Sentence examples for logistics regression from inspiring English sources

Prescreening using univariate logistics regression analysis followed by unconditional binary logistic regression analysis showed that maternal age ≤20 years, low educational level, history of one or more spontaneous abortions, history of one or more stillbirths, pregnancy-induced hypertension, anemia, premature rupture of membranes, oligohydramnios, preterm birth, and BMI <18.5 were risk factors.

*p < 0.05; **p < 0.01; ***p < 0.001; ORU: univariate odds ratio obtained using logistic regression; ORm: odds ratio obtained from stepwise multivariate logistics regression analysis using univariately significant variables as candidate variables; NS: not statistically significant in multivariate analysis.

*p < 0.05; **p < 0.01; ***p < 0.001; ORU: univariate odds ratio obtained from logistic regression models; ORm: odds ratios obtained from stepwise multivariate logistics regression analysis, using univariately significant variables as candidate variables; NS: not statistically significant in multivariate analysis.

This model was estimated via logistics regression (Logit).

Linear logistics regression (LLR) combines discriminative and parametric approaches.

The antihypertensive strategies and their association with patients' outcomes were evaluated with logistics regression.

Univariate and multivariate logistics regressions were performed to calculate odds ratios (OR).

Logistics regressions were first conducted to estimate the odds of moderate occupational sitting time and high occupational sitting time (against low occupational sitting time) due to demographic characteristics, health behaviours and work characteristics (job grade, working hours).

Fixed effect logistics regressions were used to predict whether the main outcome variables changed with time in the intervention and comparison settings, controlling for clustering at the clinic level.

Table  6 displays the results for 24 adjusted logistics regressions which examine the relationship between the four trust actors and six risk perceptions after adjusting for familiarity, age and gender.

Multivariate analyses were performed by conditional logistical regression by using a backward elimination model with Logistics (8 ).