Sentence examples for binary logistic regression showed from inspiring English sources

Binary logistic regression showed a significant difference on low uptake between those who lived within 2 Kms of River Nyando compared to those who lived more than 2 Kms from the river.

Binary logistic regression showed that model fit was good with a non-significant Hosmer and Lemeshow statistic (p = 0.476), −2 log-likelihood of 1741.5, and a 78.8% overall correct classification percentage.

Binary logistic regression showed no significant association between age, gender and family history of communicable diseases with the metabolic syndrome.

Binary logistic regression showed that PRL-3 expression was a predictor of distant metachronous metastasis in univariate analysis (P=0.000; Table 2).

This was significantly associated with type of practice and awareness about stem cell use in dentistry but binary logistic regression showed awareness as the only determinant of knowledge.

Binary logistic regression showed stenting (odds ratio 0.189, 95% confidence interval [CI] 0.059 0.602, P=0.005) and body weight (odds ratio 0.957, 95% CI 0.918 0.998, P=0.038) to protect against acute HF in older patients with acute MI.

Using "ALT/ULN <1x", "METAVIR A0", and "ActiTest A score 0.00-0.35" as reference categories, univariate binary logistic regressions showed significant necroinflammatory effects on the false positivity of LSM to stage fibrosis in the 32 patients with METAVIR F1, F2 and F3.

The results of binary logistic regressions show that local governments' socio-economic characteristics and their locations influence the implementation of compact city-related urban policy instruments.

Estimation results from binary logistic regression show that number of living children, region, place of residence, marital status and accessibility have a significant association on the non-utilization of postnatal care services.

To test whether MHD was a significant predictor of CVD in left-sided patients, we carried out binary logistic regression, which showed that MHD shows borderline significance (P=0.056).

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.