-brain signal averaged over a fixed region in atlas space, (iii) signal from a ventricular region of interest, and (iv) signal from a region centered in the white matter.

Then, the proposed method enabled successful ordinal regression, i.e., successful estimation of prediction error degrees.

The Blackard map was developed at the 250 m spatial resolution [14] using tree-based regression (i.e., Cubist).

The "differenced" regression, i.e. (b) =1, does have slightly lower levels of significance on the effect of the fraction foreign born.

In the literature examples may be found of localization treated as a problem of regression, i.e., estimating an actual physical position and quoting a mean positioning error ([3, 12], etc).

In addition to carrying out the regressions as multiple-regressions (i.e., regressing all 100 timeseries into the data simultaneously), we performed separate analyses using single-regressions (i.e., regressing each timeseries into the data one at a time, independently).

After estimating these models, we calculate the residuals from the regressions (i.e. the term ε it in Eq. 3) and the correlations between these.

We compute the final indices by combining the results of the country sub-regressions, i.e. we standardize the results across countries, but separately for gender and year.

This paper uses simultaneous regressions (i.e. path analysis in SAS's PROC CALIS) to estimate the relationship between field saturation, field transferability, field-of-study mismatch, overqualification and wages.

We first correlated the slavemaker and host colony abundance in each plot, for each community separately, using three linear regressions (i.e., host densities explaining slavemaker densities).