Sentence examples for preferred regression from inspiring English sources

For our preferred regression in column 6, the coefficient of −0.0253 implies that HOPE reduced the number of STEM graduates by 12.6percentt.

The performance of the preferred regression models were compared in terms of their means, standard deviations (SDs), and the 10th, 50th, and 90th percentiles of the observed and predicted values across two frequently used measures of disease severity: cancer stage (ranging from 1-4) and the Eastern Cooperative Oncology Group (ECOG) performance status (ranging from 0 to 3).

In order to avoid artifacts introduced by outliers with respect to dN or the expression level, we have preferred robust regression over standard least squares regression.

However, we preferred Poisson regression models because detailed analyses using Cox regression were unfeasible owing to the large size of the study dataset.

We preferred multiple regression analysis as it is relatively simple to develop, to use, and to incorporate in clinical practice.

Negative binomial regression is preferred over Poisson regression for modeling the count data because of the overdispersion that is common with actual research data [ 40, 77].

Negative binomial regression was preferred over Poisson regression wherever there was over-dispersion with the Poisson model (i.e. the variance in the mean number of heterozygous SNPs within MIs being substantially higher than the mean).

Logistic regression was preferred above Cox regression analyses because we were mainly interested in estimating the absolute risk difference (and the effect of known confounders on this risk difference), rather than assessing the relative risk of receiving benefits for a person at risk per unit time.

Initial exploration of the prediction sample data indicated the presence of possible overdispersion (variance (δ=1.37) exceeded mean (μ=0.4) count of cases), so negative binomial regression was preferred to Poisson regression since it explicitly models any overdispersion with an extra dispersion parameter.

For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis.

When multiple classes of the dependent variable can be ranked, then ordinal logistic regression is preferred to multinomial logistic regression [ 61].